Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Profesi Pendidikan Journal of Educational Technology and Learning Creativity
Hadi Almohab
Nusa Putra University
Computer Science & IT Education

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

Found 2 Documents
Search

 1 
Deep Learning CNN for Pneumonia Detection: Advancing Digital Health in Society 5.0 Hadi Almohab
Jurnal Ilmiah Profesi Pendidikan Vol. 10 No. 4 (2025): November
Publisher : Fakultas Keguruan dan Ilmu Pendidikan, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jipp.v10i4.4001

Abstract

Pneumonia merupakan masalah kesehatan global yang serius dan menyumbang tingkat morbiditas serta mortalitas yang tinggi, terutama di wilayah dengan keterbatasan alat diagnostik dan sumber daya kesehatan. Penelitian ini bertujuan mengembangkan model Convolutional Neural Network (CNN) berbasis deep learning untuk mendeteksi pneumonia secara otomatis menggunakan citra X-ray dada. Metode yang digunakan meliputi pelatihan model pada dataset berlabel dengan serangkaian teknik pra-pemrosesan, seperti normalisasi, augmentasi data, dan peningkatan kualitas citra untuk memperbaiki ketahanan dan kemampuan generalisasi model. Hasil pengujian menunjukkan bahwa model yang dioptimalkan mencapai akurasi uji 91,67%, dengan nilai ROC-AUC 0,96 dan PR-AUC 0,95, yang menandakan performa kuat dalam membedakan pneumonia dari citra normal. Kesimpulannya, model CNN ini memiliki potensi signifikan sebagai alat bantu diagnostik yang cepat, konsisten, dan andal, serta mendukung visi Society 5.0 dalam integrasi kecerdasan buatan untuk meningkatkan layanan kesehatan dan kesejahteraan masyarakat.
Machine Learning-Based Multi-Sensor IoT System for Intelligent Indoor Fire Detection Anggy Pradifta Junfithranaa; Hadi Almohab; Deshinta Arrova Dewi
Journal of Educational Technology and Learning Creativity Vol. 4 No. 1 (2026): June
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jetlc.v4i1.2614

Abstract

Purpose of the study: This study aims to develop an intelligent indoor fire detection system by integrating low-cost Internet of Things (IoT) sensors with machine learning-based multi-sensor data fusion to improve early fire hazard detection accuracy while reducing false alarms compared to conventional single-sensor fire detection systems. Methodology: The system is implemented using an ESP32 microcontroller connected to temperature, humidity, flame, and sound sensors for real-time data acquisition. A dataset of 1,500 sensor samples is collected and labeled into Normal, Fire-Risk, and Fire classes. Decision Tree, Support Vector Machine, and Random Forest classifiers are trained and evaluated using Python-based machine learning libraries. Main Findings: Experimental results indicate that the Random Forest model outperforms the other classifiers, achieving 95% overall accuracy, perfect recall for fire events, and a Macro ROC-AUC score of 0.993. Feature importance analysis reveals that humidity and temperature are the most influential parameters for early fire detection in indoor environments. Novelty/Originality of this study: This study proposes a lightweight intelligent fire detection framework that integrates multi-sensor Internet of Things data including temperature, humidity, flame, and sound signals with machine learning–based classification for indoor environments. Unlike conventional systems that rely on single-sensor or threshold-based detection, the proposed approach utilizes multi-sensor data fusion and ensemble learning to improve early fire-risk identification while remaining computationally efficient for low-cost platforms such as the ESP32 microcontroller.
Co-Authors Anggy Pradifta Junfithranaa Deshinta Arrova Dewi