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All Journal ARRUS Journal of Engineering and Technology Jurnal Ilmiah Informatika dan Komputer
Purnawan, Hendra
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Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science

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Talent Management Employee Development by Using Certainty Factor Method of Expert System Hamid, Aditia Putra; Al Hakim, Rosyid Ridlo; Sungkowo, Aming; Trikolas, Trikolas; Purnawan, Hendra; Jaenul, Ariep
ARRUS Journal of Engineering and Technology Vol. 1 No. 1 (2021)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/jetech568

Abstract

Talent management is a factor that determines success in the business environment because talent management requires quantitative and qualitative skills. This study aims to implement the certainty factor (CF) method of an expert system for employee development talent management. This research using a certainty factor (CF) method to design an expert system framework. Due to the focus on our research aim, we provide a certainty factor calculation with mathematical modeling for calculating talent management employee development in X Company. The confidence level is 93.55% for a recommendation of not promotion of the job; for 52.38% is a recommendation that can be proposed for promotion, but HRD will evaluate in some time; for 98.73% is a recommendation for promotion of the job. We used CF calculation that can provide the level of confidence (in %). The calculation of the certainty factor (CF) method can be used for recommending job promotion in some companies.
Using Backpropagation Neural Network for Polyvinylchloride Ceiling Price Modeling Purnawan, Hendra; Putra, Ryan; Fauzi, Rifqi; Setiawan, Antonius; Jaenul, Ariep; Al-Hakim, Rosyid; Nugroho, Habibie; Kuntjoro, Yanif
Jurnal Ilmiah Informatika dan Komputer Vol. 1 No. 1 (2024): Juni 2024
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/caz0ac86

Abstract

Sales predictions on building material products today have applied an artificial neural network approach. One of the products of building material that need to be predicted for sales is polyvinylchloride (PVC) ceilings. Most companies haven’t implementing prediction technique for the sale of PVC ceilings, so this study aims to predict PVC ceiling sales with the backpropagation neural network (BPNN) method using the R algorithm. Unit gradients are calculated using the average absolute per cent error value (MAPE) to minimize the total square errors of network output. The results showed that the network architecture used was 4 to 6-1 and obtained an accuracy of 88% based on the lowest MAPE value.
Co-Authors Al-Hakim, Rosyid Ariep Jaenul Fauzi, Rifqi Hamid, Aditia Putra Kuntjoro, Yanif Nugroho, Habibie Putra, Ryan Rosyid Ridlo Al-Hakim Setiawan, Antonius Sungkowo, Aming Trikolas, Trikolas