The Implementation of Open Data in Indonesia

The Implementation of Open Data in Indonesia Dani Gunawan1, Amalia Amalia2 1

Department of Information Technology, 2Department of Computer Science University of Sumatera Utara Medan, Indonesia [email protected], [email protected]

Abstract—Nowadays, public demands easy access to nonconfidential government data, such as public digital information on health, industry, and culture that can be accessed on the Internet. This will lead departments within government to be efficient and more transparent. As the results, rapid development of applications will solve citizens’ problems in many sectors. One Data Initiatives is the prove that the Government of Indonesia supports data transparency. This research investigates the implementation of open data in Indonesia based on Tim BernersLee five-star rating and open stage model by Kalampokis. The result shows that mostly data in Indonesia is freely available in the Internet, but most of them are not machine-readable and do not support non-proprietary format. The drawback of Indonesia’s open data is lack of ability to link the existing data with other data sources. Therefore, Indonesia is still making initial steps with data inventories and beginning to publish key datasets of public interest. Keywords—open data; RDF; semantic web; five-star rating; open stage model

I.

INTRODUCTION

Information technology is able to simplify data exchange among institutions. Semantic web provides framework to exchange the data among several applications, institutions or community [1]. Moreover, semantic web can be utilized to support data transparency. Semantic web is the technology behind linked open data, a paradigm to connect all the data available in the Internet. This is in accordance with the government intention to data transparency that supports the realisation of good governance. To realize data transparency through institutions, it can be done by applying open data policy. Open data policy is implemented by United States and many countries [2] [3]. European Union, Lithuania and Slovakia utilize open data to fight against corruption [4] [5] [6]. Moreover, there are three major benefits that are supposed to bring to the economy and public space. These benefits are behind main driving forces to implement open data policy. These are the following [7] [8] [9]: • Stimulate private sector innovation, providing data for business decision making and promote the data science industry to unlock economic value

• Increasing the public’s capacity to monitor government and participate more to achieve the formulation of public policy. This will be providing political and social benefits. • Remove duplications, improve access to the information and optimize process to consider operation improvement in public sector. Optimizing process can be done through benchmarking and reengineering. Open data movement is also began in Indonesia with the government support for publishing public data by institutions in the form of One Data Initiatives [10]. The government releases open data from several institutions through portal that can be accessed in http://data.go.id. Many researches show the utilization of open data in Indonesia such as enhancing the productivity and competitiveness of fishery SMEs [11], generating cultural heritage metadata [12], and the national budget transparency initiative at ministry of finance [13]. Open Data Barometer is an organization that calculates the ranking based on government readiness to open their data, the implementation to fulfil open data and the impact of open data to citizens’ live. To have a good impact to citizens live, the data cannot stand alone and should be processed to have more meaningful information. According to Open Data Barometer report in 2015, Indonesia’s ranking is 40 over 92 countries [14]. It goes down 4 level than previous year. This research will review the country’s effort to implement open data policy in Indonesia. The measurement used to assess the implementation of open data is five-star scheme that introduced by Tim Berners-Lee [15]. In addition, we will also use the measurement that was proposed by Kalampokis [16] called stage model of open data. The rest of the paper will be organized as follows: Section II will provide the related works in implementation of open data in Indonesia and other countries. Section III defines the difference between open data and linked data. Section IV explains the research methodology to obtain the data that is used to measure the implementation of open data. Section V discusses the assessment result of open data. This section also discusses the existing condition of the open data implementation by the governments in Indonesia. Finally, section VI will conclude the whole work.

II. RELATED WORKS Previous study shows the open data maturity level according to the assessment conducted from several online resources (websites) in Indonesia [17]. They assessed seven government websites such as Open Government Indonesia, Satu Pemerintah - One Government, UKP-PPP (Unit Kerja Presiden bidang Pengawasan dan Pengendalian Pembangunan - Presidential Working Unit for Supervision and Management of Development), Bappenas (Badan Perencanaan Pembangunan Nasional – National Development Planning Agency), BPS (Badan Pusat Statistik – Statistics Indonesia), LAPOR and Indonesian Data Portal. In our research, we assess thirty-one government websites from three categories, province, big cities, and ministries. To measure open data maturity level, they used Tim Berners-Lee five-star rating scheme. They observed the available file types in those websites. The result is most of the websites only awarded one-star rating. The best rating is awarded to Indonesian Data Portal with three-star rating. As our research also uses Tim-Berners Lee five-star rating scheme to measure open data maturity level, we also observe the availability of certain types of format data. To complete the assessment, we use a stage model of open data to depict data relation among online resources. This model also require observation to available file types. A stage model of open data also used by research which study policy and implementation of open data in Bulgaria [18]. In this research, there are a few types of parameters such as storage of data in public sector, automatic data transmission, data structured in public administration, and data availability for public. They measure readiness by calculating number of data format in public sector. There are several number of data format, include doc/docx (Microsoft Word), HTML (HyperText Markup Language), mdb (Microsoft Access), PDF (Portable Document File), xls/xlsx (Microsoft Excel), XML (eXtensble Markup Language. Other than digital data format, they also measure the usage of paper-based record. They mixed digitalbased files and paper-based files to observe the number of available. Instead of measuring paper-based files, we use digitalbased files only. Because our research only focus on online resources. Another research evaluates the implementation of open data in Croatian government [19]. However, they only evaluate the implementation regarding the policy. They conclude that finding the right balance between the expectations of open data users and open data publishers is crucial to achieve a self-sustaining level. One of the upcoming challenges to the implementation of open data is appropriate legal framework for charging and protecting intellectual property rights. Instead of evaluating the implementation of open data in Indonesia, our paper focuses on reviewing the implementation. III. OPEN DATA AND LINKED DATA A. Open Data Open data can be used, re-used or redistributed freely. Everything that a website shares to the Internet user is without restriction such as registration or non-public license is called open data. The implementation of open data usually different among other countries.

According to Open Government Data [20], the data that is made public in a way that complies the eight principles below will be considered as open data: 1. Complete Public data refers to the data that is not subject to valid privacy, security or privilege limitations. All public data is made available. 2. Primary Data is not in aggregate or modified forms. It is available as collected at the source, with the highest possible level of granularity. 3. Timely To preserve the value of the data, it is made available as soon as possible. 4. Accessible Wide range of users and access to the data for the widest range of purposes. 5. Machine processable The available data is reasonably structured to allow automatic processing by the machine. 6. Non-discriminatory Users are not required to register in order to access the data. 7. Non-proprietary No entity has exclusive control over the data format for the public data 8. License free There is no any copyright, patent, trademark or trade secret regulation is attached to the data. However, reasonable privacy, security and privilege restrictions may be allowed. Open data maturity level can be measured by using five-star rating or stage model of open data. In this research we measure the open data implementation in Indonesia by using both methods. Tim Berners-Lee has proposed five-star rating scheme to classify the open data implementation. The data publishers can be awarded to their data sets according to following criteria. 1. 1-Star The data are available on the web in whatever format with an open license. For example, the data that have been made public might be available in PDF, JPG or DOC. Although this data is available and can be obtained easily, to process them is an arduous task. This file format is not machine-readable. 2. 2-Star The data are available on the web with machine-readable structure data. For example, Microsoft Excel instead of scanned tables. What made this rating different with the previous one is the processing manner.

3. 3-Star The data are available on the web with machine-readable structure data, plus non-proprietary format. For example, publishing data in comma-separated version (CSV) instead of Microsoft Excel. 4. 4-Star The data are available on the web with machine-readable structure data, with non-proprietary format, and use open standards from World Wide Web Consortium (W3C), such as Resource Description Framework (RDF) to identify things. By using RDF, people can point their data to our data 5. 5-Star The data publisher is awarded 5-star rating if the data match all criteria in 4-star rating, plus link the data to other people’s data to provide context. Another measurement method is stage model of open data. This model is introduced by Kalampokis et al. There are four main stages of opening data as the basis of this model. Each stage model depicts the increasing of sophistication and difficulty. It also depicts the increasing of potential of unlocking value. The first stage consists of aggregating government data. This stage is crucial because it has to overcome a number of economic, cultural, legislative and technological barriers that many organizations are unable or unwilling to share their data to be available in public. In this stage the public sector agencies and units build their inventories of data, export it to the wellknown machine-readable format, and release the data to the public. The commonly used machine-readable format are csv (comma separated version), JSON (JavaScript Object Notation), XML and others.

B. Linked Data 5-star scheme is intended to measure how well data are integrated to the web. 1-star data should be downloaded and processed it manually, while 5-star data could be accessed online, uses URIs to identify the resources in the data. It also contains links to other sources. The scheme is primarily focused in the formats and technologies being used. To obtain 5-star rating, the data should be published in W3C open standards format, such as RDF. RDF addresses one fundamental issue in Semantic Web, managing distributed data. The basic building of the RDF is called the triple. It represents a cell that contains subject, predicate and object. Subject is the identifier of the cell. Predicate specify the property of the cell. Object is the value of the cell. Table I shows the triples as subject, predicate and object. The graph visualization in Fig. 1 express the same information presented in Table I. The data about Medan, Deli Serdang or North Sumatera are available in DBpedia, the large knowledge base that extracted from Wikipedia [21]. As DBpedia uses RDF to represent their data, our data in Table I can be linked to DBpedia to obtain more TABLE I.

Subject Dani Dani Medan Deli Serdang Romi Romi

RDF TRIPLES

Predicate wrote livedIn partOf partOf friend livedIn

Object Journal Medan North Sumatera North Sumatera Dani Deli Serdang

The second stage focuses on data sharing effort as the step further by providing the integration for open government data contained in different databases. This stage faces significant barriers in both technological and organizational. On the technological barrier, the format should be compatible each other to support integration. The linked open data paradigm is able to achieve data integration. Challenge is bigger on the organizational barrier. Data integration could reveal the duplicated data, errors and inconsistencies among the available data in organizations. This could lead to misleading decisionmaking which can erode the public trust. The third stage depicts further opportunities by integrating formal government data with the formal non-government data from the private sector, the media or the civil society organizations. Those data can be potentially useful as they provide further knowledge for a particular object of interest. Integration of open government data with those data will provide greater economic value. The government should convince private companies and NGOs to share their data in order to support positive economic benefit by integrating them. The final stage depicts the integration of formal government and non-government data with social data such as from social networks. Social data can be obtained from the citizens who voluntarily shared the information and often express opinions, beliefs, attitudes and values.

Fig. 1 Graph Visualization of RDF

Fig. 2 RDF Relation to Other Data Source

information about Medan, Deli Serdang or North Sumatera. For example, our table does not have the data about latitude or longitude of Medan. By linking our data to DBpedia, we could extract the data about Medan such as the mayor, latitude, longitude, population, etc. The concept of linked data in RDF is similar with Relational Database Management System (RDBMS). Relation between two tables are provided by primary key and foreign key. Different approach used in RDF data. Instead of using primary and foreign key, RDF uses the same Uniform Resource Identifier (URI) to link between to data sources. Relation between data sources is illustrated in Fig. 2. According to the illustration in Fig. 2 we can inferred that Dani lives in Medan, which is part of North Sumatera. Also we can point the city where Dani lives in a map based on latitude (lat) and longitude (long) provided. If portal data.go.id provides RDF data, the user could integrate them with other sources, therefore, the information extracted will be more meaningful. IV. RESEARCH METHODOLOGY In this research we took the data from several categories, such as big cities, provinces and ministries. We observe ten official websites for each category. Also, we observe open data implementation in portal data.go.id. For the big cities categories, the cities were chosen based on the large population. Those are Jakarta, Surabaya, Medan, Bandung, Bekasi, Tangerang, Depok, Semarang, Palembang and Makassar. For the provinces and ministries categories, we choose the top ten provinces/ministries in providing public information based on komisiinformasi.go.id criteria [22]. We include all the sub domains which are related with the root domain. For example, http://jakarta.go.id is the root domain of Jakarta city website. We also took the data from its sub domains, such as http://pelayanan.jakarta.go.id, http://data.jakarta.go.id, etc. To find the desired documents, we use two most comprehensive search engines, Google and Bing, represented in the Table II as G and B respectively. We realize that the result might be different from both search engines. However, by using two search engines, we could have another point of view. We applied specific keywords to get the desired result. We perform search command based on site and file type. For example, to yield the data about number of Microsoft Excel documents available in Medan city, the keyword is “site:pemkomedan.go.id filetype:xls” (without quotes). The same rule applies for both search engines. We search several file types such as Adobe Portable Data Format (PDF), Microsoft Word (doc), Microsoft Excel (xls), Microsoft Powerpoint (ppt), Comma-Separated Version (CSV), Open Document Format (odt, ods, odp), Resource Description Framework (RDF) and Web Ontology Language (OWL). We do not include Microsoft Word and Powerpoint results because they are represented by PDF. Other formats such as Open Document Format and OWL are not included because we only find three documents in Open Document Format and no one OWL formatted documents from 10 cities’ websites. We still include RDF in the table although no city published the data in RDF format to show the level of open data implementation.

V. RESULT AND DISCUSSION According to Table II, Table III and Table IV, most of the cities, provinces and ministries are willing to publish their data. However, most of this data are published in PDF format. Publishing data in PDF format is not recommended, as extracting data from PDF is an arduous task. It also applies for Microsoft Word and Microsoft Powerpoint file format. It is common that there are also some paragraphs beside the data itself. Therefore, user will need more effort to get the data. There are some improvements in the development of PDF so that the user could extract data from PDF easily. Tagged PDF and structured PDF can be used to include additional information. Besides PDF, Microsoft Excel is another choice to publish the data. It is widely used in daily basis. Unlike PDF, it is one of the machine-readable formats. However, this is a proprietary format owned by Microsoft. To extract the data from Microsoft Excel we might need another extra plugin. Common options to avoid the proprietary format is using Comma-Separated Version (CSV) format. According to Table II, Jakarta is the only city that publishes data in CSV, a nonproprietary and machine-readable format. For the category province, Aceh is the only province that provide data in CSV. This condition is shown in Table III. Moreover, according to Table IV, PU is the only province that provide data in CSV. The lack of machine-readable format used for publishing data shows that the governments are not aware on how the data extracted by the third party. Therefore, according to the Table II, Table III and Table IV, there is no city or province or ministry that publish data in RDF format. This might be caused by the RDF format is not a common data format compared to CSV and XLS format. To understand the RDF, one should have advanced technical skill. However, the common way to make the data linked to others is by utilizing RDF format. This format is not popular in Indonesia yet.

TABLE II.

OPEN DATA IMPLEMENTATION IN BIG CITIES PDF (1-star)

City

XLS (2-star)

G

B

Jakarta

106000

Surabaya

CSV (3-star)

RDF (4-star) G

G

B

G

B

B

11200

771

298

15

0

0

0

6670

6710

129

22

0

0

0

0

Medan

3340

802

9

0

0

0

0

0

Bandung

3030

3850

12

15

0

0

0

0

Bekasi

777

819

5

3

0

0

0

0

Tangerang

1550

1360

142

3

0

0

0

0

Depok

5700

1620

13

23

0

0

0

0

Semarang

2220

2330

191

402

0

0

0

0

Palembang

3360

275

25

12

0

0

0

0

Makassar

1

0

0

0

0

0

0

0

TABLE III.

TABLE V.

OPEN DATA IMPLEMENTATION IN MINISTRIES PDF (1-star)

Ministry

XLS (2-star)

G

G

B

Kemenkeu

30100

10200

145

16

0

0

0

0

PU

20200

18200

122

52

16

0

0

0

Kemenperin

8080

4130

1750

25

0

0

0

0

Dephub

29300

8520

15

11

0

0

0

0

Kemkes

3650

1740

662

31

0

0

0

0

Pertanian

54400

31300

7520

87

0

0

0

0

KKP

20900

6530

127

80

0

0

0

0

Kominfo

2780

2650

55

3

0

0

0

0

Menpan

1370

2070

69

6

0

0

0

0

Setneg

13800

312

119

2

0

0

0

0

B

B

OPEN DATA IMPLEMENTATION IN PROVINCES PDF (1-star)

Province

B

RDF (4-star)

G

TABLE IV.

G

CSV (3-star)

XLS (2-star) G

B

CSV (3-star)

RDF (4-star)

G

G

G

B

B

B

Aceh

6580

2140

35

1

13

0

0

0

Jatim

34000

11300

1540

495

0

0

0

0

Kaltim

9560

1440

117

44

0

0

0

0

NTB

1260

1010

23

24

0

0

0

0

Jateng

11500

6800

120

201

0

0

0

0

Jabar

27800

6870

258

263

0

0

0

0

Kalbar

5480

1400

9

0

0

0

0

0

Banten

6380

2370

38

21

0

0

0

0

Sumsel

3170

635

37

4

0

0

0

0

Jogja

4170

3970

132

214

0

0

0

0

In national level, the government is more aware about publishing the open data. Recently, in the time of this paper is writing, portal data.go.id accommodate 1211 datasets, divided by 18 groups. There are 31 institutions that published their data through portal data.go.id. To simplify data analysis, data provided should be able to be read by the machine. Portal data.go.id supports several data formats that can be easily read by the machine, such as Microsoft Excel, CSV, JSON, XML, KML and some other data formats. format. Table V shows number of datasets in various machine-readable file format from several categories. CSV is the most widely used data format, followed by Microsoft Excel. However, in this portal we cannot find a single file with RDF data format. As previously stated, we cannot find a file with RDF format in all cities, provinces, ministries official websites or in open

Categories

OPEN DATA IN PORTAL DATA INDONESIA CSV

XLS

JSON

XML

KML

Pangan

72

12

0

0

0

Energi

77

10

0

0

0

Infrastruktur

43

17

3

0

1

Maritim

64

49

1

5

0

Kesehatan

113

16

0

0

2

Pendidikan

124

39

0

0

0

Ekonomi

272

54

0

0

1

Industri

97

38

0

0

1

Pariwisata

22

2

0

0

0

Reformasi Birokrasi

125

13

1

0

2

data portal. We might conclude that open data implementation in Indonesia only reach 3-star of maximum 5-star. 3-star means the data are available in non-proprietary format and machinereadable. Furthermore, the lack of data in RDF format means that the all data available cannot be linked to other data sources. The result shows that most of the websites in big cities, provinces or ministries only provides the one or two-star documents. We propose several strategies to increase the level of openness data in Indonesia, such as changing the policy, implementing the rule, socialization of open data and technical training. According to government rule PP No. 82 Tahun 2012 about system and operation of electronic transaction, there are specification for the software developer to deliver the software. In our observation, we cannot find the item that specifically mention the output of the software. We propose to append the item regarding the output of the software. For example, the output should be in machine readable format, such as CSV or RDF. The next step is implementing the rule after the policy has been changed. This is the most difficult part because of the lack of understanding about the need of open data. Besides, some other obstacles may come from the software developer and also the governance. For example, both software developer and the governance do not fully understand the rule and disobey the important parts of the rule. The lack of understanding about open data is the problem in Indonesia. This is shown with the two stars rating in the result. To increase the governance understanding about open data, the scheduled socialization about open data regularly is required. Technical training especially in open data is required for the information technology staffs. Publishing data in RDF files format is recommended to ensure data relation among files. There are some tools to convert Microsoft excel files or CSV to RDF [23]. This action requires particular skills of the staffs. Most of the data we found are not in standardized structure. The similar sort of data could be presented differently among other data publishers. For the data publishers it is not a big problem. However, the third party who will use the same sort of

data from many data publishers will have problems when processing them. The government should issue the policy to standardized the data structure for each field. Standardized data can be obtained by applying ontology. Ontology is formal specification of concept that can be shared each other. In data level, integration can be done by utilising the same ontology to establish information exchange. For example, e-Commerce ontology can be combined with other ontologies, such as The Ticket Ontology (TIO), The Open Directory Project (ODP), The Vehicle Ontology (VSO), and so on [24]. Semantic web technology that utilise the ontology is expected to facilitate different information systems, data or policy in each institution. As we have plethora of data, we need the strategy to provide simplicity of accessing and processing data. Data can be modeled by ontology, and the implementation can be done by utilizing RDF. By doing this strategy, hopefully simplicity of accessing and processing data will be reached. VI. CONCLUSION

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After observing open data implementation from several big cities, province and ministries, we found that the most available files are PDF. There are a small number of files available in machine readable format. According to the five-star scheme by Tim Berners-Lee, most of the government websites are awarded 1-star. A few websites are awarded 2-star and almost no website is awarded 3-star. There is no website is awarded 4-star and 5star. This result also shows that Indonesia is still in the first stage according to open stage model. Indonesia is still aggregating the government data and publish it without linking to the other data. We expect that the government has not taken the open data issue seriously yet. To increase the open data implementation, we suggest policy changing, rule implementation, socialization of open data and technical training to provide open data.

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