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INDONESIA
Journal of Information Technology and Computer Science
Published by Universitas Brawijaya
ISSN : 25409433     EISSN : 25409824     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
The Journal of Information Technology and Computer Science (JITeCS) is a peer-reviewed open access journal published by Faculty of Computer Science, Universitas Brawijaya (UB), Indonesia. The journal is an archival journal serving the scientist and engineer involved in all aspects of information technology, computer science, computer engineering, information systems, software engineering and education of information technology. JITeCS publishes original research findings and high quality scientific articles that present cutting-edge approaches including methods, techniques, tools, implementations and applications.
Arjuna Subject : -
Articles 255 Documents
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Prediction of Wastewater Treatment Revenue Based on Volume and Number of Transactions Using the Long Short-Term Memory (LSTM) Method Maulana, Aashif Amiruddin; Khaulasari, Hani; Novitasari, Dian Candra Rini; Pramono, Wahyu Joko
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103806

Abstract

This study aims to develop a prediction model for the total Revenue value of the operational activities of the Keputih Surabaya Sewage Sludge Treatment Plant (IPLT) using the Long Short-Term Memory (LSTM) method. The data used is daily data on total transactions and total Revenue from January 2022 to April 2025. Data normalization using the Min-Max method and outlier detection and handling using the IQR and median imputation techniques are examples of preprocessing steps. The model input structure is formed by utilizing Partial Autocorrelation Function (PACF) analysis to ascertain the number of lags. In this study, 405 model combinations are tested with different parameters, including activation function, number of Epochs, learning rate, and ratios of training and testing data. According to the findings, the model that has the optimal parameters a training and testing data ratio of 80:20, 50 Epochs, a learning rate of 0.002, a Tanh activation function, and 100 neurons can produce predictions for total Revenue with a Mean Absolute Percentage Error (MAPE) of 18.18%. The revenue for the following six months was then forecast using this model; the highest revenue forecast was IDR 3,740,085.00, while the lowest was IDR 1,966,628.25. According to these results, LSTM can accurately forecast time series-based income fluctuations and may find use in the waste management industry's financial decision-making and strategic planning processes.
The Sentiment Analysis Of Indonesian Startup Application Reviews Using TF-IDF+SVM and FastText: A Comparative Study Aini Nabilah; Nurlayli Indah Sari; Mira Afrina; Ali Ibrahim
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103807

Abstract

The rapid rise of startups in Indonesia makes user reviews on the Google Play Store a valuable data source for understanding user perceptions and satisfaction. These unstructured reviews contain insights supporting product development and business strategies. This study analyzes sentiments in Indonesian startup app reviews and compares two classification methods: TF-IDF + Linear SVM and fastText, implemented using Google Colab. Reviews were collected in September 2025 using google-play-scraper; 4,000 reviews were retrieved and refined into 3,152 unique reviews after cleaning and preprocessing. Sentiment labeling used ratings (1–2 negative, 4–5 positive); because the neutral class was limited, this study focuses on balanced binary classification with 1576 positive and 1576 negative reviews. The process involves data scraping, text preprocessing, model training, and evaluation using accuracy, precision, recall, and F1-score metrics, with Linear SVM chosen as an efficient baseline for high-dimensional sparse TF-IDF features. Results show that fastText achieves 91.88% accuracy and an F1-macro of 0.9184, slightly outperforming TF-IDF + SVM (F1-macro 0.9103), suggesting that the embedding-based approach better captures semantic nuances of Indonesian text. Future work may extend this study to ABSA to assess sentiments toward price, UI/UX, and customer service for deeper technopreneurship insights in Indonesia.
Enhancing Brain Tumor MRI Classification Performance Using EfficientNetV2-B3 with an Efficient Channel Attention Module Navira Rahma Salsabila; Lailil Muflikhah; Edita Rosana Widasari
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103846

Abstract

Early identification of brain tumors using magnetic resonance imaging helps doctors make quick and informed decisions about treatment. Although recent deep learning approaches achieve high accuracy, many rely on complex architectures that increase computational cost and limit interpretability. In order to overcome these constraints, this work proposes a system for four-class brain tumor classification utilizing a public MRI dataset of 3,264 images that is built on EfficientNetV2-B3 and an Efficient Channel Attention (ECA) module used after feature extraction and Grad-CAM. The ECA module enhances cross-channel feature representation with minimal computational overhead. Experimental results indicate consistent performance gains over the baseline model, with accuracy increasing from 97.58% to 99.09% and macro-averaged F1-score from 97.51% to 99.08%. Despite the strong baseline, these gains are achieved without increasing architectural complexity. Grad-CAM visualizations support model interpretability by highlighting tumor-relevant regions that contribute most to the classification decisions. Overall, the proposed framework provides a balanced trade-off between classification accuracy, computational efficiency, and interpretability within the evaluated setting.
An Expert System for Early Risk Diagnosis of Breast Cancer Using Fuzzy Mamdani and Case-Based Reasoning Rumahorbo, Cicilia Angelica; Arief Andy Soebroto; Putra Pandu Adikara; Diah Prabawati Retnani
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103854

Abstract

Breast cancer remains one of the leading causes of morbidity and mortality among women worldwide, making early detection essential to improve treatment outcomes. However, early-stage breast cancer symptoms are often subjective and non-specific, which complicates initial risk assessment. This study proposes an expert system for early breast cancer risk diagnosis by integrating Fuzzy Mamdani and Case-Based Reasoning (CBR). The Fuzzy Mamdani method is employed as the primary inference mechanism to model uncertainty in symptoms and risk factors using linguistic rules, while CBR is utilized as a decision support component by leveraging similarities with previously validated clinical cases. The dataset consists of 150 patient records, of which 123 cases are used as the case base and 27 cases are employed for system evaluation. Experimental results show that the proposed system achieves an accuracy of 92.59% compared to expert judgments. These findings indicate that the integration of Fuzzy Mamdani and Case-Based Reasoning provides an interpretable and adaptive approach for early breast cancer risk assessment and has potential as a screening support tool.  
Hyperparameter Optimization of Extreme Gradient Boosting Using Particle Swarm Optimization For Diabetic Nephropathy Prediction Argaputri, Maulida Khairunisa; Lailil Muflikhah; Prasetio, Barlian Henryranu
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103859

Abstract

Diabetic Nephropathy (DN) is a critical complication with a mortality rate 20-40 times higher than in non-diabetic nephropathy patients, necessitating precise machine learning models to determine whether a patient has nephropathy. Extreme Gradient Boosting (XGBoost) has emerged as a prominent machine learning model for medical diagnostics, with several studies validating its superiority in medical classification. Nevertheless, a significant limitation of XGBoost lies in the complexity of manual hyperparameter tuning. To address this limitation, an automated optimization algorithm is requisite to systematically identify the optimal hyperparameter configuration. This study focuses on optimizing Extreme Gradient Boosting (XGBoost) hyperparameters using Particle Swarm Optimization (PSO), with the F1-Score as its fitness function. To evaluate its effectiveness, the performance of this hybrid XGBoost-PSO model was compared against the baseline XGBoost model. The results showed that the hybrid model outperformed the baseline model, achieving a consistent improvement of 0.02 (2%) across all evaluation metrics. Notably, the F1-Score increased from 0.91 to 0.93, while the Recall metric improved from 0.93 to 0.95. Furthermore, the PSO algorithm efficiently identified the Global Best (GBest) hyperparameters at the 9th iteration. In conclusion, the XGBoost-PSO model provides a robust medical diagnostic tool that maintains a stable performance to enhance clinical judgment.

Page 26 of 26 | Total Record : 255

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