p-ISSN: 2301-5373

e-ISSN: 2654-5101

Jurnal Elektronik Ilmu Komputer Udayana

Volume 8, No 3. February 2020

Data Warehouse Model For Population Registration In Kerambitan Village Tabanan Regency

Fathiyarizq Mahendra Putraa1, Ida Bagus Gede Dwidasmaraa2

aInformatics Department, Mathematics and Science Faculty, Udayana University Jalan Raya Kampus Unud, Bukit, Jimbaran, Bali, Indonesia

1Fathiyarizq.mahendra@gmail.com

2dwidasmara@unud.ac.id

Abstract

Kerambitan village is one of the 15 villages in Kerambitan sub-district, Tabanan regency, Kerambitan village has 7 service hamlets. Kerambitan village population has more than 3,338 inhabitants consisting of 1,700 men and 1,638 women with a sex ratio of 103.79. . To realize good governance, it needs to be recorded well so that the information can be utilized by related agencies which can be used for receiving assistance. During this time the process of recording population events such as recording the receipt of assistance is done manually where the information is done in tabular form so it does not clearly describe the respective information groups.Data warehouse (DW or DWH), also known as enterprise data warehouse (EDW), is a system used for reporting and analyzing data, and is considered a core component of business intelligence, The purpose of building a data warehouse is to provide a system that allows the right data to reach the right end user at the right time. Thus, the main purpose of implementing this data warehouse system is to provide relevant and timely information in an easily understood format so that service decisions to the public can be made more efficiently and effectively. The purpose of this research is to form a data warehouse model scheme of population records owned by kerambitan village government and through modeling this data warehouse also provides reliable information from a collection of data, in addition this research also retrieves some information from the data warehouse in build based on kerambitan village population records. In this study I chose to use the fact constellation scheme because there are fact tables that are interconnected with dimension tables and also fact tables that are related to other fact tables. The design model of the data warehouse is designed based on 3 excel table files, each of which has 1 table from the Government of Kerambitan Villages. In this case, 3 reports are proposed to be made based on the query results from the data warehouse. Through the application of a data warehouse that was formed, the executive or government can conduct analysis of the reports generated based on various dimensions that exist.

Keywords: Data Warehouse, Government, Kerambitan Villages, Data Warehouse Model, Fact Constellation Scheme,

  • 1.    Introduction

Kerambitan village is one of the 15 villages in Kerambitan sub-district, Tabanan regency, Kerambitan village has 7 service hamlets. Kerambitan village population has more than 3,338 inhabitants consisting of 1,700 men and 1,638 women with a sex ratio of 103.79 [1]. in the course of Kerambitan village government became one of the vital parts of the community in administration, law, planning and population, which can create good governance for people who are in the Kerambitan village environment,

To achieve good governance. all data needs to be recorded properly so that the information can be utilized by related institutions that can be used to receive assistance such as assistance from social services, health services, infrastructure and so on, which is one of the implementations of egovernment, E-government (short for electronic government) is the use of technological communications devices, such as computers and the Internet to provide public services to citizens and other persons in a country or region. the term consists of the digital interactions between a citizen and their government (C2G), between governments and other government agencies (G2G), between

government and citizens (G2C), between government and employees (G2E), and between government and businesses/commerces (G2B). The most expected advantage of e-government is increased efficiency, convenience, and better accessibility of public services.[4]

During this time the process of recording population events such as recording the receipt of assistance is done manually where the information is done in tabular form so it does not clearly describe the respective information groups, so the data recorded is not able to provide information quickly and has not been able to provide information that can help the process of taking decision.

Information Systems is a combination of information technology and the activities of people who use the technology to support operations and management. [9] In a very broad sense, the term information system that is often used refers to interactions between people, algorithmic processes, data, and technology. In this sense, the term is used to refer not only to the use of information and communication technology (ICT) organizations, but also to the ways in which people interact with this technology in supporting decisions processes. [9] So that with adequate information technology support will help in the decision process that is fast and precise.

Population record data from kerambitan village which can be utilized by processing it into information, with the data being thought can be more efficient with the data warehouse, so that the data obtained is able to provide information and decision making.

In computing , a data warehouse ( DW or DWH ), is a system used for reporting and data analysis , and is considered a core component of business intelligence . [5] DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place [2] that are used for creating analytical reports for workers throughout the enterprise. [3] Data warehouse a computer environment in order to use databases so that strategic information becomes faster and more reliable. The data warehouse is created by an ETL (Extraction Transformation Loading) process that gets data from a Transactional Processing System (TPS) or OLTP (OnLine Transactional Processing)[8].

The purpose of this research is to form a data warehouse model scheme of population records owned by kerambitan village government and through modeling this data warehouse also provides reliable information from a collection of data, in addition this research also retrieves some information from the data warehouse in build based on kerambitan village population records. implementing a data warehouse system can provide positive value for the company, including:

  • •    Decision makers can access data records better

This is obtained by making decision makers to be able to access data that was previously unavailable, unknown, or unrelated information because the information is distributed to all existing database distributions, thus requiring data warehouse to integrate all of the data.

  • •    Increased productivity of decision makers in government

Data warehouse integrates data from various separate systems into a form that provides a complete view of a government institution. Through the process of converting data into more meaningful information, the data warehouse enables executives to carry out more substantial, accurate, and consistent analyzes so that they can support the decision making process in their administrative areas.

Through this research a data warehouse model and application will be formed that can assist executives in analyzing data that were previously still in the form of tabular documents but also integrated data that will facilitate the understanding of information content, so that it can be useful as a material consideration in decision making process carried out.

  • 2.    Related Works

in a previous study of data warehouses under the title "Data Warehouse Model for Fire Fighting Operations at the DKI Jakarta Provincial Fire Service" presents the design of 2 data warehouse design models for the DKI Jakarta fire department. The first data warehouse design model is categorized as a fact constellation schema, where there are 3 fact tables, 2 dimensional tables and 1 sub dimensional table, while the second data warehouse design model is a fact constellation schema which only consists of only 3 fact tables. Both of these data warehouse models are displayed with the intention that the application of data warehouse based on this fire extinguisher data will look different. The data warehouse design model is designed based on 4 excell file tables, each of which has 1 table downloaded from the DKI Jakarta fire department website on the data.jakarta.go.id website [6].

While the article titled “Designing Warehouse Data Model In Supporting Shipping Services Company” by Tanty Oktavia where in his research formulating a data warehouse model and

application design in accordance with the results of the needs analysis, which could later support the shipping service company, which in this study involved PT. Atlas Transindo Raya as the object of research. The methodology used in this study uses analysis and design methods. Where the analysis method is carried out literature study, conduct surveys and interviews, identify information needed by executives in decision making, and define data warehouse requirements to be built based on the Nine-Steps Methodology. Whereas the design method is done by designing a data warehouse application, as an interface display that contains supporting features from the user side. The results of this study are in the form and model of data warehouse applications that are formed based on operational data, processed in various dimensions, so that they can form a report to meet the needs of the executive for information [7].

  • 3.     Research Method

    3.1.    Collecting Data

Data collection in this study was obtained from literature study methods or secondary data that can be obtained from the Government of Kerambitan Villages, where the data obtained in the form of data as follows:

  • 1.  Population Data of Kerambitan Village in 2018.xls

  • 2.  Regional Incentive Assistance Recipient Data.xls

  • 3.    Central Incentive Assistance Beneficiary Data.xls

Here are some data i obtained from government of kerambitan village:

  • a.    Population Data of Kerambitan Village in 2018.xls

In this Excel file there are 3160 data row records that consist of 16 columns that contains : NIK (ID Number), No_KK (Family Registry Number), nama lengkap (Full_name), Jenis Kelamin (Gender), tanggal lahir (date of birth), umur (age), agama (religion), status kawin (marital status), SHDK (relationship status in family),pekerjaan (last education), occupation, address, nama kecamatan (district name), nama desa (village name), nama banjar (hamlet name)

  • 5    Lk Ajo kk • nama lengkap∙ jk

B r5l02042lftl020424 I PUTU AGUMG MAHAPUTRAL

12'⅛i020422Λw204iw i puiu Daniswaral

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17 *51020404ft 102041WI PUTU GEDE HENDRA □ARMAWANL

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23 r5102042βftl02U419<ORS I NVOMAN ARIA SUYASAL

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26 r51 0204061'510204IMI HYOMAH VECt ARI GUNAL

27 r51020409ft 1020419< I DEWANYOMAH SUKIAHA S PDL

28 r51020444 ftlO2O41<KNI MADE DUARMINIP

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30 r5IOS0408 '51020403 IDA BAGUS PANCASURYANA,S IKOM L

31 r51020430 ⅛ 1020403' IDA BAGUS QEDE ASWIN PRADIPTAL

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24 Hindu

22 Hindu

51 Hindu

50 Hindu

20 Hindu

10 Hindu

73 Hindu

50 Hindu

44 Hindu


9 Hindu

33 Hindu

2 Hindu

I Hindu

37 Hindu

55 Hindu

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60 Hindu

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36 Hindu

4 Hindu

63 Hindu


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Figure 1. Population Data of Kerambitan Village in 2018

  • b.    Regional Incentive Assistance Recipient Data.xls

In this Excel file there are 277 data record rows consisting of 26 columns that contains : NIK (ID Number), No_KK (Family Registry Number), nomor kartu bantuan (beneficiary card id),nama lengkap (Full_name),alamat (address), nama kecamatan (district name), nama desa (village name), nama banjar (hamlet name), SHDK (relationship status in family), tanggal lahir (date of birth), TMT, Nama Faskes (Name of Health Facilities), status peserta (member status), iuran (health fee), Tanggal Registrasi (Registration Date)

Imo. Nik Imikdukcapil kkdukcapil Nomorkartu                   nama                              Namadidukcapil

128 5102047112370023'510204711237002351020419030827190002105226549   NI KETUT TEMEN                           NI KETUT TEMEN

204 h ■ *043112370012 5102043112370012 5102041903082719 0002105555027   I WAYAN REBAH                            I WAYAN REBAH

277 5102045404520∞1 51020454045200015102041903083073 0002108951098   NI WAYAN SURYATI                         NI WAYAN SURYATI

61 5102047112520030 5102047112520030 51020419030829710002105225201 NI KETUT RISTI                                NI KETUT RISTI

r72

5102043112470022 5102043112470022 51020419030829710002105225425

I WAYAN SUJANA

I MADE SUDJANA

276

5102040906680001'51020409066800015102041903083307 0002108951087

I PUTU 5UDIAR5A

I PUTU 5UDIAR5A

271

5102042912500∞7 5102042912500007 5102041903082890 0002108951032

I KETUT MERTA

I KETUT MERTA

275

5102047112650045 5102047112650045 5102041903083012 0002108951076

NI NENGAH SRIASIH

NI NENGAH SRIASIH

30

5102O4711266007251020471126600725102O419030829800O02105224582

NI NENGAH SUMERTI

NI NENGAH SUMERTI

'188

5102047112530032510204711253003251020419030834650002105279965

NI KETUT NYANDRI

I GUSTI PUTU ASTINI

269

5102042405560001'510204240556000151020419030827920002108951019

I KETUT KARIADA

I KETUT KARIADA

270

5102046512570001510204651257000151020419030827920002108951021

NI NYOMAN ARlANI

NI NYOMAN ARIANI

273

5102046912500010 5102046912500010 5102041903082979 0007108951054

NI NENGAH LODER

NI NENGAH LODER

274

5102045106950001                                0002108951065

NI WAYAN ARI WINA WULANDARI

⅛4

5102043012S40∞SS102043012540005 5102041903082905∞0210S224661

I NYOMAN KAMAR

I NYOMAN KAMAR

35

5102047012580002'5102047012580002 5102041903082905 0002105224683

Ni NYOMAN Budiasih

Ni NYOMAN Budiasih

'36

5102043012630005510204301263000551020419030829050002105224705

IKETUT KAKAK

I KETUT KAKAK

37

5102043012660∞3510204301266000351020419030859000002105224727

I NENGAH KIKIK

I Iketutsuina

80

5102045003900∞2 5102045003900002 5102041903082905 0∞2105225583

LUH KOMANG SANISTRI DEWI

LUH KOMANG SANISTRI DEWI

'130

510204S112070∞2'510204S112070002'5102041903082905 000210S226584

LUH KETUT AYU DESlTA ANGGAREN∣ DEWI

ILUH KETUT AYU DESlTA ANGGARANI DEWI

272

5102043012650003'5102043012650003 5102041903082905 0002108951043

I NENGAH KIKIK

I NENGAH KIKIK

3

5102045609560001'510204560956000151020419030834110002105224029

NI WAYAN LANDRI

NI WAYAN LANDRI

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NI WAVAN Cl IOVATI

Kl ICTl A NV∩MΛN NAnDI

Figure 2. Regional Incentive Assistance Recipient Data

  • c.    Central Incentive Assistance Beneficiary Data.xls

In this Excel file there are 549 data record rows consisting of 11 columns that contains : Kode Wilayah (Region Code),NOKA_BPJS, PSNOKA, alamat (address), nama (name), No_KK (Family Registry Number), NIK (ID Number),tanggal lahir(date of birth), jenis kelamin (gender), SHDK (relationship status in family)

Figure 3. Central Incentive Assistance Beneficiary Data

  • 3.2.    Designing Data Warehouses

At this stage the model design is based on the results of the analysis of the problems faced Because the data is taken directly from Kerambitan village government, there is no OLTP (Online Transactional Processing) or TPS (Transactional Processing System) design which is usually used for reading data to be moved to the data warehouse. So the Data Warehouse model that is built will be formed based on existing Excel data files, so that the existing Excel data files will be manually entered into the data warehouse and subsequently based on the data warehouse there will be several opportunity reports that can be generated for the need to support the decision-making system of Kerambitan village government.

  • 4.     Result and Discussion

    4.1.   Model Desain Data Warehouse

Figure 4. Fact Constellation model data warehouse

Based on an analysis of 3 excell tables obtained from Kerambitan village government, a data warehouse is modeled in Figure 4 below where we have 3 fact tables namely : Penduduk (Representation of Population Data), pbi_jkn (Representation of Regional Incentive Assistance Recipient Data), bantuan_bpjs (Representation of Central Incentive Assistance Beneficiary Data). in this study I chose to use the fact constellation scheme because there are fact tables that are interconnected with dimension tables and also fact tables that are related to other fact tables.

Forming the tables in the data warehouse is formed based on the 3 Excell files mentioned above, namely:

  • 1.  Population Data of Kerambitan Village in 2018.xls

  • 2.  Regional Incentive Assistance Recipient Data.xls

  • 3.    Central Incentive Assistance Beneficiary Data.xls

  • 4.2.   Making the Extraction Transformation and Loading (ETL) process

Making the Extraction Transformation and Loading (ETL) process is based on making reports that will be adjusted to the data contained in the proposed data warehouse design model. In this case, 3 reports are proposed to be made based on the query results from the data warehouse model mentioned above and the reports are:

  • 1.  Population Reports that have a number of Regional and central incentive assistance

recipients

  • 2.  The Population Report that has a recipient card for regional incentive assistance with

work is farmers

  • 3.    population reports that have data on recipients of central incentive assistance with work as casual daily laborers

Following below explains how the three reports are built with the data warehouse model above as illustrated in Figure 4 above, following the SQL statement statement needed to pull data from each table in the data warehouse model above.

  • 1.    Population Reports that have a number of Regional and central incentive assistance recipients

Following is the sql statement for making the report :

SELECT

bantuan_bpjs.PSNOKA_BPJS,pbi_jkn.NO_KARTU,pbi_jkn.NIK,pbi_jkn.NKK,penduduk.N

AMA_LENGKAP,pisat.pisat,pbi_jkn.TMT,                        pbi_jkn.ID_FASKES,

pbi_jkn.iuran,pbi_jkn.tgl_registrasi FROM pbi_jkn INNER JOIN penduduk INNER JOIN bantuan_bpjs INNER JOIN pisat ON penduduk.NIK = pbi_jkn.NIK AND pbi_jkn.PISAT = pisat.ID_PISAT AND penduduk.NIK = bantuan_bpjs.NIK

÷ Opsi

PSN0KA_BPJS  N0_KARTU  NIK             NKK            NAMA_LENGKAP pisat    TMT      ID_FASKES  iuran  tgl_registrasi

820230884      2105224749  5102042708620001 5102041903082887 IWAYAN SUNARSA PESERTA 2017-01-01 1           23000  2016-11-25

Figure 5. Population that have a number of Regional and central incentive assistance recipients

  • 2.    The Population Report that has a recipient card for regional incentive assistance with work is farmers

Following is the sql statement for making the report :

SELECT     penduduk.NIK,     penduduk.NKK,     penduduk.NAMA_LENGKAP,

penduduk.ALAMAT,     penduduk.PEKERJAAN,     pekerjaan.NAMA_PEKERJAAN,

pbi_jkn.NO_KARTU,pbi_jkn.ID_FASKES, faskes.NAMA_FASKES

FROM penduduk INNER JOIN pbi_jkn INNER JOIN pekerjaan inner JOIN faskes ON penduduk.NIK = pbi_jkn.NIK AND pbi_jkn.ID_FASKES = faskes.ID_FASKES AND penduduk.pekerjaan = pekerjaan.ID_PEKERJAAN AND pekerjaan.NAMA_PEKERJAAN = 'PETANI/PEKEBUN' ;

Figure 6. The Population Report that has a recipient card for regional incentive assistance with work is farmers

  • 3.    population reports that have data on recipients of central incentive assistance with work as casual daily laborers

Following is the sql statement for making the report :

SELECT bantuan_bpjs.PSNOKA_BPJS, bantuan_bpjs.NOKA_BPJS, bantuan_bpjs.NIK, penduduk.NAMA_LENGKAP,   jk.JK   ,   shdk.SHDK   ,penduduk.PEKERJAAN,

pekerjaan.NAMA_PEKERJAAN

FROM bantuan_bpjs INNER JOIN penduduk INNER JOIN jk INNER JOIN shdk INNER JOIN pekerjaan ON bantuan_bpjs.NIK = penduduk.NIK AND bantuan_bpjs.JK = jk.ID_JK AND bantuan_bpjs.SHDK = shdk.ID_SHDK AND penduduk.PEKERJAAN = pekerjaan.ID_PEKERJAAN AND pekerjaan.NAMA_PEKERJAAN = 'BURUH HARIAN LEPAS'

+ Opsi

PSN0KA_BPJS

NOKA_BPJS

NIK

NAMA_LENGKAP

JK

SHDK

PEKERJAAN

NAMA_PEKERJAAN

1054757158

1054757158

5102043112560030

I NYOMAN NGARA

LAKI-LAKI

ANAK

3

BURUH HARIAN LEPAS

820042852

820042852

5102040501640002

I NYOMAN NATA

LAKI-LAKI

ANAK

3

BURUH HARIAN LEPAS

820072361

820072361

5102041412650001

I MADE S∪NARTA

LAKI-LAKI

ANAK

3

BURUH HARIAN LEPAS

820077399

820072361

5102043112670006

I NYOMAN SURATA

LAKI-LAKI

MERTUA

3

BURUH HARIAN LEPAS

820082665

820082665

5102040611690001

ANAKAGUNG MAYUN SUARDIKA

LAKI-LAKI

ANAK

3

BURUH HARIAN LEPAS

820091068

820091068

5102040811720001

I MADE PUTRA SANA

LAKI-LAKI

ANAK

3

BURUH HARIAN LEPAS

820096784

820096784

5102043012500002

I WAYAN PUSPA

LAKI-LAKI

ANAK

3

BURUH HARIAN LEPAS

820112038

820112038

5102042308780001

i putu Mertaadi

LAKI-LAKI

ANAK

3

BURUH HARIAN LEPAS

820218126

820218126

5102043112470019

ANAKAGUNG KETUT CAKRA

LAKI-LAKI

ANAK

3

BURUH HARIAN LEPAS

820218554

820218126

5102041101720002

ANAKAGUNG NYOMAN ARTIKA

LAKI-LAKI

FAMILI LAIN

3

BURUH HARIAN LEPAS

820221489

820221489

5102040504670001

I NENGAH SUJANA

LAKI-LAKI

ANAK

3

BURUH HARIAN LEPAS

820230884

820230884

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I WAYAN SUNARSA

LAKI-LAKI

ANAK

3

BURUH HARIAN LEPAS

820238499

820238499

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i NENGAH Widiarta

LAKI-LAKI

ANAK

3

BURUH HARIAN LEPAS

820239827

820239614

5102040103860001

i NYOMAN Wiardana

LAKI-LAKI

FAMILI LAIN

3

BURUH HARIAN LEPAS

820255149

820254014

5102043112560028

I MADE BAKTI

LAKI-LAKI

MERTUA

3

BURUH HARIAN LEPAS

Figure 7. population reports that have data on recipients of central incentive assistance with work as casual daily laborers

  • 5.    Conclusion

Based on the results of data collection, analysis, and database design carried out in Kerambitan village government, then some conclusions can be drawn as follows:

  • 1.    Through the application of a data warehouse that was formed, the executive or government can conduct analysis of the reports generated based on various dimensions that exist.

  • 2.    Besides functioning as a reporting support tool, the resulting data warehouse application can also be used as a tool to analyze decisions based on existing population and beneficiary data

  • 3.    Through this data warehouse, it can also provide information about residents in Kerambitan villages

The suggestions regarding the results of research conducted are:

  • 1.  It is necessary to design an information system in the form of web / desktop in order to

facilitate the provision of interactive information

  • 2.  Conducting further research on the application of data mining systems so that the analysis

process can be carried out in more depth and patterned based on existing approaches in the concept of data mining.

References

  • [1]    Badan    Pusat    Statistik.    (2019).    Retrieved    19    September    2019,    from

https://tabanankab.bps.go.id/publication/2017/09/14/08a44f34e33e11ed4b4e3e22/kecamatan-kerambitan-dalam-angka-2017.html

  • [2]    Chris Merrick | December 4, 2., & <!-- #author-avatar --> Chris MerrickDecember 4, 2. (2019). 9 Reasons Data Warehouse Projects Fail. Retrieved 19 September 2019, from https://blog.rjmetrics.com/2014/12/04/10-common-mistakes-when-building-a-data-warehouse/

  • [3]    Exploring data warehouses and data quality. (2019). Retrieved 19 September 2019, from https://web.archive.org/web/20180726071809/https://spotlessdata.com/blog/exploring-data-warehouses-and-data-quality

  • [4]    Jeong, C. (2007). Fundamental of development admistration. Puchong, Selangor: Scholar Press.

  • [5]    Kroenke, D., & Auer, D. (2011). Database processing. Upper Saddle River, N.J.: Prentice Hall.

  • [6]    Munawar, K. (2019). Model data warehouse untuk Operasional petugas pemadam kebakaran Pada dinas pemadam kebakaran provinsi DKI Jakarta. Jurnal Ilmu Komputer, XII(1), 29-38.

  • [7]    Oktavia, T. (2015). PERANCANGAN MODEL DATA WAREHOUSE DALAM MENDUKUNG PERUSAHAAN JASA PENGIRIMAN. Seminar Nasional Informatika (SEMNASIF), 1(5), E-93 - E-100.

  • [8]    Subekti, M., Junaidi, Warnars, H., & Heryadi, Y. (2017). The 3 steps of best data warehouse model design with leaning implementation for sales transaction in franchise restaurant. 2017 IEEE International Conference On Cybernetics And Computational Intelligence (Cyberneticscom). doi: 10.1109/cyberneticscom.2017.8311704

  • [9]    Trustworthy Refinement Through Intrusion-Aware Design. (2019). Retrieved 19 September 2019, from

http://web.archive.org/web/20070903115947/http://www.sei.cmu.edu/publications/documents/03.r eports/03tr002/03tr002glossary.html

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