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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Creative Information Technology Journal Jurnal Pilar Nusa Mandiri Syntax Literate: Jurnal Ilmiah Indonesia Technomedia Journal EXPLORE EDUMATIC: Jurnal Pendidikan Informatika M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Journal of Information Systems and Informatics Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat MEANS (Media Informasi Analisa dan Sistem) Jurnal Teknik Informatika (JUTIF) Journal of Electrical Engineering and Computer (JEECOM) Information System Journal (INFOS) SmartComp Intechno Journal : Information Technology Journal Scientific Journal of Informatics
Anggit Dwi Hartanto, Anggit Dwi
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Analisis Perbandingan Algoritma SVM, Random Forest dan Logistic Regression untuk Prediksi Stunting Balita Febriyanti, Nada Rizki; Kusrini, Kusrini; Hartanto, Anggit Dwi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29407

Abstract

The prevalence of stunting in Banjarmasin City in 2023 reached 26.5%, exceeding the WHO target (below 20%). Stunting impacts physical growth, cognitive development, and long-term economic productivity. The purpose of this study is to compare the performance of SVM, random forest, and logistic regression algorithms in classifying the stunting status of toddlers. The approach we use is comparative quantitative with machine learning methods for health data classification. Data totaling 2,231 under-five records were obtained from the Banjarmasin City Health Office. We used age, weight, height, and z-score information. Data preprocessing includes handling missing values, categorical data transformation, numerical data standardization, and feature selection. The dataset was divided into 70:30 and 80:20 ratios using stratified sampling with 5-fold cross-validation. Our results show that SVM is the best model, with accuracy 92%, precision 91%, recall 99%, F1-score 95%, and AUC 99%, followed by random forest (accuracy 91%, AUC 98%) and logistic regression (accuracy 92%, AUC 97%). SVM showed superior performance due to its ability to find the optimal hyperplane that maximally separates stunted and non-stunted classes, as well as its effectiveness in handling non-linear data through kernel tricks. SVM's good generalization ability on new data makes it a top choice as a predictive tool for stunting prevention in Banjarmasin City.
Analisis Perbandingan Metode Tf-Idf dan Word2vec pada Klasifikasi Teks Sentimen Masyarakat Terhadap Produk Lokal di Indonesia Hendrawan, Ivan Rifky; Utami, Ema; Hartanto, Anggit Dwi
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 11, No 3 (2022): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v11i3.3902

Abstract

Secara umum, proses menghasilkan setiap ulasan produk pada dasarnya terkait dengan tingkat rating, yang membuat pengguna memberikan komentar yang bias.Analisis sentimen dapat diterapkan pada ulasan produk Marketplace sehingga dapat digunakan sebagai saran perbaikan produk untuk penjual dan pesaing sehingga dapat mengetahui produk apa yang disenangi dan dibutuhkan oleh masyarakat.Penelitian ini  menggunakan algoritma XGBoost dengan menggunakan dataset bahasa Indonesia yang dikombinasikan dengan TF-IDF dan Word2vec dan akan dievaluasi kombinasi mana yang lebih baik dalam mengklasifikasikan data teks yang tidak seimbang.Berdasarkan penelitian yang telah dilakukan, dua vector space TF-IDF dan Word2vec menghasilkan nilai F1-Score yang berbeda pada algoritma klasifikasi XGBoost, kombinasi Word2vec+XGboost menghasilkan nilai F1-Score lebih tinggi 0.941% dibanding TF IDF+XGBoost 0.940%. Hal ini dikarenakan word2vec lebih baik karena memiliki keunggulan dapat melihat hubungan semantik antar kata.Kata kunci: word2vec, tfidf, sentimen analisis, XGBoost,
Co-Authors Adiatma, Biva Candra Lutfi Aji Santoso, Bayu Ananda, Fadhilah Dwi arif nur rohman Arif Santoso Astica, Yustikamasy ATIK NURMASANI Budi, Agung Prasetio Christyawan, Fajar Ema Utami Fajri, Ika Nur Febriyanti, Nada Rizki Fiardin, Tsintani Tisaga Fitriyani, Nurul Khasanah Hartatik Hedy Leoni Hendrawan, Ivan Rifky Hidayat, Jati Arif Iedwan, Aisha Shakila Iskandar, Ahmad Fikri Julianto, M. Rayhan Wahyu Kencana, Adi Kresna Khairullah, Irfan Khalil Kurniawan, Aas Andri Kurniawan, Febri Dwi Kusnawi Kusnawi Kusrini Linda, Kumara Dewi Mauliza, Nia Pratama, Jibran Yoga Purwoko, Joshua Dwi Giovani Ramadhan, Muhammad Jauharul Ramadhani, Zanuarestu Rasyid, Muhammad Khulqi Safira, Livia salisah, Tuhfatus Sari, Bunga Permata Setiadi F., A. Farhan Setiawan, Annas Setiawanda, Yongky Budi Syahrudin, Erwin Tara, Yudi Tuhpatussania, Siti Wahyu Hidayat wahyuni, wenti ayu Wasistha, Dyah Wicaksono, Anggito Whiku Wisnu Nugroho Aji, Wisnu Nugroho Yoga Pristyanto Yulianto, Yogi Yusa, Aldo