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All Journal International Journal of Electrical and Computer Engineering JRSI (Jurnal Rekayasa Sistem dan Industri) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Journal of Applied Data Sciences Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology Jurnal Ipteks Terapan : research of applied science and education Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Komtekinfo
Mutiana Pratiwi
Universitas Putra Indonesia YPTK Padang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Recommendation for Prospective Permanent Employees using the Simple Additive Weighting Method Ahmad Haidir; Gushelmi; Mutiana Pratiwi
Journal of Computer Scine and Information Technology Volume 10 Issue 4 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i4.113

Abstract

The rapid development of technological progress has made the use of personal computer technology increase significantly, where this use has made computers into branches that can still be developed, one of which is creating a decision-making system. Decision Support System is a computer-based system that is intended to assist decision making by utilizing certain data and models to solve various semi-structured problems. The application of Decision Support Systems can be found in various fields, one of which is a decision support system for prospective employees. This study aims to design a system that can provide the best decision in determining permanent employees at J&T Express Kotanopan. The method used in this study is the SAW (Simple Additive Weighting) method, with a website-based decision support system that can be used without time and place constraints, it can help J&T Express in selecting permanent employees. The results of testing this method have an accuracy level of more than 90% based on the data tested. Based on the results of the highest value obtained using the SAW method, this study was successful in determining permanent employees at J&T Express Kotanopan
Adaptive Integration of Optuna Optimization and Stacking Ensemble Learning for Automated Work Competency Classification Mutiana Pratiwi; Sarjon Defit; Muhammad Tajuddin
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1228

Abstract

Artificial intelligence and machine learning are increasingly used to automate analytical and decision processes, including the evaluation of human competencies. However, traditional models often face challenges in accuracy and generalization when applied to linguistic data from interviews. This study aims to develop a model that integrates Optuna optimization and stacking ensemble learning to enhance the accuracy and interpretability of competency classification. Interview transcript data were processed using natural language processing techniques such as cleaning, tokenization, case folding, stopword removal, and stemming to ensure textual consistency. The text was then transformed into numerical representations using term frequency inverse document frequency weighting. To handle class imbalance, the synthetic minority oversampling technique was employed. Optuna was applied to optimize the hyperparameters of base models, including support vector classifier, Naïve Bayes, random forest, gradient boosting, and XGBoost. These optimized models were combined through a stacking ensemble to form the final classifier. The proposed model achieved an accuracy of 94 percent and a precision of 95 percent with macro and weighted F1 scores of 0.94. The results demonstrate stable and balanced performance across all competency categories, including analytical thinking, initiating action, problem solving, and work standards. Comparative analysis with previous studies in sentiment analysis, medical diagnosis, and financial forecasting confirmed that the integration of Optuna and stacking produces more robust and generalizable outcomes. The integration of Optuna optimization and stacking ensemble learning effectively improves classification performance while maintaining interpretability. The model demonstrates strong potential for automated competency evaluation in recruitment and human resource analytics. This framework can be extended to other linguistic datasets to support transparent and data-driven decision-making in artificial intelligence applications.
Co-Authors Aggy Pramana Gusman Ahmad Haidir Arsyah, Ulya Ilhami Devia Kartika Gema, Rima Liana Gushelmi Husna Arsyah, Rahmatul Irawadi Putra Johan Harlan Jufriadif Na`am, Jufriadif Marfalino, Hari Muhammad Tajuddin Randi Romi Hardianto Sarjon Defit Shary Lusinia Silky Safira Surmayanti, Surmayanti Widia Marta