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All Journal Jurnal Ilmu dan Teknologi Kelautan Tropis PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Syahputri, Sabilla Hamda
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Library & Information Science

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Comparison of ARIMA and LSTM Methods in Predicting Jakarta Sea Level Hilal, Yanuar Nurul; Nainggolan, Gibson Daniel Andrianto; Syahputri, Sabilla Hamda; Kartiasih, Fitri
Jurnal Ilmu dan Teknologi Kelautan Tropis Vol. 16 No. 2 (2024): Jurnal Ilmu dan Teknologi Kelautan Tropis
Publisher : Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jitkt.v16i2.52818

Abstract

Dalam menghadapi risiko yang signifikan akibat kenaikan permukaan air laut yang dipicu oleh perubahan iklim, Jakarta sebagai kota pesisir memiliki kebutuhan mendesak untuk mengembangkan strategi yang efektif guna mengantisipasi dan memitigasi potensi dampak negatif. Dalam menghadapi tantangan ini, prediksi menjadi kunci untuk mengantisipasi dan meminimalkan dampak negatif yang mungkin timbul dari kenaikan permukaan air laut. Oleh karena itu, penelitian dilakukan dengan tujuan memperbandingkan kinerja dua metode prediksi, yaitu Autoregressive Integrated Moving Average (ARIMA) dan Long Short-Term Memory (LSTM). Kedua metode ini diaplikasikan untuk meramalkan tinggi permukaan air laut hingga akhir tahun 2023. Dalam mengevaluasi kualitas kedua model prediksi, digunakan metrik kinerja seperti Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), dan Root Mean Square Error (RMSE). Hasil analisis menunjukkan bahwa model ARIMA (1,1,4) lebih efektif dalam memprediksi tinggi permukaan air laut dibandingkan LSTM. ARIMA (1,1,4) memiliki nilai MAE 7,19, MAPE 4,86%, dan RMSE sebesar 10,35. Sementara itu, hasil forecasting dari kedua model didapatkan bahwa ketinggian permukaan air laut Jakarta diprediksi relatif stabil. Studi ini diharapkan dapat memberikan kontribusi yang signifikan dalam pemahaman serta mitigasi potensi dampak kenaikan permukaan air laut di Jakarta sebagai hasil dari perubahan iklim.
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 Fitri Kartiasih Hilal, Yanuar Nurul Marsisno, Waris Nainggolan, Gibson Daniel Andrianto