Techsalerator's Job Openings Data for Indonesia: A Comprehensive Resource for Employment Insights

Techsalerator's Job Openings Data for Indonesia is an essential resource for businesses, job seekers, and labor market analysts. This dataset offers a detailed overview of job openings across various sectors in Indonesia, consolidating and categorizing job-related information from multiple sources, including company websites, job boards, and recruitment agencies.

Key Data Fields

• Job Posting Date: Captures the date a job is listed. This is crucial for job seekers and HR professionals to stay updated on the latest opportunities and trends.
• Job Title: Specifies the position being advertised. This helps in categorizing and filtering job openings based on industry roles and career interests.
• Company Name: Lists the hiring company. This information assists job seekers in targeting their applications and helps businesses track competitors and market trends.
• Job Location: Provides the job's geographic location within Indonesia. Job seekers use this to find regional opportunities, while employers analyze regional talent and market conditions.
• Job Description: Includes details about responsibilities, required qualifications, and other relevant aspects. This is vital for candidates to determine if they meet the requirements and for recruiters to communicate expectations clearly.

Top 5 Job Categories in Indonesia

1. Information Technology (IT): Growing demand for software developers, IT support specialists, and cybersecurity experts as digital infrastructure expands.
2. Healthcare: Opportunities for doctors, nurses, medical technicians, and public health workers, driven by both public and private sector needs.
3. Education: Positions for teachers, administrators, and educational consultants, reflecting the country’s focus on improving literacy and education standards.
4. Manufacturing and Engineering: High demand for engineers, production managers, and skilled laborers due to the country’s industrial growth and development.
5. Retail and Customer Service: Roles in sales, customer service, and store management, driven by Indonesia’s expanding consumer market.

Top 5 Employers in Indonesia

1. Gojek: A leading technology company with frequent openings in IT, operations, and customer service.
2. Bank Mandiri: One of the largest banks in Indonesia, offering roles in finance, banking operations, IT, and customer service.
3. Unilever Indonesia: A major consumer goods company with opportunities in marketing, sales, supply chain, and R&D.
4. Pertamina: The state-owned oil and gas company, providing positions in engineering, operations, and corporate services.
5. Universitas Indonesia: The largest university in the country, regularly hiring for academic, administrative, and research roles.

Accessing Techsalerator’s Data

To access Techsalerator’s Job Openings Data for Indonesia, please contact info@techsalerator.com with your specific needs. We will provide a customized quote based on the data fields and records you require, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Job Posting Date
• Job Title
• Company Name
• Job Location
• Job Description
• Application Deadline
• Job Type (Full-time, Part-time, Contract)
• Salary Range
• Required Qualifications
• Contact Information

Techsalerator’s dataset is a valuable tool for those looking to stay informed about job openings and employment trends in Indonesia, aiding businesses, job seekers, and analysts in making strategic decisions.

The number of employed persons in Indonesia increased to 144642000 in 2024 from 138632511 in 2023. This dataset provides - Indonesia Employed Persons - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

The demand for data professionals in Indonesia is rapidly growing, but what does it really take to land a job in this exciting field? This dataset aims to answer that question by providing a snapshot of the Data & Analytics job market in Indonesia.

The data consists of 555 job postings collected from the leading job portal, Jobstreet Indonesia, over a 30-day period from August 25, 2025, to September 24, 2025. It has been curated for analysis, focusing on key attributes that define a candidate's profile and a company's needs.

Note: This is a cleaned version of the original dataset. Columns containing direct links, short descriptions, and tool counts have been removed for clarity and privacy purposes.

Content

The dataset includes the following columns:

• posisi: The job title being offered (e.g., "Data Analyst", "Business Intelligence").
• perusahaan: The name of the hiring company.
• kota: The city where the job is located.
• provinsi: The province where the job is located.
• gaji: The offered monthly salary in Indonesian Rupiah (IDR). Contains null values.
• tools: A text field listing the required tools and technologies (e.g., "SQL, Python, Tableau").
• pendidikan: The required educational background. Contains null values.
• pengalaman: The minimum years of work experience required. Contains null values.
• deskripsi_lengkap: The full text of the job description, ideal for Natural Language Processing (NLP) tasks. This column has been scrubbed to remove personal contact information.
• level: The career level for the position (e.g., "Entry Level", "Mid Level", "Senior Level").

Acknowledgements

This dataset was collected from publicly available job postings on Jobstreet Indonesia. We acknowledge and thank Jobstreet and all the companies who listed these opportunities.

Inspiration

This dataset is a great starting point for aspiring data professionals and researchers. Here are a few questions you could try to answer:

• What are the most in-demand tools for Data Analysts in Indonesia?
• Which cities and provinces are the true hotspots for data jobs?
• What are the key themes and responsibilities mentioned in job descriptions? (A great opportunity for NLP and topic modeling!)

Unemployment Rate in Indonesia increased to 4.85 percent in the third quarter of 2025 from 4.76 percent in the first quarter of 2025. This dataset provides the latest reported value for - Indonesia Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Time series data for the statistic Contributing family workers, male (% of male employment) (modeled ILO estimate) and country Indonesia. Indicator Definition:Contributing family workers are those workers who hold "self-employment jobs" as own-account workers in a market-oriented establishment operated by a related person living in the same household.The indicator "Contributing family workers, male (% of male employment) (modeled ILO estimate)" stands at 5.97 as of 12/31/2023. Regarding the One-Year-Change of the series, the current value constitutes an increase of 1.06 percent compared to the value the year prior.The 1 year change in percent is 1.06.The 3 year change in percent is -8.06.The 5 year change in percent is -3.83.The 10 year change in percent is -14.39.The Serie's long term average value is 7.35. It's latest available value, on 12/31/2023, is 18.72 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2015, to it's latest available value, on 12/31/2023, is +3.78%.The Serie's change in percent from it's maximum value, on 12/31/1998, to it's latest available value, on 12/31/2023, is -32.59%.

Labor Force Participation Rate in Indonesia increased to 69.30 percent in 2023 from 68.63 percent in 2022. This dataset provides - Indonesia Labor Force Participation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Comprehensive dataset containing 10 verified Employment agency businesses in Central Sulawesi, Indonesia with complete contact information, ratings, reviews, and location data.

By the middle of the 1990s, Indonesia had enjoyed over three decades of remarkable social, economic, and demographic change and was on the cusp of joining the middle-income countries. Per capita income had risen more than fifteenfold since the early 1960s, from around US$50 to more than US$800. Increases in educational attainment and decreases in fertility and infant mortality over the same period reflected impressive investments in infrastructure. In the late 1990s the economic outlook began to change as Indonesia was gripped by the economic crisis that affected much of Asia. In 1998 the rupiah collapsed, the economy went into a tailspin, and gross domestic product contracted by an estimated 12-15%-a decline rivaling the magnitude of the Great Depression. The general trend of several decades of economic progress followed by a few years of economic downturn masks considerable variation across the archipelago in the degree both of economic development and of economic setbacks related to the crisis. In part this heterogeneity reflects the great cultural and ethnic diversity of Indonesia, which in turn makes it a rich laboratory for research on a number of individual- and household-level behaviors and outcomes that interest social scientists. The Indonesia Family Life Survey is designed to provide data for studying behaviors and outcomes. The survey contains a wealth of information collected at the individual and household levels, including multiple indicators of economic and non-economic well-being: consumption, income, assets, education, migration, labor market outcomes, marriage, fertility, contraceptive use, health status, use of health care and health insurance, relationships among co-resident and non- resident family members, processes underlying household decision-making, transfers among family members and participation in community activities. In addition to individual- and household-level information, the IFLS provides detailed information from the communities in which IFLS households are located and from the facilities that serve residents of those communities. These data cover aspects of the physical and social environment, infrastructure, employment opportunities, food prices, access to health and educational facilities, and the quality and prices of services available at those facilities. By linking data from IFLS households to data from their communities, users can address many important questions regarding the impact of policies on the lives of the respondents, as well as document the effects of social, economic, and environmental change on the population. The Indonesia Family Life Survey complements and extends the existing survey data available for Indonesia, and for developing countries in general, in a number of ways. First, relatively few large-scale longitudinal surveys are available for developing countries. IFLS is the only large-scale longitudinal survey available for Indonesia. Because data are available for the same individuals from multiple points in time, IFLS affords an opportunity to understand the dynamics of behavior, at the individual, household and family and community levels. In IFLS1 7,224 households were interviewed, and detailed individual-level data were collected from over 22,000 individuals. In IFLS2, 94.4% of IFLS1 households were re-contacted (interviewed or died). In IFLS3 the re-contact rate was 95.3% of IFLS1 households. Indeed nearly 91% of IFLS1 households are complete panel households in that they were interviewed in all three waves, IFLS1, 2 and 3. These re-contact rates are as high as or higher than most longitudinal surveys in the United States and Europe. High re-interview rates were obtained in part because we were committed to tracking and interviewing individuals who had moved or split off from the origin IFLS1 households. High re-interview rates contribute significantly to data quality in a longitudinal survey because they lessen the risk of bias due to nonrandom attrition in studies using the data. Second, the multipurpose nature of IFLS instruments means that the data support analyses of interrelated issues not possible with single-purpose surveys. For example, the availability of data on household consumption together with detailed individual data on labor market outcomes, health outcomes and on health program availability and quality at the community level means that one can examine the impact of income on health outcomes, but also whether health in turn affects incomes. Third, IFLS collected both current and retrospective information on most topics. With data from multiple points of time on current status and an extensive array of retrospective information about the lives of respondents, analysts can relate dynamics to events that occurred in the past. For example, changes in labor outcomes in recent years can be explored as a function of earlier decisions about schooling and work. Fourth, IFLS collected extensive measures of health status, including self-reported measures of general health status, morbidity experience, and physical assessments conducted by a nurse (height, weight, head circumference, blood pressure, pulse, waist and hip circumference, hemoglobin level, lung capacity, and time required to repeatedly rise from a sitting position). These data provide a much richer picture of health status than is typically available in household surveys. For example, the data can be used to explore relationships between socioeconomic status and an array of health outcomes. Fifth, in all waves of the survey, detailed data were collected about respondents¹ communities and public and private facilities available for their health care and schooling. The facility data can be combined with household and individual data to examine the relationship between, for example, access to health services (or changes in access) and various aspects of health care use and health status. Sixth, because the waves of IFLS span the period from several years before the economic crisis hit Indonesia, to just prior to it hitting, to one year and then three years after, extensive research can be carried out regarding the living conditions of Indonesian households during this very tumultuous period. In sum, the breadth and depth of the longitudinal information on individuals, households, communities, and facilities make IFLS data a unique resource for scholars and policymakers interested in the processes of economic development.

Indonesia Employment Rate: Bali data was reported at 98.630 % in 2018. This records an increase from the previous number of 98.520 % for 2017. Indonesia Employment Rate: Bali data is updated yearly, averaging 97.960 % from Aug 1984 (Median) to 2018, with 33 observations. The data reached an all-time high of 99.140 % in 1984 and a record low of 93.960 % in 2006. Indonesia Employment Rate: Bali data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.GBA020: Employment Rate: by Province.

The dataset consist of three categories; image subsets, burned area masks and quicklooks. The image subsets are derived from Landsat-8 scenes taken during the years 2019 and 2021. Each image has a size of 512x512 pixels and consists of 8 multispectral. The sequence of band names from band 1 to band 7 of the image subset is same as the sequence of band names of landsat-8 scene, except for band 8 of the image subset which is band 9 (cirrus band) in the original landsat-8 scene. The image subsets are saved in GeoTIFF file format with the latitude longitude coordinate system and WGS 1984 as the datum. The spatial resolution of image subsets is 0.00025 degree and the pixel values are stored in 16 bit unsigned integer with the range of value from 0 to 65535. The total of the dataset is 227 images which containing object of burned area surrounded by various ecological diversity backgrounds such as forest, shrub, grassland, waterbody, bare land, settlement, cloud and cloud shadow. In some cases, there are some image subsets with the burned areas covered by smoke due to the fire is still active. Some image subsets also overlap each other to cover the area of burned scar which the area is too large. The burned area mask is a binary annotation image which consists of two classes; burned area as the foreground and non-burned area as the background. These binary images are saved in 8 bit unsigned integer where the burned area is indicated by the pixel value of 1, whereas the non-burned area is indicated by 0. The burned area masks in this dataset contain only burned scars and are not contaminated with thick clouds, shadows, and vegetation. Among 227 images, 206 images contain burned areas whereas 21 images contain only background. The highest number of images in this dataset is dominated by images with coverage percentage of burned area between 0 and 10 percent. Our dataset also provides quicklook image as a quick preview of image subset. It offers a fast and full size preview of image subset without opening the file using any GIS software. The quicklook images can also be used for training and evaluating the model as a substitute of image subsets. The image size is 512x512 pixels same as the size of image subset and annotation image. It consists of three bands as a false color composite quicklook images, with combination of band 7 (SWIR-2), band 5 (NIR), and band 4 (red). These RGB composite images have been performed contrast stretching to enhance the images visualizations. The quicklook images are stored in GeoTIFF file format with 8 bit unsigned integer.

This work was financed by Riset Inovatif Produktif (RISPRO) fund through Prioritas Riset Nasional (PRN) project, grant no. 255/E1/PRN/2020 for 2020 - 2021 contract period.

The diffusion in Ni-Co-Re and Ni-Co-Ru systems have been investigated in the temperature range 1323–1523 K through the determination of the interdiffusion coefficients. The main interdiffusion coefficients of these systems were larger than the cross interdiffusion coefficients, indicating that the influences of the own concentration gradients of the elements were generally still dominant. From the ratio of the cross interdiffusion coefficient to the main one, |\tildeDijk⁄\tildeDiik|, it was found that the effect of Co on the diffusion of Re was more appreciable than that of Ru. Moreover, by the comparison between the diffusion in the binary Ni-Re and in the ternary Ni-Co-Re, it was clear that the presence of Co reduced the Re diffusivity in Ni. The results of this work reflect two folds; the attractive force exists between Co and Re, and the interatomic bonding of Co-Re seems to be stronger than that of Ni-Re. Materials Trasnsactions Vol. 48 No. 10 2007 p: 2718-1723

Introduction

This dataset contains 604 public company financial statement annually in IDX (Bursa Efek Indonesia), largest number that I can see in kaggle :D. Company that's not included in this dataset either do not report their financial statement or contains some irrelevant publishing date.

Usability

• EDA
• Classifier Stock
• Fundamental Analysis
• Financial Statement Analysis

Wanna Contribute?

Please leave a message on suggestions!

Appendix

Type:

Type	Description	Translate (in Indonesia)
BS	Balance Sheet/Statement of FInancial Position	Laporan Posisi Neraca / Laporan Posisi Keuangan
IS	(Consolidated) Income Statement	Laporan Laba/Rugi (Konsolidasian)
CF	Statement of Cash Flow	Laporan Arus Kas

Account:

Account	Type	Translate (in Indonesia)
Accounts Payable	BS	Utang Usaha
Accounts Receivable	BS	Piutang Usaha
Accumulated Depreciation	BS	Akumulasi Penyusutan
Additional Paid In Capital (PIC) / Share Premium	BS	Saham premium
Allowance For Doubtful Accounts Receivable (AFDA)	BS	Cadangan Piutang Usaha
Buildings And Improvements	BS	Bangunan dan Pengembangan
Capital Stock	BS	Saham
Cash And Cash Equivalents	BS	Kas dan Setara Kas
Cash Cash Equivalents And Short Term Investments	BS	Kas, Setara Kas, dan Investasi Jangka Pendek
Cash Equivalents	BS	Setara Kas
Cash Financial	BS	Kas yang berhubungan dengan aktiviatas keuangan
Common Stock	BS	Saham Biasa
Common Stock Equity	BS	Ekuitas Saham Biasa
Construction In Progress	BS	Konstruksi yang Sedang Berlangsung
Current Assets	BS	Aset Lancar
Current Debt	BS	Utang Lancar
Current Debt And Capital Lease Obligation	BS	Utang Lancar dan Kewajiban Sewa Kapital
Current Liabilities	BS	Liabilitas Lancar
Finished Goods	BS	Barang Jadi
Goodwill	BS	Nilai Tambah (Goodwill)
Goodwill And Other Intangible Assets	BS	Nilai Tambah (Goodwill) dan Aset Tidak Berwujud Lainnya
Gross Accounts Receivable	BS	Piutang Usaha Bruto
Gross PPE	BS	Aktiva Tetap Bruto (Properti, Pabrik, dan Peralatan)
Inventory	BS	Persediaan
Invested Capital	BS	Kapital yang Diinvestasikan
Investmentsin Joint Venturesat Cost	BS	Investasi dalam Usaha Patungan dengan Harga Perolehan
Land And Improvements	BS	Tanah dan Pengembangan
Long Term Debt	BS	Utang Jangka Panjang
Long Term Debt And Capital Lease Obligation	BS	Utang Jangka Panjang dan Kewajiban Sewa Kapital
Long Term Equity Investment	BS	Investasi Ekuitas Jangka Panjang
Machinery Furniture Equipment	BS	Mesin, Perabotan dan Perlengkapan
Minority Interest	BS	Kepentingan Minoritas
Net Debt	BS	Utang Bersih
Net PPE	BS	Aktiva Tetap Bersih (Properti, Pabrik, dan Peralatan)
Net Tangible Assets	BS	Aset Berwujud Bersih
Non Current Deferred Taxes Assets	BS	Aset Pajak Tangguhan Non Lancar
Non Current Deferred Taxes Liabilities	BS	Liabilitas Pajak Tangguhan Non Lancar
Non Current Pension And Other Postretirement Benefit Plans	BS	Rencana Pensiun Non Lancar dan Manfaat Pasca Pensiun Lainnya
Ordinary Shares Number	BS	Jumlah Saham Biasa
Other Current Liabilities	BS	Liabilitas Lancar Lainnya
Other Equity Interest	BS	Kepentingan Ekuitas Lainnya
Other Inventories	BS	Persediaan Lainnya
Other Non Current Assets	BS	Aset Non Lancar Lainnya
Other Non Current Liabilities	BS	Liabilitas Non Lancar Lainnya
Other Payable	BS	Hutang Lainnya
Other Properties	BS	Properti Lainnya
Other Receivables	BS	Piutang Lainnya
Payables	BS	Utang
Pensionand Other Post Retirement Benefit Plans Current	BS	Rencana Pensiun dan Manfaat Pasca Pensiun Lainnya Saat Ini
Prepaid Assets	BS	Aset Dibayar Dimuka
Properties	BS	Properti
Raw Materials	BS	Bahan Baku
Retained Earnings	BS	Laba Ditahan
Share Issued	BS	Saham yang Diterbitkan
Stockholders Equity	BS	Ekuitas Pemegang Saham
Tangible Book Value	BS	Nilai Buku Berwujud
Total Assets	BS	Total Aset
Total Capitalization	BS	Total Kapitalisasi
Total Debt	BS	Total Utang
Total Equity Gross Minority Interest	BS	Total Ekuitas Bruto dengan Kepentingan Minoritas
Total Liabilities Net Minority Interest	BS	Total Liabilitas Bersih dengan Kepentingan Minoritas
Total Non Current Assets	BS	Total Aset Non Lancar
Total Non Current Liabilities Net Minority Interest	BS	Total Liabilitas Non Lancar Bersih dengan Kepentingan Minoritas
Total Tax Payable	BS	Total Utang Pajak
Treasury Shares Number	BS	Jumlah Saham Treasuri
Work In Process	BS	Pekerjaan dalam Proses
Working Capital	BS	Modal Kerja / Kapital Jangka Pendek
Beginning Cash Position	CF	Posisi Kas Awal
Capital Expenditure	CF	Pengeluaran - Kapital
Capital Expenditure Reported	CF	Pengeluaran - Kapital yang Dilaporkan
Cash Dividends Paid	CF	Dividen Tunai yang Dibayarkan
Cash Flowsfromusedin Operating Activities Direct	CF	Arus Kas yang Digunakan dalam Aktivitas Operasional Langsung
Changes In Cash...

Wages in Manufacturing in Indonesia increased to 3270191 IDR/Month in the third quarter of 2025 from 3090532 IDR/Month in the first quarter of 2025. This dataset provides - Indonesia Average Monthly Wages in Manufacturing - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains a curated collection of labeled images featuring common aquatic weed species found in Merauke, Papua, Indonesia. The primary objective of this dataset is to support the development of machine learning models capable of detecting and classifying aquatic weeds based on visual input from field environments.

The images were captured in natural habitats such as irrigation canals, rice paddies, and wetlands where aquatic weeds typically proliferate. Each image is annotated with species-level labels based on visual characteristics and expert verification.

Labelled Classes (Water Weed Species): Ludwigia octovalvis – Commonly known as water primrose, a semi-aquatic creeping herb found in wet soils and shallow waters.

Bacopa monnieri – Known as Brahmi or water hyssop, a low-growing, succulent herb used in traditional medicine, often seen floating or creeping along muddy water edges.

Marsilea crenata – Often referred to as water clover, identified by its four-lobed clover-like leaves, usually found in flooded rice fields.

Limnocharis flava – Also called yellow velvetleaf or sawah lettuce, with broad, spade-shaped leaves and commonly invading paddies.

Eichhornia crassipes – Better known as water hyacinth, a free-floating weed with bulbous stalks and purple flowers.

Ipomoea aquatica – Also known as water spinach or kangkung, a fast-growing vine in water bodies.

Monochoria vaginalis – A small aquatic plant with oval leaves and purple-blue flowers, often found in rice fields.

Model Labeling Purpose: The labeling supports the training of a deep learning-based image classification or object detection model aimed at:

Automatic recognition of aquatic weed species

Early detection and monitoring of weed infestations

Supporting decision-making in sustainable weed management for agricultural and environmental health

This dataset can be used in environmental research, agricultural monitoring, precision farming, and the development of weed recognition mobile applications.

This dataset is about artists. It has 1 row and is filtered where the artworks is Woman on Stairs, Indonesia. It features 9 columns including birth date, death date, country, and gender.

Consumer Confidence in Indonesia increased to 121.20 points in October from 115 points in September of 2025. This dataset provides - Indonesia Consumer Confidence - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Indonesia Employment: By Status: Employee data was reported at 49,231.568 Person th in 2018. This records an increase from the previous number of 48,047.068 Person th for 2017. Indonesia Employment: By Status: Employee data is updated yearly, averaging 29,440.793 Person th from Aug 1997 (Median) to 2018, with 22 observations. The data reached an all-time high of 49,231.568 Person th in 2018 and a record low of 24,149.886 Person th in 2003. Indonesia Employment: By Status: Employee data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.GBA018: Employment.

Context

With the increasing number of accidents in Indonesia, analysis of accident data is still need to considered and analyzed. Moreover, traffic accident information from social media such as Twitter is easy to obtain when compared to other data source from police or government institutions. In this work, we tried to create a traffic accident dataset. We do scraping on Twitter and get 157.613 tweets, then do a text processing which involves case folding, filtering, tokenizing, and stopword removal. To find out wheter the tweet is true about accident information, we take some data as testing data then do word embedding using FastText method and classification with SVM, KNN, and Naïve Bayes algorithms then test the model with K-fold validation and confusion matrix accuracy. The results show that the validation score and accuracy score is good with 86% and 88% when SVM algorithm with linear kernel was done. This result model then applied to all dataset containing 157,613 tweets taken through the scraping process since April 2019 until April 2020 and produce predictions that can be used for further research and uploaded to www.dodyagung.com/dataset/accident.

Acknowledgements

This dataset is part of my graduate thesis and international journal paper, please cite if you use this dataset. This is the example in American Psychological Association (APA), 6th Edition format.

Saputro, D. A., & Girsang, A. S. (2020). Classification of Traffic Accident Information Using Machine Learning from Social Media. International Journal of Emerging Trends in Engineering Research, 8(3), 630–637. https://doi.org/10.30534/ijeter/2020/04832020

Header Image Source : www.kornberglawfirm.com

License

Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

Indonesia Employment: Java: DKI Jakarta data was reported at 4,726.779 Person th in 2018. This records an increase from the previous number of 4,509.171 Person th for 2017. Indonesia Employment: Java: DKI Jakarta data is updated yearly, averaging 3,497.359 Person th from Aug 1984 (Median) to 2018, with 33 observations. The data reached an all-time high of 4,861.832 Person th in 2016 and a record low of 2,024.243 Person th in 1984. Indonesia Employment: Java: DKI Jakarta data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.GBA019: Employment: by Province.

Context

Traffic accident data analysis still needs attention and analysis. This is supported by the increasing number of accidents in Indonesia. One of them is open data originating from social media such as Twitter which contains information on traffic accidents and is easy to obtain when compared to other data sources from related institutions. Named-entities are informative features which may be able to provide information about a text. This work contains an analysis of the impact that named-entities have on thematic text classification. The dataset used comes from previous research combined with the latest crawling. Preprocessing is carried out at an early stage to adjust and remove invaluable text and label named entities. Model comparisons with NE, only NE, and without NE were carried out on four methods, namely SVM, KNN, and Naive Bayes. Based on tests conducted on 1.885 data consisting of 788 accident data and 1.067 non-accident data, it was found that the SVM method with a hybrid scheme provided an increase in classification accuracy by 84.86% compared to only NE and without NE scheme of all methods. The entire dataset used in the research process can be used for further research.

Acknowledgements

This dataset is a collection of my graduate thesis and international journal papers, please cite them if you use this data.

Header Image Source : cdn.gresnews.com