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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Hamdani
Universitas Sains dan Teknologi Indonesia
Computer Science & IT Engineering

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Model Prediksi Jumlah Produksi Kelapa Sawit Menggunakan Regresi Linear Berganda di PT.Surya Argolika Reksa Irpan M irpan; Unang Rio; Karpen; Hamdani
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/dv9sd116

Abstract

Palm oil plantations are one of the strategic sectors in Indonesia’s agribusiness industry. To support production efficiency and effectiveness, a predictive model capable of accurately estimating production volume based on supporting factors is required. This study aims to develop a prediction model for palm oil production using the Multiple Linear Regression algorithm by utilizing variables such as land area (Ha), number of trees, and rainfall. The data were obtained from the operational reports of PT. Surya Argolika Reksa. The model evaluation was conducted using two data splitting scenarios: 80:20 and 70:30. The evaluation results show that for the 80:20 test data, the MAE value was 30,095.68, the MSE was 1,533,325,063.46, and the RMSE was 39,151.33. Meanwhile, for the 70:30 test data, the MAE value was 35,455.01, the MSE was 2,096,902,404.44, and the RMSE was 45,791.95. These values indicate the level of prediction error of the model in units of palm oil production. This research contributes to supporting more accurate production planning in the palm oil plantation sector based on data analysis.
STACKING ENSEMBLE MACHINE LEARNING MODEL FOR EARLY DETECTION OF CHRONIC KIDNEY DISEASE IN INDONESIA Agusviyanda; Hamdani; M. Khairul Anam; Agustin; M. Ikhsan Wibowo
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6501

Abstract

Chronic Kidney Disease (CKD) is one of the major health problems that continues to rise and requires accurate early detection to prevent progression to end-stage renal failure. This study proposes a hybrid machine learning approach to automatically detect CKD by combining data balancing techniques, ensemble learning, and cross-validation. The dataset used was obtained from the Kaggle platform, consisting of 1,089 patient records, and was balanced using the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Three boosting algorithms—Adaboost, XGBoost, and LightGBM—were used as base models and combined through a stacking approach with Logistic Regression as the meta-classifier. Evaluation was conducted using a 5-fold cross-validation scheme with accuracy, precision, recall, and F1-score as performance metrics. The results show that the stacking model achieved an average accuracy of 99.40%, outperforming individual models (LightGBM: 98.87%; Adaboost: 98.76%; XGBoost: 98.61%) and exceeding the performance of several previous studies. These findings indicate that the stacking approach, when combined with SMOTE and cross-validation, significantly enhances classification performance for CKD detection.
Co-Authors Agustin Agusviyanda Anam, M Khairul Irpan M irpan Karpen M. Ikhsan Wibowo Unang Rio