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Scientific Journal of Informatics
Published by Universitas Negeri Semarang
ISSN : 24077658     EISSN : 24600040     DOI : https://doi.org/10.15294/sji.vxxix.xxxx
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering
Scientific Journal of Informatics (p-ISSN 2407-7658 | e-ISSN 2460-0040) published by the Department of Computer Science, Universitas Negeri Semarang, a scientific journal of Information Systems and Information Technology which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences. The SJI publishes 4 issues in a calendar year (February, May, August, November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 161 Documents
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Smart Monitoring Implementation for Hosting Services Using Zabbix and Autoencoder Models Maulana, Rizky Fenaldo; Wicaksono, Ardian Yusuf; Nugraha, Irvan Surya; Sujiana, Yuandytha Putri Ade
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.33241

Abstract

Purpose: This study answers the increasing need for intelligent server monitoring systems capable of anomaly detection for computing infrastructure performance and reliability. Conventional static threshold-based monitoring systems, such as Zabbix, have limitations in identifying complex and dynamic abnormal patterns. To overcome this limitation, in this research study, the integration of an Autoencoder-based deep learning model with the Zabbix monitoring system is presented. Methods: An experimental approach was followed, wherein important server performance metrics like CPU utilization, memory utilization, and network activity were collected every 15 minutes for 40 days. The Autoencoder was trained on normal operational data to identify frequent behavioral patterns and produce reconstruction errors as anomaly scores. Deviations were alerted as anomalies when these scores exceeded an adaptive threshold. The system was deployed in a virtualized environment, with real-time notifications sent via Telegram for rapid incident response. Results: Experimental results show that the system can effectively detect different anomaly scores. Deviations were alerted as anomalies when these scores exceeded an adaptive threshold. The system was placed in a virtualized production setup, publishing real-time notifications via Telegram for prompt incident different anomaly types with high performance anomaly detection with MAE = 0.0084 and RMSE = 0.0198. Novelty: The primary contribution of this study is the development of hybrid real-time anomaly detection framework that integrates deep learning within open-source monitoring tools to be more efficient, decrease the dependence on manual configuration, and enable computing infrastructures to be more reliable and efficient.
Optimization of Logistic Regression Algorithm Using Grey Wolf Optimizer for Credit Card Fraud Detection Puspita, Wiyanda; Hakim, M. Faris Al
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.26807

Abstract

Purpose: The advancement of digital technology has significantly changed the financial transaction system, but has also led to an increase in cybercrime, especially credit card fraud. This crime poses a significant financial threat, with reported losses reaching hundreds of millions of dollars annually. This study aims to improve the effectiveness of fraud detection using the Logistic Regression (LR) algorithm, which although widely used in binary classification, is still vulnerable to challenges with imbalanced data. The goal is to optimize LR using the Grey Wolf Optimizer (GWO) to improve accuracy and reliability. Methods: This research implements a Logistic Regression (LR) model whose hyperparameters are optimized using Grey Wolf Optimizer (GWO) algorithm. The model was trained and tested on a public Kaggle dataset containing 284,807 credit card transactions. Data preprocessing includes handling outliers using Interquartile Range (IQR) method and handling class imbalance using KMeansSMOTE. Evaluation metrics include accuracy, precision, recall, f1-score, and specificity based on confusion matrix. Result: The baseline LR model achieved 99.92% accuracy, 75.18% precision, 74.73% recall, 75.45% F1-score, and 99.96% specificity. After GWO optimization, the model improved to 99.94% accuracy, 85.96% precision, 83.08% recall, 84.01% F1-score, and 99.97% specificity, showing a significant performance boost. This represents a notable improvement in key metrics for fraud detection, with an increase of 14.3% in precision, 11.2% in recall, and 11.3% in the F1-score, demonstrating a more robust model. Novelty: This study proposed the application of the Grey Wolf Optimizer (GWO) for hyperparameter tuning of a Logistic Regression model in the context of fraud detection. Unlike conventional optimization techniques that can be computationally expensive, our GWO-based approach offers an efficient and effective method for discovering optimal model settings. The optimized model not only outperforms the baseline LR but also presents a scalable and powerful solution for financial institutions to improve the accuracy of their fraud detection systems.
Migrating Monolithic to Microservices: Comparative Performance Testing Evaluation Between Architectures Wahyudin, Asep; Siregar, Herbert; Anisyah, Ani; Kusumawardani, Sekar Madu; Erlangga
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.28499

Abstract

Purpose: Various organizations and industries have migrated their systems from previously adopting monolithic architecture to microservices architecture. One of the advantages of microservices architecture that is desired to be achieved from the migration process is the performance side. Therefore, this research aims to conducted performance testing on the system that was migrated from monolithic to microservices in the previous study. Methods: This research was conducted in several stages, such as designing and implementing software and applications, creating performance testing scenarios, executing scenario testing with load testing, stability testing (soak testing), and stress testing such as load testing, soak testing, and stress testing, and finally analyzing and reporting testing results using performance indicator in terms of response time, throughput, and error rate. Result: The test results showed a significant increase in performance before and after the migration of the monolithic system to microservices. Application response time became faster, more requests could be handled, and the failure rate experienced by the system was smaller. This shows that system performance is better with the implementation of microservices architecture. Novelty: This research presents a novelty in the form of a comparative evaluation of real deployment-based system performance between Laravel monolithic architecture and Golang gRPC-based microservices on the same application, with a seven-stage performance testing approach and the use of in-depth quantitative metrics using Apache JMeter.
Improved Stroke Classification Accuracy by Using Hybrid Inception and Xception Models Atikananda, Desta; Riyadi, Slamet; Damarjati, Cahya; Andriyani, Annisa Divayu
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.29023

Abstract

Purpose: Stroke is one of the leading causes of death and disability in the world that requires a fast and accurate diagnosis system. A major challenge in classifying strokes using deep learning is data imbalances, where the number of stroke patients is much less than that of non-stroke patients. Methods/Study design/approach: This research proposes a Hybrid model approach that combines Inception and Xception architectures, and applies Synthetic Minority Over-sampling Technique (SMOTE) to balance the data distribution. The dataset used consisted of 5,110 entries with 12 stroke risk features, and evaluation was performed using accuracy, precision, recall, and F1-score metrics. Result/Findings: The results show that the Hybrid model provides the best performance with an accuracy of 92.2%, outperforming the Inception (86.28%) and Xception (89.26%) models. In addition, the Hybrid model showed high and balanced precision and recall values, reflecting its reliability in detecting stroke cases. Novelty/Originality/Value: The novelty of this research lies in combining the multi-scale feature extraction power of the Inception architecture and the depthwise separable convolution efficiency of the Xception architecture in a hybrid model. This approach is proven to excel in tabular data-based stroke classification and has the potential to be applied in automated medical diagnosis systems.
Strategy Improvement for IT Governance in Library Services Using an Adaptation of ITIL 4 and FitSM : A COBIT 2019 Evaluation Idrus, Nurul Asyikin; Widodo, Catur Edi
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.34197

Abstract

Purpose: Effective management of IT services in public libraries is critical to maintaining service continuity, optimizing resources, and ensuring user satisfaction. However, current practices face persistent challenges in workforce capability, operational consistency, and problem resolution. This study evaluates IT service governance and develops improvements across four objectives: APO07 (Managed Human Resources), DSS01 (Managed Operations), DSS02 (Managed Service Requests and Incidents), and DSS03 (Managed Problems). Methods/Study design/approach: An integrated approach was applied by combining COBIT 2019 and ITIL 4 with FitSM to fit the scale of public libraries. A capability assessment established the baseline, followed by a gap analysis to identify weaknesses. Adaptive procedures were then formulated by retaining the comprehensive guidance of ITIL 4 and integrating the lightweight, practical principles of FitSM, while excluding elements unsuited for small-scale organizations. Result/Findings: The assessment showed that all four objectives are at capability level 1 and have not reached the “fully achieved” benchmark. Gaps were identified in human resource development, operational standardization, and incident and problem handling. The proposed improvements introduce standardized processes to strengthen workforce capability, stabilize daily operations, and accelerate service request and problem resolution. Novelty/Originality/Value: This study offers a practical governance improvement model tailored for public libraries and similar small-scale public sector organizations. The integration of ITIL 4 and FitSM provides a structured yet simplified framework that supports process standardization and service quality enhancement, addressing the limitations of applying COBIT 2019 in isolation.
A Systematic Review and Bibliometric Study of Climate Change Sentiment Analysis: Trends and Approaches Kusumawati, Karisma Vinda Nissa; Indra Budi; Amanah Ramadiah; Aris Budi Santoso; Prabu Kresna Putra
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.34947

Abstract

Purpose: This study aims to map research trends in sentiment analysis on the climate change topic from the beginning of 2020 to the middle of 2025 by utilizing a Systematic Literature Review (SLR) method, along with bibliometric analysis. Climate change represents a worldwide challenge that profoundly affects both the environment and human social interactions, making it essential to comprehend public perceptions of this issue thoroughly. The escalating use of social media is driving an increase in research related to sentiment analysis, which is utilized to gain insights into public opinions and emotions. Methods: Data was collected from six leading databases such as Scopus, ScienceDirect, Taylor and Francis, IEEE Xplore, Sage Journals, and ProQuest, resulting in 3,326 articles. After a screening process using the PRISMA 2020 framework, 42 articles were selected for further analysis.   Result: The findings suggest that Twitter is the predominant platform for climate change sentiment analysis, referenced in 32 articles, while Sina Weibo is mentioned in nine articles, Reddit in two articles, and both Facebook and YouTube in one article each. Of the four approaches assessed, the leading approaches identified in this research are Machine Learning and Deep Learning. In the Machine Learning category, Naïve Bayes is the predominant approach, appearing in 18 articles, followed by Naïve Bayes, cited in 17 articles. Furthermore, Logistic Regression and Random Forest are each mentioned in 13 articles. In the field of Deep Learning methodologies, 10 articles used Convolutional Neural Networks (CNNs), nine articles featured Bi-LSTMs, six articles featured LSTMs, and 13 articles referenced Transformer-based models, particularly BERT. Furthermore, model validation primarily used cross-validation techniques, and the most referenced evaluation metrics were accuracy, recall, and F1-score in 33 articles and precision in 32 articles.   Novelty: The novelty of this research lies in the time of information collection for research on climate change sentiment analysis, spanning 2020 to the middle of 2025. The latest research on a related issue was conducted from 2008 to 2022. Furthermore, this study provides insights into research trends and includes the distribution of articles by country, separating them into Single-Country Publications (SCPs) and Multi-Country Publications (MCPs). This research also presents information on social media platforms, classification approaches, and commonly employed validation and evaluation tools, which differentiate it from prior studies. This analysis is conducted on six leading databases, producing valuable findings for researchers and policymakers.
A Hybrid YOLOv8-ResNet50 Architecture for Enhanced Cardiomegaly Prediction from Chest X-rays Faudin, Arif Nur; Farikhin, Farikhin; Syafei, Wahyul Amien
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.35225

Abstract

Abstract. Objective: This study aims to develop a reliable deep learning architecture for predicting cardiomegaly by integrating the ResNet-50 backbone into the YOLOv8 object detection framework, overcoming the challenges of detecting subtle anatomical variations and low-contrast features often found in chest radiographs. Methods: This study used a publicly available chest X-ray dataset, with rigorous data annotation to establish ground truth for the heart and thoracic cavity regions. Preprocessing included resizing input images to 640×640 pixels, automatic orientation correction, and an 80:20 data split between training and testing. Real-time data augmentation was applied to the training set. The ResNet-YOLOv8 hybrid model was trained for 150 epochs with optimized hyperparameters (learning rate, momentum, weight decay, loss weight), and performance was evaluated using metrics such as mAP, precision, recall, and confusion matrix results. Results: The experimental results show that the proposed architecture achieves high accuracy in detecting cardiomegaly, with mAP50-95 of 0.7578, precision of 0.9955, recall of 0.9962, F1 score of 0.9959, and inference latency of only 4.5 ms/img. This model is more optimal than the standard YOLOv8 variant in both accuracy and computational efficiency. Innovation: The integration of ResNet-50 into YOLOv8 significantly improves feature extraction capabilities for chest X-ray images, enabling the recognition of fine anatomical details with high precision. This innovative hybrid approach advances automated cardiomegaly detection, offering potential for large-scale, real-time implementation in clinical settings and contributing to the development of advanced AI-powered diagnostic tools.
Comparative Performance Analysis of Deep Learning Models for Cryptocurrency Price Forecasting Pambudi, Ryo; Mutiara Kusumo Nugraheni, Dinar; Puji Widodo, Aris
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.35653

Abstract

Purpose: This research aims to find an accurate cryptocurrency price prediction model to mitigate financial risks caused by high price volatility. This research compares the predictive capabilities of five Deep Learning model, namely LSTM, GRU, BiLSTM, Transformer, and Performer, for  predicting cryptocurrency prices with the highest accuracy in the digital financial market. Methods: The methods applied in this research are dataset, preprocessing data, model training, and model evaluation. The dataset used in this study, namely the price per minute data for BTC, ETH, BNB, and XRP, was obtained from Kaggle. Data processing includes normalization using MinMaxScaler and sequence generation through the Sliding Window technique. To validate each deep learning model, and four metrics consisting of MAE, MSE, RMSE, and MAPE are used for evaluation. Result: The Transformer model created the best results for the lowest MAPE value across all datasets, the smallest being BTC and ETH at 0.20%, BNB at 0.29%, and XRP at 0.36% demonstrating high accuracy and generalization. The BiLSTM was ranking second since it captured effectively the bidirectional temporal dependencies; the GRU was moderate but stable in its performance. The data showed that the accuracy of LSTM and Performer varied. Novelty: This study offers a comprehensive comparison of various Deep Learning models in detail, enabling it to find the best model for predicting cryptocurrency prices with high accuracy. This study provides valuable insights for the development of advanced deep learning-based price forecasting systems in the field of digital financial analysis. 
Improving Sentiment Analysis with a Context-Aware RoBERTa–BiLSTM and Word2Vec Branch Hardyanto, Wahyu; Aryani, Nila Prasetya; Andestian, Defin; Sugiyanto; Setyaningrum, Wahyu; Mardiansyah, M Fadil; Islam, Muhamad Anbiya Nur; Purwinarko, Aji
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.35918

Abstract

Purpose: We improve the accuracy of Twitter/X sentiment analysis with a hybrid model combining Word2Vec and the Robustly Optimized BERT Pretraining Approach (RoBERTa). However, Twitter/X text is noisy (slang/OOV) and ambiguous, so the performance of the pre-trained transformer decreases. Word2Vec is also limited to local contexts. Integrative studies of both are still limited. The idea is that Word2Vec is strong for slang/novel vocabulary (distributional semantics), while RoBERTa excels in contextual meaning; combining the two mitigates each other's weaknesses. Methods: The Sentiment140 dataset contains 1.6 million balanced tweets. The split is stratified; Word2Vec is trained solely on the training data. RoBERTa is pretrained (frozen in the first stage, then fine-tuned with some layers in the second stage). The Word2Vec and RoBERTa vectors are concatenated and processed using Bidirectional Long Short-Term Memory (BiLSTM) with sigmoid activation. Training utilizes TensorFlow and the Adam optimizer, incorporating dropout and early stopping. The decision threshold is optimized during the validation process. Result: The hybrid model achieved an accuracy of 88.09%, an F1-score of 88.09%, and an Area Under the Curve (AUC) ≈ 95.19% on the Receiver Operating Characteristic (ROC). No overfitting was observed, and the hybrid model outperformed both single baselines. The confusion matrix and ROC curve corroborate the findings. Novelty: The novelty lies in the fusion of distributional and contextual representations with a structured fusion mechanism. Limitations: Computational requirements and hyperparameter tuning are not yet extensive. Further directions: Systematic hyperparameter search and cross-validation across other large sentiment datasets to assess generalization.
Evaluating the Implementation of the FinOps Framework for Cloud Infrastructure Cost Management: A Case Study of Technology Companies in Indonesia Manurung, Hierony; Aji, Rizal Fathoni
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.36226

Abstract

Purpose: The management of cloud infrastructure costs has become increasingly relevant in light of the rapid rate of cloud service adoption, especially in Indonesia's technology space, where there is tremendous economic pressure. Cloud infrastructure cost management solutions such as the FinOps Framework are clearly intended to provide a greater degree of accountability for financial governance and to enable cloud spending optimization when implemented holistically. In this research, we want to evaluate how FinOps adoption maturity is in several technology companies, and to find the gap between FinOps Framework principles and their real-world implementation. Methods: A mixed-methods case study was employed. To gather primary data, the first step was to interview six FinOps practitioners using semi-structured interviewing methods. Thematic analysis was used to identify thematic similarities across responses. Following the development of a simplified three-point maturity score system based on the FinOps framework, we compared each participant's maturity state in reference to the variations outlined in the FinOps literature. Result: From the interviews, there was an emphasis on key processes, such as cost ownership, budgeting, and governance, which were quite centralized, reactive, and somewhat informal, although everyone was using technical optimization approaches such as rightsizing. Our quantitative analysis corroborated this, with a cohort average maturity score of only 1.53 out of 3. This indicates a general maturity score in the "Crawl" or first stage, indicating shortcomings related to the process element of the basic practices not being integrated in a systematic way.  Novelty: This research provides meaningful and helpful insights into the real challenges of tech companies in Indonesia implementing FinOps. Our research provides a clear baseline for companies working locally by measuring maturity levels and making recommendations at the end. More importantly, beyond technical optimization, it clearly identifies the need to formalise policies and to improve cooperation across functional areas. This mixed methods approach should be extended to other research by increasing sample size and allowing for a deeper exploration of the cultural context.

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