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Journal : Journal of Information Technology, Computer Engineering and Artificial Intelligence (ITCEA)

Metode Naïve Bayes Untuk Prediksi Waktu Produksi Mebel di UD. Wali Barokah Kartasura Sukoharjo Jawa Tengah Abdul Dhohir Surya Kusuma, RM; Remawati, Dwi; Sandradewi, Kumaratih
Journal of Information Technology, Computer Engineering and Artificial Intelligence (ITCEA) Vol. 1 No. 1 (2024): Journal of Information Technology, Computer Engineering and Artificial Intellig
Publisher : Redtech Putra Benua

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Abstract

Wali Barokah is one of the industrial furniture craftsmen (furniture) with the main material using teak wood. The development of the times has made many furniture entrepreneurs appear, making the competition between furniture craftsmen increasingly tight. One way for customers not to be disappointed is that business voters must serve customers according to the specified time when transacting. The attributes that will be used in classifying the production time are the name of the item, the number of orders, the difficulty, the equipment, the number of workers. The method that will be used is the the method Naïve Bayes Classifier. Based on the results of the confusion matrix test on the nave Bayes method of the dataset that has been taken on the object of research, an accuracy rate of 80% is obtained or is included in the category Good. Meanwhile, Precision is 83% and Recall is 88%.
Sistem Pendukung Keputusan Karyawan Magang Menjadi Karyawan Tetap Divisi Mekanik Menggunakan Metode AHP dan TOPSIS ( Studi Kasus di  Raihan Motor) Pulanggeni, Riko; Harsadi, Paulus; Sandradewi, Kumaratih; Febrianto, Raden Arie; Irawati, Tri
Journal of Information Technology, Computer Engineering and Artificial Intelligence (ITCEA) Vol. 3 No. 1 (2026): Journal of Information Technology, Computer Engineering and Artificial Intellig
Publisher : Redtech Putra Benua

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Abstract

The process of determining which interns are eligible for permanent appointment in the mechanics division at Raihan Motor is still conducted manually and subjectively, potentially leading to inconsistent decision-making. This study aims to develop a Decision Support System (DSS) that assists leaders in objectively and measurably determining the best interns. The Analytical Hierarchy Process (AHP) method is used to determine the importance weights of each assessment criterion, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to rank alternative employees based on the criterion weights. The assessment criteria used include discipline, technical skills, responsibility, teamwork, and work ethic. The results of the study indicate that the system can provide recommendations for interns eligible for permanent appointment based on the highest preference value, thereby making the decision-making process more objective, effective, and consistent.