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All Journal JSAI (Journal Scientific and Applied Informatics) Journal of Computer Networks, Architecture and High Performance Computing
W Handono, Felix
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Computer Science & IT Education

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Simple Additive Weighting to Determine The Best Employee in a Freight Forwarding and Logistics Company Siahaan, Fernando; Anwar, Syaiful; W Handono, Felix
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4244

Abstract

The problem is that there is no method used to determine the best employee in the company based on the criteria set by the company. The purpose of this research is to propose simple additive weighting as a method for finding the best employees according to the weighting carried out. To make decisions, there are several criteria and criteria weights that are needed as a measuring tool to assess employees who will be promoted, attendance, QSM, Quiz, leading. Period of work and team work. The weight value of each criterion is attendance 0.20, QSM 0.25, Quiz 0.15, leading 0.20, tenure 0.10 and team work 0.10. Quality service management (QSM) if sub criteria < 200 QSM value 1, sub criteria 201 - 300 QSM value 2, sub criteria 301 - 400 QSM value 3, sub criteria 401 - 500 QSM value 4, sub criteria 501 - 600 QSM value 5. The results of the analysis with the saw method obtained two employees who got the highest score who had the right to be promoted for promotion with a value of 84.25 and 82.25. the conclusion is that the SAW method is influential in supporting and facilitating decision making to determine promoted employees.
Prediction of Customer Creditworthiness with the C4.5 Algorithm at PT Menara Indonesia Company Dhoni Hanif Supriyadi; Siahaan, Fernando B; Anwar, Syaiful; W Handono, Felix
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7237

Abstract

Customer credit assessment is still carried out using traditional methods that are time-consuming and less accurate. This is evidenced by the fact that there are still customers with problematic credit who pass the loan application process. To address this issue, this research aims to develop a method using the C4.5 algorithm. The purpose of this research is to improve the efficiency and effectiveness of the company’s debt collection process. This research uses customer data from PT Menara Indonesia, which has credit loans. The data includes seven independent variables: net income, loan amount, credit score, number of arrears, tenure, assets, and loan age, as well as one dependent variable, namely credit risk. The C4.5 algorithm is applied to build a customer credit repayment prediction model. This model is tested using the k-fold cross-validation method with k = 10. The test results show that the C4.5 model has excellent performance, with an accuracy of 99.70%, precision of 99.25%, recall of 98.52%, and an F1-score of 98.88%. The advantage of this method is its ability to provide highly accurate predictions, thereby helping the company identify high-risk customers and improve the overall debt collection process
Simple Additive Weighting to Determine The Best Employee in a Freight Forwarding and Logistics Company Siahaan, Fernando; Anwar, Syaiful; W Handono, Felix
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4244

Abstract

The problem is that there is no method used to determine the best employee in the company based on the criteria set by the company. The purpose of this research is to propose simple additive weighting as a method for finding the best employees according to the weighting carried out. To make decisions, there are several criteria and criteria weights that are needed as a measuring tool to assess employees who will be promoted, attendance, QSM, Quiz, leading. Period of work and team work. The weight value of each criterion is attendance 0.20, QSM 0.25, Quiz 0.15, leading 0.20, tenure 0.10 and team work 0.10. Quality service management (QSM) if sub criteria < 200 QSM value 1, sub criteria 201 - 300 QSM value 2, sub criteria 301 - 400 QSM value 3, sub criteria 401 - 500 QSM value 4, sub criteria 501 - 600 QSM value 5. The results of the analysis with the saw method obtained two employees who got the highest score who had the right to be promoted for promotion with a value of 84.25 and 82.25. the conclusion is that the SAW method is influential in supporting and facilitating decision making to determine promoted employees.
Co-Authors Dhoni Hanif Supriyadi Fernando B Siahaan Siahaan, Fernando Syaiful Anwar