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Betrisandi, Betrisandi
Universitas Pohuwato
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Comparison of NBC and KNN in Classifying Stunting in Children in Rural Areas Betrisandi, Betrisandi; Thaib, Rahmat
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.34488

Abstract

Stunting is one of the chronic nutritional problems that remains a serious concern in Indonesia. Children who experience stunting not only experience physical growth retardation, but also cognitive development disorders that have the potential to reduce intelligence, academic achievement, and productivity in adulthood. The problem in this study is the high prevalence of stunting in children in rural areas. The purpose of this study is to analyse the performance of the Naïve Bayes Classifier (NBC) and K-Nearest Neighbour (KNN) and compare the performance of the two methods to determine the most optimal method for classifying stunting status in children in accordance with the Research Master Plan with a focus on engineering and technology for improving ICT content and the research topic of big data technology development. The research methods used included data collection through observation and interviews. Data processing and analysis were carried out by comparing the NBC and KNN methods in classifying child stunting. The results of this study indicate that the NBC method has higher accuracy, namely 95.24% and an F1-score of 97%, compared to the KNN method, which has an accuracy of 76.19% and an F1-score of 86%. Therefore, the KNN method is more optimal for use in classifying stunting in children.
KLASIFIKASI NASABAH ASURANSI JIWA MENGGUNAKAN ALGORITMA NAIVE BAYES BERBASIS BACKWARD ELIMINATION Betrisandi, Betrisandi
ILKOM Jurnal Ilmiah Vol 9, No 1 (2017)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v9i1.116.96-101

Abstract

Pendapatan untuk perusahaan asuransi ditentukan oleh jumlah premi yang dibayar oleh nasabah. Banyaknya nasabah yang tidak lancar membayar premi berpengaruh terhadap kinerja serta eksistensi perusahaan sehari-hari. Algoritma Naive Bayes berbasis Backward Elimination bertujuan untuk melakukan klasifikasi nasabah asuransi dengan hasil akurasi 85,89 % dengan delapan atribut weight yaitu umur, jangka waktu, cara bayar, premi, jumlah hari, pekerjaan, penghasilan dan mata uang
Co-Authors Abdul Yunus Labolo Anas Anas Annahl Riadi Bahrin Bahrin Bahrin Bahrin Drajana, Ivo Colanus Rally Ivo Colanus Rally Drajana Maryam Diange Maryam H Dumako Maryam Hasan Rahmat Thaib, Rahmat Thaib Sudirman S. Panna Sulaehani, Ruhmi Thaib, Rahmat Warid Yunus