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All Journal Techno.Com: Jurnal Teknologi Informasi Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) JAIS (Journal of Applied Intelligent System) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control EDUMATIC: Jurnal Pendidikan Informatika Jurnal Informatika dan Rekayasa Perangkat Lunak Teknosains : Jurnal Sains,Teknologi dan Informatika International Journal of Artificial Intelligence and Robotics (IJAIR) Mathematics and Applications (MAp) Journal Jurnal Ekonomi dan Manajemen (JEKMA) Journal of Artificial Intelligence and Digital Business Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Seminar Nasional Teknologi dan Multidisiplin Ilmu Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Kepada Masyarakat Jurnal Maisyatuna Synergy In Economic & Business Management (SEBM)
Moch. Sjamsul Hidajat
Universitas Dian Nuswantoro Kediri
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Transportation Other

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

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Klasifikasi Stunting pada Balita menggunakan Algortima Gradient Bossting Clasifier Azhari, Daffa Maulana; Hidajat, Moch Sjamsul
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27502

Abstract

Stunting is a significant public health problem, impacting the physical and cognitive growth and development of children under five. In Indonesia, stunting is a major issue caused by a lack of nutritional intake since birth, including in the city of Semarang. This study aims to compare the performance of K-Nearest Neighbor (KNN), Naïve Bayes, and Gradient Boosting Classifier algorithms in classifying stunting in toddlers, to find the best model. The data used is quantitative data from posyandu, consisting of 1288 samples with variables including Name, Gender, Age, Date of Birth, Parent's Name, Village, Rt, RW, weight, height, arm circumference, and Z-score. After data collection, a data preprocessing process is carried out to clean and prepare the data. The data was divided into training and test data with a ratio of 80:20, 70:30, and 60:40, which were then trained and tested using the three algorithms. The best model was further evaluated with K-Fold Cross Validation to assess the stability and generalizability of the predictions. Model evaluation uses accuracy, precision, recall and F1-Score metrics. The results showed that Gradient Boosting Classifier gave the best performance with 99.92% accuracy, 99.92% precision, 99.92% recall, and 99.92% F1-score. This study concludes that the Gradient Boosting Classifier is the most optimal model in the classification of stunting in toddlers, giving the best precision results.
Co-Authors Ainun Muhtadin Ali, Rabei Raad Angel Frilyaningrum Aulia Rahmawati Azhari, Daffa Maulana Bhekti Eka Ferdianto Bonifacius Vicky Indriyono Budi Widjajanto Choerul Umam Daffa Hafizh Dzamara Daffa Maulana Dea Debora Aprillia Dibyo Adi Wibowo Ery Mintorini Ery Mintorini Evita Citra Yustiqomah Ichwan Setiarso Ida Farida Iffah Irdinawati Imam Nuryanto Iqlima Zahari Jaka Prasetya karmila karmila Lewa, Andi Hallang Muhammad Burhanudin, Muhammad Nagata, Azaria Beryl Natanael, David Febrian Pratama, Zudha Prayugo, Moh. Lambang Puspa, Silfi Andriana Rizal Muhammad Affandi Setiawan, Aries Stevani Tri Wahyu Putri Toni Wibowo Tri Esti Rahayuningtyas Widyatmoko Widyatmoko Wildan Mahmud Yaacob, Noorayisahbe Bt Mohd Yulianti, Shinta Rachma