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All Journal Journal of Students‘ Research in Computer Science (JSRCS) Adpebi International Journal of Multidisciplinary Sciences
Mutoffar , Muhamad Malik
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Religion Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Algoritma Naive Bayes untuk Memprediksi Waktu Pengerjaan Uji Kompetesi Keahlian (UKK) Siswa Sekolah Menengah Kejuruan Mutaqin, Muhammad Zaenal; Mutoffar , Muhamad Malik
Journal of Students‘ Research in Computer Science Vol. 4 No. 1 (2023): Mei 2023
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/pw0wvj13

Abstract

This research is motivated by the need for a method or method that helps teachers and schools to predict the speed of time for students UKK work so that schools are more effective in preparing students to face UKK with faster processing time where the current problem is that schools are still using manual prediction methods . The hypothesis of the researchers is that by implementing the Naïve Bayes algorithms to predict the length of time the UKK Student can work, it can produce more perfect predictions so that school management is more efficient in providing solutions for students who are predicted to work slowly on SMK Bhakti Persada Bekasi . UKK is the final assessment in order to determine the achievement of competencies for vocational students. The use of Data Mining with artificial classification and intelligence models that will predict the length of time spent on UKK in terms of student completion time quickly, normally or slowly. The Algorithm method used is Naïve Bayes with the prediction accuracy of 99.11%.
Sistem Informasi Penjualan Online Berbasis Web Pada Toko Citra Parfum Yunianto , Imam; Mutoffar , Muhamad Malik; Pratama , Syadam
Journal of Students‘ Research in Computer Science Vol. 4 No. 2 (2023): November 2023
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/gs4rrd34

Abstract

This research aims to develop an online sales system (e-commerce) for Citra Parfum store in Tambun Selatan, Bekasi Regency. The store faces challenges in achieving sales targets and low customer numbers due to limited promotion through conventional channels and its hidden location within a residential area. The research utilizes the method of information system development, utilizing Visual Studio Code as a medium for creating the e-commerce website. The developed system includes the display of perfume products along with relevant information, a shopping cart, the checkout process, and online payment. This study proposes the implementation of a web-based e-commerce for Citra Parfum store. With the online sales system, it is expected that the number of sales transactions will increase, the marketing reach will expand, and buyers will be able to transact more easily. The research results are also expected to provide practical benefits to the management of Citra Parfum in improving customer satisfaction through the implementation of the online sales system.
Comparison of Decision Tree, KNN and Naïve Bayes Methods In Predicting Student Late Graduation In the Informatics Engineering Department, Institute Business XYZ Yunianto, Imam; Kurniawan, Ade; Mutoffar , Muhamad Malik
Adpebi International Journal of Multidisciplinary Sciences Vol. 1 No. 1 (2022)
Publisher : Asosiasi Dosen Peneliti Ilmu Ekonomi dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54099/aijms.v1i1.304

Abstract

Solving the problem of student late graduation has been a lot of research done before, with various methods and algorithms. Likewise, the comparison of various methods to predict student graduation. However, there is no comparison of the Naïve Bayes, Decision Tree, and KNN methods using data from the Informatics Engineering Department in Institute Business XYZ. From this study by comparing the three methods, the Naïve Bayes method is ranked first with an accuracy rate of 66.67%, Precison 80% and Recall 66.67%. Rank 2 is the KNN algorithm with an accuracy rate of 55.56%, Precision 66.67% and Recall 66.67% and the last is the Decision Tree algorithm with an accuracy rate of 46%, Precison 48.3% and Recall 61.67%
Co-Authors Ade Kurniawan Muhammad Zaenal Mutaqin Pratama , Syadam Yunianto , Imam Yunianto, Imam