Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Pengabdian Pada Masyarakat METHABDI
Ommi Alfina
Universitas Pembinaan Masyarakat Indonesia
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Immunology & microbiology Languange, Linguistic, Communication & Media Medicine & Pharmacology Physics Public Health

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Hybrid Feature Selection dan Ensemble Learning untuk Klasifikasi Risiko Stunting Anak di Indonesia Ommi Alfina; Nita Syahputri; M. Safii; Muhammad Taufiq Rustam
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp107-111

Abstract

Stunting is a chronic nutritional problem that remains a major public health issue in Indonesia. This study aims to develop a classification model for stunting risk in children using a combination of hybrid feature selection and ensemble learning methods. The dataset used is derived from socio-economic and health data obtained from the Central Statistics Agency and open datasets. The research method includes data preprocessing, feature selection, model development using Random Forest and Gradient Boosting combined with a Voting Classifier, and evaluation using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The results show that the proposed model achieves high performance with accuracy reaching 98% and ROC-AUC close to 1. The hybrid feature selection successfully improves model efficiency by selecting relevant features. This study demonstrates that the integration of feature selection and ensemble learning can produce an accurate and interpretable model for early detection of stunting risk.
Implementasi Smart Attendance System Berbasis RFID dan Face Recognition untuk Meningkatkan Efisiensi Presensi Siswa Pada SMK PAB 8 Sampali Medan Ommi Alfina; Nita Syahputri; Habib Nurlutman Hasibuan; M. Safii; Muhammad Taufiq Rustam
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 6 No 1 (2026): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methabdi.Vol6No1.pp42-49

Abstract

The development of digital transformation in the field of education encourages schools to adopt technology that can improve the effectiveness of academic administration, particularly in the student attendance system. SMK PAB 8 Sampali Medan still faces several problems in the conventional attendance process such as delays in recording, potential manipulation of attendance, and suboptimal monitoring of student attendance data. This community service activity aims to implement a Smart Attendance System based on RFID and Face Recognition to improve the efficiency, accuracy, and security of the student attendance process. The implementation of the activity is carried out through stages of observing the school's needs, system design, installation of RFID devices and face recognition cameras, training teachers and school operators in using the system, and assistance in implementation. The results of the activity show that the implemented system is capable of accelerating the student attendance process in real-time, minimizing recording errors, improving student attendance discipline, and facilitating the school in monitoring and digitally summarizing attendance data. In addition, this activity also improves technology literacy for teachers and administrative staff in supporting the digital transformation of education within the school environment.
Co-Authors Habib Nurlutman Hasibuan M. Safii Muhammad Taufiq Rustam Nita Syahputri