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All Journal Journal of System and Computer Engineering Bulletin of Information Technology (BIT)
Arisha, Andriansyah Oktafiandi
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Computer Science & IT Decision Sciences, Operations Research & Management

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Crop Recommendation Based on Soil and Weather Conditions Using the K-Nearest Neighbors Algorithm Yuliyanto, Yuliyanto; Sahibu, Supriadi; Imran, Taufik; Arisha, Andriansyah Oktafiandi; Munawirah, Munawirah
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.1955

Abstract

The national food self-sufficiency program demands innovation in optimizing the selection of agricultural commodities based on environmental and weather conditions. This challenge is rooted in a fundamental problem faced by farmers—achieving harmony among soil characteristics, weather patterns, and suitable crops. In support of this initiative, it is necessary to develop a crop recommendation system based on machine learning that utilizes key soil and weather condition parameters. This study employs the K-Nearest Neighbors (KNN) algorithm, which functions by identifying the optimal value of ‘K’ to maximize classification accuracy. The KNN algorithm is implemented in a crop recommendation system to classify 1,100 datasets representing ideal growing conditions for 11 crop types. These datasets were generated using a normal distribution approach with a 5% variation from the mean values, and were validated using a clipping function to ensure the data remained within ideal ranges. The results of this study demonstrate that the KNN algorithm achieves high accuracy 96,67% in utilizing soil and weather parameters to generate crop recommendations. The average probability score for the recommended crops was 83.33%. Based on experimental testing, rice was recommended during the rainy and extreme rainy seasons, soybeans were recommended during the dry season, and mung beans were most suitable during extreme dry conditions.
Implementasi Naïve Bayes untuk Klasifikasi Peminatan Program Studi pada Penerimaan Mahasiswa Baru di Fakultas Ilmu Komputer Unika Munawirah, Munawirah; Arisha, Andriansyah Oktafiandi
Bulletin of Information Technology (BIT) Vol 6 No 3: September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i3.2142

Abstract

Fakultas Ilmu Komputer Universitas Tomakaka memiliki dua program studi, yaitu Sistem Informasi dan Teknik Informatika. Namun, dalam praktiknya, calon mahasiswa baru sering mengalami kebingungan dalam menentukan jurusan yang sesuai dengan kemampuan dan latar belakang akademiknya. Pemilihan program studi umumnya didasarkan pada tren jurusan favorit, dorongan eksternal, atau preferensi sosial tanpa mempertimbangkan jurusan asal di sekolah sebelumnya. Kondisi tersebut berpotensi menimbulkan ketidaksesuaian minat yang berdampak pada risiko penurunan motivasi belajar, pindah jurusan, berhenti kuliah, atau mengalami hambatan selama masa studi. Penelitian ini bertujuan untuk mengembangkan sistem rekomendasi program studi menggunakan metode klasifikasi Naïve Bayes guna memprediksi kecenderungan peminatan program studi berdasarkan atribut input seperti jenis kelamin, asal sekolah, dan jurusan asal sekolah. Dataset yang digunakan merupakan data historis penerimaan mahasiswa baru Fakultas Ilmu Komputer Universitas Tomakaka sejak tahun akademik 2015/2016 hingga 2024/2025, sebanyak 1.046 entri data. Proses analisis mencakup tahapan data mining, mulai dari seleksi dan pembersihan data, pembagian data latih dan data uji (80:20), hingga evaluasi performa menggunakan metode Confusion Matrix. Hasil evaluasi menunjukkan akurasi sebesar 87,14%, presisi 89,91%, recall 87,70%, dan F1-score 88,76%. Model ini diimplementasikan ke dalam aplikasi berbasis website menggunakan framework Flask, guna mempermudah pemberian rekomendasi jurusan secara real-time. Pendekatan ini memberikan kontribusi sistem rekomendasi berbasis data yang membantu institusi dalam memetakan minat mahasiswa, menyusun strategi promosi yang tepat sasaran, serta memberikan intervensi awal terhadap pilihan program studi mahasiswa baru yang kurang sesuai.
Co-Authors Imran, Taufik Munawirah, Munawirah Sahibu, Supriadi Yuliyanto Yuliyanto