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All Journal Jurnal Vokasi Sisfo: Jurnal Ilmiah Sistem Informasi
Uchti Nuzul Qhinanti Lubis
Universitas Malikussaleh
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Library & Information Science Mechanical Engineering Social Sciences

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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.
Pendampingan Pembibitan dan Pemeliharaan Tanaman Kakao Pada Masyarakat Gampong Paya Gaboh Kecamatan Sawang Hafifah Hafifah; Nasruddin Nasruddin; Lukman Lukman; Fadhliani Fadhliani; Septiarini Zuliati; Uchti Nuzul Qhinanti Lubis; Mukhaiyar Azhari; Mahlil Mahlil
Jurnal Vokasi Vol 10, No 1 (2026): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/vokasi.v10i1.8299

Abstract

Kakao (Theobroma cacao) merupakan tanaman perkebunan bernilai ekonomi tinggi yang berperan sebagai bahan baku utama dalam industri cokelat. Di Indonesia, termasuk Kabupaten Aceh Utara, potensi pengembangan kakao tergolong besar. Namun, produktivitasnya masih rendah akibat terbatasnya bibit unggul yang tersedia. Untuk mengatasi permasalahan ini, telah dilaksanakan suatu kegiatan peningkatan kapasitas dalam teknik pembibitan kakao yang sesuai standar. Kegiatan dilaksanakan di Gampong Paya Gaboh, Kecamatan Sawang, Kabupaten Aceh Utara, pada Agustus hingga Oktober 2025. Metode yang diterapkan meliputi penyuluhan, pelatihan, demonstrasi, serta pendampingan teknis secara berkala. Sebelum pelaksanaan, dilakukan survei lokasi dan sosialisasi kepada masyarakat dan perangkat desa setempat. Materi pelatihan mencakup pemilihan biji, teknik persemaian, pembuatan media tanam, teknik sambung pucuk, dan pengendalian hama penyakit. Bahan praktik seperti media tanam, bibit kakao, serta perlengkapan sambung pucuk disiapkan untuk mendukung kegiatan. Hasil pelaksanaan menunjukkan antusiasme tinggi dari peserta dan minimnya pengetahuan awal terkait teknologi pembibitan kakao yang memenuhi standar mutu. Diharapkan, keterampilan teknis yang diperoleh dapat dimanfaatkan untuk menghasilkan bibit kakao berkualitas secara mandiri, guna meningkatkan produktivitas kebun dan mendukung pembangunan pertanian yang berkelanjutan
Co-Authors Almuna Ramadhani Fadhliani, Fadhliani Hafifah Hafifah Lukman Lukman Mahlil Mahlil Mira Humaira Mukhaiyar Azhari Nasruddin Septiarini Zuliati Usnawiyah