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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) KOMPUTIKA - Jurnal Sistem Komputer Prosiding Seminar Nasional Official Statistics PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Marsisno, Waris
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences

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Integrasi Citra Satelit Radar dan Data AIS untuk Monitoring Tumpahan Minyak dengan Pendekatan Machine Learning Putri Suhendi, Brigitta Aurelia; Marsisno, Waris
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2318

Abstract

Oil spill cases are routine occurrences in the Riau Archipelago Province, particularly in the northern part of Bintan Island, as one of the consequences of the island's strategic location near Singapore, a global trade and financial hub. The limitations of resources and data pose obstacles for the government in addressing this issue. Therefore, in this study, the author offers a solution by detecting oil spill areas using Sentinel-1 radar data and wind speed using the adaptive thresholding method, the best classification model from 7 machine learning algorithms and 2 deep learning algorithms. Based on the accuracy, precision, recall, and F1-score values of these 9 algorithms, the best algorithm obtained is XGBoost (Extreme Gradient Boosting). Followed by mapping and estimating the area of the oil spill at 177.54 ha, as well as identifying ships passing through the oil spill area using AIS (Automatic Identification System) data and wind direction, with the result being 2 ships passing through the area.
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.
Equipment Borrowing and Room Booking Information System at the Politeknik Statistika STIS Hadi Nugroho, Setya; 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.v2025i1.624

Abstract

The management of goods and space lending services at the Politeknik Statistika STIS is currently still done manually, resulting in various operational constraints such as limited access to information, inefficient processes, and potential errors in recording. This impacts the quality of service and the effectiveness of campus asset utilization. This study aims to design and build a website-based goods and space lending information system to address these issues. The system developers aimed to provide users with access to information on goods and space availability, simplify the loan application process, and improve the accuracy of inventory data. The system was developed using the SDLC method with a prototyping approach, while The researchers carried out the evaluation process using Black Box Testing and a PSSUQ survey survey to measure ease of use and user satisfaction. The developers successfully built the system and confirmed through Black Box Testing that all features operate correctly, and the PSSUQ evaluation shows an average score of 1.69, indicating that this system is well received and provides a high level of satisfaction for users.
Logical Modelling of Statistical Data Using the SDMX Standard: Case Study on the Quarterly Gross Regional Domestic Product Table Amandasari, Kartika; Pratama, Nano Yulian; Aditama, Farhan Satria; 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.v2025i1.641

Abstract

Poverty, as a national issue, necessitates data-driven policy planning informed byaccurate and consistent statistics. To ensure the optimal quality and consistency of statistical datareporting across diverse regions, the adoption of an international standard is crucial. TheStatistical Data and Metadata Exchange (SDMX) standard facilitates the structured exchange ofdata and metadata. This study aims to design and implement a statistical indicator data modelusing the SDMX standard to improve table consistency. We utilized Quarterly Provincial GrossRegional Domestic Product (GRDP) data as a case study and applied the Design ScienceResearch Method (DSRM) as the methodology. The results demonstrate that modeling theGRDP data using SDMX yields a uniform and highly consistent table structure, significantlyenhancing the consistency of statistical data reporting across regions.
Estimation of Energy Transition Index based on Official Statistics and Satellite Imagery Data : (Case Study: Regencies/Cities in Indonesia) Syahputri, Sabilla Hamda; 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.v2025i1.724

Abstract

Energy has a crucial role in sustaining human life, its implementation should be optimized based on the principles of sustainable development through a shift from non-renewable to renewable sources. To monitor this shift, the World Economic Forum (WEF) developed the Energy Transition Index (ETI), which measures national-level transitions using conventional statistical data. However, the ETI is limited to the country level, while more detailed assessments are needed at smaller administrative scales such as regencies and cities to capture regional specificities. This study addresses the gap by constructing an energy transition index at the regency/city level in Indonesia for 2024. The analysis integrates official statistics with satellite imagery data to overcome limitations in subnational data availability. Methodologically, Exploratory Factor Analysis and uncertainty analysis were applied. Among five scenario of uncertaincy analysis tested, scenario 1 featuring min-max normalization, unequal weighting across indicators and factors, and linear aggregation produced the most reliable results. The findings reveal that the index is composed of four main factors. Overall, Indonesia’s energy transition index values show a relatively even distribution, yet disparities remain evident across islands and between regencies/cities. Higher scores are concentrated in the western regions, while lower scores dominate the eastern parts of the country.
Co-Authors Aditama, Farhan Satria Amandasari, Kartika Amin, Asy-Syaja'ul Haqqul Arie Wahyu Wijayanto Azhar, Daris Fadellah, Putri Kurnia Hadi Nugroho, Setya Jannah, Rofa Raudhatul Latifa, Afina Permatasari, Noverlina Putri Pratama, Nano Yulian Putri Suhendi, Brigitta Aurelia Robert Kurniawan, Robert Robiul Awaliah, Mesya Anggita Sagitama, Daniel Angga Sari, Dita Dwi Wulan Sugiarto S Sukim, Sukim Sulthon Muammal, Adib Syahputri, Sabilla Hamda Taufiqqurrahman, M. watin, Rahma Yuniarto, Budi