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All Journal MDP Student Conference Journal Of Artificial Intelligence And Software Engineering
Ramadhan, Dimaz Gymnastiar
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering

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Strategi Digitalisasi Rekrutmen Pegawai di Charitas Hospital Kenten Melalui Pengembangan Aplikasi Ramadhan, Dimaz Gymnastiar; Hermaya, Nickholas Hansel; Yulistia, Yulistia
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11010

Abstract

One of the tools for submitting applications that is often used by companies is through email in the recruitment or selection process of employees. This causes difficulties for the HR department in managing digital files that often need to be reprinted for evaluation purposes. Therefore, it is necessary to develop a web-based recruitment and selection application for Charitas Hospital Kenten to improve the efficiency and effectiveness of the recruitment process. The application development method used is the Waterfall method, which includes the stages of software requirements analysis, design, implementation, and testing. The result of this research is an information system that assists in managing digital documents and enhances the efficiency of the recruitment process at Charitas Hospital Kenten .
Clustering of Accounts Receivable Billing Data Based on Customer Tariff Categories at PT PLN UP3 Palembang Ramadhan, Dimaz Gymnastiar; Yulistia, Yulistia
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6511

Abstract

The purpose of writing this final assignment is to group customers based on late payment patterns by applying the K-Means Clustering algorithm. The data used are late receivables and arrears of PT PLN Palembang customers. The results of writing this final assignment show that Cluster 1 has 10 data, Cluster 2 has 36 data, and Cluster 3 has 326 data on late payments. While in the risky payment arrears, Cluster 1 has 26 data, Cluster 2 has 36 data, and Cluster 3 has 312 data. From the evaluation results using Silhouette Score, it shows that there are 3 clusters with a value of 0,880 (Highest), which means that the clustering that was formed was successful and can be used.
Co-Authors Hermaya, Nickholas Hansel Yulistia, Yulistia