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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Darma Agung
Eliviani, Rosa
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Social Sciences

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PREDIKSI KELAYAKAN KREDIT PEMAKAI PONSEL PINTAR DI INDONESIA MENGGUNAKAN ALGORITMA K NEAREST NEIGHBOR (KNN) PASCA PANDEMI Winahyu, Raden Rara Kartika Kusuma; Eliviani, Rosa; Saputro, Vian Ardiyansyah; Winda, Athar
JURNAL DARMA AGUNG Vol 31 No 6 (2023): DESEMBER
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Darma Agung (LPPM_UDA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46930/ojsuda.v31i6.3897

Abstract

Dalam studi ini, kami bertujuan untuk menggunakan algoritma pembelajaran mesin untuk memprediksi kelayakan kredit pemakai ponsel pintar di Indonesia pasca pandemi COVID-19. Algoritma pembelajaran mesin Principal Component Analysis (PCA) dan algoritma K-means digunakan untuk mengurangi ukuran dimensi dataset dan menggolongkan peringkat kepercayaan dari dataset yang berisi 803 responden, termasuk 12 pertanyaan yang disajikan kepada pemakai ponsel pintar Indonesia pasca pandemi COVID-19. Algoritma klasifikasi KNN diterapkan untuk mengklasifikasikan kepercayaan pemakai ponsel pintar di Indonesia. Tes yang dilakukan termasuk akurasi, presisi, recall, dan F1-score. Hasil penelitian ini menunjukkan bahwa algoritma klasifikasi KNN mencapai tingkat akurasi 0,84, tingkat presisi 0,85, tingkat recall 0,84 dan skor F1 0,84.
Stunting Prediction Modeling in Toddlers Using a Machine Learning Approach and Model Implementation for Mobile Application Abdul Goffar, Eko; Eliviani, Rosa; Ayu Wulandhari, Lili
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6450

Abstract

Children’s health and development are critical for maintaining national productivity and independence, with stunting being a major concern. Stunting, a form of malnutrition, impairs growth and development, affecting millions of people globally, including a significant number in Indonesia. This study addresses the challenge of stunting by developing a predictive model using machine learning techniques to forecast stunting risks based on public health data. The literature review section discusses the factors that influence stunting, and these factors are used as features to build a stunting prediction model. Then the features were used to build a model with three machine learning algorithms Extreme Gradient Boosting (XGBoost), Random Forest, and K-Nearest Neighbor (KNN) to build and evaluate models that predict stunting. The models were trained and assessed using public datasets and the most effective algorithm was integrated into a mobile application for practical use. The results indicate that the XGBoost model outperforms the other models with an accuracy of 85%, making it the optimal choice for implementation in a mobile application. The next-best model is selected to be implemented through a mobile application so that users can directly use the model that has been built. This application aims to enhance early detection and intervention efforts for stunting, potentially improving child health outcomes and contributing to long-term productivity by building predictive models and implementing the models into a mobile application. This study contributes to the implementation of models built using public data for application in mobile applications.
Co-Authors Abdul Goffar, Eko Alfian, Nurkholis Ayu Wulandhari, Lili Candra, Nofri Wahyu Oey, Irvan Kurnia Wicaksono Saputra, Lucky Dwi Saputro, Vian Ardiyansyah Winahyu, Raden Rara Kartika Kusuma Winda, Athar