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All Journal Sainteks AKSIOMA: Jurnal Program Studi Pendidikan Matematika QALAMUNA: Jurnal Pendidikan, Sosial, dan Agama Metode : Jurnal Teknik Industri JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Nasional Komputasi dan Teknologi Informasi Psychology, Evaluation, and Technology in Educational Research Jurnal Kridatama Sains dan Teknologi Journal of Innovation and Future Technology (IFTECH) Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Sistem Informasi dan Sistem Komputer Mejuajua Jurnal Dinamika Informatika (JDI) BERBAKTI: Jurnal Pengabdian Kepada Masyarakat Jurnal Abdimas Indonesia Jurnal Bisnis Digital dan Sistem Informasi Jurnal Ilmu Komputer dan Matematika
Fida Maisa Hana
Universitas Muhammadiyah Kudus
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PREDIKSI STUNTING PADA BALITA DI RUMAH SAKIT KOTA SEMARANG MENGGUNAKAN NAIVE BAYES Widya Cholid Wahyudin; Fida Maisa Hana; Agung Prihandono
JURNAL ILMU KOMPUTER DAN MATEMATIKA Vol 4, No 1 (2023): JURNAL ILMU KOMPUTER DAN MATEMATIKA
Publisher : Universitas Muhammadiyah Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26751/jikoma.v4i1.1792

Abstract

Stunting is chronic malnutrition caused by insufficient nutritional intake over a long period of time due to the provision of food that is not in accordance with needs. This study focuses on malnutrition in toddlers. Stunting in toddlers is more common in toddlers aged 12-59 months than toddlers aged 0-24 months. Stunting can have short and long-term impacts. This study used toddler data for 2018 which was obtained from the Semarang City Health Center with toddlers aged 0-59 months. This research aims to value the classification results of stunting nutritional status in toddlers using the Naive Bayes Classifier algorithm. The Naive Bayes Classifier algorithm is one of the algorithms used for the classification process that can solve problems with large amounts of data so that it can produce a probability value for a hypothesis that is sought. It is proved by the results of testing with the Naive Bayes Classifier algorithm, which was carried out on all data in a dataset of 300 records, the accuracy achieved is 85.33%.
Co-Authors Achmad Ridwan Achmad Ridwan, Achmad Ade Ima Afifa Himayati Ade Kurnia Bayu Firdaus Afrisia, Silviana Putri Agung Prihandono Ahadin, Akbar Ihsanul Ahmad, Saiful Andi Kurniawan Arlinwibowo, Janu Avira Budianita Cikita Berlian Hakim Deka Setia Negara Dhina Cahya Rohim Diva Anastasya Dwi Elysia Dinata Eko Hari Rachmawanto Elisya Pujasari Fadhila, Vera Farikha Tuzzahro Firmansyah, Nunung Agus Hafni Aulida Hafni Aulida Harjanti, Agustina Eka Hawa Suci Hermawan Abdillah Hamka Ihsanul Ahadin, Akbar Imam Abdul Rozaq Imam Prayogo Pujiono Indanah Janu Arlinwibowo Kalimah, Sinta Nur Khoirul Umam Haqiqi Mahdarul Huda Ahmad Manggalastawa, Manggalastawa Moh Akbar Munajad Muadzah, Muadzah Nurul Iman Rahmawan Bagus Trianto Rangga Primadasa Sabiq Ismail Saharani, Safira Saiful Ulya Sayyidati Maulia Ashfa Siti Mahgfiroh Soma Setiawan Soma Setiawan P.N Syahrul Azmi Nawaf Taftazani Ghazi Pratama Tri Febriani Dewi Widya Cholid Widya Cholid Wahyudin Widya Cholid Wahyudin Yunus Mustaqim Yunus Mustaqim, Yunus