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All Journal Journal of Digital Technology and Computer Science
Sriani
Universitas Islam Negeri Sumatera Utara, Medan, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : journal of digital technology and computer science

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Application of Fuzzy Multi Criteria Decision Making (FMCDM) and TOPSIS in Selecting Prospective Employees Lailatul Badria; Sriani
Journal of Digital Technology and Computer Science Vol. 3 No. 3 (2026): August 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/dtcs.v3i3.1132

Abstract

Purpose – The primary objective of this research is to design and build a web-based Decision Support System (DSS) to evaluate and hire prospective candidates at Perumda Tirtanadi. To achieve this, a hybrid methodology combining Fuzzy Multi-Criteria Decision Making (FMCDM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is implemented. The proposed system is intended to reduce subjectivity and improve the effectiveness of the employee selection process. Methods – In this framework, FMCDM is utilized to translate subjective, linguistic evaluations into precise crisp values through a defuzzification process, which subsequently establishes the relative weights of the evaluation criteria. Afterward, these weights are integrated into the TOPSIS algorithm to calculate the final rankings of the applicants across nine specific benchmarks, namely GPA, Basic Competency Test (TKD), psychological test score, attitude, communication skills, work experience, politeness, field expertise, and personal development plan. The system was implemented as a web-based application using PHP and MySQL. Findings – The developed system successfully performed automatic FMCDM weighting and TOPSIS ranking. The ranking results were consistent with manual calculations, indicating that the proposed approach was correctly implemented and capable of producing accurate candidate rankings. Research implications – This study used recruitment data from a single organization, namely Perumda Tirtanadi Medan. Consequently, the applicability of the outcomes is restricted to the specific criteria and administrative environment of this particular utility company and may not be directly generalizable to other organizations. Originality – This study integrates FMCDM for criteria weighting and TOPSIS for candidate ranking into a web-based decision support system, providing a practical and objective approach to employee selection at Perumda Tirtanadi.
Random Forest Algorithm with SMOTE Technique for Classifying the Mental Health of Fresh Graduates Facing Career Competition Wily Supi Ramadani; Sriani
Journal of Digital Technology and Computer Science Vol. 3 No. 3 (2026): August 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/dtcs.v3i3.1149

Abstract

Purpose – This study evaluated Random Forest with the Synthetic Minority Oversampling Technique (SMOTE) for classifying questionnaire-derived career-related psychological categories among fresh graduates and descriptively compared performance before and after class balancing. Methods – Data were collected from 250 fresh graduates using a self-developed 13-item, four-point Likert questionnaire covering seven operational dimensions. A stratified 80:20 holdout split was used for the primary comparison, while 5 × 5 repeated stratified cross-validation examined stability. Standard SMOTE and random oversampling were applied only to training data, and SMOTE-generated profiles were audited in both stored-integer and continuous-interpolation representations. Findings – On the selected holdout split, Random Forest without SMOTE achieved 96.00% accuracy and a 95.93% weighted F1-score, whereas the SMOTE model reached 100.00% for both measures by correcting two Moderate-to-Poor errors. Random oversampling achieved 98.00% holdout accuracy. Across 25 repeated validation folds, mean accuracy was 96.08% without SMOTE, 95.76% with SMOTE, and 95.92% with random oversampling; the baseline–SMOTE accuracy difference was not statistically significant (Wilcoxon p = 0.412). Research implications – This study combines a controlled comparison of baseline Random Forest, standard SMOTE, and random oversampling with synthetic-profile auditing and repeated validation, while explicitly distinguishing operational score-category reproduction from independently validated psychological prediction. Originality – This study combines a controlled comparison of baseline Random Forest, standard SMOTE, and random oversampling with synthetic-profile auditing and repeated validation, while explicitly distinguishing operational score-category reproduction from independently validated psychological prediction.
Co-Authors Lailatul Badria Wily Supi Ramadani