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All Journal Jurnal Mandiri IT Journal Of Human And Education (JAHE)
Manoppo, Franky Gerald Cliford
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Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Village Apparatus Training Regarding Good Village Fund Management in Indonesia Simbolon, Rosdiana; Tabita, Marcelitha; Aktama, Gede Erik; Manoppo, Franky Gerald Cliford; Simbolon, Merson
Journal Of Human And Education (JAHE) Vol. 5 No. 1 (2025): Journal of Human And Education (JAHE)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jh.v5i1.1985

Abstract

Abstract This community service was carried out in Taduaog village, Buleleng district, North Sulawesi province, within a period of two weeks with the stages of problem formulation and solution. the author believes that training on how to use village funds for two weeks in Taduaog Village, North Sulawesi is believed to be a solution to the problem of using village funds that are less productive and still use old methods. It is hoped that with this training, the skills of the Taduaog Village apparatus, North Sulawesi can increase and become a solution to existing problems. Keywords: Vliiage Apparatus, Training, Village Fund
Comparison of decision tree and naive bayes methods in glioma classification based on clinical and molecular factors Dewi, Ni Wayan Emmy Rosiana; Putra, I Made Suwija; Simanungkalit, Erwinsyah; Manoppo, Franky Gerald Cliford
Jurnal Mandiri IT Vol. 13 No. 4 (2025): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i4.389

Abstract

This study compares the performance of Decision Tree and Naive Bayes classifiers in classifying gliomas based on clinical and molecular factors. The dataset consists of 839 patient records with features including Grade, Gender, Age, Race, and gene mutation status. The evaluation showed that the Decision Tree classifier achieved 98% accuracy on the training data and 76% on the test data, while the Naive Bayes classifier obtained 74% and 71% accuracy, respectively. Both models demonstrated strong predictive ability, with feature importance analysis highlighting the IDH1 gene mutation as a significant factor in glioma classification. This study aims to identify the most effective method for supporting clinical decision-making in glioma diagnosis. It contributes to the development of medical decision support systems and provides insight into the application of machine learning models, particularly in utilizing molecular markers such as IDH1.
Co-Authors Dewi, Ni Wayan Emmy Rosiana Gede Erik Aktama Putra, I Made Suwija Simanungkalit, Erwinsyah Simbolon, Merson Simbolon, Rosdiana Tabita, Marcelitha