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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA)
M. Safii
AMIK Tunas Bangsa Pematangsiantar, Sumatera Utara
Automotive Engineering Computer Science & IT Control & Systems Engineering

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Implementation of K-Means Clustering on High School Students Management Anggriani Dwi Kartina; M. Safii
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 1 No. 1 (2021): October 2021
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (644.606 KB) | DOI: 10.59934/jaiea.v1i1.47

Abstract

The quality of national education and teaching needs to be monitored continuously in every stage and step of educational activities. The monitoring is intended as an effort to control the quality of education and furthermore as a guarantee of the quality of education. Therefore, a method is needed to facilitate the grouping of high school student data. With the k-means clustering approach, the division of student groups can be done based on the national final exam scores. In this study, students were clustered using the K-Means algorithm. By using K-Means, it aims to facilitate the grouping of the highest and lowest Pemtangssiantar High School students. The result is a picture that shows the grouping of students based on national final exam scores.
K-Medoids Algorithm Analysis in Grouping Students' Level of Understanding of Subjects Ehrlich F.T Butarbutar; M. Safii
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 1 No. 1 (2021): October 2021
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (718.315 KB) | DOI: 10.59934/jaiea.v1i1.50

Abstract

Analysis of the teaching and learning process needs to be done as feedback on the understanding of the material for students. One of the obstacles faced by schools is that there is no method of how this feedback can be done so that student achievement is uneven. Student achievement in subjects can be seen from the results of the scores on the report cards obtained by students after taking the final semester exam. Due to the uneven achievement of students, it is necessary to make a method so that feedback analysis can be carried out on the level of student understanding of the subject. Is data mining with clustering techniques using the K-Medoids algorithm. With this algorithm, students' understanding of subjects with high potential can be grouped with high brightness average results
Implementation Data Mining of Employement Contract Exten-sion at Indosat Using Naïve Bayes Andini Fadila Sari; M. Safii; Dedi Suhendro; Irfan Sudahri Damanik
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 1 No. 1 (2021): October 2021
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (992.868 KB) | DOI: 10.59934/jaiea.v1i1.52

Abstract

Contract employees are company resources in carrying out oprasional activities for a certain time based on an agreement or contract. Every company that uses a work contrak system every year, there must be employees who are extended and not renewed. Employees will get additional contracts if they have good performance. In this case to determine whether an employee is extended or not extended his work contract, there is difficulty in determining it and requires a long time and process. Therefore, this research was conducted to help guarantee the extension of the employee’s work contract by classifier it into the labes “Eligble” and “Not Feasible” which has 4 variables for the process of employees who will be extended or not. The four variables are age, years of service, aspects of delay, achievement. In this study, the alternatives used as samples were employees at PT. Indosat Ooredoo. The number of data tested is 5 employees with two classes. From the results of the calculation of the Naïve Bayes Algorithm, it is obtained classification with 3 employees eligible class and 2 employees not eligible class. The results of this study found that the level of accuracy of 100.00%.
Co-Authors Andini Fadila Sari Anggriani Dwi Kartina Dedi Suhendro Ehrlich F.T Butarbutar Irfan Sudahri Damanik