Garuda - Garba Rujukan Digital

Bahrudi Efendi Damanik

STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

Author-ID : 5368617

Computer Science & IT Engineering

Published : 3 Documents Claim Missing Document

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Classification Techniques in Predicting New Student Admission Using the Naïve Bayes Method Suwayudhi Suwayudhi; Eka Irawan; Bahrudi Efendi Damanik
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 3 (2022): September
Publisher : Yayasan Literasi Sains Indonesia

Admission of new students is the registration process for new students entering school and the initial gate through which students enter the object of Education; this activity is the starting point for determining the smoothness of the tasks of a school, assisted by teaching staff and equipped with optimal facilities and infrastructure in teaching and learning activities, producing skilled and broad-minded students. However, the uncertainty of the number of registrants also influences the policies that will be taken in the future. Therefore, it is necessary to forecast or predict to estimate the number of students who are likely to register so that the school can prepare everything. In this study, the prediction process for new students will use a classification technique using the Naïve Bayes method. This study aims to predict the rise and fall of the number of students who register using the Naïve Bayes method. The research data was obtained by distributing questionnaires randomly to 200 respondents (students) who were about to enter high school. The data is accumulated using the help of Microsoft excel. The results obtained are that the prediction of high-class precision is 100%, while the prediction of low-class precision is 94.23%. The conclusion is that the extracurricular, cost and distance criteria need attention and improvement. This is because disinterest and low prediction are higher than interest with high prediction results.

Naïve Bayes Algorithm For Predicting Sales at the Pematang Siantar VJCakes Store Juwita Juwita; M. Safii; Bahrudi Efendi Damanik
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December
Publisher : Yayasan Literasi Sains Indonesia

Along with the development of the era, competition in the world of business and technology is overgrowing, so business people are competing to develop their business by utilizing existing technology to develop their business, and also so that their business always survives in the rapid business competition. Sales of cake products are expected to continue to increase profits, one of which is by providing products according to market demand so that there are no losses. So far, companies often experience losses because they do not have a system that can predict sales. This writing is done to implement and prove that the Naïve Bayes Algorithm can be used to predict sales of cakes at the VJCakes Pematangsiantar store. The research data is cake sales data consisting of 10 types of cakes with various sizes, tastes, and shapes, which were obtained from the VJCakes Pematangsiantar Store from June 2021 – March 2022. The results of the calculations that have been carried out show that the calculation process is manual and assisted with Rapid software. Miner is the same, which means that the calculation can be said to be successful by producing a probability table of each variable and an accuracy rate of 83.44% of the testing data that has been carried out, and knowing this can be informed to VJCakes to make better decisions in the future.

Application of Data Mining Classification C4.5 Patient Satisfaction with Tuan Rondahaim Simalungun Hospital Service Nadrah Fauziah; Muhammad Ridwan Lubis; Bahrudi Efendi Damanik
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December
Publisher : Yayasan Literasi Sains Indonesia

The purpose of this study was to produce a measuring instrument for patient satisfaction with hospital services. In order to further improve patient care. The method used in this study is the C4.5 Algorithm, where the data source used is a questionnaire/questionnaire technique which is given a questionnaire to the general public who visit the hospital. The results of this study are expected to provide input to the Tuan Rondahaim Hospital in Simalungun by using the C4.5 Algorithm. This can be done by using a decision tree model or decision tree in the C4.5 algorithm. In this study, the researchers used data from the patients of RSUD Tuan Rondahaim, totaling 105 patients through a questionnaire that the researchers distributed. The variables are Hospital Place (C1), Empathy (C2) and Responsiveness (C3). The testing process of this study uses Rapid Miner software to generate rules and a decision tree model or decision tree that will be used in determining the patient satisfaction factor for Tuan Rondahaim Hospital. The results of this study obtained 14 rules with an accuracy rate of 93.55%.

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