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All Journal Jurnal Pilar Nusa Mandiri Computer Science (CO-SCIENCE)
Yan Rianto
Nusamandiri University
Computer Science & IT

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Cross-Project Defect Prediction on AEEEM Using SMOTE–Tomek and Ensemble learning Fina Sifaul Nufus; Yan Rianto
Computer Science (CO-SCIENCE) Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i2.12521

Abstract

Cross-project defect prediction (CPDP) aims to predict software defects in a target project using data from other projects. However, most existing CPDP approaches rely on complex frameworks, such as transfer learning or sophisticated multi-source integration, making them difficult to reproduce and apply in practical software engineering environments. This study proposes a simple yet integrated hybrid preprocessing pipeline consisting of feature normalization, Principal Component Analysis (PCA), SMOTE–Tomek balancing, and decision threshold tuning to improve CPDP performance on the AEEEM dataset. Experiments were conducted under both single-source and multi-source CPDP scenarios using Random Forest (RF) and Support Vector Machine (SVM) classifiers. Performance was evaluated using the F1 Score and the Area Under the Curve (AUC). The experimental results demonstrate that the proposed approach improves prediction performance, particularly under the multi-source CPDP scenario. Compared with the more complex MSCPDP approach, the proposed method achieved a higher F1-score on four out of five target projects and consistently outperformed MSCPDP on all five projects in terms of AUC. Furthermore, the experimental analysis indicates that decision threshold tuning contributed more significantly to performance improvement than class balancing alone. In contrast, the combination of threshold tuning and SMOTE–Tomek yielded the best overall performance. These findings provide empirical evidence that a simple, reproducible preprocessing pipeline can effectively improve CPDP performance without requiring complex learning frameworks.
COMPARISON OF ARIMA, LSTM, AND GRU MODELS FOR FORECASTING SALES OF HIT AEROSOL PRODUCTS Nendi Sunendar; Yan Rianto
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6412

Abstract

A more accurate forecasting model, such as LSTM, can significantly enhance business efficiency by providing more reliable predictions of future sales, allowing for better inventory management, optimized production schedules, and more precise distribution planning. This leads to reduced costs, minimized stockouts, and improved customer satisfaction. This study evaluates the forecasting performance of ARIMA, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models using sales data from 2021 to 2023. The models are assessed based on Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Results show that LSTM outperforms the other models with a MAPE of 10.76%, followed by ARIMA at 11.23% and GRU at 11.47%. These findings highlight the advantages of deep learning methods, particularly LSTM, in capturing complex patterns and trends in time series data. The study demonstrates the potential of these models to optimize sales forecasting, aiding decision-making processes in production and distribution planning.
APPLICATION OF ARTIFICIAL NEURAL NETWORK METHODS TO DETECT HEART ATTACKS Nasir Hamzah; Yan Rianto
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6413

Abstract

A heart attack is a medical emergency caused by restricted blood flow to the heart, commonly leading to myocardial infarction due to blood clots or fat accumulation. Early detection of heart disease is crucial to support prevention efforts and assist healthcare professionals in timely diagnosis and treatment. This study applies the Backpropagation Neural Network (BPNN) algorithm as an intelligent computing method for heart attack detection. Experimental results demonstrate a prediction accuracy of 96.47%, confirming the effectiveness of artificial neural networks in identifying heart attacks in patients. These findings highlight the potential of BPNN as a reliable and precise early detection system, which can support more accurate clinical decision-making and improve the effectiveness of heart attack prevention and treatment.
Co-Authors Fina Sifaul Nufus Nasir Hamzah Nendi Sunendar