Analisis Faktor Untuk Peningkatan Mutu Madrasah Tsanawiyah Al-Washliyah Medan Krio

LAMPIRAN

KUESIONER Perihal : Permohonan PengisianAngket Lampiran : Satu berkas

JudulSkripsi : Analisis Faktor Untuk Peningkatan Mutu Madrasah Tsanawiyah Al-Washliyah Medan Krio

Dengan hormat,

Dalam rangka penulisan skripsi Program Sarjana Sains, Departemen Matematika Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Sumatera Utara, maka saya mohon bantuan kesediaan saudara/i untuk mengisi kuesioner ini dengan sebaiknya. Kuesioner ini akan digunakan dalam analisis peningkatan mutu yang nantinya dapat menjadi masukan dalam perbaikan dan peningkatan mutu di sekolah Madrasah Tsanawiyah Al-Washliyah Medan Krio

Saya menjamin kerahasiaan data yang saudara/i berikan, karena jawaban tersebut hanya sebagai bahan penelitian dan tidak untuk dipublikasikan.Atas kesediaan dan kerjasamanya saya ucapkan terima kasih.

Tingkat Kepentingan

Pengisian kuesioner ini bertujuan untuk mengetahui pendapat anda mengenai variabel apa yang paling penting untuk meningkatkan mutu Madrasah Tsanawiyah Al-Washliyah Medan Krio.

Berilah tanda silang (X) padaskala (1, 2, 3, 4 dan 5) yang tersedia sesuai dengan pilihan anda.

Tingkat Kepentingan: 1 = Sangat Tidak Penting 2 = Tidak Penting

3 = Cukup Penting 4 = Penting

5 = Sangat Penting

KEMENTERIAN RISET, TEKNOLOGI DAN PENDIDIKAN TINGGI UNIVERSITAS SUMATERA UTARA

FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM Jalan Bioteknologi No. 1 Kampus USU Medan 20155 Telp/Fax. (061) 8214290

No DaftarPertanyaan Tingkat Kepentingan I. TANGIBLE (BUKTI FISIK)

1 Kelengkapan alat selama proses belajar

mengajar 1 2 3 4 5

2 Kebersihan dan kerapian ruangan 1 2 3 4 5 3 Kenyamanan ruangan, misal: kursi, meja,

kipasangin, AC, tidak berisik 1 2 3 4 5 4 Kelengkapan fasilitas misal: toilet, musholla,

kantin, tempat sampah, danfotocopy 1 2 3 4 5 5 Kelayakan fasilitas 1 2 3 4 5 6 Penampilan guru dan pegawai yang rapi dan

sopan 1 2 3 4 5

7 Tersedianya tempat sampah yang cukup di

lingkungan sekolah 1 2 3 4 5

8 Area bermain 1 2 3 4 5

II. DIMENSI RELIABILITY (KEHANDALAN)

9 Guru yang kompeten sesuai dengan keahliannya 1 2 3 4 5 10 Reward (penghargaan) bagi anak didik, bahkan

guru dan pegawai yang aktif dan berprestasi 1 2 3 4 5 11 Proses belajar mengajar yang aktif dan kreatif 1 2 3 4 5 III. DIMENSI RESPONSIVENESS (DAYA TANGGAP)

12 Respon yang baik dalam menerima kritik dan

saran dari orangtua anak didik 1 2 3 4 5 13 Menjaga hubungan yang baik antar guru,

pegawai dan orangtua anak didik dengan kegiatan tertentu

1 2 3 4 5 IV. DIMENSI ASSURANCE (JAMINAN)

14 Keamanan siswa selama di lingkungan sekolah 1 2 3 4 5 15 Administrasi yang jelas dan transparan 1 2 3 4 5 V. DIMENSI EMPATHY (KEPEDULIAN)

16 Keramahan guru dan pegawai saat menerima/memberikan kritik dan saran kepada orangtua anak didik

1 2 3 4 5 17 Guru dan pegawai yang siap membantu masalah

anak didik 1 2 3 4 5

18 Memberikan bantuan kepada anak didik yang

kurang mampu 1 2 3 4 5

LAMPIRAN 2

DATA PENELITIAN RESPONDEN

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18

4 4 3 3 4 1 2 5 2 2 3 3 2 3 3 2 2 2

4 4 4 3 4 3 2 4 1 2 3 4 3 2 2 2 1 1

4 3 3 3 4 2 2 4 1 2 2 3 2 2 2 1 2 1

4 3 3 4 5 1 3 4 1 2 3 2 1 3 1 1 3 1

4 3 4 4 4 1 3 4 1 2 3 2 2 2 1 2 2 1

4 3 3 3 4 5 2 3 2 2 2 2 2 3 2 2 1 1

4 3 4 4 5 2 2 3 1 1 2 1 1 1 2 1 1 1

5 4 3 5 5 3 4 5 4 3 3 4 3 3 3 1 2 1

5 2 4 5 5 1 2 5 2 2 2 2 1 2 4 2 1 1

4 4 4 3 3 3 2 4 3 2 3 3 3 4 3 3 3 1

4 4 3 3 4 1 2 5 1 2 2 1 2 3 2 1 1 1

1 3 5 5 4 1 2 5 1 1 2 3 2 4 3 2 2 1

2 3 4 5 4 2 3 3 2 2 3 2 2 3 2 1 2 3

3 4 4 5 4 2 3 3 2 2 2 5 4 2 2 3 2 5

3 3 2 3 2 1 3 1 3 3 1 3 3 2 2 2 2 1

3 3 2 3 2 1 3 1 3 3 2 3 3 2 2 2 2 1

3 3 2 3 2 1 3 1 3 3 1 3 3 2 2 2 2 1

3 3 1 4 1 2 3 1 2 1 3 1 1 2 3 1 2 1

3 4 4 3 3 1 2 2 2 2 3 3 2 3 2 3 2 1

3 4 3 3 3 2 2 2 2 2 2 2 3 2 3 2 2 3

4 3 4 4 5 2 2 3 1 2 2 3 3 2 2 3 2 2

2 4 4 5 3 1 2 2 2 2 2 3 3 5 3 2 2 2

3 3 4 3 4 2 2 3 3 3 2 3 3 5 4 2 2 2

2 3 4 4 2 1 2 1 1 2 2 2 1 2 2 1 3 3

4 3 4 4 5 2 2 3 1 1 2 1 1 1 1 1 1 1

3 3 4 4 3 2 2 3 2 1 4 2 3 3 2 3 3 2

3 3 4 4 3 2 3 4 1 2 3 1 3 2 2 2 1 1

4 4 5 4 4 1 2 2 1 2 2 2 2 2 4 2 1 1

4 2 3 4 3 1 2 3 1 4 3 2 3 2 3 3 2 1

4 1 3 2 2 2 1 4 1 2 1 3 4 3 4 2 2 1

4 2 3 3 2 1 1 1 1 2 3 2 2 1 3 2 1 2

4 2 4 4 3 1 3 5 2 3 4 2 1 2 3 4 3 2

4 2 3 5 3 2 3 5 2 3 2 5 3 4 4 4 2 2

4 2 4 4 2 1 4 4 1 4 4 4 4 3 1 1 1 1

4 2 4 4 3 2 4 4 2 4 3 4 2 3 3 1 1 1

4 2 4 3 4 2 5 4 1 3 4 4 3 2 3 1 2 2

4 2 4 4 4 1 4 3 2 1 2 4 2 2 4 2 2 3

4 1 4 2 4 1 2 4 1 2 1 2 1 2 2 2 1 1

3 2 2 3 3 1 4 4 1 2 3 2 1 2 4 2 2 3

4 3 3 3 3 2 3 3 2 2 3 2 3 2 3 3 4 3

4 3 4 4 3 2 3 4 2 3 2 3 3 3 3 3 2 3

4 3 4 4 3 2 4 4 2 3 2 3 3 3 3 3 2 3

4 2 3 3 3 2 2 2 2 3 3 2 4 3 3 3 3 2

3 1 3 4 3 2 3 4 1 2 2 5 5 1 2 1 1 1

2 1 2 2 2 1 1 3 2 1 1 1 1 1 2 1 1 2

2 1 2 3 3 1 2 3 1 2 1 3 2 2 1 1 1 1

1 2 4 3 3 1 2 4 1 2 2 3 2 1 1 1 1 1

4 2 3 4 5 2 3 4 2 4 3 3 3 2 3 2 3 2

4 1 2 2 2 3 4 4 3 2 3 3 3 2 3 2 2 3

3 1 3 2 3 1 2 4 1 3 4 3 4 2 4 2 4 1

2 1 3 2 4 1 2 4 2 1 2 4 1 4 2 1 2 1

3 1 2 2 3 1 3 3 1 2 3 1 2 2 3 1 2 2

3 1 2 2 2 1 1 2 1 1 1 3 3 2 1 1 1 1

4 1 2 1 2 1 1 4 1 2 2 4 4 1 4 1 2 1

4 1 1 2 2 1 1 4 1 1 1 1 2 2 1 2 1 2

2 1 2 2 3 1 1 2 1 1 1 2 3 2 1 1 1 1

3 2 3 2 2 2 2 3 1 1 2 3 4 3 2 2 3 1

3 3 3 3 3 2 3 3 2 2 2 4 4 4 2 3 4 3

3 2 3 4 4 3 4 4 1 3 3 5 4 3 2 2 2 3

4 1 2 2 2 1 3 4 1 2 2 2 3 1 2 3 2 1

3 1 4 3 3 1 3 4 1 3 3 4 4 3 2 3 2 1

3 1 3 4 3 1 2 4 1 3 3 4 4 3 2 3 3 1

4 2 3 2 3 2 2 4 1 4 3 4 4 2 2 2 2 1

3 2 2 2 4 1 2 4 2 3 1 5 4 2 2 3 2 2

4 1 2 2 3 1 2 4 1 4 3 4 2 1 1 2 1 4

2 2 3 4 4 2 3 4 2 4 3 4 4 3 2 3 2 1

2 4 5 4 3 2 2 2 1 2 2 3 3 1 1 2 1 1

2 4 2 3 1 2 3 1 2 3 1 5 3 1 1 1 1 1

2 3 3 4 2 1 2 1 1 2 2 3 4 1 1 1 1 1

4 4 4 3 1 1 4 5 1 1 2 5 4 1 2 1 4 2

2 4 4 4 2 1 2 2 4 1 1 2 1 3 1 2 4 2

2 4 4 3 1 2 3 2 1 3 2 5 4 1 1 2 2 1

3 4 3 3 1 2 3 2 1 3 2 5 4 2 1 2 2 1

3 4 4 3 2 4 1 5 3 3 2 2 2 4 2 2 2 3

2 5 4 1 3 1 2 4 3 2 1 2 4 3 2 2 2 1

2 5 5 1 4 3 2 5 3 5 1 5 5 5 1 4 4 5

3 3 4 3 2 2 2 4 2 2 2 4 4 2 2 2 3 2

2 4 5 4 4 1 2 2 1 2 1 3 3 2 2 1 1 2

2 3 4 3 3 2 3 4 2 2 2 3 4 2 2 2 2 2

3 3 4 4 2 1 3 2 1 2 1 5 4 2 1 2 2 1

2 1 4 4 2 1 2 2 1 2 1 4 5 1 2 2 2 1

3 3 4 4 3 1 2 5 1 1 2 5 4 3 2 1 4 2

3 3 4 4 3 2 2 5 1 2 3 5 4 1 1 2 4 1

3 4 3 3 2 1 3 4 1 1 2 5 4 1 2 1 1 2

3 4 3 3 2 2 3 4 1 1 1 5 4 1 1 1 2 2

2 4 4 3 2 1 3 5 2 3 1 3 2 3 1 2 3 1

2 4 4 3 2 1 2 5 2 2 1 2 4 3 3 2 4 2

2 5 5 3 2 1 3 3 1 2 1 5 4 2 2 1 3 1

LAMPIRAN 3 Succesive Detail

Col Category Freq Prop Cum Density Z Scale

1 1 2 0,023 0,023 0,054 -1,996 1,000

2 21 0,241 0,264 0,327 -0,630 2,240

3 29 0,333 0,598 0,387 0,247 3,190

4 33 0,379 0,977 0,054 1,996 4,246

5 2 0,023 1,000 0,000 8,161 5,739

2 1 18 0,207 0,207 0,286 -0,817 1,000

2 18 0,207 0,414 0,390 -0,218 1,879

3 27 0,310 0,724 0,334 0,595 2,559

4 21 0,241 0,966 0,076 1,819 3,449

5 3 0,034 1,000 0,000 4,594

3 1 2 0,023 0,023 0,054 -1,996 1,000

2 15 0,172 0,195 0,276 -0,858 2,084

3 26 0,299 0,494 0,399 -0,014 2,958

4 38 0,437 0,931 0,133 1,484 3,979

5 6 0,069 1,000 0,000 5,294

4 1 3 0,034 0,034 0,076 -1,819 1,000

2 15 0,172 0,207 0,286 -0,817 1,999

3 32 0,368 0,575 0,392 0,188 2,925

4 30 0,345 0,920 0,149 1,402 3,917

5 7 0,080 1,000 0,000 5,069

5 1 5 0,057 0,057 0,115 -1,576 1,000

2 25 0,287 0,345 0,368 -0,399 2,123

3 31 0,356 0,701 0,347 0,528 3,064

4 19 0,218 0,920 0,149 1,402 3,909

5 7 0,080 1,000 0,000 4,860

6 1 46 0,529 0,529 0,398 0,072 1,000

2 33 0,379 0,908 0,165 1,329 2,367

3 6 0,069 0,977 0,054 1,996 3,355

4 1 0,011 0,989 0,030 2,274 3,874

5 1 0,011 1,000 0,000 4,370

7 1 8 0,092 0,092 0,165 -1,329 1,000

2 40 0,460 0,552 0,396 0,130 2,293

3 29 0,333 0,885 0,194 1,201 3,399

4 9 0,103 0,989 0,030 2,274 4,379

5 1 0,011 1,000 0,000 8,161 5,412

8 1 8 0,092 0,092 0,165 -1,329 1,000

2 14 0,161 0,253 0,320 -0,665 1,833

3 17 0,195 0,448 0,396 -0,130 2,406

4 35 0,402 0,851 0,233 1,039 3,200

5 13 0,149 1,000 0,000 4,351

9 1 48 0,552 0,552 0,396 0,130 1,000

2 28 0,322 0,874 0,208 1,143 2,301

3 9 0,103 0,977 0,054 1,996 3,196

4 2 0,023 1,000 0,000 4,086

10 1 17 0,195 0,195 0,276 -0,858 1,000

2 41 0,471 0,667 0,364 0,431 2,227

3 21 0,241 0,908 0,165 1,329 3,236

4 7 0,080 0,989 0,030 2,274 4,089

5 1 0,011 1,000 0,000 5,030

11 1 21 0,241 0,241 0,312 -0,702 1,000

2 36 0,414 0,655 0,368 0,399 2,155

3 25 0,287 0,943 0,115 1,576 3,173

4 5 0,057 1,000 0,000 4,296

12 1 8 0,092 0,092 0,165 -1,329 1,000

2 22 0,253 0,345 0,368 -0,399 1,990

3 25 0,287 0,632 0,377 0,338 2,765

4 16 0,184 0,816 0,266 0,901 3,397

5 16 0,184 1,000 0,000 4,240

13 1 12 0,138 0,138 0,220 -1,090 1,000

2 18 0,207 0,345 0,368 -0,399 1,882

3 26 0,299 0,644 0,373 0,368 2,583

4 28 0,322 0,966 0,076 1,819 3,519

5 3 0,034 1,000 0,000 4,811

14 1 18 0,207 0,207 0,286 -0,817 1,000

2 37 0,425 0,632 0,377 0,338 2,166

3 23 0,264 0,897 0,180 1,262 3,126

4 6 0,069 0,966 0,076 1,819 3,882

5 3 0,034 1,000 0,000 4,594

15 1 21 0,241 0,241 0,312 -0,702 1,000

2 38 0,437 0,678 0,358 0,463 2,185

3 19 0,218 0,897 0,180 1,262 3,110

4 9 0,103 1,000 0,000 4,031

16 1 29 0,333 0,333 0,364 -0,431 1,000

2 39 0,448 0,782 0,295 0,778 2,244

3 16 0,184 0,966 0,076 1,819 3,279

4 3 0,034 1,000 0,000 8,161 4,304

17 1 25 0,287 0,287 0,341 -0,561 1,000

2 41 0,471 0,759 0,312 0,702 2,248

3 12 0,138 0,897 0,180 1,262 3,143

4 9 0,103 1,000 0,000 3,925

18 1 49 0,563 0,563 0,394 0,159 1,000

2 23 0,264 0,828 0,255 0,945 2,224

3 12 0,138 0,966 0,076 1,819 2,997

4 1 0,011 0,977 0,054 1,996 3,602

5 2 0,023 1,000 0,000 8,161 4,069

LAMPIRAN 4

X1 _X2 X3 _X4 X5 _X6 X7 _X8 X9 _X10 X11 _X12 X13 _X14 X15 _X16 X17 _X18

4,000 4,000 3,000 3,000 4,000 1,000 2,000 5,000 2,000 2,000 3,000 3,000 2,000 3,000 3,000 2,000 2,000 2,000 4,246 3,449 3,979 2,925 3,909 3,355 2,293 3,200 1,000 2,227 3,173 3,397 2,583 2,166 2,185 2,244 1,000 1,000 4,246 2,559 2,958 2,925 3,909 2,367 2,293 3,200 1,000 2,227 2,155 2,765 1,882 2,166 2,185 1,000 2,248 1,000 4,246 2,559 2,958 3,917 4,860 1,000 3,399 3,200 1,000 2,227 3,173 1,990 1,000 3,126 1,000 1,000 3,143 1,000 4,246 2,559 3,979 3,917 3,909 1,000 3,399 3,200 1,000 2,227 3,173 1,990 1,882 2,166 1,000 2,244 2,248 1,000 4,246 2,559 2,958 2,925 3,909 4,370 2,293 2,406 2,301 2,227 2,155 1,990 1,882 3,126 2,185 2,244 1,000 1,000 4,246 2,559 3,979 3,917 4,860 2,367 2,293 2,406 1,000 1,000 2,155 1,000 1,000 1,000 2,185 1,000 1,000 1,000 5,739 3,449 2,958 5,069 4,860 3,355 4,379 4,351 4,086 3,236 3,173 3,397 2,583 3,126 3,110 1,000 2,248 1,000 5,739 1,879 3,979 5,069 4,860 1,000 2,293 4,351 2,301 2,227 2,155 1,990 1,000 2,166 4,031 2,244 1,000 1,000 4,246 3,449 3,979 2,925 3,064 3,355 2,293 3,200 3,196 2,227 3,173 2,765 2,583 3,882 3,110 3,279 3,143 1,000 4,246 3,449 2,958 2,925 3,909 1,000 2,293 4,351 1,000 2,227 2,155 1,000 1,882 3,126 2,185 1,000 1,000 1,000 1,000 2,559 5,294 5,069 3,909 1,000 2,293 4,351 1,000 1,000 2,155 2,765 1,882 3,882 3,110 2,244 2,248 1,000 2,240 2,559 3,979 5,069 3,909 2,367 3,399 2,406 2,301 2,227 3,173 1,990 1,882 3,126 2,185 1,000 2,248 2,997 3,190 3,449 3,979 5,069 3,909 2,367 3,399 2,406 2,301 2,227 2,155 4,240 3,519 2,166 2,185 3,279 2,248 4,069 3,190 2,559 2,084 2,925 2,123 1,000 3,399 1,000 3,196 3,236 1,000 2,765 2,583 2,166 2,185 2,244 2,248 1,000 3,190 2,559 2,084 2,925 2,123 1,000 3,399 1,000 3,196 3,236 2,155 2,765 2,583 2,166 2,185 2,244 2,248 1,000 3,190 2,559 2,084 2,925 2,123 1,000 3,399 1,000 3,196 3,236 1,000 2,765 2,583 2,166 2,185 2,244 2,248 1,000 3,190 2,559 1,000 3,917 1,000 2,367 3,399 1,000 2,301 1,000 3,173 1,000 1,000 2,166 3,110 1,000 2,248 1,000 3,190 3,449 3,979 2,925 3,064 1,000 2,293 1,833 2,301 2,227 3,173 2,765 1,882 3,126 2,185 3,279 2,248 1,000 3,190 3,449 2,958 2,925 3,064 2,367 2,293 1,833 2,301 2,227 2,155 1,990 2,583 2,166 3,110 2,244 2,248 2,997

4,246 2,559 3,979 3,917 4,860 2,367 2,293 2,406 1,000 2,227 2,155 2,765 2,583 2,166 2,185 3,279 2,248 2,224 2,240 3,449 3,979 5,069 3,064 1,000 2,293 1,833 2,301 2,227 2,155 2,765 2,583 4,594 3,110 2,244 2,248 2,224 3,190 2,559 3,979 2,925 3,909 2,367 2,293 2,406 3,196 3,236 2,155 2,765 2,583 4,594 4,031 2,244 2,248 2,224 2,240 2,559 3,979 3,917 2,123 1,000 2,293 1,000 1,000 2,227 2,155 1,990 1,000 2,166 2,185 1,000 3,143 2,997 4,246 2,559 3,979 3,917 4,860 2,367 2,293 2,406 1,000 1,000 2,155 1,000 1,000 1,000 1,000 1,000 1,000 1,000 3,190 2,559 3,979 3,917 3,064 2,367 2,293 2,406 2,301 1,000 4,296 1,990 2,583 3,126 2,185 3,279 3,143 2,224 3,190 2,559 3,979 3,917 3,064 2,367 3,399 3,200 1,000 2,227 3,173 1,000 2,583 2,166 2,185 2,244 1,000 1,000 4,246 3,449 5,294 3,917 3,909 1,000 2,293 1,833 1,000 2,227 2,155 1,990 1,882 2,166 4,031 2,244 1,000 1,000 4,246 1,879 2,958 3,917 3,064 1,000 2,293 2,406 1,000 4,089 3,173 1,990 2,583 2,166 3,110 3,279 2,248 1,000 4,246 1,000 2,958 1,999 2,123 2,367 1,000 3,200 1,000 2,227 1,000 2,765 3,519 3,126 4,031 2,244 2,248 1,000 4,246 1,879 2,958 2,925 2,123 1,000 1,000 1,000 1,000 2,227 3,173 1,990 1,882 1,000 3,110 2,244 1,000 2,224 4,246 1,879 3,979 3,917 3,064 1,000 3,399 4,351 2,301 3,236 4,296 1,990 1,000 2,166 3,110 4,304 3,143 2,224 4,246 1,879 2,958 5,069 3,064 2,367 3,399 4,351 2,301 3,236 2,155 4,240 2,583 3,882 4,031 4,304 2,248 2,224 4,246 1,879 3,979 3,917 2,123 1,000 4,379 3,200 1,000 4,089 4,296 3,397 3,519 3,126 1,000 1,000 1,000 1,000 4,246 1,879 3,979 3,917 3,064 2,367 4,379 3,200 2,301 4,089 3,173 3,397 1,882 3,126 3,110 1,000 1,000 1,000 4,246 1,879 3,979 2,925 3,909 2,367 5,412 3,200 1,000 3,236 4,296 3,397 2,583 2,166 3,110 1,000 2,248 2,224 4,246 1,879 3,979 3,917 3,909 1,000 4,379 2,406 2,301 1,000 2,155 3,397 1,882 2,166 4,031 2,244 2,248 2,997 4,246 1,000 3,979 1,999 3,909 1,000 2,293 3,200 1,000 2,227 1,000 1,990 1,000 2,166 2,185 2,244 1,000 1,000 3,190 1,879 2,084 2,925 3,064 1,000 4,379 3,200 1,000 2,227 3,173 1,990 1,000 2,166 4,031 2,244 2,248 2,997 4,246 2,559 2,958 2,925 3,064 2,367 3,399 2,406 2,301 2,227 3,173 1,990 2,583 2,166 3,110 3,279 3,925 2,997

4,246 2,559 3,979 3,917 3,064 2,367 3,399 3,200 2,301 3,236 2,155 2,765 2,583 3,126 3,110 3,279 2,248 2,997 4,246 2,559 3,979 3,917 3,064 2,367 4,379 3,200 2,301 3,236 2,155 2,765 2,583 3,126 3,110 3,279 2,248 2,997 4,246 1,879 2,958 2,925 3,064 2,367 2,293 1,833 2,301 3,236 3,173 1,990 3,519 3,126 3,110 3,279 3,143 2,224 3,190 1,000 2,958 3,917 3,064 2,367 3,399 3,200 1,000 2,227 2,155 4,240 4,811 1,000 2,185 1,000 1,000 1,000 2,240 1,000 2,084 1,999 2,123 1,000 1,000 2,406 2,301 1,000 1,000 1,000 1,000 1,000 2,185 1,000 1,000 2,224 2,240 1,000 2,084 2,925 3,064 1,000 2,293 2,406 1,000 2,227 1,000 2,765 1,882 2,166 1,000 1,000 1,000 1,000 1,000 1,879 3,979 2,925 3,064 1,000 2,293 3,200 1,000 2,227 2,155 2,765 1,882 1,000 1,000 1,000 1,000 1,000 4,246 1,879 2,958 3,917 4,860 2,367 3,399 3,200 2,301 4,089 3,173 2,765 2,583 2,166 3,110 2,244 3,143 2,224 4,246 1,000 2,084 1,999 2,123 3,355 4,379 3,200 3,196 2,227 3,173 2,765 2,583 2,166 3,110 2,244 2,248 2,997 3,190 1,000 2,958 1,999 3,064 1,000 2,293 3,200 1,000 3,236 4,296 2,765 3,519 2,166 4,031 2,244 3,925 1,000 2,240 1,000 2,958 1,999 3,909 1,000 2,293 3,200 2,301 1,000 2,155 3,397 1,000 3,882 2,185 1,000 2,248 1,000 3,190 1,000 2,084 1,999 3,064 1,000 3,399 2,406 1,000 2,227 3,173 1,000 1,882 2,166 3,110 1,000 2,248 2,224 3,190 1,000 2,084 1,999 2,123 1,000 1,000 1,833 1,000 1,000 1,000 2,765 2,583 2,166 1,000 1,000 1,000 1,000 4,246 1,000 2,084 1,000 2,123 1,000 1,000 3,200 1,000 2,227 2,155 3,397 3,519 1,000 4,031 1,000 2,248 1,000 4,246 1,000 1,000 1,999 2,123 1,000 1,000 3,200 1,000 1,000 1,000 1,000 1,882 2,166 1,000 2,244 1,000 2,224 2,240 1,000 2,084 1,999 3,064 1,000 1,000 1,833 1,000 1,000 1,000 1,990 2,583 2,166 1,000 1,000 1,000 1,000 3,190 1,879 2,958 1,999 2,123 2,367 2,293 2,406 1,000 1,000 2,155 2,765 3,519 3,126 2,185 2,244 3,143 1,000 3,190 2,559 2,958 2,925 3,064 2,367 3,399 2,406 2,301 2,227 2,155 3,397 3,519 3,882 2,185 3,279 3,925 2,997 3,190 1,879 2,958 3,917 3,909 3,355 4,379 3,200 1,000 3,236 3,173 4,240 3,519 3,126 2,185 2,244 2,248 2,997 4,246 1,000 2,084 1,999 2,123 1,000 3,399 3,200 1,000 2,227 2,155 1,990 2,583 1,000 2,185 3,279 2,248 1,000

3,190 1,000 3,979 2,925 3,064 1,000 3,399 3,200 1,000 3,236 3,173 3,397 3,519 3,126 2,185 3,279 2,248 1,000 3,190 1,000 2,958 3,917 3,064 1,000 2,293 3,200 1,000 3,236 3,173 3,397 3,519 3,126 2,185 3,279 3,143 1,000 4,246 1,879 2,958 1,999 3,064 2,367 2,293 3,200 1,000 4,089 3,173 3,397 3,519 2,166 2,185 2,244 2,248 1,000 3,190 1,879 2,084 1,999 3,909 1,000 2,293 3,200 2,301 3,236 1,000 4,240 3,519 2,166 2,185 3,279 2,248 2,224 4,246 1,000 2,084 1,999 3,064 1,000 2,293 3,200 1,000 4,089 3,173 3,397 1,882 1,000 1,000 2,244 1,000 3,602 2,240 1,879 2,958 3,917 3,909 2,367 3,399 3,200 2,301 4,089 3,173 3,397 3,519 3,126 2,185 3,279 2,248 1,000 2,240 3,449 5,294 3,917 3,064 2,367 2,293 1,833 1,000 2,227 2,155 2,765 2,583 1,000 1,000 2,244 1,000 1,000 2,240 3,449 2,084 2,925 1,000 2,367 3,399 1,000 2,301 3,236 1,000 4,240 2,583 1,000 1,000 1,000 1,000 1,000 2,240 2,559 2,958 3,917 2,123 1,000 2,293 1,000 1,000 2,227 2,155 2,765 3,519 1,000 1,000 1,000 1,000 1,000 4,246 3,449 3,979 2,925 1,000 1,000 4,379 4,351 1,000 1,000 2,155 4,240 3,519 1,000 2,185 1,000 3,925 2,224 2,240 3,449 3,979 3,917 2,123 1,000 2,293 1,833 4,086 1,000 1,000 1,990 1,000 3,126 1,000 2,244 3,925 2,224 2,240 3,449 3,979 2,925 1,000 2,367 3,399 1,833 1,000 3,236 2,155 4,240 3,519 1,000 1,000 2,244 2,248 1,000 3,190 3,449 2,958 2,925 1,000 2,367 3,399 1,833 1,000 3,236 2,155 4,240 3,519 2,166 1,000 2,244 2,248 1,000 3,190 3,449 3,979 2,925 2,123 3,874 1,000 4,351 3,196 3,236 2,155 1,990 1,882 3,882 2,185 2,244 2,248 2,997 2,240 4,594 3,979 1,000 3,064 1,000 2,293 3,200 3,196 2,227 1,000 1,990 3,519 3,126 2,185 2,244 2,248 1,000 2,240 4,594 5,294 1,000 3,909 3,355 2,293 4,351 3,196 5,030 1,000 4,240 4,811 4,594 1,000 4,304 3,925 4,069 3,190 2,559 3,979 2,925 2,123 2,367 2,293 3,200 2,301 2,227 2,155 3,397 3,519 2,166 2,185 2,244 3,143 2,224 2,240 3,449 5,294 3,917 3,909 1,000 2,293 1,833 1,000 2,227 1,000 2,765 2,583 2,166 2,185 1,000 1,000 2,224 2,240 2,559 3,979 2,925 3,064 2,367 3,399 3,200 2,301 2,227 2,155 2,765 3,519 2,166 2,185 2,244 2,248 2,224 3,190 2,559 3,979 3,917 2,123 1,000 3,399 1,833 1,000 2,227 1,000 4,240 3,519 2,166 1,000 2,244 2,248 1,000

2,240 1,000 3,979 3,917 2,123 1,000 2,293 1,833 1,000 2,227 1,000 3,397 4,811 1,000 2,185 2,244 2,248 1,000 3,190 2,559 3,979 3,917 3,064 1,000 2,293 4,351 1,000 1,000 2,155 4,240 3,519 3,126 2,185 1,000 3,925 2,224 3,190 2,559 3,979 3,917 3,064 2,367 2,293 4,351 1,000 2,227 3,173 4,240 3,519 1,000 1,000 2,244 3,925 1,000 3,190 3,449 2,958 2,925 2,123 1,000 3,399 3,200 1,000 1,000 2,155 4,240 3,519 1,000 2,185 1,000 1,000 2,224 3,190 3,449 2,958 2,925 2,123 2,367 3,399 3,200 1,000 1,000 1,000 4,240 3,519 1,000 1,000 1,000 2,248 2,224 2,240 3,449 3,979 2,925 2,123 1,000 3,399 4,351 2,301 3,236 1,000 2,765 1,882 3,126 1,000 2,244 3,143 1,000 2,240 3,449 3,979 2,925 2,123 1,000 2,293 4,351 2,301 2,227 1,000 1,990 3,519 3,126 3,110 2,244 3,925 2,224 2,240 4,594 5,294 2,925 2,123 1,000 3,399 2,406 1,000 2,227 1,000 4,240 3,519 2,166 2,185 1,000 3,143 1,000

LAMPIRAN 5

HASIL OUTPUT SPSS HASIL PERHITUNGAN UJI VALIDITAS

Correlation

Correlations

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 y

x1 Pearson Correlation 1 -.180 -.162 .049 .301** _{.207 .180 .271}* _{-.014 .142 .401}** _{-.120 -.160 -.065 .385}** _{.161 -.081 .004 .282}**

Sig. (2-tailed) .093 .132 .652 .004 .053 .093 .011 .897 .186 .000 .266 .137 .546 .000 .135 .453 .971 .008

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x2 Pearson Correlation -.180 1 .497** _.227* _{-.003 .225}* _{.099 -.069 .320}** _{-.020 -.168 .112 .084 .201 -.162 .024 .212}* _{.135 .356}**

Sig. (2-tailed) .093 .000 .033 .979 .035 .359 .526 .002 .854 .117 .298 .436 .060 .132 .825 .048 .212 .001

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x3 Pearson Correlation -.162 .497** _{1 .404}** _.293** _{.092 .062 .202 -.027 .036 .039 .146 .092 .246}* _{.000 .146 .180 .071 .466}**

Sig. (2-tailed) .132 .000 .000 .006 .393 .566 .060 .802 .738 .716 .175 .393 .021 .999 .174 .093 .513 .000

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x4 Pearson Correlation .049 .227* _.404** _{1 .306}** _{.054 .315}** _{-.047 .018 -.020 .253}* _{.033 -.149 .097 .099 .042 -.030 .008 .349}**

Sig. (2-tailed) .652 .033 .000 .004 .620 .003 .666 .867 .853 .017 .760 .166 .368 .359 .695 .778 .944 .001

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x5 Pearson Correlation .301** _{-.003 .293}** _.306** _{1 .168 .019 .338}** _{.023 .077 .248}* _{-.161 -.283}** _.251* _{.196 .096 -.121 .093 .376}**

Sig. (2-tailed) .004 .979 .006 .004 .118 .857 .001 .830 .474 .020 .134 .008 .018 .068 .376 .262 .390 .000

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x6 Pearson Correlation .207 .225* _{.092 .054 .168 1 .110 .144 .306}** _{.193 .176 .098 .140 .243}* _{.032 .170 .017 .214}* _.454**

Sig. (2-tailed) .053 .035 .393 .620 .118 .309 .181 .004 .071 .100 .364 .194 .022 .769 .114 .874 .045 .000

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x7 Pearson Correlation .180 .099 .062 .315** _{.019 .110 1 .128 .119 .273}* _.346** _.322** _{.102 -.012 .102 .006 .113 .161 .451}**

Sig. (2-tailed) .093 .359 .566 .003 .857 .309 .235 .269 .010 .001 .002 .343 .910 .345 .957 .293 .135 .000

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x8 Pearson Correlation .271* _{-.069 .202 -.047 .338}** _{.144 .128 1 -.024 .130 .216}* _{.157 .055 .262}* _{.163 .151 .222}* _{.114 .483}**

Sig. (2-tailed) .011 .526 .060 .666 .001 .181 .235 .828 .228 .043 .143 .608 .014 .129 .162 .038 .292 .000

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x9 Pearson Correlation -.014 .320** _{-.027 .018 .023 .306}** _{.119 -.024 1 .207 -.088 -.061 -.076 .467}** _{.192 .283}** _.262* _.253* _.383**

Sig. (2-tailed) .897 .002 .802 .867 .830 .004 .269 .828 .053 .416 .573 .483 .000 .074 .007 .014 .018 .000

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x10 Pearson Correlation .142 -.020 .036 -.020 .077 .193 .273* _{.130 .207 1 .274}** _.286** _.280** _.228* _{.069 .393}** _{.067 .138 .488}**

Sig. (2-tailed) .186 .854 .738 .853 .474 .071 .010 .228 .053 .010 .007 .008 .033 .524 .000 .533 .199 .000

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x11 Pearson Correlation .401** _{-.168 .039 .253}* _.248* _{.176 .346}** _.216* _{-.088 .274}** _{1 -.083 -.090 .073 .292}** _{.169 .134 .043 .400}**

Sig. (2-tailed) .000 .117 .716 .017 .020 .100 .001 .043 .416 .010 .441 .403 .499 .006 .115 .215 .692 .000

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x12 Pearson Correlation -.120 .112 .146 .033 -.161 .098 .322** _{.157 -.061 .286}** _{-.083 1 .649}** _{.000 -.161 .067 .188 .104 .395}**

Sig. (2-tailed) .266 .298 .175 .760 .134 .364 .002 .143 .573 .007 .441 .000 .995 .134 .536 .080 .334 .000

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x13 Pearson Correlation -.160 .084 .092 -.149 -.283** _{.140 .102 .055 -.076 .280}** _{-.090 .649}** _{1 .008 -.093 .210}* _.279** _{.046 .319}**

Sig. (2-tailed) .137 .436 .393 .166 .008 .194 .343 .608 .483 .008 .403 .000 .941 .387 .049 .008 .672 .002

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x14 Pearson Correlation -.065 .201 .246* _{.097 .251}* _.243* _{-.012 .262}* _.467** _.228* _{.073 .000 .008 1 .250}* _.364** _.324** _{.202 .542}**

Sig. (2-tailed) .546 .060 .021 .368 .018 .022 .910 .014 .000 .033 .499 .995 .941 .019 .000 .002 .060 .000

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x15 Pearson Correlation .385** _{-.162 .000 .099 .196 .032 .102 .163 .192 .069 .292}** _{-.161 -.093 .250}* _{1 .223}* _{.110 .131 .348}**

Sig. (2-tailed) .000 .132 .999 .359 .068 .769 .345 .129 .074 .524 .006 .134 .387 .019 .037 .309 .226 .001

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x16 Pearson Correlation .161 .024 .146 .042 .096 .170 .006 .151 .283** _.393** _{.169 .067 .210}* _.364** _.223* _{1 .350}** _.321** _.529**

Sig. (2-tailed) .135 .825 .174 .695 .376 .114 .957 .162 .007 .000 .115 .536 .049 .000 .037 .001 .002 .000

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x17 Pearson Correlation -.081 .212* _{.180 -.030 -.121 .017 .113 .222}* _.262* _{.067 .134 .188 .279}** _.324** _{.110 .350}** _{1 .269}* _.468**

Sig. (2-tailed) .453 .048 .093 .778 .262 .874 .293 .038 .014 .533 .215 .080 .008 .002 .309 .001 .011 .000

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

x18 Pearson Correlation .004 .135 .071 .008 .093 .214* _{.161 .114 .253}* _{.138 .043 .104 .046 .202 .131 .321}** _.269* _{1 .426}**

Sig. (2-tailed) .971 .212 .513 .944 .390 .045 .135 .292 .018 .199 .692 .334 .672 .060 .226 .002 .011 .000

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

y Pearson Correlation .282** _.356** _.466** _.349** _.376** _.454** _.451** _.483** _.383** _.488** _.400** _.395** _.319** _.542** _.348** _.529** _.468** _.426** ₁

Sig. (2-tailed) .008 .001 .000 .001 .000 .000 .000 .000 .000 .000 .000 .000 .002 .000 .001 .000 .000 .000

N 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88

Total Variance Explained

Component

Initial Eigenvalues

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Total

% of Variance

Cumulative

% Total

% of Variance

Cumulative

% Total

% of Variance

Cumulative %

1 _{3,308 18,377 18,377 3,308 18,377 18,377 2,487 13,817 13,817}

2 _{2,414 13,413 31,790 2,414 13,413 31,790 2,188 12,156 25,973}

3 _{1,900 10,555 42,344 1,900 10,555 42,344 2,091 11,618 37,591}

4 _{1,719 9,551 51,896 1,719 9,551 51,896 1,968 10,932 48,523}

5 _{1,259 6,996 58,892 1,259 6,996 58,892 1,624 9,021 57,544}

6 _{1,113 6,182 65,074 1,113 6,182 65,074 1,355 7,530 65,074}

7 _{,943 5,237 70,311}

8 _{,834 4,635 74,946}

9 _{,793 4,408 79,354}

10 _{,739 4,103 83,457}

11 _{,623 3,461 86,919}

12 _{,527 2,926 89,845}

13 _{,419 2,328 92,173}

14 _{,347 1,930 94,103}

15 _{,321 1,784 95,886}

16 _{,269 1,493 97,379}

17 _{,258 1,436 98,815}

18 _{,213 1,185 100,000}

Communalities Initial Extraction

x1 1,000 ,623

x2 1,000 ,693

x3 1,000 ,784

x4 1,000 ,722

x5 1,000 ,712

x6 1,000 ,692

x7 1,000 ,722

x8 1,000 ,644

x9 1,000 ,759

x10 1,000 ,518

x11 1,000 ,629

x12 1,000 ,760

x13 1,000 ,752

x14 1,000 ,628

x15 1,000 ,541

x16 1,000 ,531

x17 1,000 ,694

x18 1,000 ,310

Variabel li1 li2 li3 li4 li5 li6 x1 -0.020 -0.178 0.592 -0.284 0.313 0.249 x2 0.217 0.074 -0.244 0.702 -0.169 0.245 x3 0.113 0.122 -0.086 0.775 0.377 -0.076 x4 -0.066 -0.138 0.437 0.712 -0.012 -0.035 x5 -0.012 -0.334 0.213 0.268 0.650 0.247

x6 0.174 0.099 0.064 0.085 0.129 0.790

x7 0.035 0.316 0.692 0.271 -0.242 0.101

x8 0.173 0.177 0.129 -0.026 0.752 0.003

x9 0.670 -0.185 -0.046 0.092 -0.291 0.426 x10 0.273 0.415 0.322 -0.129 0.079 0.380 x11 0.079 -0.032 0.757 0.000 0.220 -0.012 x12 -0.008 0.858 0.042 0.134 0.013 0.057 x13 0.125 0.849 -0.109 -0.058 0.011 -0.004 x14 0.678 -0.097 -0.101 0.195 0.283 0.173 x15 0.415 -0.290 0.460 -0.153 0.174 -0.139 x16 0.665 0.148 0.131 -0.085 0.197 0.060 x17 0.686 0.281 0.010 0.122 0.027 -0.359 x18 0.521 0.074 0.089 0.060 -0.046 0.142 Component Matrixa

Component

1 2 3 4 5 6

x1 0,277 -0,634 0,341 0,018 0,083 0,146 x2 0,303 0,425 -0,583 0,193 0,196 0,077 x3 0,407 0,185 -0,494 0,482 -0,327 -0,027 x4 0,298 -0,193 -0,372 0,590 0,220 -0,250 x5 0,373 -0,538 -0,278 0,218 -0,266 0,296 x6 0,478 0,009 -0,029 -0,043 0,283 0,617 x7 0,403 -0,010 0,275 0,450 0,473 -0,237 x8 0,454 -0,189 0,176 0,098 -0,574 0,179 x9 0,477 0,127 -0,299 -0,500 0,418 0,040 x10 0,514 0,096 0,416 -0,013 0,170 0,203 x11 0,420 -0,501 0,300 0,247 0,095 -0,204 x12 0,260 0,599 0,423 0,378 -0,051 0,093 x13 0,215 0,662 0,472 0,128 -0,150 0,074 x14 0,624 0,046 -0,313 -0,325 -0,178 0,034 x15 0,383 -0,468 0,094 -0,234 -0,032 -0,333 x16 0,613 0,065 0,123 -0,331 -0,126 -0,104 x17 0,487 0,348 0,021 -0,207 -0,233 -0,487 x18 0,466 0,124 -0,031 -0,238 0,113 -0,085

Rotated Component Matrixa Component

1 2 3 4 5 6

x1 -0,020 -0,178 0,592 -0,284 0,313 0,249 x2 0,217 0,074 -0,244 0,702 -0,169 0,245 x3 0,113 0,122 -0,086 0,775 0,377 -0,076 x4 -0,066 -0,138 0,437 0,712 -0,012 -0,035 x5 -0,012 -0,334 0,213 0,268 0,650 0,247 x6 0,174 0,099 0,064 0,085 0,129 0,790 x7 0,035 0,316 0,692 0,271 -0,242 0,101 x8 0,173 0,177 0,129 -0,026 0,752 0,003 x9 0,686 -0,185 -0,046 0,092 -0,291 0,426 x10 0,273 0,415 0,322 -0,129 0,079 0,380 x11 0,079 -0,032 0,757 0,000 0,220 -0,012 x12 -0,008 0,858 0,042 0,134 0,013 0,057 x13 0,125 0,849 -0,109 -0,058 0,011 -0,004 x14 0,678 -0,097 -0,101 0,195 0,283 0,173 x15 0,415 -0,290 0,460 -0,153 0,174 -0,139 x16 0,665 0,148 0,131 -0,085 0,197 0,060 x17 0,670 0,281 0,010 0,122 0,027 -0,359 x18 0,521 0,074 0,089 0,060 -0,046 0,142

Correlation Matrixa

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18

Correlation x1 1.000 -.180 -.162 .049 .301 .207 .180 .271 -.014 .142 .401 -.120 -.160 -.065 .385 .161 -.081 .004 x2 -.180 1.000 .497 .227 -.003 .225 .099 -.069 .320 -.020 -.168 .112 .084 .201 -.162 .024 .212 .135 x3 -.162 .497 1.000 .404 .293 .092 .062 .202 -.027 .036 .039 .146 .092 .246 .000 .146 .180 .071 x4 .049 .227 .404 1.000 .306 .054 .315 -.047 .018 -.020 .253 .033 -.149 .097 .099 .042 -.030 .008 x5 .301 -.003 .293 .306 1.000 .168 .019 .338 .023 .077 .248 -.161 -.283 .251 .196 .096 -.121 .093

x6 .207 .225 .092 .054 .168 1.000 .110 .144 .306 .193 .176 .098 .140 .243 .032 .170 .017 .214

x7 .180 .099 .062 .315 .019 .110 1.000 .128 .119 .273 .346 .322 .102 -.012 .102 .006 .113 .161

x8 .271 -.069 .202 -.047 .338 .144 .128 1.000 -.024 .130 .216 .157 .055 .262 .163 .151 .222 .114 x9 -.014 .320 -.027 .018 .023 .306 .119 -.024 1.000 .207 -.088 -.061 -.076 .467 .192 .283 .262 .253 x10 .142 -.020 .036 -.020 .077 .193 .273 .130 .207 1.000 .274 .286 .280 .228 .069 .393 .067 .138 x11 .401 -.168 .039 .253 .248 .176 .346 .216 -.088 .274 1.000 -.083 -.090 .073 .292 .169 .134 .043 x12 -.120 .112 .146 .033 -.161 .098 .322 .157 -.061 .286 -.083 1.000 .649 -.001 -.161 .067 .188 .104 x13 -.160 .084 .092 -.149 -.283 .140 .102 .055 -.076 .280 -.090 .649 1.000 .008 -.093 .210 .279 .046 x14 -.065 .201 .246 .097 .251 .243 -.012 .262 .467 .228 .073 -.001 .008 1.000 .250 .364 .324 .202 x15 .385 -.162 .000 .099 .196 .032 .102 .163 .192 .069 .292 -.161 -.093 .250 1.000 .223 .110 .131

x16 .161 .024 .146 .042 .096 .170 .006 .151 .283 .393 .169 .067 .210 .364 .223 1.000 .350 .321

x17 -.081 .212 .180 -.030 -.121 .017 .113 .222 .262 .067 .134 .188 .279 .324 .110 .350 1.000 .269

x18 .004 .135 .071 .008 .093 .214 .161 .114 .253 .138 .043 .104 .046 .202 .131 .321 .269 1.000

Sig. (1-tailed)

x1 .046 .066 .326 .002 .027 .046 .005 .449 .093 .000 .133 .069 .273 .000 .067 .227 .485

x2 .046 .000 .017 .490 .018 .179 .263 .001 .427 .059 .149 .218 .030 .066 .413 .024 .106

x3 .066 .000 .000 .003 .196 .283 .030 .401 .369 .358 .087 .197 .010 .499 .087 .046 .256

x4 .326 .017 .000 .002 .310 .001 .333 .434 .427 .009 .380 .083 .184 .179 .348 .389 .472

x5 .002 .490 .003 .002 .059 .429 .001 .415 .237 .010 .067 .004 .009 .034 .188 .131 .195

x6 .027 .018 .196 .310 .059 .155 .091 .002 .035 .050 .182 .097 .011 .384 .057 .437 .023

x7 .046 .179 .283 .001 .429 .155 .117 .134 .005 .000 .001 .171 .455 .172 .479 .146 .067

x8 .005 .263 .030 .333 .001 .091 .117 .414 .114 .022 .072 .304 .007 .065 .081 .019 .146

x9 .449 .001 .401 .434 .415 .002 .134 .414 .027 .208 .286 .241 .000 .037 .004 .007 .009

x10 .093 .427 .369 .427 .237 .035 .005 .114 .027 .005 .003 .004 .016 .262 .000 .266 .099

x11 .000 .059 .358 .009 .010 .050 .000 .022 .208 .005 .220 .201 .249 .003 .058 .107 .346

x12 .133 .149 .087 .380 .067 .182 .001 .072 .286 .003 .220 .000 .498 .067 .268 .040 .167

x13 .069 .218 .197 .083 .004 .097 .171 .304 .241 .004 .201 .000 .470 .193 .025 .004 .336

x14 .273 .030 .010 .184 .009 .011 .455 .007 .000 .016 .249 .498 .470 .009 .000 .001 .030

x15 .000 .066 .499 .179 .034 .384 .172 .065 .037 .262 .003 .067 .193 .009 .019 .154 .113

x16 .067 .413 .087 .348 .188 .057 .479 .081 .004 .000 .058 .268 .025 .000 .019 .000 .001

x17 .227 .024 .046 .389 .131 .437 .146 .019 .007 .266 .107 .040 .004 .001 .154 .000 .006

x18 .485 .106 .256 .472 .195 .023 .067 .146 .009 .099 .346 .167 .336 .030 .113 .001 .006

Anti-image Matrices

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18

Anti-image Covariance

x1 ,541 -,060 ,131 ,017 -,116 -,129 -,054 -,128 ,044 -,024 -,081 -,002 ,060 ,140 -,208 -,119 ,024 ,089

x2 -,060 ,507 -,249 -,039 ,057 -,085 -,037 ,087 -,146 ,029 ,074 ,017 -,018 -,016 ,126 ,083 -,069 -,042

x3 ,131 -,249 ,466 -,148 -,124 -,006 ,027 -,110 ,133 -,022 ,007 -,018 -,006 -,023 -,068 -,076 -,034 ,039

x4 ,017 -,039 -,148 ,591 -,121 ,016 -,170 ,154 ,000 ,105 -,107 -,069 ,071 -,023 -,027 -,063 ,054 ,058

x5 -,116 ,057 -,124 -,121 ,573 -,032 ,067 -,146 -,003 -,034 -,026 ,024 ,070 -,093 ,015 ,027 ,104 -,079

x6 -,129 -,085 -,006 ,016 -,032 ,674 ,042 -,034 -,157 ,039 -,140 -,001 -,127 -,077 ,111 ,022 ,149 -,128

x7 -,054 -,037 ,027 -,170 ,067 ,042 ,594 -,060 -,094 -,099 -,153 -,140 ,020 ,070 -,018 ,111 -,009 -,101

x8 -,128 ,087 -,110 ,154 -,146 -,034 -,060 ,636 ,053 ,027 -,013 -,089 ,031 -,117 ,011 ,023 -,116 -,002

x9 ,044 -,146 ,133 ,000 -,003 -,157 -,094 ,053 ,480 -,104 ,142 ,027 ,097 -,144 -,116 -,073 -,109 -,002

x10 -,024 ,029 -,022 ,105 -,034 ,039 -,099 ,027 -,104 ,598 -,157 -,086 -,079 -,075 ,074 -,171 ,144 ,007

x11 -,081 ,074 ,007 -,107 -,026 -,140 -,153 -,013 ,142 -,157 ,534 ,094 ,040 -,001 -,078 -,005 -,160 ,046

x12 -,002 ,017 -,018 -,069 ,024 -,001 -,140 -,089 ,027 -,086 ,094 ,443 -,239 -,002 ,068 ,058 -,016 -,053

x13 ,060 -,018 -,006 ,071 ,070 -,127 ,020 ,031 ,097 -,079 ,040 -,239 ,425 ,030 -,069 -,095 -,105 ,078

x14 ,140 -,016 -,023 -,023 -,093 -,077 ,070 -,117 -,144 -,075 -,001 -,002 ,030 ,538 -,122 -,076 -,089 ,035

x15 -,208 ,126 -,068 -,027 ,015 ,111 -,018 ,011 -,116 ,074 -,078 ,068 -,069 -,122 ,659 ,000 ,014 -,081

x16 -,119 ,083 -,076 -,063 ,027 ,022 ,111 ,023 -,073 -,171 -,005 ,058 -,095 -,076 ,000 ,572 -,113 -,168

x17 ,024 -,069 -,034 ,054 ,104 ,149 -,009 -,116 -,109 ,144 -,160 -,016 -,105 -,089 ,014 -,113 ,574 -,108

x18 ,089 -,042 ,039 ,058 -,079 -,128 -,101 -,002 -,002 ,007 ,046 -,053 ,078 ,035 -,081 -,168 -,108 ,767

Anti-image Correlation

x1 .571a _{-,114 ,260 ,030 -,209 -,213 -,096 -,218 ,086 -,043 -,151 -,005 ,125 ,260 -,349 -,213 ,043 ,139}

x2 -,114 .552a _{-,513 -,071 ,106 -,146 -,067 ,153 -,297 ,052 ,143 ,035 -,039 -,031 ,219 ,154 -,127 -,067}

x3 ,260 -,513 .532a _{-,283 -,241 -,012 ,052 -,202 ,280 -,041 ,014 -,039 -,013 -,046 -,123 -,146 -,066 ,066}

x4 ,030 -,071 -,283 .560a _{-,208 ,026 -,288 ,252 ,001 ,177 -,190 -,135 ,141 -,042 -,044 -,108 ,092 ,085}

x5 -,209 ,106 -,241 -,208 .682a _{-,052 ,116 -,242 -,005 -,059 -,047 ,048 ,142 -,167 ,024 ,047 ,181 -,119}

x6 -,213 -,146 -,012 ,026 -,052 .550a _{,067 -,053 -,276 ,062 -,233 -,003 -,238 -,127 ,167 ,035 ,239 -,177}

x7 -,096 -,067 ,052 -,288 ,116 ,067 .571a _{-,097 -,176 -,166 -,273 -,273 ,039 ,125 -,029 ,191 -,016 -,150}

x8 -,218 ,153 -,202 ,252 -,242 -,053 -,097 .597a _{,096 ,044 -,022 -,167 ,060 -,200 ,017 ,038 -,191 -,003}

x9 ,086 -,297 ,280 ,001 -,005 -,276 -,176 ,096 .535a _{-,195 ,280 ,059 ,215 -,284 -,207 -,139 -,208 -,003}

x10 -,043 ,052 -,041 ,177 -,059 ,062 -,166 ,044 -,195 .615a _{-,279 -,166 -,157 -,132 ,117 -,292 ,246 ,010}

x11 -,151 ,143 ,014 -,190 -,047 -,233 -,273 -,022 ,280 -,279 .592a _{,194 ,084 -,003 -,131 -,008 -,288 ,071}

x12 -,005 ,035 -,039 -,135 ,048 -,003 -,273 -,167 ,059 -,166 ,194 .600a _{-,551 -,005 ,127 ,115 -,031 -,091}

x13 ,125 -,039 -,013 ,141 ,142 -,238 ,039 ,060 ,215 -,157 ,084 -,551 .570a _{,062 -,131 -,192 -,212 ,137}

x14 ,260 -,031 -,046 -,042 -,167 -,127 ,125 -,200 -,284 -,132 -,003 -,005 ,062 .717a _{-,205 -,137 -,161 ,055}

x15 -,349 ,219 -,123 -,044 ,024 ,167 -,029 ,017 -,207 ,117 -,131 ,127 -,131 -,205 .598a _{,001 ,022 -,114}

x16 -,213 ,154 -,146 -,108 ,047 ,035 ,191 ,038 -,139 -,292 -,008 ,115 -,192 -,137 ,001 .663a _{-,198 -,254}

x17 ,043 -,127 -,066 ,092 ,181 ,239 -,016 -,191 -,208 ,246 -,288 -,031 -,212 -,161 ,022 -,198 .584a _-,163

x18 ,139 -,067 ,066 ,085 -,119 -,177 -,150 -,003 -,003 ,010 ,071 -,091 ,137 ,055 -,114 -,254 -,163 .648a

HASIL PERHITUNGAN RELIABILITAS

Case Processing Summary

N %

Cases Valid 88 100.0

Excludeda _{0 .0}

Total 88 100.0

a. Listwise deletion based on all variables in the procedure.

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha Based on Standardized

Items N of Items

,711 ,711 18

KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.595 Bartlett's Test of

Sphericity

Approx. Chi-Square 446.174

df 153

Sig. .000

Item-Total Statistics Scale Mean if

Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item

Deleted

x1 41.92 48.143 .162 .711

x2 42.36 46.395 .204 .710

x3 41.72 45.654 .355 .693

x4 41.81 47.100 .224 .706

x5 42.08 46.465 .242 .705

x6 43.48 46.551 .362 .694

x7 42.59 46.222 .349 .694

x8 41.69 44.123 .338 .694

x9 43.47 47.332 .286 .700

x10 42.83 45.499 .383 .691

x11 42.90 46.760 .292 .699

x12 41.95 45.492 .235 .708

x13 42.17 47.132 .171 .713

x14 42.76 44.276 .433 .685

x15 42.88 47.214 .227 .705

x16 43.15 45.507 .440 .687

x17 43.01 45.667 .359 .693

x18 43.40 46.196 .312 .697

Communalities Initial Extraction

x1 1.000 .623

x2 1.000 .693

x3 1.000 .784

x4 1.000 .722

x5 1.000 .712

x6 1.000 .692

x7 1.000 .722

x8 1.000 .644

x9 1.000 .759

x10 1.000 .518

x11 1.000 .629

x12 1.000 .760

x13 1.000 .752

x14 1.000 .628

x15 1.000 .541

x16 1.000 .531

x17 1.000 .694

x18 1.000 .310

Extraction Method: Principal Component Analysis.

Correlation Matrixa

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18

Correlation x1 1,000 -,180 -,162 ,049 ,301 ,207 ,180 ,271 -,014 ,142 ,401 -,120 -,160 -,065 ,385 ,161 -,081 ,004

x2 -,180 1,000 ,497 ,227 -,003 ,225 ,099 -,069 ,320 -,020 -,168 ,112 ,084 ,201 -,162 ,024 ,212 ,135

x3 -,162 ,497 1,000 ,404 ,293 ,092 ,062 ,202 -,027 ,036 ,039 ,146 ,092 ,246 ,000 ,146 ,180 ,071

x4 ,049 ,227 ,404 1,000 ,306 ,054 ,315 -,047 ,018 -,020 ,253 ,033 -,149 ,097 ,099 ,042 -,030 ,008

x5 ,301 -,003 ,293 ,306 1,000 ,168 ,019 ,338 ,023 ,077 ,248 -,161 -,283 ,251 ,196 ,096 -,121 ,093

x6 ,207 ,225 ,092 ,054 ,168 1,000 ,110 ,144 ,306 ,193 ,176 ,098 ,140 ,243 ,032 ,170 ,017 ,214

x7 ,180 ,099 ,062 ,315 ,019 ,110 1,000 ,128 ,119 ,273 ,346 ,322 ,102 -,012 ,102 ,006 ,113 ,161

x8 ,271 -,069 ,202 -,047 ,338 ,144 ,128 1,000 -,024 ,130 ,216 ,157 ,055 ,262 ,163 ,151 ,222 ,114

x9 -,014 ,320 -,027 ,018 ,023 ,306 ,119 -,024 1,000 ,207 -,088 -,061 -,076 ,467 ,192 ,283 ,262 ,253

x10 ,142 -,020 ,036 -,020 ,077 ,193 ,273 ,130 ,207 1,000 ,274 ,286 ,280 ,228 ,069 ,393 ,067 ,138

x11 ,401 -,168 ,039 ,253 ,248 ,176 ,346 ,216 -,088 ,274 1,000 -,083 -,090 ,073 ,292 ,169 ,134 ,043

x12 -,120 ,112 ,146 ,033 -,161 ,098 ,322 ,157 -,061 ,286 -,083 1,000 ,649 -,001 -,161 ,067 ,188 ,104

x13 -,160 ,084 ,092 -,149 -,283 ,140 ,102 ,055 -,076 ,280 -,090 ,649 1,000 ,008 -,093 ,210 ,279 ,046

x14 -,065 ,201 ,246 ,097 ,251 ,243 -,012 ,262 ,467 ,228 ,073 -,001 ,008 1,000 ,250 ,364 ,324 ,202

x15 ,385 -,162 ,000 ,099 ,196 ,032 ,102 ,163 ,192 ,069 ,292 -,161 -,093 ,250 1,000 ,223 ,110 ,131

x16 ,161 ,024 ,146 ,042 ,096 ,170 ,006 ,151 ,283 ,393 ,169 ,067 ,210 ,364 ,223 1,000 ,350 ,321

x17 -,081 ,212 ,180 -,030 -,121 ,017 ,113 ,222 ,262 ,067 ,134 ,188 ,279 ,324 ,110 ,350 1,000 ,269

x18 ,004 ,135 ,071 ,008 ,093 ,214 ,161 ,114 ,253 ,138 ,043 ,104 ,046 ,202 ,131 ,321 ,269 1,000

Sig. (1-tailed)

x1 ,046 ,066 ,326 ,002 ,027 ,046 ,005 ,449 ,093 ,000 ,133 ,069 ,273 ,000 ,067 ,227 ,485

x2 ,046 ,000 ,017 ,490 ,018 ,179 ,263 ,001 ,427 ,059 ,149 ,218 ,030 ,066 ,413 ,024 ,106

x3 ,066 ,000 ,000 ,003 ,196 ,283 ,030 ,401 ,369 ,358 ,087 ,197 ,010 ,499 ,087 ,046 ,256

x4 ,326 ,017 ,000 ,002 ,310 ,001 ,333 ,434 ,427 ,009 ,380 ,083 ,184 ,179 ,348 ,389 ,472

x5 ,002 ,490 ,003 ,002 ,059 ,429 ,001 ,415 ,237 ,010 ,067 ,004 ,009 ,034 ,188 ,131 ,195

x6 ,027 ,018 ,196 ,310 ,059 ,155 ,091 ,002 ,035 ,050 ,182 ,097 ,011 ,384 ,057 ,437 ,023

x7 ,046 ,179 ,283 ,001 ,429 ,155 ,117 ,134 ,005 ,000 ,001 ,171 ,455 ,172 ,479 ,146 ,067

x8 ,005 ,263 ,030 ,333 ,001 ,091 ,117 ,414 ,114 ,022 ,072 ,304 ,007 ,065 ,081 ,019 ,146

x9 ,449 ,001 ,401 ,434 ,415 ,002 ,134 ,414 ,027 ,208 ,286 ,241 ,000 ,037 ,004 ,007 ,009

x10 ,093 ,427 ,369 ,427 ,237 ,035 ,005 ,114 ,027 ,005 ,003 ,004 ,016 ,262 ,000 ,266 ,099

x11 ,000 ,059 ,358 ,009 ,010 ,050 ,000 ,022 ,208 ,005 ,220 ,201 ,249 ,003 ,058 ,107 ,346

x12 ,133 ,149 ,087 ,380 ,067 ,182 ,001 ,072 ,286 ,003 ,220 ,000 ,498 ,067 ,268 ,040 ,167

x13 ,069 ,218 ,197 ,083 ,004 ,097 ,171 ,304 ,241 ,004 ,201 ,000 ,470 ,193 ,025 ,004 ,336

x14 ,273 ,030 ,010 ,184 ,009 ,011 ,455 ,007 ,000 ,016 ,249 ,498 ,470 ,009 ,000 ,001 ,030

x15 ,000 ,066 ,499 ,179 ,034 ,384 ,172 ,065 ,037 ,262 ,003 ,067 ,193 ,009 ,019 ,154 ,113

x16 ,067 ,413 ,087 ,348 ,188 ,057 ,479 ,081 ,004 ,000 ,058 ,268 ,025 ,000 ,019 ,000 ,001

x17 ,227 ,024 ,046 ,389 ,131 ,437 ,146 ,019 ,007 ,266 ,107 ,040 ,004 ,001 ,154 ,000 ,006

x18 ,485 ,106 ,256 ,472 ,195 ,023 ,067 ,146 ,009 ,099 ,346 ,167 ,336 ,030 ,113 ,001 ,006

Anti-image Matrices

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18

Anti-image Covariance

x1 ,541 -,060 ,131 ,017 -,116 -,129 -,054 -,128 ,044 -,024 -,081 -,002 ,060 ,140 -,208 -,119 ,024 ,089

x2 -,060 ,507 -,249 -,039 ,057 -,085 -,037 ,087 -,146 ,029 ,074 ,017 -,018 -,016 ,126 ,083 -,069 -,042

x3 ,131 -,249 ,466 -,148 -,124 -,006 ,027 -,110 ,133 -,022 ,007 -,018 -,006 -,023 -,068 -,076 -,034 ,039

x4 ,017 -,039 -,148 ,591 -,121 ,016 -,170 ,154 ,000 ,105 -,107 -,069 ,071 -,023 -,027 -,063 ,054 ,058

x5 -,116 ,057 -,124 -,121 ,573 -,032 ,067 -,146 -,003 -,034 -,026 ,024 ,070 -,093 ,015 ,027 ,104 -,079

x6 -,129 -,085 -,006 ,016 -,032 ,674 ,042 -,034 -,157 ,039 -,140 -,001 -,127 -,077 ,111 ,022 ,149 -,128

x7 -,054 -,037 ,027 -,170 ,067 ,042 ,594 -,060 -,094 -,099 -,153 -,140 ,020 ,070 -,018 ,111 -,009 -,101

x8 -,128 ,087 -,110 ,154 -,146 -,034 -,060 ,636 ,053 ,027 -,013 -,089 ,031 -,117 ,011 ,023 -,116 -,002

x9 ,044 -,146 ,133 ,000 -,003 -,157 -,094 ,053 ,480 -,104 ,142 ,027 ,097 -,144 -,116 -,073 -,109 -,002

x10 -,024 ,029 -,022 ,105 -,034 ,039 -,099 ,027 -,104 ,598 -,157 -,086 -,079 -,075 ,074 -,171 ,144 ,007

x11 -,081 ,074 ,007 -,107 -,026 -,140 -,153 -,013 ,142 -,157 ,534 ,094 ,040 -,001 -,078 -,005 -,160 ,046

x12 -,002 ,017 -,018 -,069 ,024 -,001 -,140 -,089 ,027 -,086 ,094 ,443 -,239 -,002 ,068 ,058 -,016 -,053

x13 ,060 -,018 -,006 ,071 ,070 -,127 ,020 ,031 ,097 -,079 ,040 -,239 ,425 ,030 -,069 -,095 -,105 ,078

x14 ,140 -,016 -,023 -,023 -,093 -,077 ,070 -,117 -,144 -,075 -,001 -,002 ,030 ,538 -,122 -,076 -,089 ,035

x15 -,208 ,126 -,068 -,027 ,015 ,111 -,018 ,011 -,116 ,074 -,078 ,068 -,069 -,122 ,659 ,000 ,014 -,081

x16 -,119 ,083 -,076 -,063 ,027 ,022 ,111 ,023 -,073 -,171 -,005 ,058 -,095 -,076 ,000 ,572 -,113 -,168

x17 ,024 -,069 -,034 ,054 ,104 ,149 -,009 -,116 -,109 ,144 -,160 -,016 -,105 -,089 ,014 -,113 ,574 -,108

x18 ,089 -,042 ,039 ,058 -,079 -,128 -,101 -,002 -,002 ,007 ,046 -,053 ,078 ,035 -,081 -,168 -,108 ,767

Anti-image Correlation

x1 .571a _{-,114 ,260 ,030 -,209 -,213 -,096 -,218 ,086 -,043 -,151 -,005 ,125 ,260 -,349 -,213 ,043 ,139}

x2 -,114 .552a _{-,513 -,071 ,106 -,146 -,067 ,153 -,297 ,052 ,143 ,035 -,039 -,031 ,219 ,154 -,127 -,067}

x3 ,260 -,513 .532a _{-,283 -,241 -,012 ,052 -,202 ,280 -,041 ,014 -,039 -,013 -,046 -,123 -,146 -,066 ,066}

x4 ,030 -,071 -,283 .560a _{-,208 ,026 -,288 ,252 ,001 ,177 -,190 -,135 ,141 -,042 -,044 -,108 ,092 ,085}

x5 -,209 ,106 -,241 -,208 .682a _{-,052 ,116 -,242 -,005 -,059 -,047 ,048 ,142 -,167 ,024 ,047 ,181 -,119}

x6 -,213 -,146 -,012 ,026 -,052 .550a _{,067 -,053 -,276 ,062 -,233 -,003 -,238 -,127 ,167 ,035 ,239 -,177}

x7 -,096 -,067 ,052 -,288 ,116 ,067 .571a _{-,097 -,176 -,166 -,273 -,273 ,039 ,125 -,029 ,191 -,016 -,150}

x8 -,218 ,153 -,202 ,252 -,242 -,053 -,097 .597a _{,096 ,044 -,022 -,167 ,060 -,200 ,017 ,038 -,191 -,003}

x9 ,086 -,297 ,280 ,001 -,005 -,276 -,176 ,096 .535a _{-,195 ,280 ,059 ,215 -,284 -,207 -,139 -,208 -,003}

x10 -,043 ,052 -,041 ,177 -,059 ,062 -,166 ,044 -,195 .615a _{-,279 -,166 -,157 -,132 ,117 -,292 ,246 ,010}

x11 -,151 ,143 ,014 -,190 -,047 -,233 -,273 -,022 ,280 -,279 .592a _{,194 ,084 -,003 -,131 -,008 -,288 ,071}

x12 -,005 ,035 -,039 -,135 ,048 -,003 -,273 -,167 ,059 -,166 ,194 .600a _{-,551 -,005 ,127 ,115 -,031 -,091}

x13 ,125 -,039 -,013 ,141 ,142 -,238 ,039 ,060 ,215 -,157 ,084 -,551 .570a _{,062 -,131 -,192 -,212 ,137}

x14 ,260 -,031 -,046 -,042 -,167 -,127 ,125 -,200 -,284 -,132 -,003 -,005 ,062 .717a _{-,205 -,137 -,161 ,055}

x15 -,349 ,219 -,123 -,044 ,024 ,167 -,029 ,017 -,207 ,117 -,131 ,127 -,131 -,205 .598a _{,001 ,022 -,114}

x16 -,213 ,154 -,146 -,108 ,047 ,035 ,191 ,038 -,139 -,292 -,008 ,115 -,192 -,137 ,001 .663a _{-,198 -,254}

x17 ,043 -,127 -,066 ,092 ,181 ,239 -,016 -,191 -,208 ,246 -,288 -,031 -,212 -,161 ,022 -,198 .584a _-,163

x18 ,139 -,067 ,066 ,085 -,119 -,177 -,150 -,003 -,003 ,010 ,071 -,091 ,137 ,055 -,114 -,254 -,163 .648a

Total Variance Explained

Com pone nt

Initial Eigenvalues

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings Total % of Varianc e Cumulativ

e % Total

% of Varianc

e

Cumulati

ve % Total

% of Varianc

e

Cumulative % 1 3,308 18,377 18,377 3,308 18,377 18,377 2,487 13,817 13,817 2 2,414 13,413 31,790 2,414 13,413 31,790 2,188 12,156 25,973 3 1,900 10,555 42,344 1,900 10,555 42,344 2,091 11,618 37,591 4 1,719 9,551 51,896 1,719 9,551 51,896 1,968 10,932 48,523

5 1,259 6,996 58,892 1,259 6,996 58,892 1,624 9,021 57,544

6 1,113 6,182 65,074 1,113 6,182 65,074 1,355 7,530 65,074

7 ,943 5,237 70,311

8 ,834 4,635 74,946

9 ,793 4,408 79,354

10 ,739 4,103 83,457

11 ,623 3,461 86,919

12 ,527 2,926 89,845

13 ,419 2,328 92,173

14 ,347 1,930 94,103

15 ,321 1,784 95,886

16 ,269 1,493 97,379

17 ,258 1,436 98,815

18 ,213 1,185 100,000

Component Matrixa Component

1 2 3 4 5 6

x1 .277 -.634 .341 .018 .083 .146

x2 .303 .425 -.583 .193 .196 .077

x3 .407 .185 -.494 .482 -.327 -.027

x4 .298 -.193 -.372 .590 .220 -.250

x5 .373 -.538 -.278 .218 -.266 .296

x6 .478 .009 -.029 -.043 .283 .617

x7 .403 -.010 .275 .450 .473 -.237

x8 .454 -.189 .176 .098 -.574 .179

x9 .477 .127 -.299 -.500 .418 .040

x10 .514 .096 .416 -.013 .170 .203

x11 .420 -.501 .300 .247 .095 -.204

x12 .260 .599 .423 .378 -.051 .093

x13 .215 .662 .472 .128 -.150 .074

x14 .624 .046 -.313 -.325 -.178 .034

x15 .383 -.468 .094 -.234 -.032 -.333

x16 .613 .065 .123 -.331 -.126 -.104

x17 .487 .348 .021 -.207 -.233 -.487

x18 .466 .124 -.031 -.238 .113 -.085

Extraction Method: Principal Component Analysis. a. 6 components extracted.

Rotated Component Matrixa Component

1 2 3 4 5 6

x1 -.020 -.178 .592 -.284 .313 .249

x2 .217 .074 -.244 .702 -.169 .245

x3 .113 .122 -.086 .775 .377 -.076

x4 -.066 -.138 .437 .712 -.012 -.035

x5 -.012 -.334 .213 .268 .650 .247

x6 .174 .099 .064 .085 .129 .415

x7 .035 .316 .692 .271 -.242 .101

x8 .173 .177 .129 -.026 .752 .003

x9 .686 -.185 -.046 .092 -.291 .426

x10 .273 .790 .322 -.129 .079 .380

x11 .079 -.032 .757 3.100E-5 .220 -.012

x12 -.008 .858 .042 .134 .013 .057

x13 .125 .849 -.109 -.058 .011 -.004

x14 .678 -.097 -.101 .195 .283 .173

x15 .415 -.290 .460 -.153 .174 -.139

x16 .665 .148 .131 -.085 .197 .060

x17 .670 .281 .010 .122 .027 -.359

x18 .521 .074 .089 .060 -.046 .142

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 15 iterations.

Component Transformation Matrix Comp

onent 1 2 3 4 5 6

1 .708 .217 .400 .302 .329 .303

2 .186 .702 -.547 .211 -.357 -.037

3 -.119 .580 .424 -.683 .030 -.049

4 -.599 .325 .357 .615 .152 -.083

5 -.036 -.108 .362 .071 -.792 .472

6 -.300 .081 -.320 -.121 .335 .821

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

LAMPIRAN 6

PERHITUNGAN KMO DAN MSA

Untuk menghitung KMO dan MSA maka diperlukan matriks korelasi sederhana dan matriks korelasi parsial yang semua entrinya telah dikuadratkan. Berikut ini akan disajikan matriks korelasi sederhana dan matriks korelasi parsial yang semua entrinya telah dikuadratkan

MATRIKS KORELASI SEDERHANA [Rij]

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18

x1 1,00 -0,18 -0,16 0,05 0,30 0,21 0,18 0,27 -0,01 0,14 0,40 -0,12 -0,16 -0,07 0,39 0,16 -0,08 0,00

x2 -0,18 1,00 0,50 0,23 0,00 0,22 0,10 -0,07 0,32 -0,02 -0,17 0,11 0,08 0,20 -0,16 0,02 0,21 0,13

x3 -0,16 0,50 1,00 0,40 0,29 0,09 0,06 0,20 -0,03 0,04 0,04 0,15 0,09 0,25 0,00 0,15 0,18 0,07

x4 0,05 0,23 0,40 1,00 0,31 0,05 0,31 -0,05 0,02 -0,02 0,25 0,03 -0,15 0,10 0,10 0,04 -0,03 0,01

x5 0,30 0,00 0,29 0,31 1,00 0,17 0,02 0,34 0,02 0,08 0,25 -0,16 -0,28 0,25 0,20 0,10 -0,12 0,09

x6 0,21 0,22 0,09 0,05 0,17 1,00 0,11 0,14 0,31 0,19 0,18 0,10 0,14 0,24 0,03 0,17 0,02 0,21

x7 0,18 0,10 0,06 0,31 0,02 0,11 1,00 0,13 0,12 0,27 0,35 0,32 0,10 -0,01 0,10 0,01 0,11 0,16

x8 0,27 -0,07 0,20 -0,05 0,34 0,14 0,13 1,00 -0,02 0,13 0,22 0,16 0,06 0,26 0,16 0,15 0,22 0,11

∑ = (rij)= x9 -0,01 0,32 -0,03 0,02 0,02 0,31 0,12 -0,02 1,00 0,21 -0,09 -0,06 -0,08 0,47 0,19 0,28 0,26 0,25

x10 0,14 -0,02 0,04 -0,02 0,08 0,19 0,27 0,13 0,21 1,00 0,27 0,29 0,28 0,23 0,07 0,39 0,07 0,14

x11 0,40 -0,17 0,04 0,25 0,25 0,18 0,35 0,22 -0,09 0,27 1,00 -0,08 -0,09 0,07 0,29 0,17 0,13 0,04

x12 -0,12 0,11 0,15 0,03 -0,16 0,10 0,32 0,16 -0,06 0,29 -0,08 1,00 0,65 0,00 -0,16 0,07 0,19 0,10

x13 -0,16 0,08 0,09 -0,15 -0,28 0,14 0,10 0,06 -0,08 0,28 -0,09 0,65 1,00 0,01 -0,09 0,21 0,28 0,05

x14 -0,07 0,20 0,25 0,10 0,25 0,24 -0,01 0,26 0,47 0,23 0,07 0,00 0,01 1,00 0,25 0,36 0,32 0,20

x15 0,39 -0,16 0,00 0,10 0,20 0,03 0,10 0,16 0,19 0,07 0,29 -0,16 -0,09 0,25 1,00 0,22 0,11 0,13

x16 0,16 0,02 0,15 0,04 0,10 0,17 0,01 0,15 0,28 0,39 0,17 0,07 0,21 0,36 0,22 1,00 0,35 0,32

x17 -0,08 0,21 0,18 -0,03 -0,12 0,02 0,11 0,22 0,26 0,07 0,13 0,19 0,28 0,32 0,11 0,35 1,00 0,27

x18 0,00 0,13 0,07 0,01 0,09 0,21 0,16 0,11 0,25 0,14 0,04 0,10 0,05 0,20 0,13 0,32 0,27 1,00

LANJUTAN LAMPIRAN 6

MATRIKS KORELASI PARSIAL

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18

x1 0,05 0,07 0,33 0,00 0,03 0,05 0,01 0,45 0,09 0,00 0,13 0,07 0,27 0,00 0,07 0,23 0,49

x2 0,05 0,00 0,02 0,49 0,02 0,18 0,26 0,00 0,43 0,06 0,15 0,22 0,03 0,07 0,41 0,02 0,11

x3 0,07 0,00 0,00 0,00 0,20 0,28 0,03 0,40 0,37 0,36 0,09 0,20 0,01 0,50 0,09 0,05 0,26

x4 0,33 0,02 0,00 0,00 0,31 0,00 0,33 0,43 0,43 0,01 0,38 0,08 0,18 0,18 0,35 0,39 0,47

x5 0,00 0,49 0,00 0,00 0,06 0,43 0,00 0,41 0,24 0,01 0,07 0,00 0,01 0,03 0,19 0,13 0,19

x6 0,03 0,02 0,20 0,31 0,06 0,15 0,09 0,00 0,04 0,05 0,18 0,10 0,01 0,38 0,06 0,44 0,02

x7 0,05 0,18 0,28 0,00 0,43 0,15 0,12 0,13 0,01 0,00 0,00 0,17 0,45 0,17 0,48 0,15 0,07

x8 0,01 0,26 0,03 0,33 0,00 0,09 0,12 0,41 0,11 0,02 0,07 0,30 0,01 0,06 0,08 0,02 0,15

A = (aij)=

x9 0,45 0,00 0,40 0,43 0,41 0,00 0,13 0,41 0,03 0,21 0,29 0,24 0,00 0,04 0,00 0,01 0,01

x10 0,09 0,43 0,37 0,43 0,24 0,04 0,01 0,11 0,03 0,00 0,00 0,00 0,02 0,26 0,00 0,27 0,10

x11 0,00 0,06 0,36 0,01 0,01 0,05 0,00 0,02 0,21 0,00 0,22 0,20 0,25 0,00 0,06 0,11 0,35

x12 0,13 0,15 0,09 0,38 0,07 0,18 0,00 0,07 0,29 0,00 0,22 0,00 0,50 0,07 0,27 0,04 0,17

x13 0,07 0,22 0,20 0,08 0,00 0,10 0,17 0,30 0,24 0,00 0,20 0,00 0,47 0,19 0,02 0,00 0,34

x14 0,27 0,03 0,01 0,18 0,01 0,01 0,45 0,01 0,00 0,02 0,25 0,50 0,47 0,01 0,00 0,00 0,03

x15 0,00 0,07 0,50 0,18 0,03 0,38 0,17 0,06 0,04 0,26 0,00 0,07 0,19 0,01 0,02 0,15 0,11

x16 0,07 0,41 0,09 0,35 0,19 0,06 0,48 0,08 0,00 0,00 0,06 0,27 0,02 0,00 0,02 0,00 0,00

x17 0,23 0,02 0,05 0,39 0,13 0,44 0,15 0,02 0,01 0,27 0,11 0,04 0,00 0,00 0,15 0,00 0,01

x18 0,49 0,11 0,26 0,47 0,19 0,02 0,07 0,15 0,01 0,10 0,35 0,17 0,34 0,03 0,11 0,00 0,01

LANJUTAN LAMPIRAN 6

KUADRAT MATRIKS KORELASI SEDERHANA

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 Jumlah

x1

0,032 0,026 0,002 0,091 0,043 0,033 0,073 0,000 0,020 0,161 0,014 0,026 0,004 0,148 0,026 0,007 0,000

0,707 x2

0,032 0,247 0,052 0,000 0,051 0,010 0,005 0,102 0,000 0,028 0,013 0,007 0,041 0,026 0,001 0,045 0,018 0,677

x3

0,026 0,247 0,163 0,086 0,008 0,004 0,041 0,001 0,001 0,002 0,021 0,008 0,061 0,000 0,021 0,033 0,005 0,729

x4

0,002 0,052 0,163 0,094 0,003 0,099 0,002 0,000 0,000 0,064 0,001 0,022 0,009 0,010 0,002 0,001 0,000 0,526

x5

0,091 0,000 0,086 0,094 0,028 0,000 0,114 0,001 0,006 0,061 0,026 0,080 0,063 0,038 0,009 0,015 0,009 0,721

x6

0,043 0,051 0,008 0,003 0,028 0,012 0,021 0,094 0,037 0,031 0,010 0,020 0,059 0,001 0,029 0,000 0,046 0,493

x7

0,033 0,010 0,004 0,099 0,000 0,012 0,016 0,014 0,075 0,119 0,104 0,010 0,000 0,010 0,000 0,013 0,026 0,546

x8

0,073 0,005 0,041 0,002 0,114 0,021 0,016 0,001 0,017 0,047 0,025 0,003 0,068 0,027 0,023 0,049 0,013 0,544

∑ = (r2_ij)

x9

0,000 0,102 0,001 0,000 0,001 0,094 0,014 0,001 0,043 0,008 0,004 0,006 0,218 0,037 0,080 0,069 0,064 0,741

x10

0,020 0,000 0,001 0,000 0,006 0,037 0,075 0,017 0,043 0,075 0,082 0,079 0,052 0,005 0,154 0,005 0,019 0,670

x11

0,161 0,028 0,002 0,064 0,061 0,031 0,119 0,047 0,008 0,075 0,007 0,008 0,005 0,085 0,029 0,018 0,002 0,751

x12

0,014 0,013 0,021 0,001 0,026 0,010 0,104 0,025 0,004 0,082 0,007 0,421 0,000 0,026 0,004 0,035 0,011 0,803

x13

0,026 0,007 0,008 0,022 0,080 0,020 0,010 0,003 0,006 0,079 0,008 0,421 0,000 0,009 0,044 0,078 0,002 0,823

x14

0,004 0,041 0,061 0,009 0,063 0,059 0,000 0,068 0,218 0,052 0,005 0,000 0,000 0,063 0,132 0,105 0,041 0,922

x15

0,148 0,026 0,000 0,010 0,038 0,001 0,010 0,027 0,037 0,005 0,085 0,026 0,009 0,063 0,050 0,012 0,017 0,563

x16

0,026 0,001 0,021 0,002 0,009 0,029 0,000 0,023 0,080 0,154 0,029 0,004 0,044 0,132 0,050 0,122 0,103 0,829

x17

0,007 0,045 0,033 0,001 0,015 0,000 0,013 0,049 0,069 0,005 0,018 0,035 0,078 0,105 0,012 0,122 0,072 0,678

x18

0,000 0,018 0,005 0,000 0,009 0,046 0,026 0,013 0,064 0,019 0,002 0,011 0,002 0,041 0,017 0,103 0,072 0,447

Jumlah

12,169

LANJUTAN LAMPIRAN 6

KUADRAT MATRIKS KORELASI PARSIAL

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 Jumlah

x1

0,002 0,004 0,106 0,000 0,001 0,002 0,000 0,201 0,009 0,000 0,018 0,005 0,075 0,000 0,005 0,051 0,236 0,714 x2

0,002 0,000 0,000 0,240 0,000 0,032 0,069 0,000 0,182 0,003 0,022 0,048 0,001 0,004 0,170 0,001 0,011 0,786

x3

0,004 0,000 0,000 0,000 0,039 0,080 0,001 0,161 0,136 0,128 0,008 0,039 0,000 0,249 0,008 0,002 0,066 0,920

x4

0,106 0,000 0,000 0,000 0,096 0,000 0,111 0,188 0,182 0,000 0,144 0,007 0,034 0,032 0,121 0,151 0,223 1,396

x5

0,000 0,240 0,000 0,000 0,003 0,184 0,000 0,172 0,056 0,000 0,005 0,000 0,000 0,001 0,035 0,017 0,038 0,751

x6

0,001 0,000 0,039 0,096 0,003 0,024 0,008 0,000 0,001 0,003 0,033 0,009 0,000 0,148 0,003 0,191 0,001 0,560

x7

0,002 0,032 0,080 0,000 0,184 0,024 0,014 0,018 0,000 0,000 0,000 0,029 0,207 0,030 0,229 0,021 0,005 0,875

x8

0,000 0,069 0,001 0,111 0,000 0,008 0,014 0,171 0,013 0,000 0,005 0,092 0,000 0,004 0,007 0,000 0,021 0,518

x9

0,201 0,000 0,161 0,188 0,172 0,000 0,018 0,171 0,001 0,043 0,082 0,058 0,000 0,001 0,000 0,000 0,000 1,097

x10

0,009 0,182 0,136 0,182 0,056 0,001 0,000 0,013 0,001 0,000 0,000 0,000 0,000 0,069 0,000 0,071 0,010 0,730

x11

0,000 0,003 0,128 0,000 0,000 0,003 0,000 0,000 0,043 0,000 0,049 0,041 0,062 0,000 0,003 0,012 0,120 0,464

x12

0,018 0,022 0,008 0,144 0,005 0,033 0,000 0,005 0,082 0,000 0,049 0,000 0,248 0,004 0,072 0,002 0,028 0,718

x13

0,005 0,048 0,039 0,007 0,000 0,009 0,029 0,092 0,058 0,000 0,041 0,000 0,221 0,037 0,001 0,000 0,113 0,700

x14

0,075 0,001 0,000 0,034 0,000 0,000 0,207 0,000 0,000 0,000 0,062 0,248 0,221 0,000 0,000 0,000 0,001 0,849

x15

0,000 0,004 0,249 0,032 0,001 0,148 0,030 0,004 0,001 0,069 0,000 0,004 0,037 0,000 0,000 0,024 0,013 0,618

x16

0,005 0,170 0,008 0,121 0,035 0,003 0,229 0,007 0,000 0,000 0,003 0,072 0,001 0,000 0,000 0,000 0,000 0,653

x17

0,051 0,001 0,002 0,151 0,017 0,191 0,021 0,000 0,000 0,071 0,012 0,002 0,000 0,000 0,024 0,000 0,000 0,543

x18

0,236 0,011 0,066 0,223 0,038 0,001 0,005 0,021 0,000 0,010 0,120 0,028 0,113 0,001 0,013 0,000 0,000 0,884

Jumlah _13,778

LANJUTAN LAMPIRAN 6

1. ��� = ∑ ∑ �

� ≠ �

=

∑�₌ ∑�_≠ � +∑�₌ ∑�_≠ �

��� = _, ,_{+ ,} = ,

2. ��� = ∑ ∑ �

� ≠ �

=

∑�₌ � +∑�₌ ��

��� = ,

, + , = ,

��� = ,

, + , = ,

��� = ,

, + , = ,

��� = ,

, + , = ,

��� = ,

, + , = ,

��� = ,

, + , = ,

��� = ,

, + , = ,

��� = ,

, + , = ,

��� = ,

, + , = ,

��� = ,

, + , = ,

��� = ,

, + , = ,

��� = ,

, + , = ,

��� = ,

, + , = ,

��� = ,

, + , = ,

��� = ,

, + , = ,

��� = _, ,_{+ ,} = ,

��� = _, ,_{+ ,} = ,

��� = _, ,_{+ ,} = ,

LAMPIRAN 7

UJI BARLETT PENDEKATAN STATISTIK CHI-SQUARE

Untuk menguji apakah matriks korelasi sederhana bukan merupakan suatu matriks identitas, maka digunakan uji Barlett dengan pendekatan statistik chi-square. Berikut ini langkah- langkah pengujiannya:

1. Hipotesis

H0 : matriks korelasi sederhana merupakan matriks identitas

H1 : matriks korelasi sederhana bukan merupakan matriks identitas

2. Statistik Uji

� = −[ � − − � + ]��|∑|

3. Taraf nyata α dan nilai � dari tabel diperoleh: α = 5% = 0,05

dengan = � �− = 8 8− = � � = ,

4. Kriteria Pengujian:

H0 ditolak apabila �_{ℎ� �} � _�

H0 diterima apabila �_{ℎ� �} � _�

5. Perhitungan � : Det(R) = 0,004

� = −[ − − + ]��| , |

= −[ − , ] − , = − , − ,

= ,

6. Kesimpulan:

�ℎ� � = , > � � = , , maka H0 ditolak. Dengan kata lain,

matriks sederhana bukan merupakan matriks identitas.

LAMPIRAN 8

PERHITUNGAN KOMUNALITAS

Variabel li1 li2 li3 li4 li5 li6

x1 -0,020 -0,178 0,592 -0,284 0,313 0,249

x2 0,217 0,074 -0,244 0,702 -0,169 0,245

x3 0,113 0,122 -0,086 0,775 0,377 -0,076

x4 -0,066 -0,138 0,437 0,712 -0,012 -0,035

x5 -0,012 -0,334 0,213 0,268 0,650 0,247

x6 0,174 0,099 0,064 0,085 0,129 0,790

x7 0,035 0,316 0,692 0,271 -0,242 0,101

x8 0,173 0,177 0,129 -0,026 0,752 0,003

x9 0,670 -0,185 -0,046 0,092 -0,291 0,426

x10 0,273 0,415 0,322 -0,129 0,079 0,380

x11 0,079 -0,032 0,757 0,000 0,220 -0,012

x12 -0,008 0,858 0,042 0,134 0,013 0,057

x13 0,125 0,849 -0,109 -0,058 0,011 -0,004

x14 0,678 -0,097 -0,101 0,195 0,283 0,173

x15 0,415 -0,290 0,460 -0,153 0,174 -0,139

x16 0,665 0,148 0,131 -0,085 0,197 0,060

x17 0,686 0,281 0,010 0,122 0,027 -0,359

x18 0,521 0,074 0,089 0,060 -0,046 0,142

53

DAFTAR PUSTAKA

Azwar, Saifuddin. 1996. Reliabilitas dan Validitas.Yogyakarta: Pustaka Pelajar Imam Ghozali. 2006. Analisis Multivariat dengan Program SPSS. Semarang :

Badan Penerbit Universitas Diponegoro.

Johnson, R. A and D. W. Wichern. (1982). Applied Multivariate Statistical Analysis, Prentice-Hall, Inc. New Jersey

Mulyasa. 2002. Kurikulum Berbasis Kompetensi. Bandung: Remaja Rosdakarya. Santoso, Singgih. 2010. Statistik Multivariat Konsep dan Aplikasi dengan

SPSS.Jakarta: PT Elex Media Komputindo.

Soedijarto. 1991. Mencari Strategi Pengembangan Pendidikan Nasional Menjelang Abad XXI. Jakarta: PT. Grasindo.

Sudjana, 1996. Teknik Analisis Regresi dan Korelasi. Bandung: Penerbit Tarsito. Suparmoko. 1991. Metode Penelitian Praktek. BPFE. Yogyakarta.

Supranto, J. 2004. Analisis Multivariate Arti dan Interpretasi. PT. Rineka Cipta Jakarta

Suyata. 1998.Perbaikan Mutu Pendidikan Transformasi Sekolah Dan Implikasi Kebijakan. Yogyakarta: IKIP Yogyakarta

Tilaar, H. A. R. 1990. Pendidikan Dalam Pembangunan Nasional Menyongsong Abad XXI. Jakarta: Balai Pustaka.

Zamroni. 2001. Paradigma Pendidikan Masa Depan. Yogyakarta: Bigraf Publishing.

27

27 BAB 3 PEMBAHASAN

3.1 Populasi Penelitian

Pengambilan data dilakukan dengan cara langsung menyebar kuesioner yaitu berupa pertanyaan-pertanyaan kepada responden penelitian. Responden penelitian ini adalah siswa kelas VII, VIII dan IX di Madrasah Tsanawiyah Al- Washliyah Medan Krio. Jumlah siswa yaitu sebanyak 707 orang.

Tabel 3.1 Populasi Penelitian

No Kelas Jumlah Kelas Jumlah Siswa Persentase

1 VII 7 Kelas 282 Siswa 39,89%

2 VIII 5 Kelas 210 Siswa 29,70%

3 IX 5 Kelas 215 Siswa 30,41%

Jumlah 17 Kelas 707 Siswa 100%

Sumber: Madrasah Tsanawiyah Al-Washliyah Medan Krio

3.2 Pengambilan Sampel

Pengambilan jumlah sampel dalam penelitian ini menggunakan teknik Slovin. Jumlah populasi yang diambil yaitu siswa kelas VII, VIII dan IX di Madrasah Tsanawiyah Al- Washliyah Medan Krio yaitu sebanyak 707 orang.

� =

_+��

Maka:

� = _{+ ,}

� = _, � = ,

Sehingga jumlah sampel yang akan diteliti dalam penelitian ini adalah sebanyak 88 orang.

Dalam penelitian ini terdapat 7 kelas yaitu kelas VII - 1 sampai VII - 7, VIII - 1 sampai VIII - 5 dan IX – 1 sampai IX - 5 di Madrasah Tsanawiyah Al- Washliyah Medan Krio. Metode yang digunakan dalam pengambilan sampelnya adalah dengan Proportionale Stratified random sampling yaitu pengambilan

vi

FACTOR ANALYSIS FOR QUALITY IMPROVEMENT OF MADRASAH TSANAWIYAH AL-WASHLIYAH

MEDAN KRIO

ABSTRACT

Factor analysis is a data analysis technique that is expected to reduce the number of variables into smaller groups called factors. The purpose of research is to identify the affect factor in the learning achievement of Mathematics used factor analysis. The sampling technique used cluster sampling. Variables used as many as 18. From the data obtained to test the validity and reliability so factor analysis used SPSS 18.0 software for windows. The analysis showed that all variables are valid. The research showed six dominant factor affecting the increase in quality of Madrasah Tsanawiyah Al-Washliyah Medan Krio. The factors are competent teachers in accordance to their expertise (18.38%), good response factor in accepting criticism and suggestions from the parents of the students ( 13.41%), a factor that active learning process and creative (10.56%), room comfort factor (9.56%), factors play area (6.99%), and factor rewards (awards) for students , even teachers and staff are active and accomplished (6.18%). The six factors were giving the diversity of gasoline at 65,07% means the six factors is a dominant factor and the rest of it can be influenced by factors others were not identified by research. Keywords : Factor anlysis, cluster sampling, school quality improvement..

vii DAFTAR ISI

Halaman

Persetujuan ii

Pernyataan iii

Penghargaan iv

Abstrak v

Abstrack vi

Daftar Isi vii

Daftar Tabel ix

Daftar Gambar x

Daftar Lampiran xi

BAB 1 Pendahuluan

1.1Latar Belakang 1

1.2Rumusan Masalah 3

1.3Batasan Masalah 3

1.4Tujuan Penelitian 4

1.5Manfaat Penelitian 4

1.6Tinjauan Pustaka 4

1.7Metode Penelitian 8

BAB 2 Landasan Teori

2.1 Kualitas 9

2.2 Penyebab Rendahnya Kualitas Pendidikan di Indonesia 11 2.3 Solusi Untuk Meningkatkan Kualitas Pendidikan di Indonesia 11

2.4 Desain Penelitian 12

2.5 Konsep Penelitian 12

2.6 Sumber dan Data Sampel 13

2.7 Metode Survei 15

2.8 Instrumen Penelitian 16

2.9 Skala Pengukuran 17

2.10 Teknik Sampling 17

2.11 Uji Validitas dan Reliabilitas 19

2.12 Analisis Faktor 20

2.13 Langkah-langkah Analisis Faktor 22

2.13.1 Tabulasi Data 22

2.13.2 Pembentukan Matriks Korelasi 22

2.13.3 Ekstraksi Faktor 24

2.13.4 Rotasi Faktor 25

viii BAB 3 Pembahasan

3.1Populasi Penelitian 27

3.2Pengambilan Sampel 27

3.3Uji Validitas 29

3.4Uji Reliabilitas 33

3.5Penskalaan Ordinal Menjadi Interval 35

3.6Proses Analisis faktor I 38

3.7Proses Analisis faktor II (Ekstraksi) 39

3.7.1 Communalities 39

3.7.2 Total Variance Explained 40

3.7.3 Scree Plot 42

3.8Proses Analisis Faktor III (Rotasi) 43

3.9Proses Analisis Faktor IV (Interpretasi Faktor) 45 BAB 4 Kesimpulan dan Saran

4.1Kesimpulan 49

4.2Saran 52

ix

DAFTAR TABEL

Halaman

Tabel 3.1 Populasi Penelitian 27

Tabel 3.2 Populasi Penilitian Tiap Strata 28

Tabel 3.3 Uji Validitas 1 29

Tabel 3.4 Contoh Perhitungan Korelasi Produck Moment 30 Tabel 3.5 Hasil Cronbach Alpha Reliability Test 35

Tabel 3.6 Penskalaan Variabel 1 35

Tabel 3.7 Hasil Penskalaan Tiap Variabel 37

Tabel 3.8 KMO and Barlet Test 38

Tabel 3.9 Measure Of Sampling Aduquacy 39

Tabel 3.10 Comunalities 40

Tabel 3.11 Total Variance Explaaned 41

Tabel 3.12 Faktor Loading 43

Tabel 3.13 Rotated Faktor Loading 44

Tabel 3.14 Bobot Variabel Pendukung Faktor Pertama 45 Tabel 3.15 Bobot Variabel Pendukung Faktor Kedua 46 Tabel 3.16 Bobot Variabel Pendukung Faktor Ketiga 46 Tabel 3.17 Bobot Variabel Pendukung Faktor Keempat 47 Tabel 3.18 Bobot Variabel Pendukung Faktor Kelima 47 Tabel 3.19 Bobot Variabel Pendukung Faktor Keenam 48

x

DAFTAR GAMBAR

Halaman

xi

DAFTAR LAMPIRAN

Lampiran 1 Kuesioner Penelitian Lampiran 2 Data Penelitian Responden Lampiran 3 Succesive Detail

Lampiran 4 Succesive Interval Lampiran 5 Output Spss

Lampiran 6 Perhitungan KMO dan MSA

Lampiran 7 Uji Barlett Pendekatan Statistik CHI SQUARE Lampiran 8 Perhitungan Komunalitas