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All Journal Jurnal Informatika dan Rekayasa Perangkat Lunak Scientific Journal of Informatics
Gilar Pandu Annanto
Universitas Wahid Hasyim
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Robust Human Gait Recognition with Convolutional Neural Network based on Gait Energy Image Fandy Indra Pratama; Akhmad Pandhu Wijaya; Gilar Pandu Annanto; Avira Budianita; Hairudin Farid Sunanda
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.37383

Abstract

Purpose: Human gait recognition is one of the developments in artificial intelligence technology. Gait recognition is a biometric recognition technique that uses no direct interaction with an object, allowing for identification of individuals based on their gait. However, this recognition faces challenges, including varying camera angles (00 - 1800), so this requires a more in-depth introduction. Methods: Therefore, based on the references, this study proposes using the Gait Energy Image (GEI) and Convolutional Neural Network (CNN) features for in-depth extraction and recognition of each image in the Casia B Dataset, which is then compared with the results of previous studies. Result: The results of this study, with the division of the Casia B Dataset 80% as training data and 20% as testing data and 11 camera angles between 00 - 1800 produced an accuracy rate of 99.48%. Novelty: So the accuracy achieved with this deep learning technique exceeds that of previous research using conventional methods and this gait pattern recognition technique can be used to be implemented in a biometric recognition system based on human gait patterns.
Klasifikasi Tingkat Demam Berdarah menggunakan Metode Naive Bayes Classifier untuk Deteksi Dini Akhmad Pandhu Wijaya; Gilar Pandu Annanto; Adek Saputra
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2025): Maret
Publisher : Universitas Wahid Hasyim

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Abstract

Nowadays, the flow of information has experienced a significant increase every day, which results in the accumulation of data in the form of text documents both online and offline. The classification of Dengue Fever data in the medical field is an essential task in predicting the disease, it can even support doctors in establishing a diagnosis, so it is important to make a diagnosis quickly in order to reduce the risk of Dengue Fever spreading in the community. The classification of dengue fever level using Naïve Bayes Classifier for early detection is the Naïve Bayes Algorithm can be used to classify the level of DD (Dengue Fever), and DBD1 (Dengue Hemorrhagic Fever Level 1), DBD2 (Dengue Hemorrhagic Fever Level 2), DBD3 (Hemorrhagic Fever) Level 3), Dengue4 (Hemorrhagic Fever Level 4) for early detection, which is taken from the result of the largest Naïve Bayes probability value. In testing the Naïve Bayes method using test data as many as 60 data.
Co-Authors Adek Saputra Akhmad Pandhu Wijaya, Akhmad Pandhu Avira Budianita Hairudin Farid Sunanda Pratama, Fandy Indra