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All Journal Cakrawala Pendidikan CAUCHY: Jurnal Matematika Murni dan Aplikasi DINAMIKA DOTCOM Prosiding SNATIKA Vol 01 (2011) POSITIF Jurnal Kajian Pembelajaran Matematika JMPM: Jurnal Matematika dan Pendidikan Matematika Jurnal Manajemen Informatika Building of Informatics, Technology and Science Jurnal Karinov Scientific Journal of Informatics International Journal of Insights for Mathematics Teaching (IJOIMT) Indonesian Values and Character Education Journal Jurnal Pengabdian Masyarakat Asia Jikom: Jurnal Informatika dan Komputer Jurnal Sistem Komputer Asia Jurnal ilmiah teknologi informasi Asia
Puji Subekti
Institut Teknologi Dan Bisnis Asia Malang
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Social Sciences Other

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Journal : Building of Informatics, Technology and Science

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ObeCheck Sebagai Platform Penerapan Metode K-Nearest Neighbors untuk Klasifikasi Obesitas Berbasis Website Farihah, Lailatul; Subekti, Puji
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6726

Abstract

The increase in obesity has become one of the major challenges in the healthcare sector, requiring quick and effective solutions for early classification and diagnosis. This study aims to develop a web-based system using the K-Nearest Neighbors (KNN) method to classify obesity based on user data, thereby assisting the public in early detection of obesity. The dataset used in this research comprises 2,111 records and 17 attributes, covering various factors related to obesity, such as weight, height, age, gender, genetic factors, and lifestyle, including dietary habits and physical activity. This dataset was obtained from the UCI Repository website. The data is processed using the K-Nearest Neighbors (KNN) method to generate an accurate and relevant obesity classification model. To evaluate the performance of the K-Nearest Neighbors (KNN) model, the dataset was split into training and testing data with a ratio of 80:20 and evaluated using a Confusion Matrix, resulting in an accuracy of 89%. Since the model demonstrates good performance in classifying test data, it can be implemented as a web-based system to test new data. This system will produce weight classification results, including categories such as "Underweight," "Normal Weight," "Overweight," and "Obesity." Thus, the public can easily and accurately classify obesity using this system.
Co-Authors Adriani Kalalembang Agus Purnomo Sidi Anjaningrum, Widiya Dewi Azwar Riza Habibi, Azwar Riza Fadhli Almu’iini Ahda Farihah, Lailatul Hanif, Rifki Henry, Rivaldo Hironimus Hari Kurniawan Ida Nuryana, Ida Jaenal Arifin Justita Dura Laksono Trisnantoro Layli Dwi Rohmatunnisa Lilis Widayanti Lukman Hakim Lukman Hakim Mufidatul Islamiyah Mufidatul Islamiyah Mufidatul Islamiyah Mufidatul Islamiyah Mufidatul Islamiyah, Mufidatul mulyaningtyas, mulyaningtyas Nur Lailatul Aqromi Puspa Dewi, Mariana Setyorini Setyorini Siti Nurul Afiyah Suastika Yulia Riska Titania Dwi Andini Wa Ode Irma Sari Widya Adhariyanty Rahayu Yogatama, Ahmad Nizar