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All Journal Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Sisfokom (Sistem Informasi dan Komputer) JSiI (Jurnal Sistem Informasi) JISA (Jurnal Informatika dan Sains) Jurnal Pengabdian Masyarakat Tekno
Tiaharyadini, Rizka
Universitas Budi Luhur
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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ANALYZING CLIMATE IMPACTS ON RICE PRODUCTION IN SUMATRA THROUGH SPATIOTEMPORAL MACHINE LEARNING MODELS Zaqi Kurniawan; Rizka Tiaharyadini; Puguh Jayadi; Windhy widhyanty
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7344

Abstract

Climate variability poses a major challenge to rice production in Sumatra, a key contributor to Indonesia’s food security. This study aims to analyze spatiotemporal climate impacts on rice yields by integrating climatic, geographical, and agricultural datasets. Historical records from 1993–2024, including rainfall, temperature, humidity, and rice production statistics, were collected from BMKG, BPS, and the Ministry of Agriculture. After preprocessing and feature selection, six machine learning algorithms—Linear Regression, Random Forest, Gradient Boosting, Support Vector Regression, Decision Tree, and K-Nearest Neighbors—were evaluated for predictive performance. Results show significant spatial heterogeneity: rainfall strongly affects yields in Aceh and North Sumatra, while temperature stress is critical in southern provinces. Among the tested models, Random Forest achieved the best accuracy (R² = 0.985), outperforming other algorithms. These findings highlight the importance of localized adaptation strategies and demonstrate the potential of ensemble machine learning to support climate-resilient rice production.
Implementation of K-Means clustering algorithm in mapping the groups of graduated or dropped-out students in the Management Department of the National University Darwis, Muhammad; Hasibuan, Liyando Hermawan; Firmansyah, Mochammad; Ahady, Nur; Tiaharyadini, Rizka
JISA(Jurnal Informatika dan Sains) Vol 4, No 1 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i1.848

Abstract

This study aims to determine the characteristics of students who are likely to graduate or drop out (DO) in the management department of the National University, Jakarta. The study was conducted by implementing the K-Means algorithm, where each data is grouped according to the closest distance to the centroid. Determination of Cluster C1 graduate or C2 drop out is based on the attributes of status of students (active, leave, out and non-active), educational status (graduated or DO), GPA, total credits taken and length of study. To facilitate the clustering process, Orange tools are used that provide K-Means algorithm features. The total data input in this study were 1988 students from various classes. As a result, a pattern or mapping of graduated or DO students was found based on the attributes mentioned earlier. Testing the results of this cluster with the silhouette method, by measuring the distance between cluster members, both C1 and C2, showed good Silhouetter value, reaching 85%. The management department, National University can use the results of this study to predict the graduation of their students.
ANALYZING CLIMATE IMPACTS ON RICE PRODUCTION IN SUMATRA THROUGH SPATIOTEMPORAL MACHINE LEARNING MODELS Zaqi Kurniawan; Rizka Tiaharyadini; Puguh Jayadi; Windhy widhyanty
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7344

Abstract

Climate variability poses a major challenge to rice production in Sumatra, a key contributor to Indonesia’s food security. This study aims to analyze spatiotemporal climate impacts on rice yields by integrating climatic, geographical, and agricultural datasets. Historical records from 1993–2024, including rainfall, temperature, humidity, and rice production statistics, were collected from BMKG, BPS, and the Ministry of Agriculture. After preprocessing and feature selection, six machine learning algorithms—Linear Regression, Random Forest, Gradient Boosting, Support Vector Regression, Decision Tree, and K-Nearest Neighbors—were evaluated for predictive performance. Results show significant spatial heterogeneity: rainfall strongly affects yields in Aceh and North Sumatra, while temperature stress is critical in southern provinces. Among the tested models, Random Forest achieved the best accuracy (R² = 0.985), outperforming other algorithms. These findings highlight the importance of localized adaptation strategies and demonstrate the potential of ensemble machine learning to support climate-resilient rice production.
Co-Authors Ahady, Nur Anif , Muhammad Arief Wibowo Arief Wibowo FIRMANSYAH, MOCHAMMAD Hasibuan, Liyando Hermawan Istifarulah, Mochamad Hanif Rifa'i Jonathan, Jeremy Muh. Darwis Muhammad Anif Puguh Jayadi Rahdiana, Ikhsan Rusdah Windhy Widhyanty Zaqi Kurniawan