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All Journal Jurnal Fisika Unand Gravity : Jurnal Ilmiah Penelitian dan Pembelajaran Fisika Jurnal Sistem Komputer dan Informatika (JSON)
Latifah Nurul Qomariyatuzzamzami
Sekolah Tinggi Meteorologi Klimatologi dan Geofisika
Astronomy Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Physics

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Aplikasi Metode Volumetrik dalam Estimasi Potensi Panas Bumi Berdasarkan Parameter Fisis Reservoir (Studi Kasus Daerah Panas Bumi “Z”) Latifah Nurul Qomariyatuzzamzami; Zuhdiyah Matienatul Iemaaniah; Alamta Singarimbun
Gravity : Jurnal Ilmiah Penelitian dan Pembelajaran Fisika Vol 9, No 2 (2023)
Publisher : Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30870/gravity.v9i2.20474

Abstract

Geothermal is a renewable energy strategically developed in Indonesia, but its utilization has not been maximized. Geothermal exploration requires high costs, so estimating energy reserves in potential areas is essential. This study was conducted to determine the electrical energy potential of geothermal area "Z" by volumetric method and determine the influence of saturation, porosity, and length of generation time on the potential of geothermal electrical energy. The volumetric method used is the lumped parameter model. The calculation was carried out with variations in water saturation 0% - 80%, rock porosity 10% - 40%, and the length of generation time 25 - 40 years. The highest estimated electrical energy of geothermal area "Z" is 188 MWe obtained at 80% water saturation, 10% porosity, and 25 years generation duration, while the lowest is 80 MWe obtained with 0% water saturation, 40% porosity, and 40 years of generation duration. At the same porosity, the greater the water saturation, the greater the value of geothermal electricity reserves of the area "Z." The greater the porosity, the greater the effect of increasing water saturation on increasing electrical energy reserves. The large porosity of rocks causes their energy to be smaller since the heat dominance of geothermal regions "Z" is given by the heat of rocks. The length of generation time affects the amount of geothermal electrical energy, that is, the longer the production time, the smaller the reserve.
Perbandingan Metode Partisi Machine Learning untuk Kerentanan Kebakaran Lahan Gambut Kalimantan pada Variasi ENSO Intan Bryliana Putri; Latifah Nurul Qomariyatuzzamzami; Purwanti Lelly Sabrina; Yahya Darmawan
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 8 No. 1 (2026): September (2026)
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v8i1.10022

Abstract

Lahan gambut Kalimantan merupakan wilayah rawan kebakaran yang polanya dipengaruhi fase El Niño -Southern Oscillation (ENSO). Penelitian ini membandingkan metode partisi acak KFOLD k=5 dan metode partisi spasial BANDS dalam pemodelan kerentanan kebakaran lahan gambut Kalimantan periode 2015 – 2024 , menggunakan algoritma machine learning tunggal melalui paket ENMTML pada tiga fase ENSO. Random Forest dengan KFOLD mencapai AUC 0,999 pada El Niño, namun diagnostik kNNDM menunjukkan statistik Wasserstein KFOLD selalu lebih tinggi dibanding BANDS yang berkisar 90.218-121.109 meter berbanding hanya 19.096-77.225 meter, sehingga performa tinggi KFOLD kurang merepresentasikan kondisi prediksi sesungguhnya. Pada metode BANDS, algoritma terbaik berbeda tiap fase yaitu Maximum Entropy default dengan AUC 0,929 pada El Niño, Boosted Regression Trees dengan AUC 0,983 pada Netral, dan Random Forest dengan AUC 0,870 pada La Niña. Kalimantan Tengah dan Kalimantan Selatan menunjukkan proporsi kerentanan tinggi hingga sangat tinggi terbesar dibanding wilayah lain pada fase ENSO yang berbeda. Variasi ENSO tidak berperan signifikan pada kerentanan kebakaran lahan gambut di Kalimantan dibandingkan dengan aktivitas konveksi lokal dan prediktor kelembaban lingkungan. Pemilihan skema partisi yang memperhitungkan struktur spasial terbukti penting untuk menghasilkan peta kerentanan yang lebih realistis dan berpotensi mendukung upaya konservasi lahan gambut yang lebih sesuai dengan karakteristik lingkungan setempat.
Nowcasting Trajektori dan Intensitas Siklon Tropis Seroja Menggunakan Temporal Convolutional Network Pertiwi Setyowati; Yosafat Donni Haryanto; Adi Mulsandi; Latifah Nurul Qomariyatuzzamzami
Jurnal Fisika Unand Vol 15 No 5 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.5.497-504.2026

Abstract

Tropical cyclones have an extremely short detection-to-peak-impact lead time, as exemplified by Tropical Cyclone Seroja, which caused catastrophic damage in East Nusa Tenggara in April 2021, while conventional methods such as the Dvorak technique, the Advanced Dvorak Technique, and Numerical Weather Prediction remain limited in update speed and objectivity. This study applies the Temporal Convolutional Network (TCN) for nowcasting the track and intensity of Tropical Cyclone Seroja as a faster, computationally lighter alternative capable of capturing nonlinear cyclone dynamics. The study uses historical best-track data from the Bureau of Meteorology (BOM) Australia covering 1973–2026 (longitude, latitude, central pressure, wind speed), with the model trained via a sliding-window approach for predictions at 6–24-hour horizons and tested on Cyclone Seroja, evaluated using RMSE, MAPE, and R². Results show TCN performs best in track prediction (longitude and latitude RMSE of 1.01–2.97 and 0.40–1.88, consistently high R² up to 24 hours), whereas intensity performance declines sharply (central-pressure RMSE of 4.01–10.40 hPa, wind-speed RMSE of 2.87–7.22 m/s, wind-speed MAPE up to 23.77%, negative R²), associated with the interaction between Cyclone Seroja and Tropical Cyclone Odette through the Fujiwhara effect. TCN has potential as an alternative to conventional methods for tropical cyclone early-warning systems in Indonesia, particularly for track prediction, with further development needed to improve intensity-prediction accuracy.
Co-Authors Adi Mulsandi Alamta Singarimbun Intan Bryliana Putri Pertiwi Setyowati Purwanti Lelly Sabrina Yahya Darmawan Yosafat Donni Haryanto Zuhdiyah Matienatul Iemaaniah