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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Nuansa Informatika Telematika JUITA : Jurnal Informatika Indonesian Journal on Computing (Indo-JC) JETT (Jurnal Elektro dan Telekomunikasi Terapan) JOIV : International Journal on Informatics Visualization Jurnal Informatika Jurnal Pilar Nusa Mandiri Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Mantik Jurnal Teknik Informatika C.I.T. Medicom International Journal of Advances in Data and Information Systems EKONOMI, KEUANGAN, INVESTASI DAN SYARIAH (EKUITAS) Tematik : Jurnal Teknologi Informasi Komunikasi Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian Masyarakat Nusantara Jurnal Abdimas Kartika Wijayakusuma Journal of Dinda : Data Science, Information Technology, and Data Analytics Naratif : Jurnal Nasional Riset, Aplikasi dan Teknik Informatika AJAD : Jurnal Pengabdian kepada Masyarakat Indonesian Journal of Business Analytics (IJBA) Formosa Journal of Applied Sciences (FJAS) Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC) Jurnal Pengabdian Tri Bhakti International Journal of Computer Technology and Science Bulletin of Intelligent Machines and Algorithms
Nur Alamsyah
Fakultas Ilmu Komputer PASIM
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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A stacking ensemble model with SMOTE for improved imbalanced classification on credit data Nur Alamsyah; Budiman Budiman; Titan Parama Yoga; R. Yadi Rakhman Alamsyah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 3: June 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i3.25921

Abstract

This research is based on a significant problem in credit risk analysis in the banking sector caused by class imbalance. We face the problem of the model’s inability to accurately identify risks in the ‘‘Charged Off’’ class. As a solution, we propose a stacked ensemble approach that utilizes synthetic minority over-sampling technique (SMOTE) to balance the class distribution. Experiments were conducted by applying SMOTE to the training data before training the credit model using gradient boosting (XGBoost) and random forest (RF) algorithms in a single ensemble. The results show significant improvements in precision, recall, and F1-score after applying SMOTE on the unbalanced classes. The updated model achieved a striking accuracy rate of 0,97 on resampled training data. This re-search clearly identifies the problem of class imbalance as a major challenge in credit risk analysis. The application of SMOTE in a stacked ensemble was found to be effective in improving model performance, making a valuable contribution to the development of more reliable credit models for better risk management and revenue generation in financial institutions.
XGBoost optimization using hybrid Bayesian optimization and nested cross validation for calorie prediction Budiman Budiman; Nur Alamsyah; Titan Parama Yoga; R Yadi Rakhman Alamsyah; Elia Setiana
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26554

Abstract

Accurately predicting calorie expenditure is crucial for wearable device applications, enabling personalized fitness and health recommendations. However, traditional models struggle with high data variability and nonlinear relationships in activity data, leading to suboptimal predictions. This study addresses these challenges by integrating extreme gradient boosting (XGBoost) with Bayesian optimization and nested cross validation to enhance predictive accuracy. Unlike previous approaches, our method systematically tunes hyperparameters using Bayesian optimization while employing nested cross validation to prevent overfitting, ensuring robust model evaluation. We utilize a dataset of daily activity records, including steps, distance, and active minutes, extracted from wearable devices. Our experimental findings indicate a substantial enhancement in prediction performance, achieving a mean squared error (MSE) of 4294.27, an Rsquared (R2) score of 0.9917, and a root mean squared error (RMSE) of 65.53. The proposed model outperforms baseline approaches such as random forest and support vector machines in terms of predictive accuracy. These findings underscore the advantage of our approach in predictive modeling. Beyond calorie estimation, the proposed methodology is adaptable to other domains requiring high-precision predictions, such as healthcare analytics and personalized recommendation systems.
REINFORCEMENT LEARNING-BASED DYNAMIC PRICING IN A STOCHASTIC DEMAND–SUPPLY ENVIRONMENT Nur Alamsyah; Budiman; Almira Nurchawilah; Wala Erpurini
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8286

Abstract

Dynamic pricing in ride-sharing platforms must balance revenue generation with stable pricing decisions under changing demand and supply. This study aims to develop and evaluate a reinforcement learning-based dynamic pricing policy that maximizes expected revenue while reducing abrupt policy-level price adjustments. A stochastic contextual environment was constructed from 1,000 historical ride records and evaluated using a leakage-safe 70/15/15 train-validation-test split. The agent was trained with Proximal Policy Optimization (PPO) using five discrete price adjustments from -10% to +10%. Expected revenue was combined with a multiplier-based stability penalty, where stability was measured from changes in the price multiplier rather than nominal price variation across heterogeneous rides. Across 30 paired test episodes, the PPO policy achieved a cumulative reward of 103,316.25 +/- 3,243.99 and expected revenue of 103,449.18 +/- 3,241.27, significantly exceeding static pricing (p < 0.001). Relative to rule-based surge pricing, PPO produced statistically indistinguishable cumulative reward (p = 0.808) while reducing multiplier volatility by 24.83%, mean absolute multiplier change by 24.21%, and action switch rate by 10.97% (all p < 0.001). These results indicate that PPO can preserve near-surge revenue while producing smoother dynamic pricing decisions within the simulated environment.
FINANCIAL DISTRESS PREDICTION USING THE DECISION TREE METHOD IN MANUFACTURING COMPANIES IN INDONESIA Ayi Mohamad Sudrajat; Hani Fitria Rahmani; Nur Alamsyah
International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC) Vol. 4 No. 4 (2026): August
Publisher : ZILLZELL MEDIA PRIMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61990/ijamesc.v4i4.828

Abstract

This study aims to predict financial distress in Indonesian manufacturing companies using the Decision Tree method. A quantitative predictive design was applied to secondary data from the annual financial statements of manufacturing companies listed on the Indonesia Stock Exchange during 2020–2024, yielding 612 firm-year observations. Financial distress was measured as a binary outcome (financially distressed versus non-financially distressed), with Current Ratio, Debt to Asset Ratio, Return on Assets, Total Asset Turnover, Sales Growth, and Firm Size as predictors. Using Python, a pruned Decision Tree achieved 84.6% accuracy, 64.7% precision, 75.9% recall, and a 69.8% F1-score for the financially distressed class. Return on Assets was the most influential predictor, followed by Debt to Asset Ratio and Current Ratio. The resulting rules show that distress reflects interacting conditions of weak profitability, high leverage, low liquidity, inefficient asset utilization, and declining sales growth. Theoretically, the study extends financial distress prediction research by demonstrating the value of interpretable machine learning in an emerging-market manufacturing context. Practically, its transparent rules provide an actionable early-warning tool for investors, creditors, managers, and regulators to identify financial vulnerability and support timely intervention.
Co-Authors Acep Hendra Aji Gautama Putrada Alamsyah, R Yadi Rakhman AlFauzi, Ihsan Almira Nurchawilah Amelia Tifany Dewi Arnold Ropen Sinaga Arnold Ropen Sinaga Ayi Mohamad Sudrajat Budi Sulistiyo Jati Budiman Danestiara, Venia R Della Octa Irmulvani Devi Silaen Dirham Triyadi Eli Nofita Sari Elia Setiana Erpurini, Wala Fauzan, Mohamad Nurkamal Habibi, Chairul Hani Fitria Rahmani Hani Fitria Rahmani Hasan Nuraripin Hermawan, Arief Karditya Ikke Dian Oktaviani Indah Susilawati Ismi Nur Muhamad Jennifer Kaunang, Valencia Claudia Kaunang, Valencia Kaunang, Valencia Claudia Jennifer Leonandri, Dino Gustaf Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Muhamad Andriansyah Nurcahya Muhamad Nurkamal Fauzan Muhammad Yusup Iskandar Muhiban, Ayi Mutaqin Akbar Nayla Nurul Azkiya Nur Imam Febrianto Nursyanti, Reni PARAMA YOGA, TITAN R Yadi Rakhman Alamsyah R. Yadi Rakhman Alamsyah Reni Nursyanti Reni Nursyanti Rijwan Rijwan Rimbun Kencana RIO ANDRIYAT KRISDIAWAN Setiana, Elia Sidik Prabowo Silvana Anggraeni, Zulmeida Syafrial Fachri Pane Tiara Nurhasanah Titan Parama Titan Parama Yoga Titan Parama Yoga Tito Sugiharto Tutik Ultsa Rahmatika Valencia Claudia Jennifer Venia Restreva Danestiara Wala Erpurini Wala Erpurini Wala Erpurini Wia Handayani Yeni