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All Journal Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) Jurnal Informatika dan Rekayasa Perangkat Lunak
Chandra, Iryanto
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Sistem Pendukung Keputusan Pemilihan Pegawai Terbaik Menggunakan Kombinasi Metode Pembobotan MEREC dan Simple Additive Weighting Chandra, Iryanto; Hadad, Sitna Hajar
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5360

Abstract

The selection of the best employees is an event that aims to appreciate and recognize the extraordinary performance of employees in a company. The implementation of the selection of the best employees is not without challenges and problems, one of the main problems is the risk of subjectivity in the assessment process, which can cause dissatisfaction among other employees if they feel that the assessment is unfair or transparent. The purpose of this study is to develop an SPK that can help in the selection of the best employees by using a combination of MEREC and SAW methods, a MEREC approach to manage and evaluate important criteria in employee selection, and integrate SAW as a mathematical method to provide weight and ranking candidates based on predetermined criteria. The recommendation for the results of the selection of the first best employee with a final SAW score of 0.8345 was obtained by Candidate 8, the second best employee with a final SAW score of 0.8253 was obtained by Candidate 6, and the third best employee with a final SAW score of 0.8068 was obtained by Candidate 3.
Combination of PIPRECIA and Multi-Attributive Ideal-Real Comparative Analysis for the Determination of Scholarship Students Hadad, Sitna Hajar; Chandra, Iryanto; Maryana, Sufiatul; Setiawansyah, Setiawansyah
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6313

Abstract

Scholarships are a form of financial assistance given to individuals to support their education. Criteria considered in the determination of scholarship recipients may include academic achievement, special talents, financial need, participation in extracurricular activities, and potential contributions to the community. The combination of weighting using PIPRECIA and MAIRCA can be a powerful approach in determining scholarship recipients. With PIPRECIA, scholarship providers can gather preferences from various relevant parties to determine the relative weight of each evaluation criterion. Furthermore, by applying MAIRCA, scholarship recipients can be evaluated based on these criteria by comparing between ideal attributes that reflect expected standards with real attributes that reflect the actual conditions of each recipient. By integrating these two methods, the process of determining scholarship recipients becomes more structured, transparent, and takes into account diverse preferences and priorities, ensuring that aid is distributed to the most deserving and needy individuals. The results of alternative rankings in determining scholarship recipients are 1st place with a final score of 0.071 obtained on behalf of Yusuf Maqdis, 2nd place with a final score of 0.068 obtained on behalf of Kurniawansyah, and 3rd place with a final score of 0.062 obtained on behalf of Ketut Purwanti.
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.
Integration of RECA Weighting and MARCOS Methods in Decision Support Systems for the Selection of the Best Customer Recommendations Asistyasari, Ayuni; Arshad, Muhammad Waqas; Chandra, Iryanto; Nuryaman, Yosep; Saputra, Very Hendra
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2025): Volume 6 Number 2 June 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i2.219

Abstract

In a competitive business environment, selecting the best customers is a strategic step to improve marketing efficiency and build profitable long-term relationships. However, this process is often constrained by subjectivity in determining criteria and evaluating alternatives. This study aims to develop an objective and measurable decision-making model by integrating of the Respond to Criteria Weighting (RECA weighting) and the method of measurement of alternatives and ranking according to compromise solution (MARCOS). The RECA weighting is used to determine the weight of criteria based on the response to their level of importance, while MARCOS is used to evaluate and rank customer alternatives based on proximity to the ideal solution. The final ranking of customers is determined using the RECA weighting method and MARCOS, which reflects the final value of each customer alternative; Customer 3 obtained the highest final score of 1.2339, indicating the best overall performance based on the established evaluation criteria. Furthermore, Customer 7 and Customer 1 are in second and third place with scores of 1.2096 and 1.1546, respectively, indicating that these three customers are the main candidates to be prioritized in the customer relationship strategy. The result of the integration of these two methods provides a decision support system that is able to generate accurate and logical customer ratings, and supports data-driven strategic decision-making. This model is expected to be an effective solution in improving the quality of business decisions, especially in managing customer relationships more on target and efficiently.
Decision Support System Based on RECA and COPRAS Methods in Performance Evaluation of Non-Permanent Employees Asistyasari, Ayuni; Chandra, Iryanto; Hadad, Sitna Hajar; Nuryaman, Yosep; Wang, Junhai
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 3 (2025): Volume 6 Number 3 September 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i3.848

Abstract

The evaluation of the performance of non-permanent employees is a significant challenge for organizations due to the high turnover rate and the limited tenure of these employees. The manual evaluation processes often lead to biases, inconsistencies, and a lack of accuracy in supporting decision-making. This research aims to develop a decision support system based on the RECA and COPRAS methods to produce a more objective, transparent, and systematic evaluation. RECA is used to determine the criteria weights proportionally based on each contribution, while COPRAS functions to assess and provide a final ranking of employee performance by considering both benefit and cost-type criteria. The research results show that this system is capable of sorting non-permanent employees fairly with ranking results of E-AS-05 with a score of 100%, E-AS-03 with a score of 97.32%, E-AS-01 with a score of 94.03%, E-AS-02 with a score of 88.34%, and E-AS-04 with a score of 82.19%. The integration of the RECA and COPRAS methods not only enhances the effectiveness of performance evaluation but also provides a tangible contribution to supporting more efficient and sustainable human resource management.
Co-Authors Aditia Yudhistira Arshad, Muhammad Waqas Asistyasari, Ayuni Megawaty, Dyah Ayu Nuryaman, Yosep Setiawansyah Setiawansyah Sitna Hajar Hadad Sufiatul Maryana Very Hendra Saputra Wang, Junhai