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All Journal INTERNATIONAL JOURNAL OF MECHANICAL COMPUTATIONAL AND MANUFACTURING RESEARCH Technologia Journal
Angga Pramudianto
STMIK AMIKOM Yogyakarta
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Application of Naïve Bayes Classifier Algorithm for Classification of Scholarship Recipients at SMA PGRI 2 Bandung Jayadi, Jayadi; Raharja, Agung Rachmat; Pramudianto, Angga; Muchsam, Yoki
International Journal of Mechanical Computational and Manufacturing Research Vol. 13 No. 2 (2024): August: Mechanical Computational And Manufacturing Research
Publisher : Trigin Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/computational.v13i2.169

Abstract

Education is one of the rights that each Indonesian citizen has. In this manner, there's a require for government consideration to assist underprivileged individuals in instruction, to be specific through grants. SMA II PGRI is one of the schools that organizes an underprivileged grant program, the grant is within the frame of educational cost waiver help. In its execution, officers regularly have trouble in deciding understudies who get grants based on foreordained criteria. In this way, it is fundamental to analyze information mining procedures utilizing naïve bayes classifiers in information preparing and is anticipated to be utilized to analyze grant grants for grant candidates. In this way it is fundamental to analyze information mining procedures utilizing naïve bayes classifiers in information handling and is anticipated to be utilized to analyze grants for grant candidates. Grant candidate information is carried out a information cleaning and decrease prepare so that the information is less but enlightening, so that it is appropriate for assist handling. At that point classification is done with Naïve Bayes classifier to deliver a classification likelihood demonstrate. The test brought about in an precision of 87.39% and was included within the great classification criteria.
Penerapan Algoritma Decision Tree dalam Klasifikasi Data “Framingham” Untuk Menunjukkan Risiko Seseorang Terkena Penyakit Jantung dalam 10 Tahun Mendatang Raharja, Agung Rachmat; Jayadi; Pramudianto, Angga; Muchsam, Yoki
Technologia Journal Vol. 1 No. 1 (2024): Tecnologia Journal-February
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/cwgzp962

Abstract

Heart disease is one of the deadliest diseases in Indonesia and in the world, many of the sufferers do not know the risk of heart disease. This research will analyze and identify factors that will affect the risk of heart disease. With the decision tree algorithm approach which is one of the methods of machine learning that can produce predictive models based on a series of logical decisions. The application is done by classifying framingham data to assess the risk of heart disease in the next 10 years. The result is a Decision Tree model used to predict the risk of heart disease based on the Framingham dataset. The model achieved 74% accuracy on the test data
Co-Authors Agung Rachmat Raharja Jayadi Jayadi Jayadi Muchsam, Yoki