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All Journal Sisfo: Jurnal Ilmiah Sistem Informasi
Mira Humaira
Universitas Malikussaleh
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Library & Information Science Mechanical Engineering

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Analisis Data Spasial Berbasis Sistem Informasi Geografis untuk Optimalisasi Produktivitas Padi dan Efisiensi Penggunaan Sumber Daya Lahan Usnawiyah Usnawiyah; Khaidir Khaidir; Mira Humaira; Tengku Dhean
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 9 No. 2 (2025): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2025
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/.v9i2.26014

Abstract

Produktivitas padi di Indonesia masih menghadapi tantangan dalam memenuhi kebutuhan pangan nasional yang terus meningkat. Kabupaten Aceh Utara, sebagai salah satu sentra produksi padi di Provinsi Aceh, menunjukkan variasi produktivitas antar wilayah yang mengindikasikan adanya ketidakefisienan dalam pemanfaatan sumber daya lahan. Penelitian ini bertujuan untuk menganalisis distribusi spasial produktivitas padi dan efisiensi penggunaan lahan berbasis Sistem Informasi Geografis (SIG) di Kabupaten Aceh Utara. Analisis dilakukan menggunakan data sekunder yang mencakup produktivitas padi tingkat kecamatan, karakteristik lahan, topografi, curah hujan, serta citra satelit untuk analisis kondisi vegetasi, yang diolah melalui pemetaan tematik dan overlay spasial. Hasil penelitian menunjukkan bahwa variasi produktivitas padi dipengaruhi terutama oleh kondisi biofisik lahan, khususnya jenis tanah dan topografi, dimana wilayah dengan tanah aluvial dan topografi datar cenderung memiliki produktivitas dan efisiensi penggunaan lahan yang lebih tinggi. Pendekatan SIG memungkinkan identifikasi zona prioritas optimalisasi produktivitas padi secara spasial dan terukur. Hasil penelitian menunjukkan bahwa faktor-faktor lingkungan tersebut memiliki pengaruh signifikan terhadap tingkat produktivitas padi, dan analisis spasial melalui SIG dapat memberikan informasi yang lebih akurat untuk perencanaan pertanian yang lebih efisien.
Analysis of Machine Learning-Based Classification Models for Determining Fertilizer Types for Rice Crop Growth: Machine Learning Approach for Optimizing Fertilizer Selection in Rice Cultivation Mira Humaira; Almuna Ramadhani; Uchti Nuzul Qhinanti Lubis; Fadhliani Fadhliani; Septiarini Zuliati; Usnawiyah Usnawiyah
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27283

Abstract

Determining the appropriate fertilizer type is essential for supporting rice plant growth and optimizing agricultural productivity. However, conventional fertilization practices still rely heavily on empirical judgment and often neglect dynamic soil and plant growth characteristics. This study aims to analyze and compare the performance of several machine learning classification models for fertilizer type determination in rice cultivation. The study employed a computational experimental approach adapted from the CRISP-DM framework using a dataset of 480 records consisting of soil and rice growth parameters, including Nitrogen (N), Phosphorus (P), Potassium (K), soil pH, moisture, and plant height. Five classification algorithms were evaluated, namely Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree, Support Vector Machine (SVM), and Random Forest. Model performance was assessed using accuracy, precision, recall, and F1-score, combined with Stratified k-Fold Cross Validation. The results showed that Random Forest achieved the best performance with an accuracy of 95.83%, precision of 95.54%, recall of 95.12%, and F1-score of 95.33%. These findings indicate that ensemble learning methods are more effective in handling heterogeneous and multivariable agricultural data than conventional classification approaches. This study contributes to the development of machine learning-based classification analysis for more accurate and data-driven fertilizer determination in rice cultivation.
Co-Authors Almuna Ramadhani Fadhliani, Fadhliani Khaidir Khaidir Septiarini Zuliati Tengku Dhean Uchti Nuzul Qhinanti Lubis Usnawiyah