Laporan Statistik Desember 2016 1
Statistik Asuransi Gempa Bumi Indonesia
2016
Indonesia Earthquake Insurance Statistics 2016
(2)
(3)
Kata Pengantar
Foreword
Bapak dan Ibu Direksi Perusahaan Asuransi yang
saya hormati,
Dengan mengucap Puji Syukur Kehadirat Tuhan
Yang Maha Esa, buku
Laporan Statistik Asuransi
Gempa Bumi Indonesia (LSAGBI) Desember 2016
telah
selesai
disusun.
Penyusunan
LSAGBI
ini
selaras dengan salah satu Visi MAIPARK yaitu
menjadi
Center
Of
Excellence
dan sebagai upaya
dalam memberikan pelayanan yang terbaik bagi
Industri Asuransi Umum di Indonesia khususnya
mengenai statistik serta pengetahuan risiko gempa
bumi.
Sebagai salah satu publikasi rutin, LSAGBI ini
memuat
hasil
kajian
risiko-risiko gempa bumi
terkait data exposure, premi nasional, jumlah risiko
dan perkembangan risiko gempa bumi. Di dalam
laporan ini kami juga menyajikan ulasan aktuaria
dan risiko exposure dari sudut pandang asuransi
gempa bumi. Kami melakukan analisis atas eksposur
utama kita di Jawa Bagian Barat yang kami lengkapi
dengan analisis Probable Maximum Loss untuk area
ini. Selain itu, data statistik nasional ini juga
memperlihatkan kecenderungan penurunan produksi
premi
dari
underwriting
risiko
bencana
oleh
perusahaan
asuransi
di
Indonesia
walaupun
penurunan
premi
ini
tidak
diimbangi
dengan
penurunan eksposurnya.
Seperti yang kita sadari bersama, dukungan dari
seluruh perusahaan asuransi umum sangat berarti
bagi kami dalam upaya pengembangan
laporan
statistik ini agar dapat digunakan sebagai panduan
yang baik dalam kita melaksanakan bisnis
sehari-hari.
Akhir
kata,
kami
menyadari
kebutuhan
penyempurnaan buku ini sangat tinggi sehingga
kritik maupun saran sangat kami harapkan. Semoga
laporan ini dapat menjadi referensi yang berkualitas
dan dapat memberikan manfaat bagi perusahaan
yang
menangani
asuransi
gempa
bumisehingga
dapat
memperkuat
industri
asuransi
umum
di
Indonesia.
Kami
mohon
maaf
apabila
masih
ditemukan
kesalahan
data dan
informasi yang
disajikan dalam buku ini.
Hormat kami,
Dear Sir / Madam,
Our gratitude to God Almighty, the
Indonesian
Earthquake Insurance Statistic Report as at
December 2016 is ready to publish. This report
publication is aligned with
MAIPARK’s
vision,
which is to be a "Center of
Excellence
and also
to provide the best service for the General
Insurance
Industry
in
Indonesia,
especially
statistic and earthquake risks knowledge.
As a regular publication, it contains earthquake
risks study related to the national Exposure,
premium, number of risks and development of the
earthquake risks management. We also include
actuarial
and
Exposure
risks
reviews
from
earthquake insurance point of view. We do an
analysis to our main exposure in West Java that
also
provide
with
Probable
Maximum
Loss
analysis for this area. The national statistics also
show decreasing trend in premium production
from disaster risk underwriting by insurance
companies in Indonesia although the decline in
premiums is not followed by a decrease in
exposure.
Support from the General Insurance Industry will
be meaningful to us in order to improve this
Statistic
Report.
We
realize
the
need
for
improvement of this report is very high so that
critics and suggestions are appreciate.
Finally, we hope that this report could be used as
a qualified reference and will be beneficial for all
general insurance industries in Indonesia. We
apologize for any possible data and information
errorspresented in this report.
Sincerely,
(4)
(5)
Gambaran Ekonomi
–
Industri Asuransi 2016
(6)
8,564.9
8,982.5
9,433.0
Year 2014
Year 2015
Year 2016
4.88%
5.02%
IDR 9.433 Trillion
IDR 378,2 Trillion
IDR 9.433 triliun adalah total Produk Domestik Bruto (PDB) Indonesia pada tahun 2016. Tiga sektor
usaha dengan kontribusi paling besar adalah: (i) Industri Pengolahan
–
21,39%, (ii) Perdagangan Besar
Eceran; Reparasi Mobil dan Sepeda Motor
–
13,31% dan (iii) Pertanian, Kehutanan dan Perikanan
–
12,82%.
IDR 9.433 Trillion is
Indonesia’s
GDP for 2016. Three sectors with highest contribution are: (i)
Processing Industry
–
21.4%, (ii) grocery, retail and automotive trading
–
13.3% and (iii) Farming,
Forestry and Fisheries
–
12.82%.
IDR 378,2 trilyun adalah besaran PDB dalam kelompok usaha Jasa Keuangan dan Asuransi. Sektor ini
berkontribusi 4% dari total PDB Indonesia pada tahun 2016, masih berada di bawah sektor Konstruksi;
Pertambangan dan Penggalian; Informasi dan Komunikasi.
IDR 378,2 Trillion is the GDP from Financial Services and Insurance sector. This sector contributes 4%
of Indonesia's total GDP by 2016, below Construction sector; Mining and excavation; Information and
Communication.
Keadaan Ekonomi 2016. Economic Outlook 2016
Indonesia mengalami pertumbuhan ekonomi sebesar 5.02% pada tahun 2016. Namun demikian laju
pertumbuhan pada sektor Jasa Keuangan dan Asuransi mencapai 8.9%, tertinggi dibandingkan sektor
lainnya. Laju pertumbuhan 8.9% ini juga menjadi laju pertumbuhan tertinggi bila dibandingkan dengan
tahun sebelumnya yaitu 4.7% pada tahun 2014 dan 8.6% pada tahun 2015.
Indonesia’s
economic growth in 2016 reached 5.02%. However, growth rates in the Financial Services
and Insurance sector was 8.9%, the highest compared to other sectors. The 8.9% growth rate is also
the highest growth rate compared to 4.7% in 2014 and 8.6% in 2015.
In trillion IDR
(7)
69.01%
9.55%
21.44%
Produksi premi Asuransi Jiwa lebih tinggi dibandingkan dengan Asuransi Umum/kerugian. Dari Total
produksi premi 2016 sebesar 199 Triliun rupiah, Asuransi Jiwa berkontribusi sebesar 69,01%.
Sedangkan Asuransi Umum/Kerugian hanya 30,99%. Dari total Asuransi Umum tersebut ada 30%
merupakan produksi premi Asuransi Umum/Kerugian dari Lini Bisnis Harta Benda.
Life Insurance premium production is higher than General Insurance/Non Life. Life Insurance
contributed 69.01% of 199 Trillion rupiah, the total premium production in 2016. While General
Insurance / Non Life is only 30.99%. The premium production of General Insurance / Non Life from the
Business Line of Property is 30% of the total Premium of General Insurance.
6
Perusahaan
Reasuransi
Reinsurance
77
Perusahaan
Asuransi Umum
General Insurance
55
Perusahaan
Asuransi Jiwa
Life Insurance
5
Asuransi
Wajib dan Sosial
Mandatory and
Social Insurance
Keadaan Asuransi 2016.
Insurance Outlook 2016
AJ
AU-O
AU-P
Uraian/Description
Premi /Premium
Asuransi Jiwa/
Life Insurance (AJ)
137,785,583
Asuransi Umum - Harta Benda/
General Insurance - Property (AU-P)
19,072,900
Asuransui Umum - Lainnya/
General Insurance - Others (AU-O)
42,797,300
In million IDR
Source: Otoritas Jasa Keuangan, 2017
Asosiasi Asuransi Umum Indonesia, 2017
(8)
Grafik ini menampilkan pertumbuhan premi dari underwiring year (UY) 2004-2016. UY 2016 masih
belum matang per 31 Januari 2017, dan diperkirakan masih bertambah sampai dengan akhir tahun
2017 nanti. Bila melihat dari pola UY sebelumnya, maka penambahan premi untuk UY 2016 sendiri
dapat mencapai kira-kira 19% atau sekitar Rp4,3 Triliun.
Dengan proyeksi UY 2016 menjadi Rp4,3 Triliun, maka dapat kita lihat terjadi penurunan produksi
premi dari UY 2015 ke UY 2016 untuk asuransi gempa bumi sebesar 19%. Hal ini berbanding
terbalik dengan pertumbuhan Jasa Keuangan dan Asuransi secara umum di Indonesia yang tumbuh
sebesar 8,9%.
This graph shows premium growth from underwriting year 2004-2016. UY 2016 is still developing
as obtained per January 31, 2017, and still increasing until the end of 2017. Looking at the previous
UY pattern, the additional premium for UY 2016 can reach approximately 19% or about Rp4.3
Trillion.
The projection of UY 2016 to 4.3 Trillion, we can see a decrease in premium production for
earthquake insurance from UY 2015 to UY 2016 by 19%. This is inversely related to the growth of
Financial Services and Insurance in general in Indonesia which grew by 8.9%.
Catatan Asuransi Gempa Bumi 2004 - 2016.
Earthquake Insurance 2004
–
2016.
3.1
3.2
3.3
Premi di UY 2016 terdistribusi 56,7% di Cresta 3
yang mencakup 3 Area yaitu 3.1- Provinsi DKI
Jakarta, 3.2-Kota Bandung dan 3.3-Kota/Kabupaten
Lain selain Jakarta dan Bandung di Provinsi Banten
dan Jawa Barat.
Premiums in UY 2016 were distributed 56.7% in
Cresta 3 that covering 3 Area ie 3.1- Province of
DKI Jakarta, 3.2-City of Bandung and 3.3-City/
Regency Other than Jakarta and Bandung in Banten
Cresta 3
56,7%
871
1,334
1,645
2,076
1,982
1,782
2,116
2,823
2,721
4,530
4,959
5,305
3,659
1,000
2,000
3,000
4,000
5,000
6,000
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
In
Bi
llion
s
(9)
Catatan Asuransi Gempa Bumi 2004 - 2016.
Earthquake Insurance 2004
–
2016.
Grafik ini menampilkan pertumbuhan eksposur dari underwiring year 2004-2016. Untuk UY 2016
dimana angka eksposur didapatkan per 31 Januari 2017 kami perkirakan masih dapat bertambah
sampai dengan akhir tahun 2017 nanti. Bila melihat dari pola UY sebelumnya, maka penambahan
eksposur untuk UY 2016 sendiri dapat mencapai kira-kira 20% atau sekitar Rp3,4 Kuadriliun.
Dengan proyeksi UY 2016 menjadi 3,4 Quadtriliun maka dapat kita lihat terjadi penurunan eksposur
dari UY 2015 ke UY 2016 untuk asuransi gempa bumi sebesar 5,6%.
This graph shows exposure growth from underwiring year 2004-2016. Exposure of UY 2016 is
obtained as of January 31, 2017, we estimate it can still increase until the end of 2017. When
looking at the previous UY pattern, the additional exposure for UY 2016 can reach approximately
20% or about Rp3.4 QuadTrillion.
The projection of UY 2016 to Rp3.4 QuadTrillion, we can see a decrease in exposure production for
earthquake insurance from UY 2015 to UY 2016 by 5.6%.
Cresta 3
53,2%
3.1
3.2
3.3
Eksposur di UY 2016 terdistribusi 53,2% di Cresta
3 yang mencakup 3 Area yaitu 3.1- Provinsi DKI
Jakarta, 3.2-Kota Bandung dan 3.3-Kota/Kabupaten
Lain selain Jakarta dab Bandung di Provinsi Banten
dan Jawa Barat.
Exposure in UY 2016 are distributed 53.2% in
Cresta 3 that covering 3 Area ie 3.1- Province of
DKI Jakarta, 3.2-City of Bandung and 3.3-City/
Regency Other than Jakarta and Bandung in Banten
and West Java Province.
692
1,026
1,458
1,606
1,481
1,498
1,671
2,156
1,974
2,759
3,115
3,642
2,884
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
In
Tri
llion
s
(10)
Dilihat dari sisi intensitas gempa yang digambarkan oleh intensitas maksimum, Gempa Aceh 2004,
Gempa Yogyakarta 2006 dan Gempa Padang 2009 adalah tiga gempa terbesar yang terjadi dari
tahun 2004.
Loss ratio
tertinggi terjadi pada event Gempa Aceh 2004 dan Gempa Padang 2009
masing-masing sebesar 77,85% dan 78,38% sementara itu Gempa Yogyakarta 2006 hanya memiliki
loss ratio
1.25%.
Fakta ini salah satunya disebabkan mekanisme gempa dimana pada gempa subduksi
(Aceh dan
Padang) intensitas tinggi dapat terjadi pada area yang sangat luas sehingga
loss ratio
sangat tinggi,
sedangkan gempa patahan (Yogyakarta) intensitas tinggi hanya berdampak pada area yang relative
lebih kecil, sehingga
loss ratio
tidak terlalu besar.
From the earthquake intensity point of view, three biggest earthquake are Aceh EQ 2004, Yogya EQ
2006 and Padang EQ 2009. the highest loss ratio experienced in Aceh and Padang events each
77,85% dan 78,38% while Yogya EQ only resulted a minor loss ratio of 1.25%.
One of the probable cause is the mechanism of earthquake where the subduction type of EQ (Aceh
and Padang) affected larger high intensity areas than the fault EQ (Yogya), which this was affected
the loss ratio of these particular events.
Catatan Asuransi Gempa Bumi 2004 - 2016.
Earthquake Insurance 2004
–
2016.
No.
Kejadian
Tanggal
Kejadian
Kekuatan
Gempa
Kedalaman
(KM)
MMI
Maksimum
Eksposure
Terdampak
Klaim
Rasio Kerugian
Number
Event
Date of Loss
Magnitude
Depth (KM)
Maximum MMI Affected Exposure
Claim
Loss Ratio
1
ACEH
26/12/04
9.1 Mw
30.00
IX
958,757.93 746,364.47
77.85%
2
YOGYA
27/05/06
6.3 Mw
12.50
IX
22,607,377.57 283,523.14
1.25%
3
PADANG
06/03/07
6.3 Mwc
11.00
VIII
5,975,640.89 28,753.80
0.48%
4
BENGKULU
12/09/07
8.4 Mw
34.00
VIII
870,834.57 61,180.16
7.03%
5
PADANG
16/08/09
6.7 Mwc
20.00
VI
3,105,777.10 42,782.94
1.38%
6
TASIKMALAYA
02/09/09
7.0 Mw
46.00
VII
37,059,693.77 33,508.29
0.09%
7
PADANG
30/09/09
7.6 Mw
81.00
IX
1,434,892.35 1,124,652.86
78.38%
8
BIMA
09/11/09
6.6 Mwc
18.00
VI
10,261,909.77 47,771.65
0.47%
9
MERAPI
25/10/10
6,912,282.28 30,534.72
0.44%
10
KELUD
13/02/14
51,122,470.86 270,749.97
0.53%
volcanic eruption
volcanic eruption
(11)
Asuransi Gempa Bumi 2012 - 2016
Earthquake Insurance 2012-2016
(12)
Tabel di atas adalah data produksi premi selama 5 tahun terakhir. Pada Okupasi Agrikultur, kenaikan
paling tinggi terjadi antara UY 2011-2012 yaitu sebesar 23,1%. Pada Okupasi Komersial dan
Residential, kenaikan paling tinggi terjadi antara UY 2012-2013 yaitu masing-masing sebesar
175,3% dan 36,6%. Pada Okupasi Industrial kenaikan paling tinggi terjadi antara UY 2013-2014
yaitu sebesar 44,7%.
Bila angka UY 2016 dibandingkan dengan UY 2012 maka kenaikan produksi premi tertinggi terjadi
pada Okupasi Komersial yaitu sebesar 52,2%. Sementara itu, premi dari okupasi agrikultur
mengalami penurunan sebesar 7.8%.
The table above is the premium production for the last 5 years. In the Agricultural Occupation, the
highest increase occurred between UY 2011-2012 that is equal to 23,1%. In the Commercial and
Residential Occupation, the highest increase occurred between UY 2012-2013, i.e 175.3% and
36.6%, respectively. In Industrial Occupation, the highest increase occurred between UY 2013-2014
which is equal to 44,7%.
If the figure of UY 2016 compared to UY 2012, the highest increase of premium production occurred
in Commercial Occupation that is equal to 52,2%. Meanwhile, the premium amount from agricultural
occupation decreased 7.8%.
Premi Asuransi Gempa Bumi 2016
Earthquake Insurance Premium 2016
UY
Agrikultural
∆
Komersial
∆
Industrial
∆
Residensial
∆
Total
∆
2012
29.42
23.1%
658.34
-13.2%
1,686.07
-0.8%
347.54
1.5%
2,721.36
-3.6%
2013
35.25
19.8%
1,812.23
175.3%
2,208.13
31.0%
474.72
36.6%
4,530.33
66.5%
2014
28.85
-18.2%
1,119.71
-38.2%
3,195.59
44.7%
614.99
29.5%
4,959.13
9.5%
2015
29.55
2.5%
1,802.33
61.0%
2,897.32
-9.3%
575.52
-6.4%
5,304.72
7.0%
2016
27.34
-7.5%
1,001.97
-44.4%
2,155.41
-25.6%
474.48
-17.6%
3,659.21
-31.0%
2012 to 2016
-7.1%
52.2%
27.8%
36.5%
34.5%
(13)
Tabel di atas adalah data produksi premi selama 5 tahun terakhir. Pada Interest Building, Others dan
Business Interuption, kenaikan paling tinggi terjadi antara UY 2012-2013 yaitu masing-masing
sebesar 47,1%, 74,5% dan 274,7%. Pada Interest Machinery, kenaikan paling tinggi terjadi antara
UY 2013-2014 yaitu sebesar 35,4%. Pada Interest Stock, kenaikan paling tinggi terjadi antara UY
2013-2014 yaitu sebesar 73,8%.
Bila angka UY 2016 dibandingkan dengan UY 2012 maka pertumbuhan tertinggi produksi premi
terjadi pada Interest Building yaitu sebesar 41,1%. Sementara pertumbbuhan terendah terjadi pada
interest Stock sebesar 17.4%.
The table above is the premium production data for the last 5 years. In the Building, Others and
Business Interuption Interest, the highest increase occurred between UY 2012-2013, i.e 47.1%,
74.5% and 274.7% respectively. In the Machinery Interest, the highest increase occurred between
UY 2013-2014 which is 35.4%. In the Stock Interest, the highest increase occurred between UY
2013-2014 which is equal to 73.8%.
If the figure of UY 2016 compared to UY 2012, the highest growth in premium production occurs in
the Interest Building that is equal to 41.1%, which the lowest growth is come from Stocks 17.4%.
Premi Asuransi Gempa Bumi 2016
Earthquake Insurance Premium 2016
UY
Building
∆
Machinery
∆
Others
∆
Stock
∆
Bussiness
Interruption
∆
Total
∆
2012
1,109.99
-14.3%
540.32
-3.6%
452.83
39.4%
377.17
2.7%
241.06
-12.5%
2,721.36
-3.6%
2013
1,632.36
47.1%
694.83
28.6%
790.30
74.5%
509.68
35.1%
903.15
274.7%
4,530.33
66.5%
2014
2,036.80
24.8%
940.76
35.4%
619.12
-21.7%
885.58
73.8%
476.87
-47.2%
4,959.13
9.5%
2015
2,754.09
35.2%
920.39
-2.2%
637.03
2.9%
576.81
-34.9%
416.38
-12.7%
5,304.72
7.0%
2016
1,565.76
-43.1%
708.82
-23.0%
620.01
-2.7%
442.68
-23.3%
321.93
-22.7%
3,659.21
-31.0%
2012 to 2016
41.1%
31.2%
36.9%
17.4%
33.5%
34.5%
(14)
Kode Ukupasi dan Keterangan
Description & Occupation Code
Jumlah Risiko
Number of Risk
Premi
Premium
Exposure
Exposure
Private Building
297
76.292
507.543.348.366,93 356.534.521.091.664,00
Trading and storage
293
60.265
454.438.237.985,54 352.215.048.368.483,00
Hotels, Entertainment, Sport,
Services
294
8.779
258.685.294.963,58 189.853.581.229.423,00
Conventional power station . .
281
242
254.200.632.649,54 184.559.210.302.184,00
Mass communication
292
9.639
184.847.249.329,61 122.983.990.617.974,00
Edible fats, edible oil and desiccated
coconut producers
274
638
135.087.154.106,32 116.242.728.581.394,00
Tobacco, cigars and cigarettes
manufacture
279
404
134.531.401.654,95 133.199.023.516.826,00
Mining (underground or above
ground) of precious metal . . .
200
26
126.611.096.193,12
88.838.188.575.950,00
Cement, Chalk, Lime and Gypsum
Industry
211
213
122.352.807.348,26 105.505.506.691.319,00
Industrial, Mining and Commercial
Machinery . . .
221
963
107.635.376.746,15 100.860.184.394.393,00
Premi Asuransi Gempa Bumi 2016
Earthquake Insurance Premium 2016
(15)
Tabel di atas adalah data eksposur selama 5 tahun terakhir. Pada Okupasi Agrikultur, kenaikan
paling tinggi terjadi antara UY 2011-2012 yaitu sebesar 16,7%. Pada Okupasi Komersial dan
Residential, kenaikan paling tinggi terjadi antara UY 2012-2013 yaitu masing-masing sebesar
117,5% dan 37,5%. Pada Okupasi Industrial, kenaikan paling tinggi terjadi antara UY 2013-2014
yaitu sebesar 38,4%.
Bila angka UY 2016 dibandingkan dengan UY 2012 maka kenaikan eksposur tertinggi terjadi pada
Okupasi Komersial yaitu sebesar 60,1%.
The table above is the exposure for the last 5 years. In the Agricultural Occupation, the highest
increase occurred between UY 2011-2012 that is equal to 16,7%. In the Commercial and Residential
Occupation, the highest increase occurred between UY 2012-2013, i.e 117.5% and 37.5%
respectively. In the Industrial Occupation, the highest increase occurred between UY 2013-2014 i.e
38,4%.
If the number of UY 2016 compared to UY 2012, the highest increase in exposure occurred at
Commercial Occupation that is equal to 60,1%.
Eksposur Asuransi Gempa Bumi 2016
Earthquake Insurance Exposure2016
In Trillion IDR
UY
Agri
∆
Commercial
∆
Industrial
∆
Residensial
∆
Total
∆
2012
23.54
-99.9%
458.52
-99.9%
1,261.13
-99.9%
231.08
-99.9%
1,974.28
-99.9%
2013
25.96
10.3%
997.42
117.5%
1,417.71
12.4%
317.64
37.5%
2,758.72
39.7%
2014
22.35
-13.9%
746.06
-25.2%
1,961.85
38.4%
385.09
21.2%
3,115.35
12.9%
2015
24.20
8.3%
1,163.69
56.0%
2,068.54
5.4%
385.82
0.2%
3,642.25
16.9%
2016
21.73
-10.2%
734.06
-36.9%
1,797.00
-13.1%
331.69
-14.0%
2,884.48
-20.8%
(16)
Tabel di atas adalah data eksposure selama 5 tahun terakhir. Pada Interest Others dan Business
Interuption kenaikan paling tinggi terjadi antara UY 2012-2013 yaitu sebesar 53,2% dan 143,1%.
Pada
Interest Machinery dan Stock kenaikan paling tinggi terjadi antara UY 2013-2014 yaitu
sebesar 31,9% dan 58,7%. Pada Interest Building kenaikan paling tinggi terjadi antara UY
2014-2015 yaitu sebesar 41,1%.
Bila angka UY 2016 dibandingkan dengan UY 2012 maka kenaikan eksposure terjadi pada Interest
Building yaitu sebesar 57,2%.
The table above is the exposure for the last 5 years. In The Others and Business Interuption
Interest, the highest increase occurred between UY 2012-2013, i.e 53.2% and 143.1%. In The
Machinery and Stock Interest, the highest increase occurred between UY 2013-2014 i.e 31.9% and
58.7%. In the Building Interest, the highest increase occurred between UY 2014-2015 i.e 41.1%.
If the number of UY 2016 compared to UY 2012, the exposure increase occurs in the Building
Interest i.e 57.2%.
Eksposur Asuransi Gempa Bumi 2016
Earthquake Insurance Exposure2016
UY
Building
∆
Machinery
∆
Others
∆
Stock
∆
Bussiness
Interruption
∆
Total
∆
2012
804.2
-16.6%
395.8
-8.3%
309.6
22.2%
281.1
-1.1%
183.6
-17.2%
1,974.3
-8.4%
2013
1,048.3
30.4%
452.1
14.2%
474.3
53.2%
337.5
20.1%
446.4
143.1%
2,758.7
39.7%
2014
1,290. 3
23.1%
596.2
31.9%
401.3
-15.4%
535.8
58.7%
291.7
-34.6%
3,115.3
12.9%
2015
1,820.3
41.1%
655.4
9.9%
449.8
12.1%
424.9
-20.7%
291.8
0.0%
3,642.3
16.9%
2016
1,195.4
-34.3%
573.0
-12.6%
486.5
8.2%
365.0
-14.1%
264.5
-9.4%
2,884.5
-20.8%
2012 to 2016
48.6%
44.8%
57.2%
29.9%
44.0%
46.1%
(17)
Eksposur Asuransi Gempa Bumi 2016
Earthquake Insurance Exposure 2016
Kode Okupasi dan Keterangan
Description & Occupation Code
Jumlah Risiko
Number of Risk
Premi
Premium
Eksposure
Exposure
Private Building
297
76,292
507,543,348,367
356,534,521,091,664
Trading and storage
293
60,265
454,438,237,986
352,215,048,368,483
Hotels, Entertainment, Sport,
Services
294
8,779
258,685,294,964
189,853,581,229,423
Conventional power station,
buildings with boiler houses . . .
281
242
254,200,632,650
184,559,210,302,184
Tobacco, cigars and cigarettes
manufacture
279
404
134,531,401,655
133,199,023,516,826
Mass communication
292
9,639
184,847,249,330
122,983,990,617,974
Edible fats, edible oil and
desiccated coconut producers
274
638
135,087,154,106
116,242,728,581,394
Cement, Chalk, Lime and Gypsum
Industry
211
213
122,352,807,348
105,505,506,691,319
Industrial, Mining and Commercial
Machinery, . . .
221
963
107,635,376,746
100,860,184,394,393
Petrochemical works
232
133
99,884,265,243
89,767,796,081,455
(18)
Tabel di atas adalah data Jumlah Risiko selama 5 tahun terakhir. Pada Okupasi Agrikultur,
penurunan paling rendah terjadi antara UY 2014-2015 yaitu sebesar 47,1%. Pada Okupasi Komersial
penurunan paling rendah terjadi antara UY 2015-2016 yaitu sebesar 32,9%. Pada Okupasi Industrial
kenaikan paling tinggi terjadi antara UY 2011-2012 yaitu sebesar 14,7%. Pada Okupasi Residensial
kenaikan paling tinggi terjadi antara UY 2013-2014 yaitu sebesar 30,3%.
Bila angka UY 2016 dibandingkan dengan UY 2012 maka kenaikan Jumlah Risiko tertinggi terjadi
pada Okupasi Residensial yaitu sebesar 24,0%.
The table above is the Number of Risk for the last 5 years. In The Agricultural Occupation, the
lowest decrease occurred between UY 2014-2015 i.e 47,1%. In The Comercial Occupation, the
lowest decrease occurred between UY 2015-2016 i.e 32,9%. In the Industrial Occupation, the
highest increase occurred between UY 2011-2012 i.e 14,7%. In Residential Occupational, the
highest increase occurred between UY 2013-2014 i.e 30.3%.
If the number of UY 2016 compared to UY 2012, the highest increase of Number of Risk occurs in
Residential Occupation i.e 24.0%.
Jumlah Risiko Asuransi Gempa Bumi 2016
Earthquake Insurance Number of Risk 2016
UY
Agrikultural
∆
Komersial
∆
Industrial
∆
Residensial
∆
Total
∆
2012
420
10.8%
94,598
3.5%
17,199
14.7%
61,627
4.2%
173,844
4.8%
2013
535
27.4%
100,976
6.7%
18,461
7.3%
70,315
14.1%
190,287
9.5%
2014
753
40.7%
124,569
23.4%
18,906
2.4%
91,629
30.3%
235,857
23.9%
2015
398
-47.1%
129,298
3.8%
17,604
-6.9%
86,333
-5.8%
233,633
-0.9%
2016
503
26.4%
86,784
-32.9%
18,744
6.5%
76,443
-11.5%
182,474
-21.9%
(19)
Tabel di atas menampilkan kejadian Gempabumi yang terjadi selama 2016. Data MMI, Kedalaman
dan Magnitude merujuk kepada data USGS. Meskipun Gempa Air Bangis berkekuatan relative
besar yaitu 7,8Mw namun hanya dirasakan pada intensitas III sehingga mengakibatkan kerugian
relative kecil. Hal ini berbeda dengan Gempa Solok dan Pidie Jaya, dengan kekuatan 6,6 Mw dan
6,5 Mw dirasakan hingga MMI VII dan IX mengakibatkan kerugian yang besar.
Pada table tersebut disajikan informasi Exposure Terdampak yang diperoleh dari data exposure
on-risk
atas risiko yang terdampak pada saat kejadian gempa. Sedangkan Loss Ratio disini
dimaksudkan sebagai perbandingan antara Jumlah Klaim terhadap Exposure Terdampak. Loss
Ratio terbesar adalah event Solok sebesar 3,68%.
The table above shows the earthquake events that occurred during 2016. MMI, Depth and
Magnitude refer to USGS data. Although the Air Bangis earthquake is relatively large i.e 7.8Mw
and felt to MMI III, it has resulted in relatively small losses. This is different from the Solok and
Pidie Jaya Earthquakes, which has magnitude of 6.6 Mw and 6.5 Mw is felt until MMI VII and IX
resulted in substantial losses.
The table is presented information Affected Exposure from data exposure onrisk for the risks that
were affected at the time of the earthquake. Loss Ratio here is intended as a comparison between
the Claim Amounts to Affected Exposure. Loss Ratio for event Solok is the largest i.e 3.68%.
Claim Events
Asuransi Gempa Bumi 2016
Earthquake Insurance Claim Events 2016
Event
Date of Loss
Magnitude
Depth (km)
Max Intensity
(MMI)
Affected Exposure
Claim
Loss Ratio
Halmahera
24/02/16
5.4 Mb
13.70
I
18,548.39
-
0.000000%
Air Bangis
02/03/16
7.8 Mw
24.00
III
2,521,420.33
10.00 0.000397%
Mentawai
05/03/16
5.4 Mw
10.00
I
5,051,567.91
10.00 0.000198%
Garut
06/04/16
6.1 Mw
29.00
IV
166,952.86
59.91 0.035884%
Bengkulu
10/04/16
5.7 Mw
41.00
V
1,071,880.14
-
0.000000%
Solok
02/06/16
6.6 Mw
50.00
VII
573,578.45 21,102.04 3.679015%
Bengkulu
12/09/16
8.4 Mw
34.00
VIII
1,775,903.72
-
0.000000%
Padang
21/09/16
4.1 Mw
162.50
I
19,320,111.46
25.00 0.000129%
Irian Jaya
17/10/16
4.7 Mw
33.40
I
2,526,522.97
-
0.000000%
Manado
27/10/16
5.8 Mw
61.00
IV
2,752,815.31
5.55 0.000202%
Malang
16/11/16
5.7 Mw
85.00
IV
6,009,281.11
-
0.000000%
Pidie Jaya
07/12/16
6.5 Mw
8.20
IX
771,388.24
2,350.00 0.304646%
(20)
Gambar 5. Peta Intensitas dan ShakeMap Gempa Solok.
Gambar 6. Peta Intensitas dan ShakeMap Gempa Pidie Jaya.
Peta di atas menggambarkan penyebaran intensitas MMI merujuk kepada data USGS.
Berdasarkan peta tersebut dapat diperoleh informasi wilayah-wilayah yang memiliki MMI
I-X+. Gempa Solok yang terjadi pada tanggal 2 Juni 2016 menimbulkan MMI maksimal VII yang
berdampak di wilayah Kota Sungai Penuh. Sedangkan Gempa Pidie Jaya yang terjadi pada 7
Desember 2016 menimbulkan MMI hingga IX yang berdampak pada wilayah Kabupaten Pidie
Jaya.
The map above illustrates the spread of MMI intensity referring to USGS data. Based on the
map it can be obtained information areas that have MMI I-X+. Solok earthquake that occurred
on June 2, 2016 raises the maximum MMI VII that impacts in the area of City of Sungai Penuh.
While the Pidie Jaya Earthquake that occurred on December 7, 2016 cause MMI to IX which
affects the Regency of Pidie Jaya.
Claim Events
Asuransi Gempa Bumi 2016
Earthquake Insurance Claim Events 2016
(21)
Katalog Gempa Bumi 2016
Earthquake Catalog 2016
(22)
Daftar Gempa Bumi 2016 Magnitudo
≥ 6.0 Mw
Earthquake List 2016 Magnitude
≥
6.0 Mw
No. Tanggal Kejadian Bujur Lintang USGS BMKG Kota Terdekat
Number Date of Loss Longitude Latitude Magnitude (Mw) Depth (KM) Magnitude (Mw)
Depth (KM) Nearest Population
1 1/11/2016 126.97 BT 3.8 LU 6.50 13 6.4 10 58 KM Tenggara KEP-TALAUD-SULUT 2 2/12/2016 119.34 BT 9.77 LS 6.30 28 6.6 10 SUMBABARAT-NTT 14 KM Barat Daya 3 3/2/2016 94.39 BT 4.92 LS 7.80 24 7.8 10 636 KM Barat Daya
KEP-MENTAWAI-SUMBAR 4 4/6/2016 107.32 BT 8.3 LS 6.10 29 6.1 10 101 KM Barat Daya
GARUT-JABAR 5 5/2/2016 104.37 BT 5.39 LS 5.70 117 6.1 115 TANGGAMUS-LAMPUNG 23 KM Barat Daya 6 6/2/2016 100.46 BT 2.29 LS 6.60 50 6.5 72 79 KM Barat Daya PESISIR
SELATAN-SUMBAR 7 6/5/2016 125.74 BT 4.63 LS 6.30 429.6 6.3 445 146 KM Barat Daya
BURUSELATAN-MALUKU 8 6/9/2016 116.24 BT 11.42 LS 6.10 19 6.2 10 SUMBAWA BARAT-NTB 286 KM Barat Daya 9 8/24/2016 122.54 BT 7.46 LS 6.0 533 6.1 537 105 KM Barat Laut FLORES
TIMUR-NTT 10 10/9/2016 127.41 BT 1.79 LU 5.8 128 6.2 117 52 KM Barat Laut
HALMAHERA BARAT-MALUT 11 10/19/2016 108 BT 5.29 LS 6.6 614 6.5 654 120 KM Timur Laut SUBANG-JABAR 12 10/27/2016 125.79 BT 1.32 LU 5.8 61 6.1 10 75 KM Tenggara
BITUNG-SULUT 13 11/8/2016 104.59 BT 8.35 LS 5.8 33 6.0 10 271 KM Barat Daya
LEBAK-BANTEN 14 11/16/2016 113.12 BT 9.32 LS 5.7 85 6.2 69 KAB MALANG-JATIM 127 KM Tenggara 15 12/5/2016 123.4 BT 7.32 LS 6.3 526 6.4 524 120 KM Timur Laut
FLORES TIMUR-NTT 16 12/7/2016 96.36 BT 5.19 LU 6.5 13 6.4 10 18 KM Timur Laut
KAB PIDIE JAYA-ACEH 17 12/21/2016 128.01 BT 7.75 LS 6.7 152 6.6 173 MALUKU BARAT DAYA 184 KM Timur Laut 18 12/30/2016 118.63 BT 9.37 LS 6.3 79 6.6 91 59 KM Barat Laut SUMBA
(23)
Ulasan Aktuaria: Cadangan Atas Risiko Bencana
(24)
Ulasan Aktuaria
Actuarial Review
CADANGAN ATAS RISIKO BENCANA
CATASTROPHIC RESERVE
Penerapan
Catastrophic Reserve di Indonesia
Otoritas Jasa Keuangan (OJK) baru-baru ini
menerbitkan
Rancangan
Surat
Edaran
OJK
(RSEOJK) Nomor /SEOJK.05/2016 tentang pedoman
pembentukan cadangan teknis bagi perusahaan
asuransi dan perusahaan reasuransi. RSEOJK
tersebut akan menggantikan Peraturan Ketua Badan
Pengawas Pasar Modal dan Lembaga Keuangan
Nomor PER-09/BL/2012 yang mulai berlaku untuk
laporan keuangan perusahaan periode 31 Desember
2017.
Termuat
beberapa
perubahan
dan
penambahan
atas
pedoman
yang
berlaku
sebelumnya, salah satu tambahan yang akan dibahas
pada ulasan aktuaria periode ini yaitu mengenai
pembentukan
cadangan
atas
risiko
bencana
(catastrophic
reserve).
Bahasan
meliputi
perbandingan penerapan di beberapa negara dan
jabaran metodologi perhitungan terkait. Dalam poin
terakhir RSEOJK tersebut menjelaskan bahwa
cadangan atas risiko katastrop dihitung berdasarkan
manfaat asuransi retensi sendiri setelah dikurangi
cadangan
premi
dengan
memperhitungkan
kemungkinan terjadinya risiko katastrop, dimana
definisi risiko bencana atau risiko katastrop yaitu
risiko kerugian yang timbul akibat terjadinya
fenomena alam atau risiko murni kecelakaan yang
menyebabkan kerugian besar bagi perusahaan.
Definisi
Probable Maximum Loss
Penerapan catastrophic reserve di berbagai belahan
dunia beragam, sesuai dengan kondisi industri
perasuransian dan tingkat perlindungan yang ingin
dicapai. Beberapa negara menjadikan
Probable
Maximum Loss (PML) sebagai dasar dalam
pembentukan
catastrophic reserve. PML adalah
suatu ukuran risiko terkait kerugian terbesar
perusahaan
yang
memungkinkan
diperkirakan
terjadi, seringkali definisi PML juga sebagai periode
ulang (return period), yang merupakan kebalikan
dari probabilitas bahwa nilai kerugian akan melebihi
suatu nilai tertentu. PML umumnya diperoleh dari
simulasi
pemodelan
bencana
yang
berisi
kemungkinan kejadian bencana beserta perkiraan
nilai
kerugiannya
dengan
menggunakan
data
kejadian (catalogue sintesis) dan karakteristik peril
secara
stokastik.
Penentuan
return
period
bergantung terhadap jenis kejadian bencana alam
(gempa bumi, banjir, longsor dll) yang memiliki
karakteristik berbeda-beda.
Implementation of Cat Reserve in Indonesia
Otoritas Jasa Keuangan (OJK) recently issued
Rancangan Surat Edaran OJK (RSEOJK) Number
/SEOJK.05/2016
about
guideline
for
the
establishment of technical reserve to insurance
and reinsurance companies. It will replace
Peraturan Ketua Badan Pengawas Pasar Modal
dan
Lembaga
Keuangan
Number
PER-09/BL/2012, begins at financial report in 31
December 2017. The contents are modifications
and additions of prior guideline, one of the
additions that will be discussed in this actuarial
commentary is the establishment of catastrophic
reserve. The topic involves comparison of
practice in other countries and methodology
calculation. The last point in RSEOJK explains
calculation of catastrophic reserve based on the
advantage in own retention insurance after
subtracted from premium reserve and consider
the occurrence probability of catastrophic risk,
which the definition of catastrophic risk is
damage risk from consequence of natural
phenomena or pure accident risk that cause
losses significantly for company.
Definition of Probable Maximum Loss
The practice of catastrophic reserve vary widely
in different part of the world, which suitable with
insurance industry and level of protection to be
achieved. Most countries makes Probable
Maximum Loss (PML) as the basis of its
establishment. PML is a measure of risk
corresponding to the largest loss the company
can reasonably be expected to experience. Often,
PML is defined as a return period, which is the
inverse of the probability that losses will exceed
a dollar threshold. Commonly PML is obtained by
simulating the Catastrophe Model, which contains
the occurrence possibility of catastrophe event
and its estimated loss respectively using
synthetic catalogue and the characteristic of
perils stochastically. Determination of return
period depend on type of catastrophe events (i.e.
earthquake, flood, landslide, etc.) that has
different characteristics.
(25)
Penerapan
Catastrophic Reserve di Negara Lain
Perusahaan asuransi umum di Jepang membentuk
catastrophic reserve hingga mencapai nilai estimasi
kerugian yang disebabkan oleh satu kejadian
bencana alam yang terjadi sekali dalam 70 tahun
(Topan Vera 1959). Regulasi asuransi bencana alam
di Meksiko menetapkan pembentukan
catastrophic
reserve dengan limit sebesar 90% dari PML untuk
return period kejadian gempa bumi sebesar 150
tahun dan probabilitas terjadi kerugian melebihi
suatu nilai kerugian terbesar tertentu yaitu sebesar
10%,
termasuk
persentil
90%
atas
kurva
kerentanan. OSFI (Office of the Superintendent of
Financial Institutions) di Kanada mengatur tentang
ERC (Earthquake Reserve Complement) sebagai
penambah dari cadangan premi gempa bumi sebagai
bentuk persiapan finansial industri perasuransian.
ERC dibentuk berdasarkan pengurangan antara
PML
250
dan beberapa komponen yaitu proteksi
reasuransi, retensi, pembiayaan dari pasar modal
dan cadangan premi gempa bumi. Perusahaan
memiliki kurun waktu selama 25 tahun untuk
membangun PML menuju level PML
500
yang harus
dicapai pada akhir tahun fiskal 2022. Agar lebih
memahami penjelasan diatas, berikut disajikan
formulasi beserta keterangan sebagai berikut:
The Practice of Catastrophic Reserve in Other
Countries
General insurance company in Japan must
establish catastrophic reserve until the amount
reaches the estimated loss caused by a natural
disaster which occurs once in 70 years (i.e.
typhoon
Vera
in
1959).
Regulation
of
catastrophic insurance in Mexico set the
establishment of catastrophic reserve until the
limit in the amount of 90% of PML for return
period of the event of 150 years and there is a
10% of probability for the maximum value of
damage that it would exceed involving a
percentile of 90% over the vulnerability curve.
OSFI (Office of the Superintendent of Financial
Institutions) in Canada regulate ERC (Earthquake
Reserve Complement) as addition of earthquake
premium reserve as financial preparedness for
insurance industry. ERC is formed by subtraction
among PML
250
and some components that is
reinsurance protections, retention, capital market
financing and earthquake premium reserve.
Companies have 25 years to build their PML to
the PML
500
level that must be reached by the end
of fiscal year 2022. In order to achieve better
understanding,
the
formulation
and
the
description of each components are
Earthquake Reserve Formula by OSFI
ERRO = EPR + ERC
ERC = PML250 + N/25 (PML500
–
PML250)
–
Reinsurance Collectable
–
Retention
–
Approved Capital Market Financing
–
EPR,
where :
ERRO
earthquake reserve required by OSFI
EPR
earthquake premium reserve, which consist of the voluntary
accumulation of the earthquake premiums as defined below. This
EPR must be less than or equal to net PML
500. Any earthquake
premium contributed to the EPR must remain in the EPR unless
there is a material decrease in exposure.
Earthquake Premiums
an amount not exceeding 75 per cent of (current year’s earned
policyholders earthquake premiums
–
cost of earthquake
reinsurance).
In the case of catastrophic reinsurance coverage not specifically
written for earthquake risks, an allocation of the premium
amount must be made. Companies should be able to demonstrate
the reasonableness of their rate-making procedures.
ERC
earthquake reserve complement, the additional component (if
necessary) of ERRO needed to achieve financial preparedness
according to the formula. The ERC must always be greater than
or equal to 0.
N
current fiscal year minus 1997
Gross PML
PML amount estimated after policyholders deductibles but
before reinsurance protection, based on the higher value
between Quebec and British Columbia total losses on personal
and commercial property caused by shake and fire.
Net PML
PML amount estimated after policyholders deductibles and after
reinsurance protection.
PML250
gross PML estimated using a 250 year event return period at a
75 per cent damageability confidence level for deterministic
models or a 250 year loss return period at a 50 per cent
damageability confidence level for probabilistic models.
PML500
gross PML estimated using a 500 year event return period at a
75 per cent damageability confidence level for deterministic
models or a 500 year loss return period at a 50 per cent
damageability confidence level for probabilistic models.
Retention
amount of retention the company is currently using to manage
its earthquake exposure subject to a maximum of 10 per cent of
Capital & Surplus
(26)
Pemanfaatan Catastrophe Model dalam pembentukan
Catastrophic Reserve
Salah satu hasil keluaran
Catastrophe Model yang
berguna bagi manajemen risiko adalah kurva
Occurrence Exceedance Probability (OEP). Kurva
OEP
merupakan
representatif
grafis
dari
probabilitas bahwa pada suatu level kerugian akan
terlewati dalam periode waktu yang ditentukan.
Secara spesifik, kurva tersebut berharga bagi
perusahaan
asuransi
dan
reasuransi
untuk
menentukan besaran dan distribusi dari kerugian
potensial portofolio, serta memperoleh nilai PML.
PML berkaitan dengan
return period yang secara
sederhana merupakan kebalikan (inverse) dari
probabilitas
exceedance tahunan. Sebagai contoh,
pada gambar 2.5 dibawah menunjukkan nilai PML
untuk
return period kerugian 250 tahun sebesar
kurang lebih 21 Juta Dolar, yang merupakan nilai
batas bawah kerugian pada probabilitas exceedance
sebesar 0,4%. Secara jelas, hal ini berarti ada
probabilitas
99,6%
bahwa
suatu
perusahaan
perasuransian menderita kerugian sampai dengan
kurang lebih 21 Juta Dolar dan ada 0,4%
kemungkinan kerugian lebih dari 21 Juta Dolar.
The Application of Catastrophe Model to Establish
Catastrophe Reserve
One of the Catastrophe Model output that has
advantage for risk management is Occurrence
Exceedance Probability (OEP) curve. An OEP
curve is a graphical representation of the
probability that a certain level of loss will be
surpassed in a given time period. Specifically, an
OEP curve is particularly valuable for insurers
and reinsurers to determine the size and
distribution of their
portfolio’s
potential losses,
and also to obtain PML. PML limits are framed in
terms of a return period that simply is inverse of
the annual probability of exceedance. In this
example, from the figure 2.5 below, it can be
seen that the PML is approximately 21 million
Dollar on return period of event 250 years, as
the lower limit on the loss at a 0.4% probability
of exceedance. Clearly, there is a 99.6%
probability for the company have loss until
approximately 21 million Dollar and there is a
0.4% probability of loss more than 21 million
Dollar.
(Source: Grossi, P., Kunreuther, H. 2005. Catastrophe Modelling: A New Approach to
Managing Risk. Springer Science + Business Media, Inc.)
(27)
Pada praktiknya, terdapat banyak kemungkinan
modifikasi perhitungan OEP. Salah satu metodologi
yang akan dibahas yaitu berdasarkan Grossi (2005).
Setiap kejadian bencana diasumsikan hanya dapat
terjadi maksimum sebanyak 1 (satu) kali, sehingga
frekuensi masing-masing kejadian bencana , yaitu
�
berdistribusi Bernoulli dengan fungsi massa
peluang berupa
There are many probable modification of
calculation OEP in practice. One of the
calculation methodology that will be discussed in
this actuarial commentary is based on Grossi
2005. Each of catastrophic event assumed only
occur maximum once, so that the frequency of
each catastrophic event , that is
�
has Bernoulli
distribution with probability mass function
=
,
��
(1
−
),
��
.
Isi tabel ELT (Earthquake Loss Table) yang
diperoleh
dari
Catastrophe
Model
kemudian
diurutkan berdasarkan mean loss dari yang terbesar
hingga terkecil, sehingga menjadi
The content of earthquake loss table from
catastrophe model then sorted by mean loss from
the largest to the smallest, so that
�
�
1
>
�
�
2
>
⋯
>
�
�
merupakan
statistik
terurut
(order
statistic).
Berdasarkan
statistika
terurut,
dapat
didefinisikan dengan menggunakan dua persamaan
berikut:
is order statistic. Based on order statistic,
is defined as two following equation:
: = 1
−
�
,
��
�� �
ℎ �
ℎ �ℎ
ℎ�
≤ �
�
(1)
: = 1
−
�
,
�� �
ℎ �
ℎ �ℎ
ℎ�
>
�
�
��
(2)
sesuai dengan persamaan (1) maka,
corresponding with the equation (1),
=
�
�
= 1
−
1
−
−
1
=1
.
apabila terdapat nilai mean loss yang sama, dimana
untuk suatu nilai
�
�
�
= �
� +1
, maka
If there are mean loss that have equal value,
which is for a , the value of
�
�
�
= �
� +1
, then
(28)
Secara implisit, penggunaan PML sebagai batas
minimum pendanaan bersifat adil bagi perusahaan
karena telah mempertimbangkan sebaran dan jenis
eksposur sesuai dengan portofolio masing-masing,
hal ini tercermin karena merupakan data masukan
untuk
Catastrophe Model, sehingga catastrophic
reserve yang dihasilkan mencerminkan nilai risiko
yang ditanggung. Suatu perusahaan yang memiliki
eksposur berukuran besar akan mencadangkan
sejumlah dana lebih besar pula terhadap perusahaan
lain yang memiliki eksposur berukuran kecil atau
eksposur sama besar namun banyak tersebar pada
daerah dengan tingkat risiko bencana yang rendah.
Akhirnya, penetapan standar kekuatan finansial dan
aturan skema perlindungan terhadap perusahaan
asuransi dan reasuransi yang memiliki liability atas
risiko katastrop akan menjadikan industri asuransi
lebih kuat dan lebih terencana sehingga diharapkan
memiliki masa depan yang baik untuk terus
berkontribusi dalam perekonomian nasional maupun
internasional.
Implicitly, the use of PML as a minimum funding
limit is fairly for the company because it has
considered spreading and type of exposure in
accordance with its respective portfolio, this is
because it is the input data for catastrophe
model, so the resulting reflects the value of the
assure risks. A company that has large exposure
will reserve a larger amount of funds against
other companies that have small exposures or
exposures as large but widely spread in areas
with low level of disaster risk. Finally, the
establishment of financial strength standards and
protection
schemes
for
insurance
and
reinsurance companies that have a liability for
catastrophic risk will make the insurance
industry stronger and more planned so it is
expected to have a good future to continue to
contribute in the national and international
economy.
Referensi/
Refferrence
:
1. Grossi, P., Kunreuther, H. 2005.
Catastrophe Modelling: A New Approach to Managing
Risk
. Springer Science + Business Media, Inc.
2. Grossi, P., Kunreuther, H., Editors. 2013.
Clarification and Errata to Catastrophe
Modeling: A New Approach to Managing Risk
.
3. 2017.
Rancangan Surat Edaran Otoritas Jasa Keuangan perihal Pedoman Pembentukan
Cadangan Teknis bagi Perusahaan Asuransi dan Perusahaan Reasuransi
. Otoritas Jasa
Keuangan.
4. PT Reasuransi Maipark
.
2010.
Cadangan Katastrofi Gempa Bumi
.
5. Hapsari, I.N., Atmaja, F.W. 2016.
Terminologi dan Perhitungan Dasar Analisa Risiko
Katastrofe
. RDI
–
PT Reasuransi Maipark.
6. 2013.
Guideline B-9
–
Earthquake Exposure Sound Practices
. Office of the
Superintendent of Financial Institutions Canada.
7. Rodr
ί
guez, N.A.R. 2016.
Regulation of Catastrophic Insurance : Mexico
. Insurance and
Surety National (CNSF-MEXICO).
8. 2007.
Fact
Book
2005
–
2006
General
Insurance
in
Japan
(
www.sonpo.or.jp/en/publication/pdf/fb2006e.pdf
). The International Department The
General Insurance Association of Japan.
(29)
(30)
West Java is the largest contributor of exposure and number of
risk i.e 1.5 trillion rupiah or 53.2% of total exposure and 79,787
number of risks. The PML simulation is performed with input
from exposure, occupation, interest and building height data of
the region
.
Jawa
Bagian
Barat
adalah
penyumbang
eksposur
dan
jumlah
risiko
terbesar
yaitu
masing-masing 1.5 triliun rupiah
atau 53.2% dari total eksposure
dan
79.787
jumlah
risiko.
Simulasi
PML
dilakukan
mempertimbangkan data okupasi,
ekposure,
interest
dan
tinggi
bangunan.
IDR
1.534
T
53.2%
79,787
Ratio to National Exposure
Number of Risks
Total Exposure
B
43.87%
C
47.15%
BI
8.98%
Eksposur per Interest
B: Building
C: Content
BI: Business Interuption
HR
27.53%
MR
4.77%
LR
67.70%
Eksposur per Tinggi
Bangunan
HR: High Rise Building
MR: Medium Rise
Building
LW: Low Rise Building
0.012%
0.273%
0.531%
1.272%
1.935%
2.047%
3.46%
0.000%
0.500%
1.000%
1.500%
2.000%
2.500%
3.000%
3.500%
(1) Disimulasikan di iAsuransi
™
(2) Hanya mensimulasikan guncangan gempa untuk interest building dan content saja
(31)
Historical Earthquake in Jakarta.
Pieces of the book "Die Erdbeben des Indischen Archipels bis Zum Jahre 1857" written by
Arthur Wichmann published in 1918.
Wichmann noted the earthquake that occurred on January 22, 1780 is felt in Jakarta.
From
other old references
, we estimate
the earthquake
intensity was at least VII MMI with the
epicenter estimated at
the
subduction zone
in southern part of java, OR, local fault in
Jakarta.
This earthquake is probably the biggest earthquake ever felt in Jakarta. Wichmann noted
that the aftershock from the earthquake was still felt in Jakarta at least until 13 December
1780, almost a year after the incident.
Catatan Gempa di Jakarta.
Potongan dari buku
Die
Erdbeben des Indischen Archipels bis Zum Jahre
1857 yang
ditulis oleh Arthur Wichmann yang diterbitkan pada tahun 1918.
Wichmann mencatat gempa yang terjadi pada 22 Januari 1780 yang dirasakan di Jakarta.
Dari deskripsi
referensi lainnya
, kami memperkirakan
intensitas gempa ini paling tidak VII
MMI dengan
episenter gempa
diperkirakan
di zona subduksi lempeng di
Selatan Jawa atau
patahan lokal di Jakarta sendiri
.
Gempa ini mungkin adalah gempa terbesar yang pernah dirasakan di Jakarta. Wichmann
mencatat aftershock dari gempa ini masih dirasakan di Jakarta paling tidak sampai dengan
13 Desember 1780, hampir satu tahun setelah kejadian.
Sources: Die Erdbeben des Indischen Archipels bis Zum Jahre 1857, 1918
Research, Development and Innovation Group, Maipark, 2017
(32)
Observatorium Mohr.
Mulai dibangun oleh Johan Mohr pada tahun 1765 dan selesai pada tahun 1768. Bangunan
ini, mungkin, adalah bangunan pencakar langit
pertama yang berdiri di Jakarta dengan
tingginya sekitar 30 meter. Ilustrasi bagnunan ini dapat dilihat pada lukisan Johannes Rach
yang menggambarkan Klenteng Jin De Yuan (Vihara Dharma Bhakti sekarang) dengan latar
belakang Observatorium Mohr (sumber: Arsip Perpustakaan Nasional).
Bangunan mengalami kerusakan berat secara struktur akibat gempa yang terjadi pada 22
Januari 1780 sehingga tidak lagi digunakan sebagai observatorium. Saat ini sisa-sisa
bangunan tidak dapat dilihat lagi, namun dipercaya bahwa lokasi observatorium ini ada di
area Glodok, di sekitar Jalan Kemurnian, tepatnya di Gang Torong.
Torong sendiri kemungkin berasal dari salah
-
lafal atas kata Belanda Toren yang
dalam Bahasa Indonesia berarti Menara .
Mohr
Observatory
It was built by Johan Mohr in 1765 and completed in 1768. The building, most likely, is the
first "skyscraper" building in Jakarta with the height of about 30 meters. Illustration of the
building was painted by Johannes Rach depicting Jin De Yuan Temple (Dharma Bhakti
Temple now) with background of Mohr Observatory (source: National Library archive).
The building structures was severely damaged due to the earthquake that occurred on
January 22, 1780 so it is no longer used as an observatory. Currently the remains of the
building can not be seen again, but it is believed that the location of this observatory is in
the area of Glodok, around Jalan Kemurnian, in Gang Torong.
Torong himself may be derived from "mis-pronunciation" for the Dutch word "Toren"
which in Indonesian means "Tower".
(33)
Data Detail
Detail Data
(34)
Detail Data
Detail Data
Desember 2016
Tab le 2.1
Dalam Rupiah
Amount
%
Amount
%
Amount
%
Amount
%
Amount
%
BANDA ACEH 1,1 3.887.973.784.587,20 0,20 4.631.184.023.255,03 0,17 6.475.470.127.856,06 0,21 6.078.889.378.887,71 0,17 1.290.809.652.665,53 0,04 MEDAN 1,2 39.930.432.753.351,10 2,02 48.022.219.701.013,20 1,74 52.108.491.977.901,30 1,67 63.971.010.160.823,10 1,76 39.660.652.063.716,60 1,37 OTHERS 1,3 39.955.090.750.405,30 2,02 49.698.880.730.421,50 1,80 58.546.862.994.854,90 1,88 72.980.439.422.052,30 2,00 31.543.259.510.407,10 1,09
NORTH SUMATERA 1 83.773.497.288.343,60 4,24 102.352.284.454.690,00 3,71 117.130.825.100.612,00 3,76 143.030.338.961.763,00 3,93 72.494.721.226.789,30 2,51
PADANG 2,1 19.057.509.310.139,10 0,97 27.255.920.995.628,50 0,99 30.371.668.475.169,30 0,97 36.691.953.915.733,70 1,01 21.712.902.342.711,70 0,75 PALEMBANG 2,2 19.002.600.284.678,90 0,96 23.229.423.263.542,00 0,84 21.681.739.572.366,00 0,70 33.804.577.962.742,60 0,93 10.929.500.713.238,80 0,38 OTHERS 2,3 134.584.557.967.246,00 6,82 150.972.998.453.966,00 5,47 201.126.177.692.770,00 6,46 291.560.184.622.143,00 8,00 199.997.080.244.813,00 6,93
SOUTH SUMATERA 2 172.644.667.562.064,00 8,74 201.458.342.713.137,00 7,30 253.179.585.740.305,00 8,13 362.056.716.500.619,00 9,94 232.639.483.300.763,00 8,07
JAKARTA 3,1 400.178.211.429.859,00 20,27 435.509.747.123.867,00 15,79 657.274.895.593.947,00 21,10 614.671.253.265.775,00 16,88 525.292.946.002.536,00 18,21 BANDUNG 3,2 11.047.671.688.987,80 0,56 10.569.155.344.981,00 0,38 11.931.177.385.427,90 0,38 16.376.276.621.582,70 0,45 38.319.064.281.669,90 1,33 OTHERS 3,3 662.600.932.721.902,00 33,56 987.924.686.310.558,00 35,81 1.125.271.475.245.590,00 36,12 1.009.114.935.603.370,00 27,71 970.845.665.192.685,00 33,66
WEST JAVA 3 1.073.826.815.840.750,00 54,39 1.434.003.588.779.410,00 51,98 1.794.477.548.224.970,00 57,60 1.640.162.465.490.720,00 45,03 1.534.457.675.476.890,00 53,20
SEMARANG 4,1 7.152.358.175.509,66 0,36 8.897.919.788.005,73 0,32 8.633.225.679.742,90 0,28 14.628.262.030.225,10 0,40 18.310.096.657.351,80 0,63 YOGYAKARTA 4,2 9.305.354.884.895,54 0,47 8.575.091.703.780,61 0,31 14.173.252.946.765,00 0,45 395.704.206.157.153,00 10,86 16.089.822.775.850,90 0,56 OTHERS 4,3 102.786.967.600.585,00 5,21 136.822.997.958.859,00 4,96 147.501.933.285.596,00 4,73 197.467.815.456.252,00 5,42 172.042.095.703.775,00 5,96
CENTRAL JAVA 4 119.244.680.660.990,00 6,04 154.296.009.450.645,00 5,59 170.308.411.912.104,00 5,47 607.800.283.643.630,00 16,69 206.442.015.136.978,00 7,16
SURABAYA 5,1 51.264.598.090.249,70 2,60 134.263.168.475.156,00 4,87 76.721.427.230.315,60 2,46 74.468.101.155.800,20 2,04 59.868.961.289.120,10 2,08 OTHERS 5,2 242.018.455.804.202,00 12,26 233.213.828.062.384,00 8,45 339.837.153.558.289,00 10,91 373.409.575.818.694,00 10,25 267.144.671.544.593,00 9,26
EAST JAVA 5 293.283.053.894.452,00 14,86 367.476.996.537.540,00 13,32 416.558.580.788.604,00 13,37 447.877.676.974.494,00 12,30 327.013.632.833.713,00 11,34
KALIMANTAN 6 101.470.729.177.791,00 5,14 128.384.115.815.410,00 4,65 143.455.664.012.414,00 4,60 161.577.754.961.137,00 4,44 138.424.761.726.260,00 4,80
UJUNG PANDANG 7,1 10.444.854.149.882,30 0,53 14.184.711.558.838,50 0,51 24.286.053.554.519,00 0,78 19.845.266.682.189,10 0,54 17.315.654.766.166,60 0,60 OTHERS 7,2 54.000.807.954.031,10 2,74 39.313.808.513.031,70 1,43 99.196.918.662.308,80 3,18 134.802.452.331.010,00 3,70 125.724.261.519.719,00 4,36
SULAWESI 7 64.445.662.103.913,30 3,26 53.498.520.071.870,20 1,94 123.482.972.216.828,00 3,96 154.647.719.013.199,00 4,25 143.039.916.285.885,00 4,96
OTHER ISLANDS 8 65.594.970.726.182,70 3,32 317.250.758.401.997,00 11,50 96.752.011.275.839,20 3,11 125.101.957.964.710,00 3,43 229.968.143.407.862,00 7,97
1.974.284.077.254.480,00 100,00 2.758.720.616.224.700,00 100,00 3.115.345.599.271.680,00 100,00 3.642.254.913.510.280,00 100,00 2.884.480.349.395.140,00 100,00
U/Y 2016
As At 31/12/2016
National Aggregate Exposure By Cresta Zone
T O T A L
U/Y 2015
(35)
Detail Data
Desember 2016
Reasuransi MAIPARK | 31
(36)
Detail Data
Detail Data
Desember 2016
Table 2.3
Amount % Amount % Amount % Amount % Amount %
Building 804,229,599,899,853.00 40.74 1,048,349,473,052,820.00 38.00 1,290,296,525,005,220.00 41.42 1,820,298,353,037,060.00 49.98 1,195,399,704,797,040.00 41.44 Machinery 395,825,191,670,355.00 20.05 452,117,181,591,479.00 16.39 596,214,444,173,001.00 19.14 655,388,811,517,528.00 17.99 573,046,809,741,510.00 19.87 Others 309,557,841,717,582.00 15.68 474,344,808,757,433.00 17.19 401,340,028,232,438.00 12.88 449,829,469,571,238.00 12.35 486,506,516,776,063.00 16.87 Stock 281,045,067,821,749.00 14.24 337,535,210,092,717.00 12.24 535,786,640,886,770.00 17.20 424,923,281,005,429.00 11.67 365,022,972,796,062.00 12.65 Bussiness Interruption 183,626,376,144,946.00 9.30 446,373,942,730,248.00 16.18 291,707,960,974,244.00 9.36 291,814,998,379,017.00 8.01 264,504,345,284,470.00 9.17 T O T A L 1,974,284,077,254,490.00 100.00 2,758,720,616,224,700.00 100.00 3,115,345,599,271,680.00 100.00 3,642,254,913,510,280.00 100.00 2,884,480,349,395,140.00 100.00
National Aggregate Exposure By Interest
In IDR
Interest
U/Y 2014
U/Y 2015
U/Y 2016
As At 31/12/2016
(37)
Detail Data
Desember 2016
Reasuransi MAIPARK | 33
(38)
Detail Data
Detail Data
Desember 2016
Table 2.3
Amount % Amount % Amount % Amount % Amount %
Building 804,229,599,899,853.00 40.74 1,048,349,473,052,820.00 38.00 1,290,296,525,005,220.00 41.42 1,820,298,353,037,060.00 49.98 1,195,399,704,797,040.00 41.44 Machinery 395,825,191,670,355.00 20.05 452,117,181,591,479.00 16.39 596,214,444,173,001.00 19.14 655,388,811,517,528.00 17.99 573,046,809,741,510.00 19.87 Others 309,557,841,717,582.00 15.68 474,344,808,757,433.00 17.19 401,340,028,232,438.00 12.88 449,829,469,571,238.00 12.35 486,506,516,776,063.00 16.87 Stock 281,045,067,821,749.00 14.24 337,535,210,092,717.00 12.24 535,786,640,886,770.00 17.20 424,923,281,005,429.00 11.67 365,022,972,796,062.00 12.65 Bussiness Interruption 183,626,376,144,946.00 9.30 446,373,942,730,248.00 16.18 291,707,960,974,244.00 9.36 291,814,998,379,017.00 8.01 264,504,345,284,470.00 9.17 T O T A L 1,974,284,077,254,490.00 100.00 2,758,720,616,224,700.00 100.00 3,115,345,599,271,680.00 100.00 3,642,254,913,510,280.00 100.00 2,884,480,349,395,140.00 100.00
National Aggregate Exposure By Interest
In IDR
Interest
U/Y 2014
U/Y 2015
U/Y 2016
As At 31/12/2016
(39)
Detail Data
Desember 2016
Reasuransi MAIPARK | 35
(1)
Lampiran 7 / Attachment 7
SKALA TARIF PREMI ATAU KONTRIBUSI
GANGGUAN USAHA (
BUSINESS INTERRUPTION
)
Penetapan tarif Premi atau Kontribusi untuk jaminan gangguan usaha (business interruption) berlaku skala sebagai berikut:
Indemnity Period
Persentase (%) dari Tarif Premi atau Kontribusi
1 bulan 20% x 100% tarif Premi atau Kontribusi 2 bulan 30% x 100% tarif Premi atau Kontribusi 3 bulan 40% x 100% tarif Premi atau Kontribusi 4 bulan 50% x 100% tarif Premi atau Kontribusi 6 bulan 60% x 100% tarif Premi atau Kontribusi 9 bulan 80% x 100% tarif Premi atau Kontribusi 12 bulan 100% x 100% tarif Premi atau Kontribusi 15 bulan 96% x 100% tarif Premi atau Kontribusi 18 bulan 93% x 100% tarif Premi atau Kontribusi 21 bulan 91.5% x 100% tarif Premi atau Kontribusi 24 bulan 90% x 100% tarif Premi atau Kontribusi 30 bulan 87% x 100% tarif Premi atau Kontribusi 36 bulan 85% x 100% tarif Premi atau Kontribusi 48 bulan 83% x 100% tarif Premi atau Kontribusi
Untuk indemnity period lebih dari 48 bulan penetapan persentase dari tarif Premi atau Kontribusi diserahkan kepada underwriter Perusahaan
(2)
Lampiran 8 / Attachment 8
KODE OKUPASI (3 DIGIT)
Penetapan tarif Premi atau Kontribusi Asuransi Harta Benda berdasarkan kode okupasi (3 digit) sebagai berikut:
KODE OKUPASI KETERANGAN
200 Mining (underground or above ground) of precious metal (Gold, Silver, Platinum and other precious metals) including primarily smelting and refining
201 Mining (underground or above ground) of Aluminium including Smelting and Refinery
202 Iron Mines, Blast Furnaces, Iron Foundries and Primary metal product
203 Steelworks and Rolling Mills, termasuk juga pembuatan besi dan baja paduan. Termasuk kegiatan tungku pembakar selain blast furnaces, steel converter, pabrik penggulungan dan finishing; produksi besi kasar dalam bentuk dasar seperti balok; produksi besi campuran.
204 Exploration and Production of Crude Petroleum and Natural Gas, Terminals, and geothermal
205 Mines, Other than Iron, Aluminium, Precious Metal, Salt
206 Foundries, Reduction Plants (Smelting and Refinery) for Metals (Excluding Iron, Alumunium and Precious Metal)
207 Coal and Lignite Extraction, Asphalt Mines 208 Salt mines
209 Peat extraction, Peat processing
210 Stone, gravel and sand extraction installations 211 Cement, Chalk, Lime and Gypsum Industry
212 Asbestos Products, Cut Stone and Stone products, (Animal) Bones and/or Shell crushing for feed, Mineral Wool
213 Brick and Structural Clay Tile, and structural Clay products (batubata, genteng, ubin)
214 Porcelain, Earthenware, Stoneware, Pottery, Clay Refractories, Ceramic Wall and tiles factory
215 Glass works, Glass blowing plants
216 Glass Products Made of Purchased Glass, lenses, safety glass, mirror 217 Precious stone workshops and processing plant
218 Emery and abrasive materials factories
219 Tarcoated chippings manufacture, asphalt and roofing felt factories, Asphalt Goods
220 Foil and sheet making plant, Forging Works, Locksmiths, Constructional Metalworks
221 Industrial, Mining and Commercial Machinery, Metal Coating Services, Coating Processes Using Flammable Materials including dipping and spray painting 222 Manufacture of electrical apparatus, Wet & dry batteries, Measuring and
Precision apparatus photographic apparatus, Scientific laboratories 223 Cable and wire factories
224 Vehicle, Railways carriage and locomotive, Aircraft, Ships construction and assembling plants
(3)
225 Manufacture and or assembly of telecommunication or computer apparatus and Integrated circuits (IC)
226 Radio and Television Receiver factories, and or assembling plant, audio and audio-video recorder and or player factories and or assembling plants, manufacture of parts for radio and television receivers, audio and audio-video recorders and or players
227 Light Bulb Factories, Manufacture of Electronic Tubes and Fluorescent Tubes, Neon Signs, LED
228 Watches and clocks and their component parts factories
229 Metal jewelry and precious metal goods factories/diamond polishing factories 230 Chemical products, pharmaceutical products
231 Oil Processing 232 Petrochemical works
233 Storage Tanks (non terminal)
234 Manufacturer of plastic articles, Foam plastic, Synthetics resin
235 Manufacture of Artificial Fibres (Petrochemical Feedstock Process), Establishments primarily engaged in the manufacture of cellulosic fibres, such as rayon, viscose, cellulose acetate, cigarette tow, nitrocellulose fibers, etc, in the form of monofilament, yarn, staple or tow suitable for further manufacturing on spindles, looms, knitting machines or other textile processing equipment.
236 Manufacture of film, photographic paper, magnetic tape and celluloid batch adhesive tape (selotip)
237 Manufacture of tooth paste, soap, detergents, polishes, etheric oils, essence, parfumes, and cosmetic products, gelatine non-synthetic adhesives, starches, candles and wax goods, natural resins
238 Chemical fertilizer factories (conventional)
239 Powders, explosives, matches and firework factories 240 Spinning Mills, prespinning process
241 Weaving, pre-weaving process
242 Dressing (including but not limited to printing) and finishing 243 Mixed processes (Spinning, Weaving, Dressing and Finishing) 244 Processing of textile waste and non woven process
245 Ropemakers, stringmakers, sackmakers, blankets weaving mills, carpet manufacturer
246 Knitting mills, knitwear factories, stocking factories, glove factories hosiesry mills, lamp wick factories, clothing and underwear manufacturers, sewing works, furrires, skin processing works, custom tailors, umbrella factories, felt goods factories, lace
247 Upholste y, ushio ake ’s a d saddle ’s wo kshops 248 Cotton wool and capoc factories
249 Laundries, pressers, dry (chemical) cleaning, cleaning of feathers and down 250 Wood pulp, cellulose based on wood pulp and linoleum factories
251 Paper, cardboard and hardboard factories 252 Cartoon and paper goods factories, bookbinders
253 Newspaper printers, other printers, art printing works, lithographers, screen printing on paper (sablon), typeplate workshops, block workshops, photogravure workshops
254 Coloured paper and/or wallpaper printing works 255 Straw and rush goods factories
(4)
256 Leather production and tanneries 257 Shoe factories
258 Leather goods factories
259 Rubber goods factories, foamed rubber factories, tyre factories, vulcanizing works, rubber factories (non estate risk), tyre cord manufacturers
260 Saw ills, a pe te ’s shops, pa uet fa to ies, sawi g a d utti g of fi ewood 261 Furniture manufacture, cabinet makers, wooden boat builders, bamboo, rattan
and wooden carvings and handicrafts
262 Manufacture of wood fibre board, hardboard masonite, wood chipboard, particle board, pressed wood and plywood, veneers
263 Tu e ’s shop, oa h uilde ’s shop, pipes, walki g-sticks and picture frame factories, pencil factories
264 Wicker – Work Factories (anyaman bambu) 265 Cork goods factories such as shuttle cock factories 266 Broom, brush and paintbrush factories
267 Timber impregnation installations, plywood coating installation 268 Wooden musical instruments factories
269 Charcoal producers and other industries in main category 26 270 Corn mills, paddy (rice), gambir
271 Sugar mills, chocolate factories, sweets factories
272 Food paste producers, bakeries, processed food factories
273 Preserves factories (jams etc), pabrik sari gula, monosodium glutamate (MSG) producers, meat, meat products and fish product factories
274 Edible fats, edible oil and desiccated coconut producers 275 Dairy Product
276 Coffee roasting plants and coffee extract factories, malting 277 Cold stores, ice factories, abattoirs, butchers dying installation
278 Beweries, beverage producers, spirits, liquors producers, distilleries and vinegar Factories.
279 Tabacco, cigars and cigarettes manufacture 280 Hydro-electric power stations
281 Conventional power station, buildings with boiler houses and steam houses 282 Nuclear power stations
283 Overhead power transmission lines and networks
284 Voltage and current transformer stations, junction installation in the open 285 Rubbish incineration installations for power generation and heating
286 Gas Works Flammable, filling services, including acethylene, oxygen, nitrogen, argon, etc, non pipelines
287 Waterworks, Pumping Stations, Sewage Works, Ice Rinks, Water treatment plant (WTP), WWTP) (including Underground Pipes)
290 Transport and traffic 291 Construction firms 292 Mass communication 293 Trading and storage
294 Hotels, Entertainment, Sport, Services 295 The caring services
296 Motor vehicle repair shops, services station and pools 297 Private Building
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298 Property of public bodies 299 Special covers for building
300 Kina
301 Coklat
303 Kopi
304 The
305 Minyak sereh
306 Kapok (dari hasil buah) 307 Kelapa dan kopra 308 Karet (Gutta percha) 309 Gula bibit
310 Perkebunan serat, serat campuran dan kapok yang belum terproses 311 Pabrik tapioka di perkebunan
312 Damar dan terpentin
313 Cengkeh dan rempah-rempah
314 Padi, tanpa pengerjaan termasuk padi di tempat terbuka 315 Holtikultura, sayur-sayuran, kentang dan tidak ada pengeringan 316 Kelapa sawit
317 Tembakau
318 Gambir, barang-barang ditempat terbuka
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