Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Eko Hardiyanto
Statistics Jawa Timur
Computer Science & IT

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Personalized Route Recommendation Method for Field Survey Officers using Social Media Information and Administrative Border Maps Eko Hardiyanto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.130

Abstract

Changes in business processes in pandemic conditions are a must. The field survey were most affected, not only the interview process but also the route selection to survey location. To support the field survey officer, it is necessary to provide alternative route choices to the survey location as fast as possible. This research proposed a methodology that combines three information source, administrative border maps, google maps services, and information from social media that elaborated to provide best recommendation route to the assigned survey location. The combination of three different sources can enhance the current existing route that only relies on google map services. Our mechanism was tested on custom my maps application provided by Google and evaluated using system usability scale. This research aims to give the personalized route to field survey officers based on the assigned survey location and information from social media. The limitation of this research is that the social media channels used are still few, in the future, this research can be leveraged by integrating other platforms owned by the government and other public services to enrich the information.
The Application of Retrieval-Augmented Generation (RAG) in Developing an Intelligent Risk Management Platform: A Case Study at Statistics Jawa Timur I Putu Agus Wahyu Dupayana; Eko Hardiyanto
Joint International Conference 2026: The 2nd Indonesia Forum on Labour and Productivity (IFLP) & The 4th International Conference on Data Science and Official Statistics (ICDSOS) Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.591

Abstract

Risk management is a crucial element in the governance of modern organizations, especially for public institutions such as Statistics Indonesia (BPS), which is responsible for providing official state statistics. Currently, the conventional methodology at Statistics Jawa Timur remains manual, relying on spreadsheet software, which results in slow and unresponsive processes for addressing dynamic risks. This condition reduces the effectiveness of internal controls, particularly with a massive strategic agenda like the 2026 Economic Census (SE2026) approaching. To address these limitations, this research proposes the development of Kadiri-A Risk Management Information System and Worksheet, an intelligent system that integrates Artificial Intelligence (AI) technology, specifically Large Language Models using the RetrievalAugmented Generation (RAG) method. The Kadiri system is designed to transform risk management from a reactive to an initiative-taking process, accelerating the identification, analysis, and mitigation recommendations by leveraging BPS internal knowledge base. The RAG methodology enables an AI model, such as Google Gemini, to provide contextual and relevant suggestions based on the organization's historical data. The outcome of this development is a digital platform that speeds up risk analysis, enhances accountability, and aligns with the bureaucracy reform agenda.
Co-Authors I Putu Agus Wahyu Dupayana