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All Journal JRSI (Jurnal Rekayasa Sistem dan Industri) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Riset Informatika
Saifur Rohman Cholil
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Computer Science & IT Control & Systems Engineering Industrial & Manufacturing Engineering

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Modeling Productive Land Determination Using Entropy-Mabac Method Based on Multicriteria Data in Central Java Province Mutiatun Nafisah; Saifur Rohman Cholil
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9538

Abstract

Central Java Province has a diversity of land use characteristics that reflect the potential as well as challenges in regional development, so that optimization of productive land is important to support economic growth, community welfare, and environmental sustainability. For this reason, this research was conducted with an objective approach using the Entropy method in determining the weight of each criterion based on actual data variations, as well as the Multi-Attributive Border Approximation Area Comparison (MABAC) method to systematically evaluate and rank the level of land productivity in 35 districts/cities. The results of the analysis show that Demak, Brebes, and Rembang districts ranked the highest in land productivity with the highest score of 0.249, while Wonogiri and Banjarnegara districts ranked the lowest with scores of -0.392 and -0.234. Validation using the Spearman Rank test resulted in a correlation coefficient of 0.82, indicating strong agreement between the method results and historical data. The findings show that the combination of Entropy and MABAC methods is effective in determining productive land, and the results are relevant as a basis for formulating sustainable land use policies, including recommendations for irrigation development, farmland protection, and strengthening spatial policies for low productivity areas.
Decision Support System for New Employees Candidat Selection PT. Dawam Prima Perkasa Using Aras Method Web Based Saifur Rohman Cholil; Enggar Satrio Prisiswo
Jurnal Rekayasa Sistem & Industri Vol 7 No 02 (2020): Jurnal Rekayasa Sistem & Industri
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jrsi.v7i2.422

Abstract

Employees are one of the supporting factors for a company, because by having qualified employees,according to the qualifications and criteria needed by the company, then the company can develop and moveforward in the future. No exception, PT. Dawam Prima Perkasa who is in need of new employees who aresuitable to work in the company. However, PT. Dawam Prima Perkasa is faced with a problem where thelarge number of prospective employees who take the selection test will cause the number of incoming filesto be adjusted to the criteria owned by the company and require a lot of time, making it prone to file errors.To minimize the occurrence of errors and the length of time used, a Decision Support System was created todetermine the best prospective employees who will work in the company. The Decision Support SystemMethod used is the Additive Ratio Assessment (ARAS). This research has gone through the process ofvalidating the Spearman rank correlation and the value is 0.950. Based on these results, the ARAS methodcan be used in selecting prospective new employees at PT. Dawam prima Perkasa.
Reward Decision Support System and Punishment on Food Companies Using MABAC Method Eka Putri Rachmawati; Saifur Rohman Cholil; Siti Asmiatun
Jurnal Rekayasa Sistem & Industri Vol 9 No 02 (2022): Jurnal Rekayasa Sistem & Industri
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jrsi.v9i02.542

Abstract

The improvement of a company performances cannot be separated from the performance of each employee.Periodic evaluation of employee performance becomes a routine task of the Human Resources General Affair(HRGA) team that takes a long time and effort because it is still done manually. The results of employeeperformance that have been assessed as a reference of determination of rewards and punishment. TheDecision Support System (DSS) is made with the aim of making it easier for HRGA to determine employeerewards and punishments based on assessor factors that have been determined by the company. The criteriachosen are attendance, loyalty and responsibility, work competency and work results (quality and quantityresults). The DSS methods that can be applied to this process is the Multi-Attributive Border Approximationarea Comparison (MABAC) Method. This method is used because consistent in solutions and expert for usein logical decision making. The research data was taken from 11 samplings of bakery division employeeperformance data for 1 year. Data is obtained from attendance and assessments from supervisors of eachdivision. The results of this research, is a decision support system web based in order to determine thereward and punishment method using MABAC.
APPLICATION OF THE ENTROPY–MARCOS METHOD IN A DECISION SUPPORT SYSTEM FOR SELECTING TOURIST DESTINATIONS IN SEMARANG Ekayulianti, Eka Yulianti; Saifur Rohman Cholil
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i2.509

Abstract

Tourism is an important sector that supports regional economic growth. In the Semarang area, the increasing number of tourist destinations provides many options for visitors, but it can also make it difficult for tourists to determine the most appropriate destination based on several considerations. Therefore, this study aims to develop a decision support system for selecting tourist destinations using a multi-criteria decision-making approach. The criteria used in this study include travel time, ticket price, tourist facilities, and destination rating. The data were obtained from digital tourism information and questionnaire responses from 64 respondents. The Entropy method was used to determine objective criteria weights based on data variation, while the MARCOS method was applied to rank tourist destination alternatives based on their proximity to ideal and anti-ideal solutions. The results show that Kota Lama is the most recommended tourist destination with the highest final score, followed by Lawang Sewu and Pagoda Avalokitesvara. The validation results using Spearman correlation analysis produced a coefficient value of 0.9879, indicating a very strong agreement between the ranking results generated by the Entropy–MARCOS model and tourist preference rankings. This study contributes by integrating objective weighting and ranking methods using digital tourism data to provide a structured approach for tourism decision support systems.
Co-Authors Eka Putri Rachmawati Ekayulianti, Eka Yulianti Enggar Satrio Prisiswo Mutiatun Nafisah Siti Asmiatun, Siti