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All Journal Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Sisfokom (Sistem Informasi dan Komputer) JSiI (Jurnal Sistem Informasi) JISA (Jurnal Informatika dan Sains) Jurnal Pengabdian Masyarakat Tekno
Tiaharyadini, Rizka
Universitas Budi Luhur
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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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.
Deep Learning-Based Detection and Classification of Rice Leaf Diseases Using ResNet-50 with Augmentation and K-Fold Cross-Validation Zaqi Kurniawan; Rizka Tiaharyadini; M Saddam Ryuga Octoramdhani; Radiz Dirgantara
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12716

Abstract

Rice sustains over half of the global population; however, leaf diseases such as blight, brown spot, and leaf smut significantly reduce crop yields. Early and accurate detection is essential for supporting sustainable agriculture practices. This study proposes a ResNet-50-based deep learning model for rice leaf diseases classification using a dataset of 1,150 field images collected from Sleman, Indonesia, which was expanded to 2,000 images through augmentation techniques, including rotation, flipping, zooming, and brightness adjustment. Model performance was evaluated using both hold-out validation and 10-fold cross-validation with accuracy, precision, recall, and F1-Score metrics. The application of data augmentation improved hold-out validation accuracy from 82.4% to 88.1%. Meanwhile, 10-fold-cross-validation yielded a substantially higher average accuracy of 99.6%. This discrepancy suggests potential sensitivity to data partitioning and indicates the need for careful interpretation, as cross-validation may introduce optimistic estimates under certain conditions. Although the proposed approach demonstrates strong performance in distinguishing visually similar diseases, this study limited by the use of a single-region dataset, which may affect generalizability. Therefore, the integration of ResNet-50, augmentation, and cross-validation shows promising results for early disease detection, while further validation on more diverse datasets is required to support it application in real-world precision agriculture systems.
Optimalisasi literasi digital kader posyandu remaja melalui metode problem-based dalam pengelolaan media sosial Reva Ragam Santika; Rizka Tiaharyadini; Windhy Widhyanty
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 10, No 4 (2026): August (In Progress)
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v10i4.40268

Abstract

Abstrak Perkembangan teknologi digital mendorong perlunya peningkatan kapasitas kader posyandu Remaja dalam memanfaatkan media sosial sebagai media edukasi dan diseminasi infromasi kesehatan, namun keterbatasan pemahaman dan keterampilan digital masih menjadi kendala sehingga peran belum dilakukan secara optimal. Kegiatan Pengabdian ini bertujuan meningkatkan kapasitas digital kader Posyandu Remaja melalui pelatihan Problem Based Learning (PBL) yang dipilih karena memungkinkan peserta belajar dari permasalahan autentik dalam pengelolaan media sosial sehingga keterampilan literasi digital dapat diterapkan secara langsung dalam konteks tugas kader.  Pelatihan dilaksanakan melalui beberapa tahapan meliputi beberapa tahapan , meliputi analisis situasi mitra, identifikasi kebutuhan, penyusunan tim pelaksana, orientasi masalah, penyelidikan kelompok, presentasi hasil, refleksi, serta tindak lanjut.  Evalusi dilakukan melalui pretest dan posttest yang menunjukan peningkatan presentase  46,75% menjadi 89, 25% Hasil tersebut mengindikasikan adanya peningkatan kemampuan peserta dalam berpikit kritis, kreativitas digital, serta kepercayaan diri dalam mengelola media sosial sebagai sarana komunikasi publik. Temuan ini menunjukan bahwa penerapan Problem-Based Learning (PBL) berpotensi meningkatkan kapasitas digital kader Posyandu Remaja dan layak dipertimbangkan sebagai model pelatihan berbasis komunitas. Kata kunci: pelatihan digital;  problem based learning; posyandu remaja; media sosial;  pemberdayaan masyarakat. Abstract The rapid advancement of digital technology has highlighted the need to strengthen the capacity of adolescent POSYANDU cadres to utilize social media as a platform for health education and the dissemination of health information. However, limited digital knowledge and practical skills continue to hinder the effective use of social media for these purposes. This community service program aimed to enhance the digital capacity of adolescent POSYANDU cadres through Problem-Based Learning (PBL), which was selected because it enables participants to learn from authentic problems encountered in social media management, thereby facilitating the direct application of digital literacy skills in their roles as health cadres. The program was implemented through several stages, including situational analysis, needs assessment, team preparation, problem orientation, group investigation, presentation of solutions, reflection, and follow-up activities. The program evaluation employed pre-test and post-test assessments, which showed an increase in the average score from 46.75% to 89.25%. These findings indicate improvements in participants' critical thinking, digital creativity, and confidence in managing social media as a medium for public communication. Overall, the findings suggest that the implementation of Problem-Based Learning (PBL) has the potential to enhance the digital capacity of adolescent POSYANDU cadres and may serve as a promising community-based training model for strengthening digital literacy and community empowerment. Keywords: digital training; problem based learning; adolescent posyandu; social media; community empowerment
Co-Authors Ahady, Nur Anif , Muhammad Arief Wibowo Arief Wibowo FIRMANSYAH, MOCHAMMAD Hasibuan, Liyando Hermawan Istifarulah, Mochamad Hanif Rifa'i Jonathan, Jeremy M Saddam Ryuga Octoramdhani Muhammad Anif Muhammad Darwis Puguh Jayadi Puguh Jayadi Radiz Dirgantara Rahdiana, Ikhsan Rusdah Windhy Widhyanty Windhy Widhyanty Zaqi Kurniawan