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All Journal Jurnal Ilmiah Universitas Batanghari Jambi Syntax Literate: Jurnal Ilmiah Indonesia KOMPUTA : Jurnal Ilmiah Komputer dan Informatika JUKI : Jurnal Komputer dan Informatika Jurnal Computer Science and Information Technology (CoSciTech) Journal of Dinda : Data Science, Information Technology, and Data Analytics STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer J-Icon : Jurnal Komputer dan Informatika Kohesi: Jurnal Sains dan Teknologi Jurnal Informatika: Jurnal Pengembangan IT
Prabowo, Ary
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Pendekatan Naive Bayes Campuran untuk Klasifikasi Email Spam dengan Metode Machine Learning Lainnya Aditya, Bintang; Kristy Wijaya, Marchello; Prabowo, Ary
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 2 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i2.17166

Abstract

Nowadays, email is a communication media that is often used in the digital era, with various advantages offered by email, accompanied by the rise of email spam which can disrupt the comfort of its users and accessibility on the email service provider platform. Using manual spam filtering techniques has proven to be very time-consuming and labor-intensive, so an alternative technique is needed that can perform sorting automatically using Machine Learning. This research aims to develop a form of spam detection model that uses a mixed Naive Bayes approach that combines various forms of TF-IDF feature representation with various statistical features that can calculate message length, number of capital letters, and various number of links, and compare its performance with various other algorithm approaches consisting of Support Vector Machine, Logistic Regression, and Random Forest, this study uses a public dataset containing examples of 5,572 emails containing important emails and spam emails combined. The evaluation form will be calculated using the metrics Accuracy, Precision, Recall, F1-Score, and Training Time. The results of the experiment explain that Naive Bayes with Mixture is able to produce an accuracy of 96.4% with advantages in calculating computational efficiency, but Random Forest has the highest accuracy level reaching 97.9%. So it shows that this research proves that Naive Bayes with various mixed approaches is worthy of being applied to an Email Spam detection system that requires high speed and efficiency.
Klasifikasi kecanduan smartphone mahasiswa universitas esa unggul menggunakan machine learning dan sas-sv Verrel, Anggoro; Maulana, Irfan Zidny; Liu, Vico Andrean; Prabowo, Ary
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.9817

Abstract

The digital era has made smartphones an inseparable part of students' lives, but it also raises the risk of addiction that negatively impacts academic achievement and mental health. This research aims to develop and evaluate machine learning models capable of classifying the level of smartphone addiction among Esa Unggul University students. Data were collected from 32 student respondents through an online questionnaire adopting the validated psychometric instrument, the Smartphone Addiction Scale-Short Version (SAS-SV). Addiction levels were categorized into two classes: 'High', which refers to the gender-specific addiction risk threshold from Kwon et al. (2013), and 'Moderate', which includes scores below that threshold. Four classification algorithms—Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, and Random Forest—were implemented to compare their performance. To address class imbalance in the data, the SMOTE oversampling technique was applied to the training data. Model evaluation was based on accuracy, precision, recall, and F1-score. The research results show that the Logistic Regression model achieved the best performance with an accuracy of 1.0000 and an average F1-score of 1.00 on the test data. Nevertheless, it should be noted that this perfect performance was obtained from a very limited test data size (8 samples), so generalization requires further validation. Feature importance analysis from the Random Forest model identified that the question related to Planned tasks/work often interrupted by smartphone use (Q0) was the most dominant predictor. These results indicate that machine learning models based on psychometric scales have initial potential as a screening and exploratory tool to identify students at risk of smartphone addiction, but require extensive development and validation on larger data before practical implementation.
Penerapan Transfer Learning VGG-16 untuk Mendeteksi Penyakit Mata Manusia Berbasis Citra Fundus Willy, Willy; Prabowo, Ary
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.9291

Abstract

Eye disorders represent a serious global health issue that can lead to a decline in quality of life and even permanent blindness. Early diagnostic for eye diseases such as glaucoma, diabetic retinopathy, age-related macular degeneration, cataract, myopia, and hypertension is crucial to prevent more severe complications. The objective of this study is to develop an image classification model for fundus images using a transfer learning approach with the VGG-16 architecture. The dataset used is ODIR-5K, which includes eight classes of eye diseases. The research stages involve image preprocessing, data augmentation, class balancing using SMOTE, and CNN for training the model. The model training process was conducted over 80 epochs with a combination of freezing layers, fine-tuning, and hyperparameter tuning. Model evaluation was carried out using metrics such as accuracy, precision, recall, F1-score, confusion matrix, and ROC AUC curve. The results show that the developed model achieved an accuracy of 89% compared to the previous study which only reached 45%, with a macro average F1-score of 0.89. The model demonstrated excellent performance in classes such as Hypertension, Glaucoma, and Myopia, although challenges remain in distinguishing the Diabetes and Normal classes. Therefore, the VGG-16-based approach has proven effective for multi-class classification of fundus images, and the results of this study may serve as a foundation for developing deep learning-based diagnostic support systems in the field of ophthalmology.
Co-Authors Adigunawan, Adigunawan Aditya, Bintang Akbar, Habibullah Azzahra, Fayza Bambang Irawan Binastya Anggara Sekti Budi Tjahjono Desmond Orix Khoermala Dzikrya, Kaysa Herwanto, Agus Innova, Zacky Kalagi, Richard Kevin Maruli Kristy Wijaya, Marchello Latumapayahu, Febrian Firmansyah Liu, Vico Andrean Maulana, Irfan Zidny Nizirwan Anwar Pasha, Muhammad Kemal Pramuditya, Benno Ramadhan, Ridwan Ratna Yulika Go Saputra, Ragil Raditya Sharen Ming Sianggara Suryana, Septiara Pratama Verrel, Anggoro Willy, Willy