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All Journal INOVTEK Polbeng - Seri Informatika
Agus Purnomo
University of Logistics and International Business (ULBI), Bandung
Computer Science & IT

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A PSO-Optimized Stacking Ensemble with Hybrid SMOTE-NC and Tomek Links for Bid-Based Winning Prediction in Procurement Projects Kokoh Handoko; Agus Purnomo; Erna Mulyati
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/dft7w786

Abstract

This research aims to establish a classification model for the prediction of procurement winning outcomes based on bid value and owner cost estimation data. The main challenge in procurement analysis lies in severe class imbalance and complex non-linear relationships among pricing and procurement attributes. The research object utilizes procurement tender data from PT Pos Indonesia, including project owner, owner cost estimation, bid value, and procurement method. The proposed approach integrates hybrid SMOTE and Tomek links for class balancing, regulation-driven feature engineering, and a stacking ensemble model optimized using particle swarm optimization. The stacking framework combines Random Forest, Extra Trees, and Gradient Boosting as base learners. The experimental evaluation demonstrates that the proposed approach delivers the strongest performance, achieving an AUC of 0.92, an accuracy of 0.89, and an F1-Macro of 0.81, thereby surpassing all individual classifiers and homogeneous ensemble methods considered in this study. This study concludes that the hybrid optimization-based ensemble approach is effective for improving procurement winning prediction accuracy and provides a reliable decision-support tool for data-driven and regulation-compliant procurement processes.
Applying Clustering Techniques for Customer Segmentation Based on Shipping Behavior, Cost, and Satisfaction in Logistics Services Jaka Sunara; Agus Purnomo; Maniah
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/h62gy548

Abstract

In modern logistics operations, behavioral data-based customer segmentation plays a crucial role in optimizing service delivery and achieving competitive differentiation. This study proposes a clustering-based approach using K-Means, Agglomerative, and Gaussian Mixture to segment sender-level customer profiles in a logistics network based on shipping cost and delivery duration, while customer satisfaction is used for post-cluster profiling and interpretive analysis. A comprehensive preprocessing pipeline is implemented, including temporal feature engineering and sender-based statistical aggregation. Grid search is used for hyperparameter tuning, and clustering performance is evaluated using the Silhouette Score, Calinski-Harabasz Index, and Davies-Bouldin Index. The results indicate that K-Means with two clusters achieves the highest silhouette score (0.843), outperforming the aggregative and Gaussian mixture models. Principal Component Analysis (PCA) reveals clear separability between clusters labeled as Efficient Senders and Costly & Slow Senders. These findings provide actionable information for logistics service providers to improve pricing strategies, delivery efficiency, and customer satisfaction through intelligent segmentation.
Co-Authors Erna Mulyati Jaka Sunara Kokoh Handoko Maniah