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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 35 Documents
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Performance Comparison of Random Forest, Support Vector Machine and Neural Network in Health Classification of Stroke Patients Sari, Windy Junita; Melyani, Nasya Amirah; Arrazak, Fadlan; Anahar, Muhammad Asyraf Bin; Addini, Ezza; Al-Sawaff, Zaid Husham; Manickam, Selvakumar
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 1: PREDATECS July 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i1.1119

Abstract

Stroke is the second most common cause of death globally, making up about 11% of all deaths from health-related deaths each year, the condition varies from mild to severe, with the potential for permanent or temporary damage, caused by non-traumatic cerebral circulatory disorders. This research began with data understanding through the acquisition of a stroke patient health dataset from Kaggle, consisting of 5110 records. The pre-processing stage involved transforming the data to optimize processing, converting numeric attributes to nominal, and preparing training and test data. The focus then shifted to stroke disease classification using Random Forest, Support Vector Machines, and Neural Networks algorithms. Data processing results from the Kaggle dataset showed high performance, with Random Forest achieving 98.58% accuracy, SVM 94.11%, and Neural Network 95.72%. Although SVM has the highest recall (99.41%), while Random Forest and ANN have high but slightly lower recall rates, 98.58% and 95.72% respectively. Model selection depends on the needs of the application, either focusing on precision, recall, or a balance of both. This research contributes to further understanding of stroke diagnosis and introduces new potential for classifying the disease.
Applying A Supervised Model for Diabetes Type 2 Risk Level Classification Dhani, Ahmad; Lestari, Danur; Ningrum, Meriana Prihati; Fakhrizal, M. Andhika; Gandini, Ganis Lintang
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1105

Abstract

Diabetes can lead to heart attacks, kidney failure, blindness, and increased risk of death. This research was conducted with the aim of classifying a diabetes risk dataset. In this context, performance comparison was carried out on three supervised learning algorithms: K-Nearest Neighbor, Naive Bayes, and Random Forest, against a dataset containing information on specific indicators related to diabetes risk. Additionally, this study also aimed to evaluate the accuracy comparison of the results produced by these three algorithms. The results of this research show that Random Forest performs very well in detecting diabetes, prediabetes, and non-diabetes, with high precision, recall, and F1-score levels. Meanwhile, although the results are still below Random Forest, both Naive Bayes and K-NN still demonstrate significant performance, especially regarding prediabetes cases. In conclusion, from the comparison results, the Random Forest algorithm shows the highest accuracy level at 99%, followed by K-Nearest Neighbor with an accuracy of 85%, while Naive Bayes has the lowest accuracy rate of 74%. This research indicates that the Random Forest algorithm excels in classifying data compared to the other two algorithms.
A Deep Learning Approach Bassed on Classification to Detect Facial Skin Defect Rizalde, Alika Rahmarsyarah; Mubarak, Haykal Alya; Khairunnisa, Batrisia; Fatan, Mohd. Adzka
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1558

Abstract

As people are more active, facial skin is often neglected, which can lead to acne, eye bags, and redness. In this study, deep learning models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Generative Adversarial Networks (GANs) are used to classify facial skin damage. DenseNet201 and MobileNetV2 architectures were also used to evaluate the models in this study. The dataset used consists of facial skin disease photos collected from the Kaggle database. The model was trained and tested to classify the types of skin damage after going through data collection and preprocessing stages. The results showed that the GANs model and the DenseNet201 and MobileNetV2 architectures were the best models, with test accuracy values of 89% for the GANs model, 88% for the DenseNet201 architecture, and 89% for the MobileNetV2 architecture. These results show that deep learning approach techniques can help classify and find facial skin problems well. and it is expected that it will be a great progress in the field of dermatology and skin health.
Comparative Analysis of Weather Image Classification Using CNN Algorithm with InceptionV3, DenseNet169 and NASNetMobile Architecture Models Wulandari, Vina; Sari, Windy Junita; Al-Sawaff, Zaid Husham; Manickam, Selvakumar
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1608

Abstract

Rapid weather changes have a significant impact on various aspects of human life, including social and economic development. Weather analysis traditionally relies on data from Doppler radar, weather satellites, and weather balloons. However, advancements in computer vision technology provide new opportunities to enhance weather prediction systems through image recognition and classification. Studies evaluating and comparing deep learning architectures for weather image classification remain limited.This research utilizes Convolutional Neural Networks (CNN) to classify weather images using three architectures: InceptionV3, DenseNet169, and NASNetMobile. The results show that InceptionV3 achieved 97.94% accuracy on training data, 92.34% on validation data, and 93.81% on test data. DenseNet169 achieved 98.09% accuracy on training data, 88.46% on validation data, and 92.33% on test data. NASNetMobile achieved 96.51% accuracy on training data, 87.82% on validation data, and 89.97% on test data. Based on these results, InceptionV3 is the optimal choice for weather classification due to its consistent performance.This research addresses the gap in evaluating CNN architectures for weather data and contributes to improving weather monitoring systems, early disaster warnings, and applications reliant on accurate predictions. These findings also provide a foundation for the development of advanced technologies in image analysis and weather forecasting in the future.
Implementation of Gated Recurrent Unit, Long Short-Term Memory and Derivatives for Gold Price Prediction Putri, Amanda Iksanul; Syarif, Yulia; Aisyi, Nasywa Rihadatul; Waeyusoh, Nuralisa
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1609

Abstract

Gold is a precious metal with high resale value, often considered a safe investment as its price typically rises with inflation, attracting investors. However, even slight changes in gold prices can have significant impacts. To build an accurate forecasting model, this study applies and compares Gated Recurrent Unit (GRU), Bidirectional Gated Recurrent Unit (Bi-GRU), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms on global gold prices. GRU and LSTM are recurrent neural networks designed to capture patterns in sequential data, where GRU uses a simplified gating mechanism to retain essential information, and LSTM, with its more complex gates, helps manage long-term dependencies in data. Bi-GRU and Bi-LSTM process data bidirectionally, capturing context from both past and future sequences for better prediction accuracy. This research uses data from Yahoo Finance (01-01-2014 to 12-06-2024) and experiments with optimization techniques (Adam, AdamW, Adamax, and Nadam), batch sizes (8, 16, and 32), time steps (10, 20, and 30), and a learning rate of 0.0001, trained for 1000 epochs with checkpoints and early stopping. Bi-GRU with Nadam, batch size 8, and 20 time steps proved most effective, with MSE of 4.1153, RMSE of 2.0286, MAE of 1.5881, and MAPE of 0.8857%. Forecasts using this model predict a 20-day decline in gold prices.
Sentiment Analysis of Twitter Reviews on Google Play Store Using a Combination of Convolutional Neural Network and Long Short-Term Memory Algorithms Ningrum, Meriana Prihati; Mutia, Risma; Azmi, Habil; Khalifah, Habibah Dian
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1625

Abstract

In this era of rapidly evolving technology, the use of social media has become widespread and has become a major platform for sharinhabibahdian.khalifah@ogr.deu.edu.trg people's opinions and views. Google Play Store, as one of the main platforms for digital content, provides access to various applications including Twitter, which allows users to provide reviews and ratings. This research aims to conduct sentiment analysis of Twitter reviews on the Google Play Store using two algorithms namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The data used is 4999 reviews after the scraping process. From the experimental results, an accuracy value of 84.67%, recall of 81%, and precision of 84% were obtained on CNN, and an accuracy of 82.19% recall of 69%, and precision of 87% on LSTM. From these results, it can be seen that the highabibahdian.khalifah@ogr.deu.edu.trhest accuracy value is obtained in the CNN algorithm. Although the difference in accuracy is small, the CNN algorithm provides better results in classifying sentiment analysis data on Twitter reviews on the Google Play Store.
Analysis of Zoom App User Reviews on Google Play Store Using Recurrent Neural Networks and Gated Recurrent Unit Algorithms Muta'alimah, Muta'alimah; Setiawati, Elsa; Kwok, Josephine; Hasysya, Hauriya
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1627

Abstract

The World Health Organization (WHO) declared COVID-19 a global pandemic on March 11, 2020. Technology is crucial to stop the spread of the virus. Video conferencing applications such as Zoom Cloud Meetings are essential for collaboration and communication as the government issues policies to conduct various activities from home. Zoom was released in January 2013 to become a trendy video conferencing platform until now. However, post-pandemic, the Zoom App faces challenges maintaining user satisfaction due to the reduced need for virtual meetings. This research aims to analyze user reviews of the Zoom app on the Google Play Store using the RNN and Analysis of Zoom App User Reviews on Google Play Store Using Recurrent Neural Networks (RNN) and Gated Recurrent Unit Algorithms (GRU) algorithms, determine which user reviews are positive, negative, and neutral, identify common problems with Zoom for improvement recommendations, and compare the accuracy between the RNN and GRU algorithms.  The results showed that out of 5000 reviews, 3728 sentiments were Positive, 1041 sentiments were Negative, and 231 sentiments were Neutral. The RNN algorithm achieved 86% accuracy, 86% precision, 100% recall, and 92% f1-score, while GRU achieved 83% accuracy, 87% precision, 92% recall, and 89% f1-score. Thus, RNN is superior in sentiment classification and most users are satisfied with the app, but negative reviews indicate areas that require improvement. This research provides valuable insights for developers to improve Zoom app features based on user feedback.
Deep Learning for Pneumonia Detection in Chest X-Rays using Different Algorithms and Transfer Learning Architectures Lestari, Danur; Mulya, Anggi; Tatamara, Aghnia; Haiban, Ryando Rama; Khalifah, Habibah Dian
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 1: PREDATECS July 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i1.1553

Abstract

Pneumonia is one of the lung conditions brought on by bacterial infections. An accurate diagnosis is necessary for successful treatment. A radiologist can typically diagnose the condition based on images from a chest X-ray. The diagnosis may be arbitrary for a variety of reasons, such as the indistinctness of certain diseases on chest X-ray pictures or the possibility of the illness being mistaken for another. Consequently, clinicians require guidance from computer-aided diagnosis tools. We diagnosed pneumonia using two algorithms CNN and GAN, as well as two architectures ResNet50V2 and InceptionV3. The test results show that the ResNet50V2 architecture is superior to the InceptionV3 architecture on the CNN algorithm with an accuracy of 94% versus 93%. In addition, the test results on the GANs algorithm show that the ResNet50V2 architecture is superior to the InceptionV3 architecture with an accuracy of 96%, while the InceptionV3 architecture achieves an accuracy of 92%.
Analysis Comparison Classification Image Disease Eye Using the CNN Algorithm, Inception V3, DenseNet 121 and MobileNet V2 Architecture Models Melyani, Nasya Amirah; Lubis, Ayuni Fachrunisa; Tatamara, Aghnia; Haiban, Ryando Rama; Iltizam, Muhammad; Rofiqi, Muhammad Aufi; Abdurrahman, Sakhi Hasan; Samae, Nitasnim; Shahid, Bilal; Habibullah, Muhammad; Ismail, Muhammad Ibrara
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 1: PREDATECS July 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i1.1559

Abstract

Eye disease is a significant global health problem, with more than two billion people experiencing vision impairment. Some of the main causes of visual impairment include cataracts, glaucoma, diabetic retinopathy, and age-related macular degeneration. Early detection of eye disease is very important to prevent blindness. The fundus of the eye, which includes the retina and blood vessels, is an important area in the diagnosis of retinal diseases. Fundus disease can cause significant vision loss and is one of the leading causes of blindness. Automated analysis of fundus images is used to diagnose common retinal diseases, ranging from easily treatable to very complex conditions. This research discusses eye disease image classification using several Convolutional Neural Network (CNN) architectures, namely Inception V3, DenseNet 121, and MobileNet V2. The dataset used is 4217 fundus images categorized based on the patient's health condition. Data is processed through normalization and augmentation to improve model performance. Experimental results show that MobileNet V2 has the highest accuracy of 81.3%, followed by Inception V3 with 77.3%, and DenseNet 121 with 76.7%. The use of appropriate CNN models in the classification of eye fundus images can help in early detection of eye diseases, thereby preventing further visual impairment.
Implementation of Deep Learning for Brain Tumor Classification from Magnetic Resonance Imaging Husna, Nur Alfa; Hendri, Desvita; Wajdi, Muhammad Farid; Ginting, Ella Silvana; Pramesthi, Chintya Harum
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 1: PREDATECS July 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i1.1570

Abstract

Brain tumours are a medical problem that causes many people to die in the world due to brain cancer. Brain tumours are one of the dangerous types of brain cancer. MRI is well proven in the assessment of brain tumours, although conventional imaging has limitations in evaluating the extent of the tumour. In the field of medicine, there has been an increase in large amounts of data and traditional models cannot manage such data efficiently. So there is a need for medical image analysis that can store and analyse large medical data efficiently. This research will adopt a deep understanding transfer learning approach with four models namely VGG16, VGG19, MobileNetV2 and ResNet50 to classify 2 types of image shapes that detect whether a person has a brain tumour or not using Magnetic Resonance Imaging (MRI) data with Convolution Neutral Network (CNN). The number of datasets used is 4600 MRI images with 2 classes namely Brain Tumour and Health. The hyperparameters used are image size 224x224 pixels, training data ratio 70%, test data 30%, using Adam optimizer, learning rate 0.0001, using batch size 64 and epoch value 50. The best results in this study were obtained by MobileNetV2 architecture with an accuracy of 88.77%.

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