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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal of Artificial Intelligence and Engineering Applications (JAIEA) ORAHUA : Jurnal Pengabdian Kepada Masyarakat
Safrizal
Universitas Muhammadiyah Asahan
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Other

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OPTIMIZING DECISION TREE PERFORMANCE WITH RECURSIVE FEATURE ELIMINATION FOR HIGH-DIMENSIONAL MUSHROOM CLASSIFICATION Lili Tanti; Safrizal; Yan Yang Thanri
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.v11i2.6816

Abstract

Classifying mushroom species presents a significant challenge within biological data analysis because of the wide variety of species and their distinct attributes. This research investigates the effectiveness of the Decision Tree classifier for mushroom categorization by comparing two splitting criteria, the Gini Index and Entropy. Additionally, the study employs the Recursive Feature Elimination (RFE) method for dimensionality reduction to enhance model efficiency and performance. The dataset was collected, cleaned, and analyzed exploratorily before feature selection was conducted using RFE. The Decision Tree model was trained and evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that applying RFE improved computational efficiency without compromising model accuracy. The Gini criterion provided more stable results across all metrics, while Entropy demonstrated higher precision in certain cases. Model optimization through parameter tuning produced the best parameter combination at max_depth = 5, min_samples_leaf = 5, and min_samples_split = 10. This study concludes that integrating RFE with the Decision Tree can significantly enhance the performance of high-dimensional dataset classification. The findings are expected to serve as a reference for developing efficient and accurate biological data classification models
Edukasi Dan Pendampingan Tata Kelola Bank Sampah Untuk Meningkatkan Program Desa Berkelanjutan Ramadani; Safrizal; Rahmadani Pane; Muhammad Lukman Hakim; Muhammad Farel Aditiya
ORAHUA : Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 01 (2026): Juli
Publisher : Faatuatua Media Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70404/orahua.v4i01.690

Abstract

Permasalahan pengelolaan sampah menjadi isu yang krusial dalam menciptakan pembangunan desa yang berkelanjutan, terutama di Desa Tolan yang masih mengalami minimnya kesadaran warganya terkait pengelolaan sampah rumah tangga. Sampah yang terdiri dari bahan organik dan non organik yang tidak dikelola secara efektif berpotensi menyebabkan pencemaran lingkungan, masalah kesehatan, serta mengurangi kualitas hidup masyarakat. Inisiatif pengabdian masyarakat ini bertujuan untuk meningkatkan wawasan, keterampilan, dan partisipasi masyarakat melalui program edukasi dan pendampingan pengelolaan bank sampah sebagai solusi penanganan berkelanjutan untuk sampah organik dan non organik. Metode yang digunakan dalam pelaksanaan melibatkan pendekatan partisipatif, yang mencakup pengamatan, sosialisasi, pelatihan, demonstrasi, pendampingan, dan evaluasi. Target dari kegiatan ini adalah perangkat desa, kelompok PKK, karang taruna, serta warga Desa Tolan. Hasil dari kegiatan ini menunjukkan adanya peningkatan pemahaman masyarakat tentang pengelolaan dan pemanfaatan sampah organik menjadi kompos dan sampah anorganik menjadi produk yang memiliki nilai ekonomi. Program pendampingan juga berhasil membentuk sistem administrasi bank sampah yang lebih teratur, meningkatnya partisipasi warga, dan kesadaran lingkungan di antara masyarakat. Keberhasilan program ini menunjukkan bahwa edukasi dan pendampingan dapat memperbaiki pengelolaan bank sampah dan menjadi strategi yang efektif dalam mendukung pengembangan desa yang lestari dan berwawasan.
Evolution and Impacts of AI-Based Rainfall Prediction Systems on Agricultural Management in Tropical Regions: A 20-Year Systematic Review Safrizal; Ika Safitri Windiarti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2357

Abstract

Global climate change has significantly disrupted rainfall patterns in tropical regions, posing major challenges to agricultural productivity and food security. Accurate rainfall prediction has become a critical component of data-driven agricultural management. This study conducts a systematic literature review (SLR) following the PRISMA 2020 guidelines to analyze the evolution of AI-based rainfall prediction systems and their multidimensional impacts on tropical agricultural management over the period 2008–2026. Data were sourced from Scopus using three Boolean search strings, yielding 239 records, of which 235 articles were retained after duplicate removal and quality assessment using the Mixed Methods Appraisal Tool (MMAT) with a threshold score of ≥5. Bibliometric analysis was conducted using VOSviewer and Bibliometrix (R), while thematic narrative synthesis was performed using NVivo 14. Results reveal a clear four-phase technological evolution: conventional methods (2008–2015), machine learning adoption (2016–2020), deep learning and IoT integration (2021–2023), and multimodal and large language model era (2024–2026). Technical impacts dominated the corpus (accuracy improvements of 18–35%), while social and economic impact studies remain critically underrepresented (2.6% and 0.9%, respectively). Key research gaps identified include poor model interpretability (black-box problem), limited integration with decision support systems (DSS), inadequate tropical-specific model development, and the near-total absence of longitudinal impact evaluations. This study contributes a holistic synthesis integrating technological evolution with multidimensional impact analysis, offering strategic recommendations for developing more adaptive, transparent, and equitable AI rainfall prediction systems aligned with SDG 2, SDG 13, and SDG 15
Co-Authors Ika Safitri Windiarti Lili Tanti Muhammad Farel Aditiya Muhammad Lukman Hakim Rahmadani Pane Ramadani Thanri, Yan Yang