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Lutfi Rahmatuti Maghfiroh
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INDONESIA
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Published by Politeknik Statistika STIS
ISSN : 28099842     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
International Conference on Data Science and Official Statistics International Conference on Data Science and Official Statistics (ICDSOS) 2023 is organized by Politeknik Statistika STIS and Statistics Indonesia (BPS). This international conference in collaboration with Forum Pendidikan Tinggi Statistika (FORSTAT), Ikatan Statistisi Indonesia (ISI), United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP), and United Nations Statistics Division (UNSD). The ICDSOS will bring together statisticians and data scientists from academia, official statistics, health sector and business, junior and senior professionals, in an inviting hybrid environment on November 24th - 25th, 2023. Dealing with the theme of this conference is Harnessing Innovation in Data Science and Official Statistics to Address Global Challenges towards the Sustainable Development Goals. DATA SCIENCE Machine Learning and Deep Learning Data Science and Artificial Intelligence (AI) Data Mining and Big Data Statistical Software Information System Development for Official Statistics Remote Sensing to Strengthen Official Statistics Other data science relevant topic APPLIED STATISTICS Applied Multivariate Analysis Applied Time Series Analysis Applied Spatial Statistics Applied Bayesian Statistics Microeconomics Modelling and Applications Macroeconomics Modelling and Applications Econometrics Modelling and Applications Quantitative Public Policy and Statistical Analysis Applied Statistics on Demography Applied Statistics on Population Studies Applied Statistics on Biostatistics and Public health Other applied statistics relevant topic OFFICIAL STATISTICS Official Statistics Survey Methodology Developments Data Collection Improvements Sustainable Development Goals (SDGs) Indicators Estimation Small Area Estimation (SAE) Non Response and Imputation Methods Sampling Error and Non Sampling Error Evaluation Benchmarking Regional Official Statistics Other official statistics relevant topic
Arjuna Subject : Umum - Umum
Articles 251 Documents
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Fault Modeling to Determine the Reliability Status of Rotating Machines Using Deep Learning Methods Based on Vibrations from Acoustic Emissions from Cooling Fans TOUKAP NONO, FERNAND JOSEPH; TOKOUE NGATCHA , DIANORE; OFFOLE, Florence; NDI, FRANCELIN; MOUZONG PEMI, Marcelin
Proceedings of The International Conference on Data Science and Official Statistics 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.v2025i1.569

Abstract

Modern industrial production acknowledges the increasing significance of maintenance. As of right now, maintenance is seen as a service that aims to maintain the effectiveness of systems and installations while adhering to quality, energy efficiency, and protection standards. An inventive technique to automate rotating machine maintenance procedures has been created in this study. To identify failures and flaws in the motors through their supports, where the fan blades are attached, a technique based on capturing the noises produced by their cooling fans and utilizing deep learning to diagnose problems was investigated. Two operational circumstances were envisioned: the absence of fault and the presence of fault. The machine is correctly powered and running in ideal circumstances when it is not having any issues. In contrast, failures were gradually created purposefully and then documented in order to better understand the faults. Utilizing a pre-trained network (SqueezeNet) built on the ImageNet database, the convolutional neural network (CNN)-based technique was constructed. Applying transfer learning to the spectrograms obtained from the sound emission recordings of our machine's fan in both working modes demonstrated outstanding performance (accuracy = 0.987), confirming the methodology's outstanding quality.
Water Quality Measurement in Illegal Gold Mining Areas Using Sentinel-2A MSI Satellite Images of the Batanghari River, Tebo Tengah District Sinaga, Baginda; Kurniawan, Robert
Proceedings of The International Conference on Data Science and Official Statistics 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.v2025i1.570

Abstract

Water quality in Indonesian rivers has declined due to pollution from solid and liquid waste from industrial and domestic sources. The Batanghari River, the longest river on the island of Sumatra, faces various environmental problems, including pollution from illegal mining activities. Artisanal and small-scale gold mining (ASGM) contributes to mercury release, contaminating water and soil and posing health risks to communities. Conventional monitoring methods have limitations in coverage and efficiency. Therefore, this study utilizes Sentinel-2A MSI satellite imagery to assess and map water quality conditions around illegal gold mining areas along the Batanghari River in Tebo Tengah District. The developed model uses K- Means, Fuzzy C-Means (FCM), Principal Component Analysis (PCA), and Weighted Arithmetic Water Quality Index (WAWQI) to extract water quality features. The findings indicate that WAWQI provides a more representative quantitative assessment, revealing that areas near illegal gold mining sites in Batanghari river exhibit moderately to heavily polluted water quality. This approach is expected to support water quality monitoring and assist policymakers in managing water resources and the environment.
Satellite-Based Detection of Floating Plastic Debris in Jakarta Bay (2021–2024) Santi Wilda, Marchadha; Pasaribu, Ernawati
Proceedings of The International Conference on Data Science and Official Statistics 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.v2025i1.573

Abstract

Plastic waste is a critical environmental issue in Jakarta Bay, causing ecosystem degradation and challenging coastal management. This study analyzes seasonal dynamics and spatial impacts of floating plastic debris using Sentinel-2 imagery from July 2021 to November 2024. The Floating Debris Index (FDI) and Normalized Difference Vegetation Index (NDVI) were applied, with optimum thresholds determined through ROC curve analysis. Monthly median composites were processed to minimize atmospheric noise. The results show a recurring seasonal pattern, with debris consistently peaking in June, likely influenced by monsoon driven runoff and human activities. A clear increasing trend from 2021 to 2023 was followed by a decline in 2024, coinciding with the implementation of the National Ocean Love Month program. Buffer analysis indicated that most debris accumulates within 500 m of the shoreline, particularly near river mouths, ports, and settlements, while Thiessen Polygon analysis revealed hotspots concentrated along the eastern and western coasts. These findings highlight that floating plastic debris in Jakarta Bay is strongly shaped by seasonal cycles and land-based inputs, providing critical insights for designing targeted, evidence-based waste management policies.
An Intelligent Conversational Agent Using Self-Reflective Retrieval-Augmented Generation for Enhanced Large Language Model Support in National Accounts Learning Farhan, Muhammad; ., Yunofri; Tasriah, Etjih; Hulliyyatus Suadaa, Lya; Pramana, Setia
Proceedings of The International Conference on Data Science and Official Statistics 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.v2025i1.575

Abstract

BPS Statistics Indonesia plays a strategic role in compiling balance sheet statistics as the foundation for national policy analysis. This role requires a deep understanding of the concepts, definitions, and compilation standards outlined in the System of National Accounts (SNA) manual. However, in practice, comprehending such complex technical documents is not always straightforward. To address this challenge, this study proposes the development of an intelligent conversational agent in the form of a chatbot that implements the Self-Multimodal RAG approach. This approach integrates self-reflection mechanisms to generate more accurate and relevant responses. The evaluation was conducted using the LLM-as-a-Judge framework across four metrics: answer correctness, answer relevancy, context relevancy, and context faithfulness. Experimental results demonstrate that the Self-Reflective RAG achieved a score of 80% on the answer correctness metric, with competitive performance in terms of relevancy and faithfulness. From the chatbot implementation perspective, black-box testing confirmed that all functionalities operated as expected, while system usability testing using the CSUQ instrument yielded a score of 74.704%, indicating that the chatbot is well-accepted by users.
Shadow Economy Estimation Across ASEAN Member States: MIMIC Model Approach Al Agung, Ahmad Nadifa; Agustina, Neli
Proceedings of The International Conference on Data Science and Official Statistics 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.v2025i1.578

Abstract

As a measure of official output, GDP remains incomplete, omitting the substantial economic transactions that occur within the shadow economy. The shadow economy reduces government tax revenues and weakens fiscal capacity. It also contributes to the underestimation of macroeconomic indicators. This study estimates the size of the shadow economy in ASEAN member states (AMS) using the Multiple Indicators and Multiple Causes (MIMIC) model. The model employs three causal variables and two indicator variables to capture the latent construct. Inflation, unemployment rate, and GDP per capita growth are identified as the main causal determinants. Economic growth and M2 growth are validated as significant indicators constructed for the shadow economy. The estimation covers the period from 2000 to 2023 and reveals an upward trend in the shadow economy across ten AMS, with an average size of 37.75 percent of GDP. These findings emphasize the need for policy actions that focus on maintaining price stability, promoting inclusive economic growth, and expanding formal employment opportunities to mitigate the expansion of the shadow economy.
Detection and Mapping of Invasive Alien Plant Water Hyacinth using Satellite Imagery and Machine Learning (Case Study: Rawa Pening Lake, Indonesia) Sulthon Muammal, Adib; marsisno, waris
Proceedings of The International Conference on Data Science and Official Statistics 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.v2025i1.580

Abstract

Rawa Pening Lake, one of the 15 national priority lakes in Indonesia, faces a significant threat from invasive water hyacinth (Eichhornia crassipes). This plant once covered up to 70% of the lake's surface and continued to cause ecological and socio-economic impacts as of 2024, necessitating periodic monitoring to prevent future blooms. This study aimed to identify the optimal features to characterize water hyacinth, determine the most effective classification model, and map the plant’s distribution. Adopting the CRISP-DM framework, the study utilized Sentinel-1 (radar) and Sentinel-2 (optical) satellite imagery with multispectral band features, radar bands, and composite indexes. Feature selection was performed using Jenks Natural Breaks, and classification modeling was conducted using Random Forest and Convolutional Neural Network (CNN). The results demonstrated that the CNN achieved higher accuracy in distinguishing among land cover classes. The final mapping identified water hyacinth covering 34,775 pixels, 32,627 pixels, and 34,175 pixels in June, July, and August, respectively. This approach offers a reliable method for periodic monitoring of water hyacinths in Rawa Pening Lake.
Development of Portal Pintar Utilization Evaluation Dashboard (Case Study: BPS Province of Bengkulu) Josaphat, Bony Parulian; Humaira, Rifka
Proceedings of The International Conference on Data Science and Official Statistics 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.v2025i1.581

Abstract

BPS Statistics Province of Bengkulu (BPS Provinsi Bengkulu) plays a role insupporting statistical operations in Province of Bengkulu. As a vertical agency of StatisticsIndonesia (BPS), BPS Province of Bengkulu also holds an important role in providing statisticaldata at the regional level. Naturally, BPS Province of Bengkulu also requires an integratedsystem to facilitate all activities, such as providing easier and faster access to information for allemployees—both in reporting work progress and in monitoring the implementation of activitiessuch as agenda planning, facility usage, facility loan management, and cross-unit coordination.Portal Pintar is a portal used to facilitate the management of various activities in BPS Provinceof Bengkulu. By using Portal Pintar, users can access and manage various types of informationand documents, such as activity agendas, correspondence, and facility loan applications. BPSProvince of Bengkulu then produces periodic evaluations of Portal Pintar’s utilization, which aredistributed to all employees. However, the evaluations conducted are not yet visualizedautomatically and in real time, hence the need to develop a Portal Pintar Utilization EvaluationDashboard in which visualizations are generated automatically and connected to Portal Pintar’sAPI. Through the development of this dashboard, it is expected that the evaluation of PortalPintar’s utilization will become more integrated.
The Application of Retrieval-Augmented Generation (RAG) in Developing an Intelligent Risk Management Platform: A Case Study at Statistics Jawa Timur Agus Wahyu Dupayana, I Putu; Hardiyanto, Eko
Proceedings of The International Conference on Data Science and Official Statistics 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.
Extracting Information on Aspects of Sustainable Tourism in ASEAN Using Named Entity Recognition (NER) Manalu, Sisilia; Transver Wijaya, Yuliagnis
Proceedings of The International Conference on Data Science and Official Statistics 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.v2025i1.601

Abstract

Sustainable tourism is an important issue in the ASEAN region, which has experienced rapid growth in the tourism sector but faces challenges in maintaining a balance between economic, social, and environmental aspects. Information on sustainability practices is scattered across various forms of text, making it difficult to analyze manually. This study aims to extract information on aspects of sustainability in tourism using a transformer-based Named Entity Recognition (NER) approach. Three data sources were used: government websites, online news, and travel reviews on TripAdvisor. Five transformer models were compared, namely BERT, ALBERT, DistilBERT, ELECTRA, and RoBERTa, to evaluate entity extraction performance. The dataset was divided using an 80:10:10 ratio for training, validation, and testing. The results showed that DistilBERT provided the best performance with a balance of accuracy and computational efficiency. In addition, an analysis of the distribution of sustainability aspects in ASEAN countries and Indonesia in particular was conducted to identify practices that have already been implemented. These findings are expected to contribute to the development of more sustainable tourism policies and practices in the ASEAN region and Indonesia.
The Influences of Climate Change and Social Vulnerability on Dengue Fever Incidence Rate in West Java Province 2019–2023 Hanif, Alwan Nabil; Sohibien, Gama Putra Danu; Wulansari, Ika Yuni
Proceedings of The International Conference on Data Science and Official Statistics 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.v2025i1.606

Abstract

In Indonesia, dengue fever is a serious public health problem. The increase in dengue fever cases is influenced by climate change and social vulnerability factors. This study focuses on West Java Province in 2019–2023, aiming to describe the spatial-temporal pattern of dengue fever incidence and analyze the influence of climate factors and social vulnerability using a spatial-temporal model, namely Geographically Temporally Weighted Regression (GTWR). The exploration results show a high concentration of dengue fever incidence rates in 2019, while in 2023, the intensity of dengue fever incidence decreases. The GTWR model produces local parameters across various regions and time periods, indicating that in most regencies/cities, rainfall, population density, access to inadequate sanitation, health facility ratio, and education level have a positive effect on dengue fever incidence rates, while land surface temperature and the percentage of poor people have a negative effect. From the GTWR model results, areas with high levels of dengue fever vulnerability can be identified as priorities for dengue fever management interventions. Therefore, this study contributes to early warning research and dengue fever control program planning by considering the risk of dengue fever vulnerability in each region.

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