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All Journal JGE (Jurnal Geofisika Eksplorasi) Jurnal Geosaintek JOURNAL ONLINE OF PHYSICS Seminar Nasional Lahan Suboptimal JURNAL METEOROLOGI DAN GEOFISIKA Jurnal Geofisika Jurnal Pengabdian Masyarakat (ABDIRA) Jurnal Tiyasadarma: Jurnal Pengabdian Kepada Masyarakat Jurnal Geologi dan Sumberdaya Mineral (Journal of Geology and Mineral Resources) Intechno Journal : Information Technology Journal Jurnal Meteorologi dan Geofisika Journal of Rural Community Development (JRCD) Jurnal Geosaintek
Resta, Ichy Lucya
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Nursing Physics Social Sciences Other

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Rainfall Prediction Using Random Forest with Synthetic Minority Over-Sampling Technique (SMOTE) Sri Bintang Marpaung; Ichy Lucya Resta; Tugiyo Aminoto
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2939

Abstract

Rainfall is a crucial meteorological parameter that directly affects maritime activities and port operations, particularly in coastal regions. Tanjung Perak Port, as one of the busiest ports in Indonesia, is highly vulnerable to weather disturbances due to rainfall variability. This study develops a rainfall prediction model based on machine learning using the Random Forest algorithm optimized with the Synthetic Minority Over-Sampling Technique (SMOTE) to address data imbalance. Daily meteorological data for 2013-2024 were obtained from NASA POWER, including rainfall, minimum and maximum temperature, relative and specific humidity, wind speed, and surface pressure. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). SMOTE improved the model's ability to identify high-intensity rainfall events. The combined 2024-2025 evaluation produced an MAE of 1.457 mm, an RMSE of 1.819 mm, and R² of 0.861. The model therefore captures seasonal rainfall patterns and has potential as a decision-support tool for weather-risk mitigation at Tanjung Perak Port.
Sosialisasi Penggunaan Aplikasi Origin dan ImageJ untuk Analisis Sampel Material pada Mahasiswa Program Studi Fisika FST Universitas Jambi Febri Berthalita Pujaningsih; Frastica Deswardani; Alrizal Alrizal; Jesi Pebralia; M Ficky Afrianto; Lucky Zaehir Maulana; Rista Mutia Anggraeni; Samsidar Samsidar; Yoza Fendriani; Husnul Hamdi; Ichy Lucya Resta
Jurnal Pengabdian Masyarakat (ABDIRA) Vol 6, No 1 (2026): Abdira, Januari
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/abdira.v6i1.1284

Abstract

In the digital era, the ability to process and analyze experimental data has become an important skill in the field of materials science and engineering. Origin is a commercial software widely used for numerical data analysis and scientific visualization, while ImageJ is open-source software that functions in image processing of microscopic characterization results. Considering the importance of mastering both software, a socialization activity and basic training on the use of Origin and ImageJ was held for students of the Physics Study Program, Faculty of Science, University of Jambi. This activity aims to enable students to operate the software in processing research data independently and in accordance with scientific publication standards. Evaluation of the training was carried out by assessing the level of student understanding, which showed an increase in basic skills in using Origin for quantitative data analysis and ImageJ for digital image analysis.
Co-Authors -, Maison Adhitya, Bagus Afrianto, Muhammad Ficky Agus Kurniawan Mastur Airunisa, Airunisa Alrizal Alrizal, Alrizal Alrizal, Alrizal Amalina Fauziyah, Annisa Amelia Fitri Ramadhani Andriani, Aulia Anggia Harahap, Iryuvelamea Anggraini, Rista Arie Satria Astuti, Putri Efendi, Agustian Ermadani Ermadani Fadlan Risky, Khoirul Faizar Farid Febri Berthalita Pujaningsih Fernando Mersa Putra Fitri Ramadhani, Amelia Frastica Deswardani Futra Kibayutullah Gemilang, Rio Hidaya, Rizky Husnul Hamdi Husnul Hamdi ira kusuma dewi Jesi Pebralia Juventa, Juventa Kibayutullah, Futra kusuma dewi, ira Lefdiana, Aura Lucky Zaehir Maulana M Ficky Afrianto M.Z, Nazri Madyawati Latief Mahardika, Rizky Maulina Tanjung Muhammad Yuwanda Musyarofah, Leni Nurli Mz, Nasri Nasri MZ Nasri MZ Ngatijo Ngatijo Novianda, Cici Nuklirullah, Nuklirullah Randa Pranata, Abimbi Riany, Hesti Rifqi Muhammad Rikaldo Pratama Rikaldo Pratama Riski Novrianti Rista Mutia Anggraeni Rizky Mahardhika Rizky Marpaung, Ahmad Said, Yulia Morsa Samsidar Samsidar Samsidar Sarwo Sucitra Amin Satria, Arie Sefiyanti, Fani Setiawan, Rendy Agus Sitompul, Jeremia Situmorang, Salomo Sri Bintang Marpaung Syawalia, Nurlaili Tugiyo Aminoto Windhanu, Ilham Jaya Wiratama, Jarot Wiwid Joni Yoza Fendriani Yurismawan, Yurismawan