cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Jumanto
Contact Email
jumanto@mail.unnes.ac.id
Phone
+628164243462
Journal Mail Official
sji@mail.unnes.ac.id
Editorial Address
Ruang 114 Gedung D2 Lamtai 1, Jurusan Ilmu Komputer Universitas Negeri Semarang, Indonesia
Location
Kota semarang,
Jawa tengah
INDONESIA
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 20 Documents
 1   2 
Search results for , issue "Vol. 12 No. 4: November 2025" : 20 Documents clear
Migrating Monolithic to Microservices: Comparative Performance Testing Evaluation Between Architectures Wahyudin, Asep; Anisyah, Ani; Herbert; 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

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. This study aims to map research trends in the last five years (2020–2025) by utilizing a Systematic Literature Review (SLR) method along with bibliometric analysis. Data were 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. The research results indicate that Twitter is the most widely used platform for climate change sentiment analysis, followed by Sina Weibo, Reddit, Facebook, and YouTube. Out of the four approaches assessed, the leading approaches highlighted in this research are Machine Learning and Deep Learning. Furthermore, model validation primarily utilizes cross-validation techniques, and the evaluation metrics commonly referenced include accuracy, precision, recall, and F1-score. These discoveries provide valuable resources for researchers and policymakers to develop more targeted environmental communication and policy strategies.
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: A cryptocurrency's high volatility and nonlinear market dynamics make it extremely difficult to predict its price with any degree of accuracy. This study aims to evaluate and contrast the predictive capabilities of five Deep Learning architectures for the same reason: LSTM, GRU, BiLSTM, Transformer, and Performer, to identify the best model capable of predicting the price of cryptocurrencies. It is aimed at providing an empirical base for making such predictions with high reliability in such volatile financial markets. Methods: 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. An 80:20 data split is used to train and validate each deep learning model, and four metrics consisting of MAE, MSE, RMSE, and MAPE are used for evaluation. Standardized experimental protocols were guaranteed by Python-based frameworks.  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 research provides a comprehensive comparison between various models, highlighting the Transformer's self-attention mechanism as the most superior in capturing long-term temporal dependencies and nonlinear market behavior compared to other deep learning methods. These findings provide valuable insights for the development of advanced AI-based forecasting frameworks in 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 sentiment analysis with a hybrid model combining Word to Vector (Word2Vec) and the Robustly Optimized BERT Pretraining Approach (RoBERTa). 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/Study design/approach: 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. The process supports caching and training resumes. Result/Findings: 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/Originality/Value: The novelty lies in the fusion of distributional and contextual representations with resource-efficient fine-tuning. 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.
Ultra-Low-Cost Hybrid OCR–LLM Architecture for Production Grade E-KTP Extraction Saputro, Anjar Tiyo; Herlambang, Bambang Agus; Novita, Mega
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.v12i4.38200

Abstract

Purpose: The purpose of this study is to be able to avoid limitations of inexpensive ID card data extraction services and preserve privacy, which can simultaneously achieve reliable operation even under an environment with minimum infrastructure, in particular if no dependency on GPU-based servers are required. Method: The proposed approach is a microservice pipeline with three stages: (1) local lightweight pre-processing on devices, (2) Tesseract CPU-based OCR. js, (3) fast text tokenization through a small premature external LLM. The system is developed as TypeScript backend utilizing the Hono framework with all image processing taking place locally in order to keeping user data private. Result: The result of the experimental evaluations with real ID card samples is that the system can run stably in low-performance VPS (1 vCPU, 1 GB RAM) with operation cost approximately IDR 2.5047 per extraction process and its accuracy level is acceptable for use in a production environment. Moreover, the results indicate that system latency is dominated by LLM inference at the cloud. Novelty: The main contribution and novelty of this study is that we demonstrate, for the first time, a cost-effective (privacy-preserving) OCR-LLM hybrid pipeline without demanding expensive GPU models at large scale which makes our system suitable under limited storage and resource constraints on-premises or edge environments in small organizations including micro-SaaS services.
Optimized LSTM Model Using Simulated Annealing for Autoignition Temperature (AIT) Prediction as a Hazard Indicator Zahra, Nurul Izzah Abdussalam
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.v12i4.38278

Abstract

Autoignition temperature (AIT) is the minimum temperature at which a substance sparks spontaneously in air under normal atmospheric pressure without an external ignition source, such as a flame. This parameter is crucial for industrial safety, particularly in the production, processing, handling, transportation, and storage of flammable materials. However, conventional AIT measurement methods are time-consuming, expensive, and carry significant risk. As an alternative, in silico approaches based on machine learning can be used to develop AIT prediction models. Among these approaches, Long Short-Term Memory (LSTM) networks are particularly effective for modeling complex non-linear relationships. However, the performance of LSTM models is highly sensitive to the configuration of numerous hyperparameters, making manual tuning inefficient. Consequently, an automated optimization strategy is required to identify the optimal model architecture. This study aims to develop an AIT prediction model as a hazard indicator using the Long Short-Term Memory (LSTM) method optimized with Simulated Annealing (SA). Experimental results demonstrated that the proposed SA-LSTM Model with a cooling schedule of ΔT = 0.7 outperformed the unoptimized baseline architecture. The optimization process improved the R2 on the data test from 0.5682 to 0.5939 and reduced the RMSE from 74.35 K to 72.10 K. Furthermore, the MAPE decreased from 9.29% to 8.87%. These findings confirm that the SA optimized LSTM model provides a more reliable and robust hazard indicator.

Page 2 of 2 | Total Record : 20

 1   2 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 12 No. 4: November 2025 Vol. 12 No. 3: August 2025 Vol. 12 No. 2: May 2025 Vol. 12 No. 1: February 2025 Vol. 11 No. 4: November 2024 Vol. 11 No. 3: August 2024 Vol. 11 No. 2: May 2024