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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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Amazon Stock Price Prediction Using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) Tshamaroh, Muthia; Nasution, Nur Shabrina; Nadhirah, Nurin; Alfira, Rizka Ayu; Xintong, Zeng
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.v3i1.1656

Abstract

Stocks have become one of the largest and most intricate financial markets globally due to their high popularity, making them very challenging to predict as they can process millions of transactions rapidly. The objective of this study is to enhance the field by creating a dependable and accurate model for predicting the stock price of Amazon. This will be achieved via the use of advanced algorithms such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This research utilised historical data on Amazon's stock price from the past five years, which was acquired from Yahoo Finance. The data was partitioned using a hold-out validation technique, allocating 80% for training and 20% for testing. The model underwent training using different optimizers (Adam, SGD, RMSprop), batch sizes (8, 16, 32), and learning rates (0.001, 0.0001). The evaluation criteria comprised of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results suggest that the GRU model, when trained with the RMSprop optimizer using a batch size of 16 and a learning rate of 0.0001, as well as with the SGD optimizer using a batch size of either 16 or 32 and a learning rate of either 0.001 or 0.0001, produced the lowest error metrics, indicating superior performance. This study enables more precise forecasts of stock prices and more efficient investment techniques.
Classification of E-Commerce Shipping Timeliness Using Supervised Learning Algorithm Pratama, Novrian; Anrahvi, Rifka; Tambal, Ahmed; Singh, Aryanshi
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.v3i1.1855

Abstract

Developments in the e-commerce sector have increased rapidly since the onset of COVID-19, which has changed consumers' shopping habits. The growth in the number of e-commerce consumers affects the demand for long-distance delivery of goods. The problem of late delivery of goods is one of the challenges that is often experienced, and this can affect the level of customer satisfaction. This study aims to analyze whether the delivery of goods has been carried out according to schedule or has experienced delays. By using e-commerce shipping datasets obtained through the website, this research applies five supervised learning algorithms in the classification process, namely Decision Tree, Naïve Bayes Classifier, K-Nearest Neighbors (K-NN), Random Forest, and Support Vector Machine (SVM). The evaluation results show that dataset sharing using the K-Fold Cross Validation technique provides the best performance at K=8. Support Vector Machine showed the highest level of accuracy of 66.35%, followed by precision of 69.31% and recall of 66.35%. In contrast, the Naïve Bayes Classifier algorithm recorded the lowest performance with accuracy 64.22%, 97.73% precision, and 42.67% recall. These results show that the SVM algorithm is better at classifying the timeliness of delivery compared to the other four algorithms.
Lung Disease Risk Prediction Using Machine Learning Algorithms Aulia, Ananda Putri; Adelia, Qaula; Mubarak, Haykal Alya; Fatan, Mohd. Adzka; Sudarno, Sudarno
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.v3i1.1858

Abstract

Lung diseases, including lung cancer, are one of the leading causes of death in the world. Early detection is essential to increase patients' chances of recovery and reduce healthcare costs. The utilization of machine learning algorithms can be used to solve this problem. This study evaluates five machine learning algorithms, namely K-Nearest Neighbors (K-NN), Naïve Bayes Classifier (NBC), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), for lung disease prediction using a dataset of 30,000 data with 11 attributes from Kaggle. The dataset was processed through data preprocessing and divided into training and test data with a ratio of 70%:30% and 80%:20%. The algorithm performance was evaluated using precision, recall, F1-score, and accuracy metrics. The results show that RF, SVM, and DT algorithms have the highest performance, with accuracy reaching 94.72% at 70%:30% ratio. The DT algorithm, which previously showed low performance in heart disease classification, provided competitive results in lung disease prediction. This research focuses on the importance of proper algorithm selection and data organization to improve the effectiveness of disease prediction. The findings contribute to the development of artificial intelligence technology for medical applications, particularly in supporting early diagnosis of lung diseases.
Leveraging Machine Learning for Early Risk Prediction in Cirrhosis Outcome Patients Shakir, Yasir Hussein; Mandhari, Eshaq Aziz Awadh AL; Alkhazraji, Ali
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.v3i1.2015

Abstract

Millions of individuals worldwide suffer from liver cirrhosis, which is one of the primary causes of mortality. Healthcare professionals may have more opportunities to treat cirrhosis patients effectively if early death prediction is made and it is postulated that death in this cohort would be correlated with laboratory test findings and other relevant diagnoses. In this study five machine learning models, including LR, SVM, XGBoost, AdaBoost and KNN, are implemented and evaluated. The preprocessing steps included feature selection, categorical data encoding, and data balancing using SVMSMOTE. The XGBoost model demonstrated superior performance, achieving 89.55% accuracy, 89.69% precision, 89.55% recall, and an F1-score of 89.59% after balancing. These findings highlight the potential of machine learning models in accurate risk detection in patients with cirrhosis and providing valuable support in clinical decision-making and improving patient treatment.
Convolutional Neural Networks Using EfficientNetB0 Architecture and Hyperparameters on Skin Disease Classification Khairunnisa, Putri; Putra, Wahyu Eka; Yitong, Wu; Jufrizal, Abni; Makmum, Muhammad Nur Aflah
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.v2i2.1569

Abstract

Skin diseases are often caused by air temperature, environmental hygiene and personal hygiene, with symptoms such as itching, pain and redness. Contributing factors include exposure to chemicals, sunlight, infections, a weak immune system, microorganisms, and poor personal hygiene. This study uses Convolutional Neural Networks (CNN) with EfficientNetB0 model and hyperparameter optimization for skin disease classification. The dataset consists of 1158 images that have been divided into eight categories, with 80% for training data and 20% for test data. Data augmentation is applied to increase the variety of training data. Various combinations of hyperparameters such as learning rate, optimizer (Adamax and AdamW), and activation function (ReLU and LeakyReLU) were tested in 16 training scenarios. The best results was obtained from the third scenario with the original dataset, Adamax optimizer, ReLU activation function, and 0.01 learning rate, which gave a testing accuracy of 95.70%. The model also showed good generalization and low loss values. Confusion matrix analysis and classification report showed high accuracy in skin disease classification. This study concludes that EfficientNetB0 with proper hyperparameter optimization can improve the accuracy and effectiveness of skin disease diagnosis.
A Comparison of Machine Learning Algorithms in Predicting Students' Academic Performance Baye, Juanda Alra; Alfaridzi, Gemma Tahmid; Abdurrahim, Hilmy; Adinda, Abid Aziz; Athallah, Muhammad Rakha; Ramadhan, Muhammad Zahid
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.v3i2.1861

Abstract

Predicting students’ academic performance enables early interventions and data-driven planning in education. We compare five machine-learning algorithms Decision Tree, K-Nearest Neighbor, Naive Bayes, Random Forest, and Support Vector Machine on a publicly available dataset of 1,001 students, evaluated with Accuracy, Precision, Recall, and F1-Score. The Decision Tree achieved the highest performance, with perfect scores on this dataset, while SVM (?82% F1) and Random Forest (?81% F1) were competitive. These results suggest that simple, interpretable models can be highly effective when features are clean and predictive; however, the Decision Tree’s perfection also indicates potential overfitting and warrants further validation on larger, more diverse samples. The study underscores how model choice should reflect dataset characteristics and practical deployment goals in educational settings, informing early-warning systems and targeted support programs.
Sentiment Analysis of Public Opinion on the Gaza Conflict Using Machine Learning Fadri, Agil Irman; Jelita, Nur Futri Ayu; Bagaskara, Diamond Dimas; Zahra, Raudiatul
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.v3i2.2088

Abstract

The 2023 escalation of the Gaza conflict triggered widespread public discourse on the X platform, highlighting the importance of sentiment analysis for understanding public opinion on global geopolitical issues. While sentiment analysis has been widely applied to social media data, comparative evaluations of machine learning models on conflict-related datasets remain limited. This study analyzes public sentiment toward the Gaza conflict by comparing the performance of Multi-Layer Perceptron, XGBoost, and Logistic Regression models. A dataset of 2,175 tweets was processed using standard text preprocessing techniques and TF-IDF feature extraction. Model performance was evaluated using multiple train-test split scenarios. The results indicate that Logistic Regression consistently outperformed the other models, achieving the highest accuracy of 73.17% with an 80:20 data split. These findings suggest that simpler linear models may perform more robustly and efficiently than more complex approaches when applied to high-dimensional, noisy social media text data. This study provides practical insights into model selection for sentiment analysis of conflict-related discussions on social media platforms.
Comparison of Convolutional Neural Network and Recurrent Neural Network Algorithms for Indonesian Sign Language Recognition Harmade, Dani; Fathin, Afif; Zainal, Nur Jannah Nai'mah
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.v3i2.2090

Abstract

Effective communication is a fundamental human need; however, for people with hearing impairments in Indonesia, interaction relies heavily on the Indonesian Sign Language System (Sistem Isyarat Bahasa Indonesia – SIBI). Although deep learning has been widely applied in sign language recognition, comprehensive comparative studies focusing specifically on SIBI remain limited, particularly in evaluating the performance gap between different neural network architectures. This study addresses this gap by comparing the effectiveness of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in classifying SIBI hand gesture images. An augmented SIBI dataset was trained using the Adam optimizer to improve generalization and recognition performance. The experimental results reveal a significant performance difference between the two models, where CNN achieved a precision, recall, and F1-score of 94%, while RNN obtained a precision of 76% recall of 74%, and F1-score of 73%. These findings demonstrate that CNN is substantially more effective for image-based SIBI recognition because it extracts spatial features more effectively than the sequential processing mechanism of RNN. This research contributes empirical evidence for selecting appropriate deep learning architectures in SIBI recognition systems and offers practical implications for developing more accurate and reliable assistive communication technologies in educational and accessibility contexts.
Classification of Breast Cancer Ultrasound Images Using Convolutional Neural Network Aulia, Rifsya; Safira, Dina Pani; Audilla, Khaury; Raudhatul Khairiyah
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.v3i2.2104

Abstract

Breast cancer ranks among the primary contributors to female mortality, thereby underscoring the critical importance of early detection. This research employs a deep learning approach based on Convolutional Neural Networks (CNNs) to classify breast cancer using ultrasound imagery, comparing the ResNet50V2 and MobileNetV2 architectures with three optimizers: Adam, RMSprop, and SGDM. The dataset used in this study is the Breast Ultrasound Images (BUSI) dataset, obtained from Kaggle, which comprises three diagnostic categories: benign, malignant, and normal. The research workflow encompassed several stages, including data acquisition, image pre-processing involving normalization and augmentation, and dataset partitioning using the Holdout Split method, with proportions of 70% for training, 15% for validation, and 15% for testing. The experimental findings revealed that the ResNet50V2 architecture combined with the SGDM optimizer achieved the best performance, recording accuracy, precision, recall, and F1-score values of 92%. Meanwhile, MobileNetV2 with RMSprop achieved the highest performance on its architecture with 86% accuracy, 88% precision, 86% recall, and 86% F1-score. These findings prove that CNN architecture selection and optimization algorithms have a significant influence on medical image classification performance.
Classification of Corn Leaf Disease Images Using Convolutional Neural Network Algorithm Fitriani, Irma; Devi, Rahma; Sajjana, Ariandra Fokker Chaya; Irfan, Muhammad
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.v3i2.2105

Abstract

Corn leaf diseases can reduce crop yields and cause financial losses, thus requiring accurate and objective classification methods. This study aims to classify four corn leaf conditions, namely Blight, Common Rust, Gray Leaf Spot, and healthy leaves, using a Convolutional Neural Network (CNN) approach based on image processing. A systematic comparative evaluation was conducted on three CNN architectures, namely MobileNetV2, ResNet50V2, and DenseNet201, by examining the effect of architecture-optimizer pairs using Adam and RMSprop to determine the optimal model configuration. The results showed that the proposed approach was effective in classifying corn leaf diseases, with the highest accuracy of 93% achieved by the combination of DenseNet201 and the Adam optimizer. This study contributes by providing a structured comparative analysis of the performance of CNN architectures and optimizers as a reference for the development of more accurate and efficient early detection systems for plant diseases.

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