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All Journal Bulletin of Electrical Engineering and Informatics PROtek : Jurnal Ilmiah Teknik Elektro Building of Informatics, Technology and Science Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) Journal Social Science And Technology For Community Service Jurnal Ilmiah Informatika dan Ilmu Komputer CHAIN: Journal of Computer Technology, Computer Engineering and Informatics Journal of Data Science and Information System Journal of Artificial Intelligence and Technology Information Jurnal Informatika dan Rekayasa Perangkat Lunak
Sitna Hajar Hadad
Akademi Ilmu Komputer Ternate
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media

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Journal : Jurnal Teknik Informatika (JUTIF)

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COMBINATION OF LOGARITHMIC PERCENTAGE CHANGE-DRIVEN OBJECTIVE WEIGHTING AND MULTI-ATTRIBUTIVE IDEAL-REAL COMPARATIVE ANALYSIS IN DETERMINING THE BEST PRODUCTION EMPLOYEES Sitna Hajar Hadad; Subhan Subhan; Setiawansyah Setiawansyah; Muhammad Waqas Arshad; Aditia Yudhistira; Yuri Rahmanto
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2057

Abstract

The problem that occurs in the selection of the best production employees is the lack of transparency and objectivity in the selection process. Without clear procedures and well-defined criteria, employee selection decisions can be influenced by subjective preferences or irrelevant non-performance factors. This can result in injustice in employee selection and lower the morale and motivation of unselected employees. The purpose of the combination of LOPCOW and MAIRCA in determining the best production employees is to provide a holistic and adaptive framework in the employee performance evaluation process. LOPCOW allows decision makers to dynamically adjust the weight of criteria according to the level of volatility or change in the relevant environment or situation. LOPCOW offers an adaptive and responsive approach in determining the weight of criteria, enabling decision makers to respond quickly to changes occurring in the relevant environment or situation. MAIRCA is an analytical method used to assist decision makers in evaluating and selecting alternatives based on several relevant criteria or attributes. MAIRCA provides a strong framework for decision makers to make more informed and informed decisions. Combining these two methods results in a more comprehensive and accurate understanding of production employee performance, thus enabling managers to identify the most effective employees and provide rewards or development accordingly. The final results of the ranking of the best production employees obtained by JR employees get 1st place, YP employees get 2nd place, and AJL employees get 3rd place.
DYNAMIC WEIGHT ALLOCATION IN MODIFIED MULTI-ATRIBUTIVE IDEAL-REAL COMPARATIVE ANALYSIS WITH SYMMETRY POINT FOR REAL-TIME DECISION SUPPORT Hadad, Sitna Hajar; Chandra, Iryanto; Wang, Junhai; Megawaty, Dyah Ayu; Setiawansyah, Setiawansyah; Yudhistira, Aditia
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4170

Abstract

Decision Support Systems (DSS) have a crucial role in real-time decision-making, especially in the digital era that demands high speed and accuracy. Managing criterion weights in a dynamic environment presents significant challenges due to rapid and unpredictable changes in conditions. However, determining an accurate weight becomes difficult due to uncertainty, incomplete data, and subjective factors from decision-makers. In addition, changes in the external environment, such as market trends, regulations, or customer preferences, can affect the relevance of each criterion, thus requiring a real-time weight adjustment mechanism. The purpose of this study is to develop and explore the dynamic weight allocation method in symmetry point- multi-attributive ideal-real comparative analysis (S-MAIRCA) to support more accurate and responsive real-time decision-making in a dynamic environment. This research contributes to the understanding of how the weights of criteria can be adjusted automatically and responsively to changing conditions or new data, which increases the relevance and accuracy of decisions in a dynamic environment. The urgency of S-MAIRCA research is important because it often involves real-time, dynamic, and complex data. This development not only improves the adaptability of the S-MAIRCA method, but also contributes significantly to creating computer science-based applications that are more intelligent, flexible, and relevant to the evolving needs of the system. The results of the alternative ranking comparison using the CRITIC-MAIRCA, LOPCOW-MAIRCA, ROC-MAIRCA, and S-MAIRCA methods showed variations in the ranking order generated for each alternative using spearman correlation. The results of the correlation value of CRITIC-MAIRCA and LOPCOW-MAIRCA have a very high correlation of 0.993, which shows that these two methods provide almost identical rankings in alternative evaluation. Likewise, CRITIC-MAIRCA and S-MAIRCA had a high correlation of 0.979, signaling a strong similarity in ranking results despite slight differences in the approaches used by the two methods. The results of the application of the MAIRCA-S method in the development of DSS based on real-time data have a significant impact on improving the speed, accuracy, and adaptability of decisions. MAIRCA-S strengthens the validity of decision results by considering a variety of attributes on a more comprehensive scale, providing added value in the development of DSS for various industrial sectors.
Co-Authors A. Thais, Iswan Abdullah, Muksin Hi Abhishek R Mehta Abhishek R Metha Abjan Samad Abjan Samad Aditia Yudhistira Ahmad Ari Aldino Ahmad Ari Aldino Ambarita, Arisandy Arie Qur’ania Asistyasari, Ayuni Chandra, Iryanto Dedi Darwis Gibtha Fitri Laxmi Heni Sulistiani Ibrahim, Adelina Ismail, Risandi J Iswan A. Thais Julkifli Muksin Kasmawati . Megawaty, Dyah Ayu Moch Teguh Priyanto Mudar Safi Muhammad Waqas Arshad Muksin Hi Abdullah Munawir A. Hi Musa Nurnela Nurnela Nurnela Nuryaman, Yosep Palupiningsih, Pritasari Radina Hamza Hairun Rumaf, Nurdin Safi, Mudar Setiawansyah Setiawansyah Subhan Subhan Sufiatul Maryana Turuy, Seh Wang, Junhai Yuri Rahmanto