Article Per Year (5 Year)
p-Index From 2021 - 2026
6.64
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi Pendas : Jurnah Ilmiah Pendidikan Dasar DoubleClick : Journal of Computer and Information Technology Jurnal Riset Pendidikan Dasar Educate : Jurnal Teknologi Pendidikan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Edum Journal Edunesia : jurnal Ilmiah Pendidikan Didaktik : Jurnal Ilmiah PGSD STKIP Subang Jurnal Teknimedia: Teknologi Informasi dan Multimedia ELEMENTARY : Jurnal Inovasi Pendidikan Dasar Jurnal Pendidikan dan Pengajaran (JPP) Djtechno: Jurnal Teknologi Informasi Jurnal Inovasi Sains dan Teknologi (INSTEK) Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI) Bima Abdi: Jurnal Pengabdian Masyarakat JagoMIPA: Jurnal Pendidikan Matematika dan IPA LEARNING : Jurnal Inovasi Penelitian Pendidikan dan Pembelajaran EDUTECH : Jurnal Inovasi Pendidikan Berbantuan Teknologi SCIENCE : Jurnal Inovasi Pendidikan Matematika dan IPA COMMUNITY : Jurnal Pengabdian Kepada Masyarakat Edu Cendikia: Jurnal Ilmiah Kependidikan Education Achievement: Journal of Science and Research Decode: Jurnal Pendidikan Teknologi Informasi Amaliah: Jurnal Pengabdian Masyarakat Pustaka Karya : Jurnal Ilmiah Ilmu Perpustakaan dan Informasi Malcom: Indonesian Journal of Machine Learning and Computer Science Altifani : Jurnal Pengabdian Masyarakat Ushuluddin, Adab, dan Dakwah Jurnal Penelitian Ilmu Pendidikan Indonesia Amal Ilmiah: Jurnal Pengabdian Kepada Masyarakat Jurnal Amal Pendidikan PPSDP International Journal of Education Socius: Social Sciences Research Journal At- Ta'lim : Jurnal Pendidikan Jurnal Informatika
Razilu, Zila
Program Studi Pendidikan Teknologi Informasi, Universitas Muhammadiyah Kendari
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Physics Social Sciences Other

Published : 53 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Informatika

 1 
Comparative Analysis of Naïve Bayes Variants for Predicting Stunting-Risk Families Muis, Alwas; Razilu, Zila; Senga, Umy Ramadhani; Wulandari, Hutri; Andriyani, Reva
Jurnal Informatika Vol. 13 No. 1 (2026): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v13i1.11910

Abstract

Stunting is a chronic nutritional condition that adversely affects children’s physical growth and cognitive development, highlighting the need for effective early detection, particularly at the household level. This study proposes a comparative analysis of three Naïve Bayes variants Gaussian, Multinomial, and Bernoulli to identify families at risk of stunting using machine learning techniques. The dataset used in this study consists of family-level records obtained from the National Population and Family Planning Agency (BKKBN) of Southeast Sulawesi Province, comprising demographic, socioeconomic, and health-related attributes. Data preprocessing involved handling missing values, removing irrelevant attributes, and transforming categorical variables. The dataset was divided into training and testing sets using an 80:20 ratio. The main contribution of this study lies in evaluating the effectiveness of different Naïve Bayes variants for family-based stunting risk prediction, which has been rarely explored in previous studies. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results indicate that the Bernoulli Naïve Bayes model achieved the best performance, with an accuracy of 88% and balanced evaluation metrics across both classes. These findings suggest that the Bernoulli Naïve Bayes model is the most suitable approach for predicting family-level stunting risk and can support data-driven early intervention strategies.
Co-Authors Abidin, Khoirul Adi Pratama, Adi Alfiah Fajriani Alfiah Fajriani Andriyani, Reva Arif, Andi Muhammad Arsyil, Nurcholis Asgar Asmayanti, Nila Aviani, Fenti Nur Baso, M. Rifqi Fatahillah Cahyani, Gita Darman Darman Darman Darman Dermawan, Gugun Dermawan, Imam DILA, DILA Edisus Suseng Endriani, Endriani Erik Erik, Erik Fajriani, Alfiah Farhan, Burait Fatmawati, Ifan Febriadin Anggriawan Felisa, Agista Salsa Fitriani HP, Dinda Fitrianingsi, Elsa Habesia, Habesia Hadis, Hamdan Muh Hasim, La Ode Hendra Nelva Hendra Nelva Saputra Hidayath, Ahmad Insiani, Naya Iskandar, Bobi Johan, Jusni Kaulia, Yumi Landebila, Jumiatin Lilianti lilianti M, Mirnawati Mahmud Mahmud Marjin, Muh. Tri Wahyu Maulid Rahman Melinda, Elsin Muh Khalladun Attamimi Muh Tino Kabarak Ballagi Muhammad Ahyar Muhammad Akbar Al Maruf Muhammad Akbar Al Maruf Muhammad Akbar Yasin Muharam, Agil Muis, Alwas Nining Adriana Nuraeni Nuraeni Nurhasmiati Nurzaima Puspitasari, Rati Rahmawati, Rahmawati Ramadan, Aerus Ramadani, Novi Ridwan, Muh. Yusrifal Rifai, Sitti Najmia Rillan, Yasset Ririn Ririn Roziq, Fahrul Arifatul Sahir, Muhammad Sahrul Sahrul Sake, Rhesty Said Salim, Umar Samsul Saliha Samusu Samusu Satrio Pangestu Senga, Umy Ramadhani Sufari, Fadila Sulfasyah Sulfasyah, Sulfasyah Suseng, Edisus Syamsiarna Nappu, Syamsiarna Usman Usman Wulandari, Hutri Yuda Ferdiansyah Yudas, Chiko Yuardan Yuni, Sulis Tika Yunus, Ardiansyah