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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Informatika: Jurnal Pengembangan IT
Raissa Amanda Putri
State Islamic University of North Sumatra
Computer Science & IT

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Prediksi Hasil Panen Karet di Gunung Tua Menggunakan Support Vector Machine Siti Khairunnisa Siregar; Raissa Amanda Putri; Muhammad Furqan
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10040

Abstract

Penelitian ini bertujuan untuk memprediksi hasil panen karet di wilayah Gunung Tua, Kabupaten Padang Lawas Utara, dengan menggunakan algoritma Support Vector Machine (SVM). Produksi karet dipengaruhi oleh berbagai faktor musiman dan kondisi lingkungan yang menyebabkan fluktuasi hasil panen, sehingga menyulitkan perencanaan bagi petani maupun instansi terkait. Penelitian ini menerapkan pendekatan supervised learning dengan metode Support Vector Regression (SVR) untuk memodelkan prediksi hasil panen karet berdasarkan data produksi historis yang diperoleh dari instansi pertanian setempat. Tahapan penelitian meliputi pengumpulan data, prapemrosesan, normalisasi data, pelatihan model, dan pengujian. Evaluasi kinerja model dilakukan menggunakan Root Mean Square Error (RMSE) sebagai indikator tingkat kesalahan prediksi. Hasil penelitian menunjukkan bahwa model SVM mampu memprediksi hasil panen karet dengan nilai RMSE sebesar 191 dan tingkat akurasi sebesar 96,2%, yang menunjukkan bahwa model memiliki performa yang baik dalam menangkap pola data produksi. Dengan demikian, algoritma Support Vector Machine dapat dimanfaatkan sebagai alat pendukung pengambilan keputusan dalam perencanaan dan pengelolaan produksi pertanian karet
Predicting Student Learning Outcomes in Vocational Computer and Network Engineering Using Naïve Bayes Lailam Baridah; Raissa Amanda Putri
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 8 No. 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

This study applied the Naïve Bayes algorithm to predict student learning outcomes in the Basic Computer and Network Engineering subject at SMKN 1 Sipispis. A quantitative approach was employed, using data from 311 students, which consisted of both academic variables (assignments, midterm exams, and final exams) and non-academic variables (attendance, attitude, and learning interest). The dataset was preprocessed by cleaning, encoding, and splitting into training and testing sets using several ratios (90/10, 80/20, 70/30, and 60/40). The Naïve Bayes model was trained and evaluated using accuracy, precision, recall, and F1-score metrics. The best performance was achieved with the 80/20 data split, yielding an accuracy of 74.6%, demonstrating the model’s ability to capture probabilistic relationships between academic and non-academic factors. These findings indicate that the Naïve Bayes algorithm can effectively classify student performance levels such as Fair, Good, and Excellent, providing a reliable foundation for an automated decision support system. The developed web-based system can help teachers identify students at risk of declining performance early, enabling more adaptive and data-driven educational interventions
Co-Authors Lailam Baridah Muhammad Furqan Siti Khairunnisa Siregar