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All Journal Paradigma Journal of Decision Support Systems and Multi-Criteria Decision Making (JODESMA)
Abhishek R. Mehta
Parul University
Computer Science & IT

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Integration of LODECI Weighting Method and SPOTIS in Employee Performance Evaluation Based on Multi-Criteria Decision Making  Fadila Shely Amalia; Dedi Darwis; Abhishek R. Mehta
Paradigma - Jurnal Komputer dan Informatika Vol. 28 No. 1 (2026): March 2026 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v28i1.12508

Abstract

Employee performance evaluation in many organizations often faces challenges due to numerous assessment criteria and potential subjectivity in the decision-making process, making the evaluation results less consistent and objective. Multi-Criteria Decision Making (MCDM) methods have been widely used to address this problem; however, previous approaches generally still rely on subjective weight determination and do not fully consider the stability of results against data variation. Therefore, this study aims to develop a more objective and stable decision-making model by integrating the LODECI method to determine criteria weights based on data and the SPOTIS method to rank alternatives based on their distance from the ideal solution. Five evaluation criteria are used, namely productivity, work quality, discipline, teamwork, and responsibility, with data collected from eight employees as alternatives. The analysis process was carried out through the stages of constructing a decision matrix, calculating criterion weights using LODECI, and ranking using SPOTIS which produced a total distance value as a quantitative evaluation metric. The research results show that GS Employee achieved the smallest distance value of 0.058, thus ranking first, followed by CR Employee with a value of 0.086 and AN Employee with a value of 0.321. These findings indicate that the proposed model is capable of providing more measurable and consistent evaluation results. The main contribution of this study lies in the integration of objective weighting and ideal-solution-based ranking methods supported by sensitivity analysis, thereby producing a performance evaluation system that is more reliable, transparent, and robust compared to previous approaches.
TEKNO Method: Total Evaluation based on Knowledge-driven Normalized Optimization for Multi-Criteria Decision Making Yuri Rahmanto; Ryan Randy Suryono; Dedi Darwis; Abhishek R. Mehta; Auliya Rahman Isnain
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 2 (2027): March 2027
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i2.7

Abstract

Multi-criteria decision making (MCDM) methods play an important role in supporting decisions involving multiple criteria with different characteristics and levels of importance. However, differences in normalization procedures, criterion treatment, and aggregation mechanisms can lead to variations in the resulting preference structures. This study proposes a new MCDM method, namely total evaluation based on knowledge-driven normalized optimization (TEKNO), which integrates normalization, relative evaluation, criterion weighting, optimization-based normalization, and total evaluation into a unified decision-making framework. The proposed method is designed to transform heterogeneous decision information into comparable evaluation values while preserving the relative contribution of each criterion. The applicability of TEKNO is evaluated through two decision-making case studies involving new store location selection and leasing customer selection. The evaluation framework includes ranking analysis, comparison with established MCDM methods, Spearman rank correlation analysis, and sensitivity analysis under variations in criterion weights. The results show that TEKNO achieves a Spearman rank correlation coefficient of 1.0000 for the new store location case and 0.9964 for the leasing customer selection case, indicating very strong agreement with the reference rankings. In addition, the ranking remains unchanged across the tested sensitivity scenarios, demonstrating the stability of TEKNO under variations in criterion weights. These findings indicate that TEKNO provides a transparent, systematic, and stable alternative for MCDM applications for practical decision support where reliable ranking, methodological transparency, and robustness across alternative evaluation conditions are required. Nevertheless, broader validation using diverse datasets, decision domains, weighting schemes, and statistical evaluation techniques is required to further establish its generalizability and comparative performance.
Co-Authors Dedi Darwis Fadila Shely Amalia Isnain, Auliya Rahman Ryan Randy Suryono Yuri Rahmanto