V. CONCLUSION AND RECOMMENDATION
5.1. Conclusion
By using Geoinfarmatics techniques the research concluded that: a.
By comparing ULS and Satscan on poverty and food insecurity cases in Java, this research pointed out that ULS had a more precise and stable
performance compared to Satscan. ULS was suggested as an alternative to thematic maps often used by government institutions. Maps based on
SatscanULS were more precise compared to thematic maps because spatial scanning methods could not only detect whether an area was a
critical area or not, but also conducted hypothesis testing whether the area was significantly different or not compared to surrounding areas and used
the geographical information data to enhance the accuracy of results. b.
Based on the joint hotspots of poverty, unemployment, and food scarcity there were nine areas Banyumas, Batang, Cilacap, Demak, Kab. Madiun,
Kota Pekalongan, Kulonprogo, Pemalang, Purworejo considered as the most critical area that needed more attention from the government. Most
of these areas were located in Central Java. There were only three cities that were not either a poverty, food scarcity, nor an unemployment
hotspots, which were Kota Batu, Kota Salatiga, Kota Serang. c.
Main factors causing the joint hotspots were identified by using Ordinal Logistic Regression Model. Factors related to the hotspot were school
facilities, village trade, village industry, village services, slum areas, and proportion of families without electricity, and proportion of credit
facilities.
5.2. Recommendation
Further research on other methods used for hotspot detection should be done. In this research, ULS has better performance than Satscan, it should be
simulatedapplied not only in Java but also in Indonesia where there is also a large body of oceans separating the islands. Development towards tools that can be used
to enhance the practicality in SatscanULS outputs is also needed. This study hopefully would become a pioneer in further studies at a national level and
improvements in results can be done by exploring the possibilities of other covariates and using other sufficient models.
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APPENDENCIES
Appendix 1 . Municipalities with the Highest Proportion of Poverty in Java
Rank Municipality
Priority Rank
Municipality Priority
1 Trenggalek
44.70 First 25
Pemalang 28.29 Second
2 Batang
43.07 First 26
Kebumen 27.36 Second
3 Bojonegoro
37.62 First 27
Tasikmalaya 27.29 Second
4 Lumajang
36.25 First 28
Magetan 26.99 Second
5 Jember
35.16 First 29
Banjar 26.91 Second
6 Pacitan
34.52 Second 30
Kab.Kediri 26.54 Second
7 Situbondo
34.51 Second 31
Purbalingga 26.28 Second
8 Garut
34.09 Second 32
Brebes 25.89 Second
9 Temanggung
33.96 Second 33
Gunung Kidul 25.00 Second
10 Magelang
33.38 Second 34
Kab.Madiun 24.86 Third
11 Kab.Blora
33.33 Second 35
Grobogan 24.73 Third
12 Kab.Malang
32.78 Second 36
Jombang 24.16 Third
13 Banjarnegara
32.57 Second 37
Kab.Blitar 24.13 Third
14 Bondowoso
32.55 Second 38
Sragen 23.23 Third
15 Kab.Probolinggo 29.88 Second
39 Sukoharjo
23.15 Third 16
Purworejo 29.32 Second
40 Nganjuk
22.93 Third 17
Ngawi 29.20 Second
41 Kulon Progo
22.81 Third 18
Wonosobo 29.15 Second
42 Kota.Belitar
21.88 Third 19
Rembang 29.10 Second
43 Kota Pasuruan
21.88 Third 20
Boyolali 29.08 Second
44 Tegal
21.05 Third 21
Ponorogo 29.04 Second
45 Demak
20.57 Third 22
Tuban 28.91 Second
46 Kota Mojokerto
20.48 Third 23
Wonogiri 28.67 Second
47 Kota Pekalongan
20.24 Third 24
Cilacap 28.61 Second
48 Banyumas
20.07 Third
Appendix 2 . Municipalities with the Highest Proportion of Food Insecurity
Household in Java
Municipality Priority
Composite Indicators
Priority Quartile
Municipality Priority
Composite Indicators
Priority Quartile
Kudus 89.35 Third
First Grobogan
71.33 Third
Second Batang
83.44 Second First
Kab.Mojokerto 71.20
Third Second
Pati 82.39 Third
First Situbondo
71.15 First
Second Pemalang
80.73 Second First
Tangerang 71.14
Second Second
Temanggung 80.39 Third
First Demak
70.94 Second
Second Jepara
80.25 Third First
Banyuwangi 70.76
Second Second
Magetan 79.75 Third
First Nganjuk
70.18 Third
Second Kuningan
79.37 Second First
Lamongan 69.82
Second Second
Kab.Blora 79.32 Second
First Kab.malang
69.48 Second
Second Kab.Madiun
77.94 Third First
Garut 69.44
Second Third
Gresik 76.61 Third
First Sidoarjo
69.33 Third
Third Kab.Kediri
76.04 Third First
Klaten 68.91
Third Third
Wonogiri 76.02 Third
First Semarang
68.84 Third
Third Purbalingga
75.92 Second First
Karanganyar 68.45
Third Third
Sukoharjo 75.52 Third
First Brebes
68.27 First
Third Bantul
75.31 Third First
Kab.probolinggo 67.84
First Third
Jombang 75.27 Third
First Kab.Pasuruan
67.55 Second
Third Kulon Progo
75.09 Third First
Banjarnegara 66.99
Second Third
Pekalongan 74.73 Second
First Bojonegoro
66.78 Second
Third Banyumas
74.70 Third Second
Bondowoso 66.08
First Third
Magelang 73.86 Third
Second Bandung
65.88 Third
Third Cilacap
73.73 Second Second
Pacitan 65.63
Third Third
Jember 73.58 First
Second Bogor
65.60 Third
Third Kab.Blitar
73.28 Third Second
Rembang 65.25
Third Third
Sleman 72.39 Third
Second Cirebon
64.64 Second
Third Gunung
Kidul 71.98 Third
Second Ngawi
63.92 Second
Third Purworejo
71.95 Third Second
Kebumen 63.33
Second Third
Boyolali 71.90 Third
Second Tuban
63.16 Second
Third Lumajang
71.43 Second Second
Tulungagung 62.98
Third Third
Ponorogo 71.35 Third
Second
Appendix 3 . Municipalities with High Proportion of Unemployment in Java
Rank Municipality
Estimated Unemployment
Rank Municipality
Estimated Unemployment
1 Kota
Sukabumi 57.6 150, 292
11 Serang
47.4 850, 183 2
Pandeglang 56.0 609, 530
12 Kota Cilegon
46.6 153, 561 3
Banjar 55.9 91, 798
13 Cilacap
45.5 778, 119 4
Karawang 53.2 1, 003, 417
14 Kota Jakarta
Timur 45.3 956, 622
5 Lebak
52.4 609, 751 15
Cimahi 44.8 182, 885
6 Tasikmalaya
49.3 786, 981 16
Kota Tasik 44.6 242, 973
7 Subang
49.1 672, 345 17
Sukabumi 44.6 972, 578
8 Bandung
49.1 1, 965, 429 18
Garut 44.0 982, 371
9 Kota Bogor
48.4 398, 457 19
Cianjur 44.0 898, 658
10 Kota Madiun
47.9 91, 767 20
Kota Belitar 44.0 55, 883
Appendix 4. Side-by-side comparison map of different Poverty Hotspots Using
50, 40, 30, 20, 10 and 5 Maximum Cluster Size a.
Satscan
Maximum Cluster Size 5
Maximum Cluster Size 10
Maximum Cluster Size 20 Maximum Cluster Size 30
Maximum Cluster Size 40
Maximum Cluster Size 50
b. ULS
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