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All Journal TEKNIK INFORMATIKA J@TI (TEKNIK INDUSTRI) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmiah Teknik Industri JURNAL PENDIDIKAN TAMBUSAI Jurnal Review Pendidikan dan Pengajaran (JRPP) Dinasti International Journal of Education Management and Social Science JATI (Jurnal Mahasiswa Teknik Informatika) Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Jurnal Manajemen Pendidikan dan Ilmu Sosial (JMPIS) Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) Jurnal Abdimas Kartika Wijayakusuma Innovative: Journal Of Social Science Research JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah Land Journal Prosiding Seminar Nasional dan Call Paper STIE Widya Wiwaha Competitive INOVTEK Polbeng - Seri Informatika Spektrum Industri
Erna Mulyati
Program Studi Teknik Industri Politeknik Pos Indonesia, Jl. Sariasih No.54, Bandung
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Journal : INOVTEK Polbeng - Seri Informatika

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A PSO-Optimized Stacking Ensemble with Hybrid SMOTE-NC and Tomek Links for Bid-Based Winning Prediction in Procurement Projects Handoko, Kokoh; Purnomo, Agus; Mulyati, Erna
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.
Co-Authors Adawiyah, Anida Adri Sapta Firmansyah Aghitsna Iqbal Alif, Aghitsna Iqbal Agus Purnomo Agus Purnomo Alfian, Muhammad Andrey Budi Susanto Casmadi, Yohanes Dani Ramdani, Dani Dodi Permadi, Dodi Firdhaus, Saputra Aulia Hamidin, Dini Handoko, Kokoh Hidayat, Syamsu Kencana Verawati Krisnawanti Kusdanu, Saptono Lasmana, Hendri Lestiani, Melia Eka Lusel, Rikardo Riki Made Prima Widyapurna Mandasari, Cindy Maniah Maniah, Maniah Muhammad Amin Muhammad Ibnu Choldun Rachmatullah Mustafia*, Mustafia Nizar, Catur Ahaddi Noviana Noviana Pardede, Nia Prawakca, Meilasari Cita Reswadi, Oshelby Riskandar, Aris Riyadi, Faris Akbar Salsabila, Syifa Saputra, Hijrah Simbolon, Justin Goklas Sondakh, Eduard Sugesti, Hesti Wahyudi, Arif Yudha Waskito, Saptono Kusdanu Widyawan, Gerardio Septa