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All Journal Jurnal Rekayasa elektrika ABDIMU: Jurnal Pengabdian Muhammadiyah
Ramiady, Luthfiar
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Religion Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Improved Histogram of Oriented Gradient (HOG) Feature Extraction for Facial Expressions Classification Ramiady, Luthfiar; Arnia, Fitri; Oktiana, Maulisa; Novandri, Andri
Jurnal Rekayasa Elektrika Vol 20, No 3 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v20i3.34044

Abstract

Facial expression classification system is one of the implementations of machine learning (ML) that takes facial expression datasets, undergoes training, and then utilizes the trained results to recognize facial expressions in new facial images. The recognized facial expressions include anger, contempt, disgust, fear, happy, sadness, and surprise expressions. The method employed for facial feature extraction utilizes histogram-oriented gradient (HOG). This study proposes an enhancement method for HOG feature extraction by reducing the feature dimension into multiple sub-features based on gradient orientation intervals, referred to as HOG channel (HOG-C). Classifier testing techniques are divided into two methods for comparisonsupport vector machines (SVM) with HOG features and SVM with HOG-C features. The testing results demonstrate that SVM with HOG achieves an accuracy of 99.9% with an average training time of 18.03 minutes, while SVM with HOG-C attains a 100% accuracy with an average training time of 18.09 minutes. The testing outcomes reveal that the implementation of SVM with HOG-C successfully enhances accuracy for facial expression classification.
Stock Literacy Education Through the Utilization of Digital Applications to Enhance Investment Interest among Generation Z in Banda Aceh Pristiwa, Nara; Fatma, Surya; Mazas, Ichsan Akmal; Fitri, Aida; Ramiady, Luthfiar
ABDIMU: Jurnal Pengabdian Muhammadiyah Vol 5, No 2 (2025): Vol 5 No 2 Desember 2025
Publisher : Universitas Muhammadiyah Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37598/abdimu.v5i2.2540

Abstract

This community service program aims to enhance stock literacy among Generation Z in Banda Aceh through the utilization of digital investment applications. Generation Z is widely recognized as a technologically literate group, yet their knowledge and awareness of investment, particularly in the stock market, remain limited. To address this issue, the program was designed in the form of interactive education and training, focusing on the fundamentals of stock investment, practical guidance on using digital stock applications, and simple strategies for responsible investing. The methods employed included socialization, direct demonstrations of application usage, and hands-on mentoring. The results of this activity indicate an improvement in participants’ understanding of stock investment concepts and an increased interest in engaging with digital investment platforms that are legal and trustworthy. It is expected that this initiative will encourage Generation Z in Banda Aceh to become more financially literate, actively participate in the capital market, and contribute to both local and national economic growth.
Co-Authors Fatma, Surya Fitri Arnia Fitri, Aida Mazas, Ichsan Akmal Novandri, Andri Oktiana, Maulisa Pristiwa, Nara