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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer AdMathEduSt: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Jurnal Khatulistiwa Informatika Insect (Informatics and Security): Jurnal Teknik Informatika METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Inotera Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) Jurnal Ilmiah Mandala Education (JIME) Jurnal Ilmiah Tatengkorang Jurnal Pengabdian Masyarakat Borneo IJISTECH J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Masyarakat Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Journal of Advanced Health Informatics Research Jurnal Informatika Polinema (JIP) Jurnal Multidisiplin Pengabdian Masyarakat (JMPM)
Hanif, Kharis Hudaiby
Universitas Borneo Tarakan
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Journal : METHOMIKA: Jurnal Manajemen Informatika

 1 
PENERAPAN ALGORITMA DECISION TREE, SVM, NAÏVE BAYES DALAM DETEKSI STUNTING PADA BALITA Hanif, Kharis Hudaiby; Muntiari, Novita Ranti
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 1 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No1.pp105-109

Abstract

Stunting is a toddler's body condition that is short according to body length according to age (PB/U), ≤ 2 Standard Deviations (SD), with a z-score between -3 standard deviations (SD). Where checking the stunting status of toddlers takes quite a long time because it is done manually and is also prone to errors. Therefore, it is hoped that a system can classify toddler examination data quickly and accurately to predict children's stunting status. Building a system that uses an algorithm to classify the stunting status of toddlers usingdecision tree, naïve bayes, andSVM. With what level of accuracy is the best of the 3 algorithms? Results from testing with 30% testing data and 70% training data using an algorithmdecision tree, naïve bayes, and SVM. Accuracy level test resultsdecision tree by 99%,naïve bayes of 48%, and SVM of 95%. So, the algorithm with the highest level of accuracy isdecision tree amounts to 99%. Wallet hiredecision tree better for detecting stunting in toddlers
PENERAPAN ALGORITMA DECISION TREE, SVM, NAÏVE BAYES DALAM DETEKSI STUNTING PADA BALITA Hanif, Kharis Hudaiby; Muntiari, Novita Ranti
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 1 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No1.pp105-109

Abstract

Stunting is a toddler's body condition that is short according to body length according to age (PB/U), ≤ 2 Standard Deviations (SD), with a z-score between -3 standard deviations (SD). Where checking the stunting status of toddlers takes quite a long time because it is done manually and is also prone to errors. Therefore, it is hoped that a system can classify toddler examination data quickly and accurately to predict children's stunting status. Building a system that uses an algorithm to classify the stunting status of toddlers usingdecision tree, naïve bayes, andSVM. With what level of accuracy is the best of the 3 algorithms? Results from testing with 30% testing data and 70% training data using an algorithmdecision tree, naïve bayes, and SVM. Accuracy level test resultsdecision tree by 99%,naïve bayes of 48%, and SVM of 95%. So, the algorithm with the highest level of accuracy isdecision tree amounts to 99%. Wallet hiredecision tree better for detecting stunting in toddlers
Co-Authors Abdul Fadlil Abdul Fadlil Adrian Premy Ashary Annida Zakkiah, Annida Zakkiah Anton Yudhana Arif Fadllullah Aris, Ibrahim Asma Asma Awang Pradana, Awang Dedy Harto Dewi Astria Faroek Eko Prasetyo Fadllullah, Fadllullah Faqih, Radiansyah fath, Aqil Miftahul Hafidz, Muhammad Nasir Harina Fitriyani Herawati, Lily Indah Chairun Nisa Irgi Ahmad Fahrezi Mochammad Imron Awalludin Muhammad Aditya, Muhammad Aditya Muntiari, Novita Ranti Murdianto, Deny Noviani, Doris Novita Ranti Muntiari Nur Hasanah Perangin Angin, Nur Hasanah Perangin Angin Perangin Angin, Nur Hasanah Purwono P Putri Ananda Ramadhani Rahmatullah Rahmatullah Ririn Ariyanti, Ririn Rudy Rusydi Umar Sabtiandy, Dhandy Sakti, Fijaya Bima Dwi susi susi Umami, Nurrahmi Wahdana, Wahdana Wahyuni Arsianti, Rika