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All Journal JOMLAI: Journal of Machine Learning and Artificial Intelligence
Zulaini Masruro Nasution
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Computer Science & IT Engineering

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C4.5 Algorithm Classification for Determining Smart Indonesia Program Recipients at MIS Al-Khoirot Weni Ratna Sari Oktapia Ningse; S Sumarno; Zulaini Masruro Nasution
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 1 (2022): March
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1329.579 KB) | DOI: 10.55123/jomlai.v1i1.165

Abstract

The purpose of the research is to assist the school in selecting student data as recipients of the PIP (Smart Indonesia Program) to be more objective and practical and to assist in increasing the accuracy of the targeting of the recipients of the PIP funds. In this study using Data Mining techniques using the C4.5 algorithm. The source of the research data used was obtained from observations and interviews with the MIS Al-Khoirot Tambun Nabolon Pematang Siantar school. The research variables used were parents' occupations, parents' income, KKS (Prosperous Family Card) holders, SKTM holders (Poor Certificate). In this study, the alternative used as a sample is the data of MIS Al-Khoirot students. The results of this study found that the most dominant attribute was the SKTM holder with a gain of 0.833764907, besides that this study produced 8 (eight) rules with an accuracy rate of 98.00%. Based on this, it can be concluded that the C4.5 algorithm can be used for the classification of the Determination of Smart Indonesia Program Recipients at MIS Al-Khoirot
Implementation of Genetic Algorithm for Subject Scheduling at SD Taman Cahya Pematangsiantar Muhammad Irfan; Muhammad Ridwan Lubis; Zulaini Masruro Nasution
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 2 (2022): June
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (707.227 KB) | DOI: 10.55123/jomlai.v1i2.940

Abstract

The teaching schedule in schools is essential in teaching and learning activities; this schedule aims to support, facilitate, and improve the quality of education. With a teaching schedule, teaching and learning activities will run smoothly and efficiently. Until now, the scheduling of lessons in several schools is still done conventionally by the curriculum department, with previously held meetings for the division of tasks with the supervising teacher. The conventional teaching scheduling system for school teachers will be deemed less effective. In addition to requiring very high accuracy and relatively few estimates, this method also allows for errors. Therefore, this study aims to implement a genetic algorithm to optimize subject scheduling and apply the teacher scheduling model generated by the genetic algorithm in a web application. The data of this study were collected from observations at SD Taman Cahya Pematangsiantar. As a result, scheduling using genetic algorithms can generate schedules automatically, displaying the plan on the day and hour of each teacher's teaching schedule, thus creating an optimal solution for scheduling. In addition, applying the Genetic Algorithm is faster and easier in the process of making a schedule for setting teacher teaching hours so that it does not take a long time.
Clustering Production of Plantation Crops by Province Using the K-Means Method Azhari Abdillah Simangunsong; Indra Gunawan; Zulaini Masruro Nasution
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (683.427 KB) | DOI: 10.55123/jomlai.v1i4.1661

Abstract

The purpose of this research is to classify the results of plantation crop production each year based on provinces in Indonesia, so that it can be known which provinces produce the most plantation crop production and which produce less. In this study using the K-Means Algorithm Data Mining technique. The data source for this research was collected based on plantation data obtained from the Indonesian Central Bureau of Statistics (BPS). The data used is data from 2018-2020 which consists of 34 provinces. The results of this study are groupings which are divided into 3 Clusters, namely low Clusters, medium Clusters, and high Clusters. Based on the results of calculations using the K-Means Algorithm, 6 items (Provinces) were obtained for high Clusters, 2 Provinces for medium Clusters and 27 Provinces for low Clusters. The conclusion that can be obtained is that the grouping of plantation crop production in Indonesia can be solved by applying the K-Means algorithm.
Co-Authors Azhari Abdillah Simangunsong INDRA GUNAWAN Muhammad Irfan Muhammad Ridwan Lubis S Sumarno Weni Ratna Sari Oktapia Ningse