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JURIKOM (Jurnal Riset Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 2407389X     EISSN : 27157393     DOI : http://dx.doi.org/10.30865/jurikom.v7i1.1927
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JURIKOM (Jurnal Riset Komputer) membahas ilmu dibidang Informatika, Sistem Informasi, Manajemen Informatika, DSS, AI, ES, Jaringan, sebagai wadah dalam menuangkan hasil penelitian baik secara konseptual maupun teknis yang berkaitan dengan Teknologi Informatika dan Komputer. Topik utama yang diterbitkan mencakup: 1. Teknik Informatika 2. Sistem Informasi 3. Sistem Pendukung Keputusan 4. Sistem Pakar 5. Kecerdasan Buatan 6. Manajemen Informasi 7. Data Mining 8. Big Data 9. Jaringan Komputer 10. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informati dan komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 902 Documents
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Perbandingan Kinerja SVM, Random Forest dan XGBoost pada Aplikasi Access by KAI Menggunakkan ADASYN Epriyanti, Nadia; Meiriza, Allsela; Yunika Hardiyanti, Dinna
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.9139

Abstract

The rapid growth of digital applications has heightened the need to understand user perceptions more thoroughly, particularly through sentiment analysis of user-generated reviews. In practice, sentiment classification often faces challenges related to class imbalance, especially when neutral reviews are significantly fewer than positive or negative ones. This imbalance can limit a model’s ability to accurately detect all sentiment categories. This study examines the comparative performance of three machine learning algorithms Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) by applying the Adaptive Synthetic Sampling (ADASYN) technique to address class imbalance. This study differs from previous similar research by conducting a simultaneous comparative analysis of three algorithms using the ADASYN method in the context of Access by KAI application reviews, which has not been examined in prior studies. Experimental results indicate that after implementing ADASYN, model accuracies reached 75.17% for SVM, 84.06% for RF, and 83.17% for XGBoost. Although accuracy slightly decreased after oversampling, the F1-scores for the neutral class improved to 0.13 (SVM), 0.05 (RF), and 0.14 (XGBoost). Before applying ADASYN, the models achieved accuracies of 85.88% (SVM), 85.13% (RF), and 85.37% (XGBoost), but they were unable to effectively recognize neutral sentiments, with F1-scores of 0.00 for SVM and RF, and 0.03 for XGBoost. These findings suggest that ADASYN enhances model sensitivity to neutral sentiment, with XGBoost demonstrating the most consistent and robust performance in sentiment classification for the Access by KAI application.
Penerapan Knowledge Discovery dalam Perbandingan Kinerja LSTM, Random Forest, dan SVR untuk Peramalan Harga Beras Provinsi Sumatera Selatan Bahri, Cheisya Andini; Tania, Ken Ditha
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.9140

Abstract

Rice is a primary staple food in Indonesia, particularly in South Sumatra Province. In February 2024, BBC News Indonesia reported that the price of premium rice surged to Rp18,000 per kilogram, marking the highest price in the country’s history. To anticipate and predict similar spikes in the future, this study applies a Knowledge Discovery approach and compares three machine learning models: LSTM, Random Forest, and SVR. The approach follows the stages of data selection, cleaning, transformation, modeling, and evaluation to uncover hidden patterns in historical data. The dataset, obtained from the official PIHPS Nasional website, consists of 1,412 daily rice price records from January 2020 to May 2025. Model performance was evaluated using MAPE, MAE, and RMSE metrics. The findings indicate that the SVR model outperformed LSTM and Random Forest, delivering the most accurate results. For the Super Quality II rice category, SVR achieved a MAPE of 0.00 percent, MAE of 40.93, and RMSE of 52.54. SVR also consistently produced the lowest prediction errors in other categories, such as Low Quality I (MAE 59.39) and Medium Quality I (MAE 38.92). This research is expected to serve as a foundation for developing machine learning–based food price monitoring systems to support more responsive policies and maintain rice price stability in the future.

Page 91 of 91 | Total Record : 902

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