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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research
Rizal Lamusu
Universitas Muhammadiyah Gorontalo, Gorontalo
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Efektivitas Media Pembelajaran Online Terhadap Hasil Belajar Siswa Muhammadiyah di Gorontalo Mohamad Ilyas Abas; Nursetiawati Nursetiawati; Rizal Lamusu; Irawan Ibrahim
TIN: Terapan Informatika Nusantara Vol 3 No 4 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v3i4.4108

Abstract

This research aims to analyze the learning methods that have been carried out so far at Muhammadiyah schools in Gorontalo. Learning media such as WhatsApp, Google Form, Zoom, Google Meet and so on are effective and have an impact on student learning outcomes. It is true that online learning has experienced setbacks in terms of learning achievement, but this is important to know the development of learning at Muhammadiyah schools in Gorontalo. The research method used is qualitative with descriptive analysis. The results of the research show that online learning has generally been successfully implemented for students at Gorontalo Muhammadiyah schools, but its implementation has not run optimally compared to offline learning
Penerapan Algoritma Naive Bayes Untuk Sistem Klasifikasi Status Gizi Bayi Balita Mohamad Ilyas Abas; Rizal Lamusu; Widya Eka Pranata; Syahrial Syahrial; Irawan Ibrahim; Wahyudin Hasyim; Verliana Kiayi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.508

Abstract

Infants and toddlers are in a critical period of rapid growth and development, often referred to as the "golden age." During this stage, regular nutritional assessments are essential to monitor health status and detect potential nutritional problems early. This study aims to classify the nutritional status of infants and toddlers using the Naïve Bayes algorithm, a probabilistic classification method based on Bayes' theorem with a strong assumption of attribute independence. The main attributes used in the classification system include age, weight, and height. The dataset consists of 700 records of infants and toddlers collected from previous observations. The results show that the Naïve Bayes algorithm can be effectively implemented for nutritional status classification, achieving a system accuracy of 88.14%. This indicates that the method performs well and has the potential to be utilized in decision support systems for child health monitoring.
Co-Authors Ibrahim, Irawan Mohamad Ilyas Abas Nursetiawati Nursetiawati Syahrial Syahrial Verliana Kiayi Wahyudin Hasyim Widya Eka Pranata