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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL INSTEK (Informatika Sains dan Teknologi) Conference on Innovation and Application of Science and Technology (CIASTECH) Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Scientific Student Journal for Information, Technology and Science Jurnal Buana Pengabdian
Sutan Faisal
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Religion Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Development of AI-Based Public Safety System with Face Recognition Using CNN and SVM Models in Real-Time Alifa, Naila Ratu; Yana Cahyana; Rahmat, Rahmat; Sutan Faisal
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9524

Abstract

Sexual crimes are an increasing problem, with many cases difficult to identify due to the limitations of existing surveillance systems. This study aims to develop an Artificial Intelligence (AI)-based system using Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for gender identification in order to support sexual crime investigations. The methods used include processing facial image datasets, training models using CNN for feature extraction, and SVM for gender classification. The results showed that the CNN model achieved an accuracy of 90.15%, while the SVM model only achieved an accuracy of 82.16%. Further evaluation with a confusion matrix showed that CNN was more accurate in classifying gender than SVM. With these results, the developed system has the potential to help authorities identify perpetrators of sexual crimes more quickly and accurately. The dataset used consists of 23,706 grayscale facial images of 48x48 pixels, with a balanced distribution of male and female samples. The CNN architecture includes three convolutional blocks and achieves 90.15% accuracy. Although designed for real-time operation, inference speed needs further validation using FPS or latency metrics on specific hardware platforms.
Sentiment Analysis of User Reviews of the AdaKami Online Loan App from the App Store Using SVM and Naive Bayes Azzahra, Wava Lativa; Jamaludin Indra; Rahmat, Rahmat; Sutan Faisal
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9536

Abstract

This study aims to classify sentiments on user reviews of the AdaKami online loan application, which are obtained through web scraping techniques from the Apple App Store platform. A total of 2000 reviews were collected, then selected and 1000 reviews were selected to be manually labeled by two linguistic experts, to ensure the validity of the classification. Sentiments are divided into three categories, namely negative, neutral, and positive. The classification model was built using two machine learning algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB). The evaluation was carried out by measuring accuracy, precision, recall, F1-score, as well as through confusion matrix and cross-validation. The results showed that SVM performed better, with an accuracy of 97.5%, an F1-score of 0.97, and an average cross-validation accuracy of 84.69%. In contrast, Naïve Bayes recorded an accuracy of 81.4% and an F1-score of 0.77. The results of the paired t-test showed that the difference in performance between the two models was statistically significant (p < 0.05). The SVM model was then applied to predict 971 unlabeled reviews, and the results showed a dominance of negative sentiment. Wordcloud visualizations reinforced this finding, with words such as “bilih”, “bunganya”, and “teror” as the most frequently occurring words. These findings prove that SVM is more effective in classifying online loan review sentiments, as well as providing important insights for developers in understanding user perceptions and experiences.
Co-Authors Adi Rizky Pratama Afga Ilyasa Ahmad Fauzi Alifa, Naila Ratu Aqib Zhaky Ayuni Evita Rahayu Azzahra, Wava Lativa Bagus Priambada Baihaqi, Kiki Ahmad Daffa Alwan Deni Yusup Habib Abdullah Hanny Handayani Ilham Azis Annaba Indra, Jamaludin Isnaeni Hidayah Jorke Alkawiyu Lestari, Santi Arum Puspita Madi Naashir Muhammad Chayim Mustabinnur Nurofik Rahmat Rahmat Rija Nur Hijriyya Rissa Ilmia Agustin Rizki Nur Annisa Rizki Septian Akbar Roby Rupiat Santi Lestari Santi Pertiwi Hari Sandi Siregar, Amril Mutoi Suja Priyanto Syamsul Tri Andika Tatang Rohana Tohirin Al Mudzakir Windyani Eka Putri Yana Cahyana