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All Journal Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Helda Yenni
Universitas Sains Dan Teknologi Indonesia
Computer Science & IT Control & Systems Engineering

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MODEL PREDIKSI PRODUKTIVITAS PADI MENGGUNAKAN XGBOOST DAN RANDOM FOREST Yoga Safitra Anugraha; Helda Yenni; Wirta Agustin; Hadi Asnal
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i1.4169

Abstract

Rice is a strategic commodity in ensuring national food security in Indonesia. Predicting rice productivity is a critical issue due to the decreasing harvest area and fluctuating production. This study aims to develop and compare the performance of two machine learning algorithms, namely Extreme Gradient Boosting (XGBoost) and Random Forest, in predicting rice productivity based on harvest area and total production data. The dataset consists of rice productivity data from 38 provinces in Indonesia over the period 2018 to 2024. The models were evaluated using three data splitting ratios (70:30, 80:20, and 90:10) and four evaluation metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). The results show that both models perform well, with Random Forest achieving the highest R² value of 0.887 and the lowest RMSE of 2.939 on the 90:10 split, indicating higher accuracy. XGBoost, while slightly lower in accuracy (R² = 0.781), produced more stable predictions across varying input scales. When tested on new data, both models showed consistent performance, demonstrating generalization capabilities. These findings indicate that machine learning models are effective in modeling and forecasting agricultural productivity and can serve as decision-support tools for policymakers and agricultural stakeholders. The models can be utilized for strategic planning, resource allocation, and improving agricultural productivity in the future.
Implementasi Algoritma Regresi Linear Untuk Memprediksi Harga Laptop Risky Harahap; Karpen,; Helda Yenni; Muhamad Jamaris
BETRIK Vol. 16 No. 02 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/rnnp7x70

Abstract

The development of laptop technology has driven the need for accurate price predictions to assist consumers in making purchasing decisions appropriately and efficiently. This study implements a Linear Regression algorithm to predict laptop prices based on 4 main features including Brand, Processor, RAM, and GPU. The dataset used consists of 11,768 data obtained from the Kaggle platform which is processed through preprocessing, feature transformation, and model evaluation stages with various performance metrics. The analysis results show that the RAM feature has the most significant influence on laptop prices, followed by Processor, Brand, and GPU. The developed Linear Regression model successfully achieved an R-squared value of 0.6453, which indicates that the model is able to explain 64.53% of the variation in laptop prices based on the analyzed features. This study contributes to the development of an accurate laptop price prediction system and provides a practical tool to support data-based purchasing decisions effectively and efficiently.
Prediksi Jumlah Titik Ruang Terbuka Hijau (RTH) Menggunakan Metode Regresi Linier dan Model Random Forest M. Azzuhri Dinata; Helda Yenni; Wirta Agustin; Aguston
BETRIK Vol. 16 No. 02 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/d81adt50

Abstract

The development and preservation of Green Open Space (GOS) is an important part of maintaining environmental balance, especially in the Sumatra Ecoregion. This study aims to predict the number of GOS points using a linear regression approach and the Random Forest algorithm. The data used include variables such as area and forest area from several provinces in Sumatra. Model performance evaluation was carried out using MAE, RMSE, and coefficient of determination (R²) metrics. The analysis results show that the Random Forest model has superior performance compared to linear regression, with an MAE value of 5.52, RMSE of 5.88, and R² of 0.74. Meanwhile, linear regression was only able to achieve an R² of 0.45. These findings indicate that Random Forest is more effective in capturing non-linear data patterns and more accurate in predicting the number of GOS points. This study contributes to the use of data science technology to support sustainable environmental planning, as well as becoming a basis for data-based spatial planning policy making
Co-Authors Aguston Hadi Asnal Karpen, M. Azzuhri Dinata Muhamad Jamaris Risky Harahap Wirta Agustin Yoga Safitra Anugraha