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PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Published by Institut Riset dan Publikasi Indonesia
ISSN : 3024921X     EISSN : 30248043     DOI : https://doi.org/10.57152/predatecs
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering
PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science is a scientific journal published by the Institute of Research and Publication Indonesian (IRPI) or Institut Riset dan Publikasi Indonesia (IRPI). The main focus of PREDATECS Journal is Engineering, Data Technology and Computer Science. PREDATECS Journal is written in English consisting of 8 to 12 A4 pages, using Mendeley reference management and similarity/ plagiarism below 20%. Manuscript submission in PREDATECS Journal uses the Open Journal System (OJS) system using Microsoft Word format (.doc or .docx). The PREDATECS Journal review process applies a Closed System (Double Blind Reviews) with 2 reviewers for 1 article. Articles are published in open access and open to the public.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 42 Documents
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Depression Classification in University Students using A Machine Learning Approach Based on Multi-Layer Perceptron Azzahra, Fatimah; Pohan, Muhammad Rafiq; Rafiq, Ainul Mardhiah Binti Mohammed; Salim, Imran Hazim Bin Abdullah; Ayub, Azwa Nurnisya Binti; Nizam, Nuralya Medina Binti Mohammad
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 2: PREDATECS January 2026
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i2.2107

Abstract

Depression among university students is a critical mental health concern, often exacerbated by academic pressure and social adaptation. While prior studies have utilized Multi-Layer Perceptron (MLP) models to achieve up to 78% accuracy, the effectiveness of these systems remains highly sensitive to architectural design and optimization strategies. To address this gap, this study systematically evaluates the performance of modern MLP architectural variants including DenseNet, ResMLP, and ResNet paired with SGD, Adam, and RMSprop optimizers. Using a dataset of 1,025 student records, the methodology integrates Chi-Square feature selection and Min-Max normalization, followed by an 80:20 Hold-Out validation. Results demonstrate that the ResNet-RMSprop synergy yields a superior accuracy of 83.86%, significantly outperforming traditional MLP benchmarks . By identifying the optimal combination of deep learning structures and optimization algorithms, this research provides a more robust and precise technical foundation for AI-driven early detection systems in academic settings.
Comparative Study of Convolutional Neural Network Architectures and Optimizers for Flower Image Classification Yulisara, Ekatri; Husna, Nayla; Martin, David; Ariesta, Candrawati
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 2: PREDATECS January 2026
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i2.2110

Abstract

This study aims to comparatively evaluate the performance of different Convolutional Neural Network (CNN) architectures and optimization algorithms for flower image classification. Three widely used CNN architectures DenseNet201, InceptionV3, and MobileNetV2 are implemented using transfer learning with pre-trained ImageNet weights and tested with two optimizers, Adam and RMSProp. The experiments are conducted on the Flowers Recognition dataset consisting of five flower classes: daisy, dandelion, rose, sunflower, and tulip. Image normalization and data augmentation are applied to improve model generalization, while performance is evaluated using accuracy, precision, recall, and F1-score. The main contribution of this study lies in a systematic comparison of CNN architectures and optimizers within a unified experimental framework, which is rarely addressed in previous studies. The results show that DenseNet201 combined with the Adam optimizer achieves the highest classification accuracy of 90%, followed by MobileNetV2 with RMSProp, while InceptionV3 yields the lowest accuracy of 85%. These results confirm that the research objective is achieved, demonstrating that both CNN architecture and optimizer selection significantly influence flower image classification performance.

Page 5 of 5 | Total Record : 42

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