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All Journal Jurnal Geliga Sains: Jurnal Pendidikan Fisika POSITRON Jurnal Penelitian Fisika dan Aplikasinya (JPFA) JURNAL ILMIAH PENDIDIKAN FISIKA AL BIRUNI JOURNAL ONLINE OF PHYSICS Berkala Ilmiah Pendidikan Fisika Jurnal Fisika FLUX Jurnal Inovasi dan Pembelajaran Fisika Sriwijaya University Learning and Education International Conference JIPFRI (Jurnal Inovasi Pendidikan Fisika dan Riset Ilmiah) JPPPF (Jurnal Penelitian dan Pengembangan Pendidikan Fisika) JIPF (Jurnal Ilmu Pendidikan Fisika) Seminar Nasional Pendidikan IPA Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Ilmiah Pendidikan Fisika Spektra: Jurnal Fisika dan Aplikasinya Formatif: Jurnal Ilmiah Pendidikan MIPA JMM (Jurnal Masyarakat Mandiri) Jurnal Pengelolaan Laboratorium Pendidikan Jurnal Ilmu Fisika Indonesian Physical Review Jurnal Inovasi Pendidikan Jurnal Teori dan Aplikasi Fisika Kappa Journal Bubungan Tinggi: Jurnal Pengabdian Masyarakat Jurnal Pendidikan Fisika dan Sains (JPFS) Wahana Dedikasi : Jurnal PkM Ilmu Kependidikan Magnetic: Research Journal of Physics and It’s Application JIFP (Jurnal Ilmu Fisika dan Pembelajarannya) Journal of Mathematics Science and Computer Education Progressive Physics Journal Jurnal Pendidikan dan Pengabdian Masyarakat Research in Education, Technology, and Multiculture Jurnal Pendidikan MIPA JIPFRI (Jurnal Inovasi Pendidikan Fisika dan Riset Ilmiah) BULETIN FISIKA
Melly Ariska
Physics Education, Faculty Of Teacher Training And Education, Sriwijaya University, Palembang-Prabumulih Street KM 32 Indralaya, Ogan Ilir, South Sumatra 32155, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Health Professions Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Social Sciences Other

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Analysis of Rainfall Patterns in Sulawesi Using the Empirical Orthogonal Function (EOF) Method and Composite Analysis Ariska, Melly; Setiyowati, Devi Ariska; Siahaan, Sardianto Markos; Seprina, Iin; Firdausi, Huriyatul; Taufiq, Taufiq
POSITRON Vol 15, No 2 (2025): Vol. 15 No. 2 Edition
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam, Univetsitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/positron.v15i2.91149

Abstract

topography, and ocean-land interactions, which shape weather patterns and rainfall intensity variability. This study analyzes rainfall patterns in Sulawesi Island from 1981 to 2015 using the Empirical Orthogonal Function (EOF) method and composite analysis with machine learning. The results show that the EOF method successfully identifies three primary modes of rainfall variability. EOF Mode 1 captures negative anomalies, while EOF Mode 2 and EOF Mode 3 capture both positive and negative rainfall anomalies. EOF Mode 1 is the dominant component, explaining nearly 70% of the total variance. EOF Modes 2 and 3 capture additional variations on a smaller scale, and collectively, these three modes explain 88.53% of the total rainfall variability. Meanwhile, composite analysis reveals that global factors such as ENSO and the Indian Ocean Dipole (IOD) also influence rainfall variability, impacting drought periods and extreme rainfall events. During El Niño and positive IOD phases, rainfall deficits occur, potentially leading to prolonged droughts. Conversely, during La Niña and negative IOD phases, Sulawesi experiences a significant rainfall surplus, increasing the risk of hydrometeorological disasters such as floods and landslides.
Machine Learning to Predict Climate Change in Coastal Areas of Indonesia Huriyatul Firdausi; Melly Ariska; Sardianto Marcos Siahaan; Hamdi Akhsan; Yenny Anwar; Iin Seprina; Taufiq Taufiq
BULETIN FISIKA Vol. 27 No. 1 (2026): BULETIN FISIKA
Publisher : Departement of Physics Faculty of Mathematics and Natural Sciences, and Institute of Research and Community Services Udayana University, Kampus Bukit Jimbaran Badung Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/BF.2026.v27.i01.p05

Abstract

Indonesia's coastal regions face significant threats from climate change, including rainfall uncertainty, rising temperatures, and sea level rise. This study aims to explore the potential of machine learning algorithms in predicting climate parameter changes in the coastal areas of Minangkabau, Pesawaran, and Maritim Panjang. Daily climatological data obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) were used as the basis for model training. Three primary algorithms were tested Random Forest, XGBoost, and Long Short-Term Memory (LSTM) selected for their capability to handle complex and temporal data. The research methodology included data preprocessing, model training, cross-validation, and predictive performance evaluation using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Preliminary results show that LSTM excels in time series prediction, while XGBoost offers a good balance between speed and accuracy. These findings indicate that machine learning-based approaches have strong potential as decision-support tools for climate change mitigation and adaptation planning in Indonesia’s coastal regions.
Application of Physics-Informed Neural Networks (PINNs) for the Numerical Solution of the Time-Independent Schrödinger Equation Akhsan, Hamdi; Khoirun Nisa; Nurhikmah, Putri; Wailaina; Ariska, Melly
Progressive Physics Journal Vol. 6 No. 2 (2025): Progressive Physics Journal
Publisher : Program Studi Fisika, Jurusan Fisika, FMIPA, Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/6881zx10

Abstract

This work investigates the application of Physics-Informed Neural Networks (PINNs) for numerical solutions to the time-independent Schrödinger equation of the quantum harmonic oscillator in one, two, and three spatial dimensions. Fully connected neural architectures are constructed to approximate wavefunctions over finite symmetric domains, while the corresponding energy eigenvalues are treated as trainable parameters. The training strategy utilizes randomly sampled interior points to enforce the Schrödinger operator residual and boundary points to impose vanishing wavefunction constraints. For the 1D quantum harmonic oscillator, the learned ground-state wavefunction yields an energy of E = 1.2939 after 12,000 iterations. In the 2D configuration, convergence is achieved at E = 2.1352 within 14,000 iterations, whereas the 3D model attains E = 2.6377 after 12,000 iterations. These values agree with the expected trend of increasing ground-state energy with dimensionality, although deviations from exact analytical values indicate that PINNs may experience optimization challenges and sensitivity to sampling density and boundary enforcement. Despite these limitations, the trained models successfully capture the characteristic spatial symmetries and Gaussian-like envelope of harmonic oscillator eigenstates across all dimensions. These findings demonstrate that PINNs offer a flexible, mesh-free alternative for solving stationary quantum systems, particularly when analytical or conventional numerical approaches become impractical. The method shows strong potential for higher-dimensional quantum applications, even though further refinement such as improved sampling, loss balancing, and network depth remains necessary to suppress residual error and enhance eigenvalue accuracy.
IMPLEMENTATION OF MACHINE LEARNING FOR RAINFALL PREDICTION IN SMOKE-PRONE AREAS OF SOUTH SUMATRA Rahmannisa, Amanda; Ariska, Melly; Siahaan, Sardianto Markos; Seprina, Iin
Jurnal Ilmu Fisika dan Pembelajarannya (JIFP) Vol 9 No 2 (2025): Jurnal Ilmu Fisika dan Pembelajarannya (JIFP)
Publisher : Program Studi Pendidikan Fisika, UIN Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/h8s3w172

Abstract

Haze caused by forest and land fires is a serious problem in South Sumatra Province. One mitigation effort that can be made is to improve the accuracy of rainfall predictions, because rain plays an important role in reducing the potential for fires. This study implements machine learning methods, namely XGBoost and ConvLSTM, to predict spatiotemporal rainfall in areas prone to haze. The results show that ConvLSTM is capable of providing better predictions than the baseline, especially during periods of haze, by considering missing data imputation and masking techniques for disrupted satellite conditions. Increasingly apparent climate change in tropical regions has had a significant impact on rainfall patterns, particularly in South Sumatra, which is one of Indonesia's main agricultural and plantation centers. High rainfall variability can lead to the risk of flooding and drought, as well as disrupting productivity in the education, health, and economic sectors. Therefore, a more accurate rainfall prediction approach is needed to support climate adaptation planning and disaster risk mitigation. This study aims to compare the performance of three approaches to daily rainfall prediction, namely the ConvLSTM-based method, XGBoost, and Persistence, using daily observation data from BMKG for the South Sumatra region for the period 1981–2020. The input variables include average air temperature (Tavg), humidity, sunshine duration, and wind speed, while rainfall is used as the prediction target. The analysis was conducted through a time series approach, statistical distribution, and model performance evaluation using the quantitative metrics Root Mean Square Error (RMSE) and Critical Success Index (CSI). The results show that the ConvLSTM model produced the highest accuracy with an average RMSE of 10 mm/day and a CSI of 0.53, which is better than XGBoost (RMSE 12 mm/day; CSI 0.48) and the persistence method (RMSE 15 mm/day; CSI 0.40). Distribution analysis indicates that light to moderate rainfall occurs more frequently, while extreme rainfall occurs sporadically. The correlation heatmap shows that rainfall has a moderate positive relationship with humidity and a negative relationship with solar radiation, while average temperature and wind play a smaller role. The main contribution of this study is to provide empirical evidence that spatiotemporal deep learning methods such as ConvLSTM are superior in modeling the complexity of tropical rainfall dynamics compared to classical machine learning approaches and simple models. These findings can serve as a basis for the development of early warning systems and interactive climate dashboards at the regional level, while enriching the literature on rainfall prediction in tropical regions using an integrative approach.
Co-Authors Abidin Pasaribu Abidin Pasaribu Abidin Pasaribu, Abidin Adam Darmawan Ade Kurniawan Ade Kurniawan Agustina, Atika Al Fatih, Zaky Alawiyah, Sakinah Amanda, Karenina Andriani, Nelly Apit Fathurohman Ari Widodo Arini Rosa Sinensis Atika Agustina Az Zahra, Lutfiah Azizah Putri Berimah Berimah, Azizah Putri Diah Kartika Sari Dina Maulina Dwi Purnomo Aji Dwicahyani, Rania Efrinalia, Winta Ernalida Ernalida Fena Siska Putriyani Firdausi, Huriyatul Fitra Ritonga, Ahmad Fitriyani Fitriyani Frida Ramadian Gelby Pradina Paramitha Hamdi Akhsan Hartono Hartono Hartono Hartono Hartono Hartono Hartono Hartono Herlambang, Dominikus Krisna Huriyatul Firdausi Husna, Tsabita Ida Sriyanti Ida Sriyanti Iful Amri Iin Seprina Iskhaq Iskandar Ismet Ismet Ismet, Ismet Jesi Pebralia Ketang Wiyono Ketang Wiyono Ketang Wiyono KHOIRUN NISA Kistiono Kistiono Kistiono Kistiono Kristylia Sury Laras Sapitri, Cindy Leni Marlina Manurung, Nia Three May Sari Melati, Pegi Meli Asma Desti Melvany, Nanda Eva Milka, Ikbal Adrian Mindia Vanessa Pratiwi, Sri Muhamad Yusup Muhammad afrizal Muhammad Aufa Riyaldo Muhammad Irfan Muhammad Irfan Muhammad Muslim muhammad muslim Muhammad Muslim Muhammad Muslim, Muhammad Muhammad Romadoni Muhammad Yusuf Muhammad Yusup Mulyadi Eko Purnomo, Mulyadi Eko Murnia Murniati Murniati . Muslimah, Resta Ulis Nely Andriani Nely Andriani, Nely Nilam Cahyati Novi Yusliani Nur Julia Ningsih Nurhikmah, Putri Nurjannah Nurjannah Nuzula, Khalidatun Patriot, Evelina Astra Pertiwi, Nadiar Pratiwi Ineke Anwar Putra, Guruh Sukarno Putri Maulida, Nabila Putri, Astrid Yulinda Putri, Jamiatul Khairunnisa Putriyani, Fena Siska Rahmannisa, Amanda Rahmi Susanti Rahmi Susanti Rahmi Susanti Rahmi, Ani Ramadhani, Neysya Ditha Rara, Rara Ratu Ilma Indra Putri Redondo, Fernando Eric Rini Khoirunnisa Rita Inderawati Ritonga, Ahmad Fitra Rizki Novianti, Rizki Romadoni, Muhammad Sakinah Alawiyah Salmah Rianti Saparini Saparini Sardianto Markos Siahaan Sardianto Markos Siahaan, Sardianto Markos Sari, Dwita Kartika Sary Silvhiany Sayyendra, Amelia Putri Seprina, Iin Setiyowati, Devi Ariska Siti Nur Azizah Sri Mindia Vanessa Pratiwi Sri Zakiyah Sudirman Sudirman Sudirman Sudirman Sudirman, Sudirman Suhadi Suhadi Suhanda, Alfin Sunyono - - Supari Supari Supari Supari, Supari Suryaningsih, Ruth Magdalena Syarifudin, Agus Syuhendri, Syuhendri Taufiq Taufiq Taufiq Taufiq Tine Aprianti Tita Ratna Wulan Dari Utami, Amanda Kurnia Viyanti Viyanti Wailaina Wati, Lira Diska Yenny Anwar Zahra Alwi, Zahra Zulherman Zulherman Zulherman Zulherman, Zulherman