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All Journal Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer INOVTEK Polbeng - Seri Informatika
Wirta Agustin
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.
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
Togaf Analysis in Bengkalis State Polytechnic Laboratory Information Systems Design Khairus suhada; Wirta Agustin; Rahmaddeni; Unang Rio
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/0hz5q194

Abstract

Laboratories in educational institutions have an important role in supporting learning and research. However, effective and efficient laboratory management is still a challenge, especially in recording inventory and managing consumables (BHP). The Bengkalis State Polytechnic Informatics Engineering Laboratory already has an application system, but there are still limitations in recording monthly BHP usage, borrowing facilities, and proposing equipment procurement. This research aims to design a blueprint for an integrated laboratory information system using The Open Group Architecture Framework (TOGAF) Architecture Development Method (ADM). This approach is applied to phase F: Migration Planning, which includes requirements analysis, business architecture design, information system architecture, as well as strategy and migration implementation. The results of this research produce a blueprint for a system information laboratory that includes application design, technology recommendations, and implementation stages that can be used as a guide in system development. This blueprint is expected to increase laboratory management efficiency by optimizing inventory recording, procurement planning and maintenance of laboratory services. In addition, the TOGAF ADM approach used can be adapted for laboratories in other educational settings that have similar needs.
Co-Authors Aguston Hadi Asnal Helda Yenni Khairus suhada M. Azzuhri Dinata Rahmaddeni Unang Rio Yoga Safitra Anugraha