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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 190 Documents
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Implementation of Agile Scrum in the Digital Transformation of Insurance Claims Services at Integrated Ports Muh Irfan Hs; Thoyyibah T
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.41827

Abstract

Purpose: This study develops a web-based insurance claim information system at PT Jasaraharja Putera within an integrated port environment. The conventional claim process, which relies on manual communication such as WhatsApp and email, often causes verification delays, document loss, lack of transparency, and risks of duplicate claims or fraud. The system aims to accelerate and simplify claim submission, particularly during reporting and initial verification by field officers, through real-time integration with the company’s core systems. Additionally, digital monitoring features enhance oversight of the claims process, reducing duplication and fraud potential. Methods: This research employed a descriptive qualitative method with an Agile Scrum system development approach. Data collection involved interviews with field officers and the claims team, analysis of existing business processes, system trials, and distribution of questionnaires to users. System development was conducted iteratively over several sprints, with each sprint producing features that were tested and evaluated based on user feedback. Results: Accelerate the claims input and verification process in the field. Increase transparency of claims status through a real-time monitoring dashboard. Reduce the risk of lost documents, human error, and potential duplicate claims. Achieve high user satisfaction levels based on questionnaire results at each development sprint. Novelty: This research introduces a real-time, integrated web-based claims system for port operations, applying Agile Scrum and monitoring features to prevent fraud. It offers practical solutions to public insurance claim inefficiencies and theoretical insights for advancing insurance information systems.
K-MEANS WITH PARTICLE SWARM OPTIMIZATION FOR ERROR REDUCTION IN MICRO, SMALL, MEDIUM ENTERPISE CRAFT IN YOGYAKARTA Athallah Naufal Muthahhari; Lisna Zahrotun
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.40664

Abstract

Purpose: This study aims to optimize the determination of the optimal number of clusters in the segmentation of handicraft-based Micro, Small, and Medium Enterprises (MSMEs) in Yogyakarta to support targeted and data-driven development strategies. Approach: A quantitative approach was applied to survey data collected from 145 MSMEs. The analytical pipeline consisted of four stages: (1) data acquisition through structured surveys, (2) preprocessing including encoding, mode imputation for missing values, and Min–Max normalization, (3) model development using the K-Means algorithm integrated with Particle Swarm Optimization (PSO) to automatically search for the optimal cluster number (K = 2–10), and (4) performance evaluation using Silhouette Score, Sum of Squared Error (SSE), and Mean Absolute Error (MAE). Result: The optimization process consistently converged to an optimal configuration of K = 8 clusters. Compared to standard K-Means, the proposed K-Means + PSO model reduced SSE from 54.555 to 51.676 and MAE from 0.124 to 0.116, indicating improved clustering stability and compactness. Semantic centroid analysis further revealed a hierarchical MSME structure consisting of Established Digital Adopters, Developing Potential Enterprises, and Subsistence Micro Enterprises, highlighting disparities in digital maturity and market reach. Novelty: This study contributes by integrating swarm-based optimization with centroid-driven semantic profiling, bridging algorithmic enhancement and policy-relevant interpretation. The proposed framework provides a robust and interpretable clustering model for MSME segmentation in emerging economic contexts.
Streamlining Architectural Design Services with Mobile E-Commerce Development Using Integrated Payment Gateways Galih Satrio Wicaksono; Afwan Anggara
Scientific Journal of Informatics Vol. 13 No. 2: May 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i2.36514

Abstract

Purpose: This research aims to design a mobile-based e-commerce system integrated with a payment gateway, as well as a centralized data management dashboard for A3+ Architect Design. The main focus of the research is on the efficiency of architectural design service business processes, including data management, information access, ordering mechanisms, payment integration, and real-time order monitoring for customers. Methods: The research methodology applied a systematic four-stage approach, including needs analysis to identify requirements through interviews with the A3+ studio, system model design, implementation, and testing. The system was built using client-server architecture with Flutter as the mobile application and Laravel as the web service and admin/designer dashboard. Functional validation of the system at the final stage was carried out using the black-box testing method. Result: Black box testing confirmed that all features are operating optimally and in accordance with the requirements specifications. The system successfully provides a comprehensive transaction workflow and an efficient operational dashboard for administrators and designers to support more organized data management. Novelty: The uniqueness of this research lies in the integration of a mobile application system with a web dashboard that is specifically tailored to the characteristics of the architectural services industry, which was previously dominated by manual processes. This research contributes to the digitization of customer services and increased transaction transparency, which directly impacts the strengthening of customer trust and the operational efficiency of service providers.
Design and Evaluation of an AI-Based Indonesian–Malay Bilingual Dictionary Using Machine Learning Techniques Nugroho Dwi Saputro; Aziizatul Khusniyah
Scientific Journal of Informatics Vol. 13 No. 2: May 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i2.40232

Abstract

Purpose: This study aimed to construct and evaluate an AI-based Indonesian–Malay bilingual dictionary by incorporating ML and NLP techniques through SLR. This research investigates salient issues in bilingual lexicography at the level of lexis, including limited parallel corpora, contextual semantic variability, and cultural nuances between closely related tongues. Methods: A mixed-methodology, pairing a systematic literature review (SLR) of 18 peer-reviewed studies published from 2020 to 2025 with an experiment based on prototypes. As a result of these findings, we created a mini prototype dictionary via ML-based semantic-mapped supervised and unsupervised models and Transformer representations. We evaluated the prototype using objective measures of translation accuracy and qualitative analysis of errors. Result: NLP and ML techniques are found to be effective in word-level bilingual dictionary building when integrated. Unlike direct equivalence approaches, which neglect contextual understanding in the representation of meaning groups, transformer-based representations capture high degrees of alignment in semantic generality through context. Our prototype produced suitable accuracy for most common lexical entries but struggles with informal expressions due to cultural nuances and underrepresentation of Indonesian–Malay parallel data. Novelty: This study contributes by bridging the existing literature by linking systematic review findings with an empirical implementation in the form of a functional AI-based dictionary prototype, demonstrating that combined machine learning models are effective for Indonesian–Malay lexicography and providing a repeatable framework for AI-driven bilingual dictionaries between other language pairs.
Region-Aware Double-Tail GAN with AdaIN–LADE Adaptive Style Transfer for Cinematic Anime Background Stylization in Makoto Shinkai Style Agus Purwanto; Kusrini; Ema Utami; David Agustriawan
Scientific Journal of Informatics Vol. 13 No. 2: May 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i2.40665

Abstract

Purpose: Background stylization in a specific anime art direction remains challenging because global style transfer often yields inconsistent stylization across semantic regions. Our prior Double-Tail GAN (DTGAN) with Adaptive Instance Normalization (AdaIN) and Linearly Adaptive Denormalization (LADE) can produce Shinkai-like backgrounds, but it still exhibits region-specific failures such as unstable sky gradients, over-stylized vegetation textures, and reduced edge clarity in buildings. Methods: We propose a region-aware extension of DTGAN by conditioning the generator on semantic masks (sky, vegetation, and buildings) and optimizing with coverage-aware, region-weighted objectives. Semantic masks are generated automatically using a lightweight transformer-based semantic segmentation model and refined via simple morphological filtering to stabilize mask boundaries during training. Result: Experiments on real photographs and Makoto Shinkai-style background frames show that region-aware conditioning improves both global and region-level quality compared with DTGAN without masks. The proposed method reduces global FID from 74.5 to 65.8 and LPIPS from 0.505 to 0.448, while improving sky-gradient similarity and overall palette consistency. Novelty: This work contributes (i) a practical mask-conditioned DTGAN formulation for local controllability in cinematic anime background stylization, and (ii) a coverage-aware region-weighting strategy that mitigates over-stylization and style leakage when semantic regions occupy imbalanced areas, without requiring manual mask annotation.
Contrast-Limited Adaptive Histogram Equalization for Enhancing YOLOv8-Based Industrial Bolt Defect Detection Muhammad Nurbaitullah; Abdul Syukur; Ahmad Zainul Fanani
Scientific Journal of Informatics Vol. 13 No. 2: May 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i2.41185

Abstract

Purpose: Defect detection in industrial bolts is crucial for ensuring product reliability, production safety, and consistent quality control in modern industrial environments. However, visual inspection of metal bolts remains challenging due to low contrast, uneven lighting, and reflective surfaces that often hide subtle defect patterns and reduce detection accuracy. Most existing YOLO-based approaches focus on architectural modifications to improve performance, which may increase model complexity and limit real-time applicability. Methods: This study integrates Contrast-Limited Adaptive Histogram Equalization (CLAHE) with YOLOv8 to improve defect visibility prior to detection. CLAHE enhances local contrast by redistributing pixel intensities while suppressing noise amplification, thereby strengthening feature representation for deep learning-based detection. Experiments were conducted on a publicly available industrial bolt dataset annotated via Roboflow, using a 3-fold cross-validation strategy. Performance was assessed with Precision, Recall, mAP@50, mAP@50–95, FPS, and FLOPs to evaluate accuracy and real-time feasibility. Result: Experimental results based on a 3-fold cross-validation scheme indicate that the proposed CLAHE–YOLOv8 model achieves consistent performance improvements over the baseline YOLOv8 configuration. The method obtains an average Precision of 0.9495±0.0068, Recall of 0.9028±0.0235, mAP@50 of 0.9364±0.0156, and mAP@50–95 of 0.7121±0.0037, while maintaining real-time inference performance at 29.79 FPS. These results demonstrate that contrast-based preprocessing contributes positively to detection stability and localization consistency without increasing model complexity. Novelty: The novelty of this research lies in demonstrating that data-level contrast enhancement using CLAHE effectively improve industrial bolt defect detection performance without architectural modification, offering a practical and computationally efficient solution for real-time industrial inspection systems.
Hybrid Genetic Algorithm and Adaptive Momentum Backpropagation Model with Support Vector Regression Kernel Function for Short-Term Electricity Load Forecasting Safitri Laela; Sirajuddin; Abdillah; Syaharuddin; Saba Mehmood; Wasim Raza
Scientific Journal of Informatics Vol. 13 No. 2: May 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i2.41475

Abstract

Purpose: Short-term electricity load forecasting (STLF) plays an important role in power system management because electricity demand is dynamic and influenced by factors such as community activities, weather conditions, and seasonal patterns. However, conventional forecasting methods often have limitations in modeling nonlinear and fluctuating electricity load data. Therefore, this study aims to develop a more accurate and stable forecasting model by integrating artificial intelligence methods within a Graphical User Interface (GUI)-based system. Methods: This research proposes a hybrid model combining Genetic Algorithm (GA), Adaptive Momentum Backpropagation (AMBP), and Support Vector Regression (SVR). GA is used to optimize SVR parameters, SVR performs nonlinear regression forecasting, and AMBP improves learning stability. The dataset consists of electricity load data from Gunung Sari District, Lombok, collected during 2015–2024 with 3,650 daily samples, divided into 80% training data and 20% testing data. Model performance was evaluated using MSE, RMSE, and MAPE. Result: The experimental results show that the proposed GA–SVR–AMBP hybrid model achieves better forecasting performance than single and partial hybrid models. In the testing phase, the model produced an MSE of 0.1556, RMSE of 0.3945, and MAPE of 1.13%, with an accuracy of 98.86%. Using the entire dataset, the model achieved an MSE of 0.4213, RMSE of 0.6491, and MAPE of 1.6953% with an accuracy of 98.30%, indicating good generalization capability and low prediction error. Novelty: The novelty of this study lies in the development of a hybrid GA–SVR–AMBP forecasting model integrated into a MATLAB-based GUI system that facilitates data analysis, model execution, and visualization of prediction results for short-term electricity load forecasting and decision support in power system management.
Interpretable XGBoost-Based Explainable AI Model for Concrete Compressive Strength Prediction Nadia Annisa Maori; Nur Aeni Widiastuti; Nor Hidayati
Scientific Journal of Informatics Vol. 13 No. 2: May 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i2.41825

Abstract

Purpose: The objective of this research is to provide more accurate predictions and transparency in the analysis process, making it easier for engineering professionals to comprehensively interpret, validate, and evaluate the model results. Methods: The algorithm used is XGBoost with the XAI-SHAP approach, which serves to interpret the contribution of each input feature, like the water-cement ratio, cement content, and concrete age, to the predicted output. This approach ensures that the model is not only accurate but also open and accountable to engineering practitioners. The research methods are data collection, model training, SHAP visualization, and performance evaluation using RMSE, MAE, and R² metrics. The dataset used is the Concrete Compressive Strength dataset from the UCI Repository, which consists of 1,030 records. Result: Subsequent to model training using the XGBoost with optimal hyperparameters identified via Random Search, model performance was evaluated across three scenarios: training data, testing data, and 5-fold cross-validation. The results indicate that the proposed XGBoost exhibits show high performance with an RMSE 4.59 ± 0.63 and R² of 93% as well as stability, which is evidenced by consistent performance metrics, without notable signs of overfitting. Novelty: The novelty of this study lies in the combination of XGBoost with the XAI-SHAP approach for predicting concrete compressive strength, providing both accuracy and interpretability that can work with nonlinear data, as well as its ability to produce results that are easily understood by construction professionals, making it a favorable choice for predicting concrete compressive strength.
Predicting Stunting Risk in Toddlers Using Interpretable Machine Learning on Imbalanced Data with Random Forest Utami; Amir Ali; Eka Wilda Faida; Titik Kuntari
Scientific Journal of Informatics Vol. 13 No. 2: May 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i2.42131

Abstract

Purpose: The prevalence of stunting in toddlers is still significant at over 20%, according to the Indonesian Nutritional Status Survey (SSGI). By creating machine learning based early prediction models especially made to handle unbalanced datasets, this project seeks to hasten the elimination of stunting. Methods: Three hybrid data balancing methods SMOTE, SMOTE + Tomek Links, and SMOTE + ENN were used in conjunction with a Random Forest algorithm. An anthropometry dataset comprising 40.444 toddlers was used to train and evaluate the models. Data cleaning, labeling, and imbalance handling comprised preprocessing. Accuracy, precision, recall, F1-score, and specificity metrics produced from a confusion matrix were then used for evaluation. Findings: Four key features significantly influence classification: ZSTB/U (0.698961), Height (0.116384), Weight (0.102773), and LiLA (0.035833). The Random Forest algorithm, paired with SMOTE-based techniques, achieved near-perfect performance across all metrics (approaching 1.0). This demonstrates excellent capability in accurately distinguishing the nutritional status of toddlers. Originality: This study offers a scientific explanation of the main stunting variables as well as a high-performance classification methodology. It offers a strong framework for early stunting diagnosis in Indonesia by successfully correcting class imbalance through SMOTE, SMOTE-Tomek, and SMOTE-ENN.
Enhancing Generalization of BISINDO Alphabet Recognition Using Anatomy-Aware Realistic 3D Keypoint Augmentation Muhammad Fa'iz Alfarisi; Denis Eka Cahyani; Samsul Setumin
Scientific Journal of Informatics Vol. 13 No. 2: May 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.42221

Abstract

Purpose: Sign language recognition systems based on 3D hand keypoints frequently experience generalization issues when trained on limited and homogeneous datasets, particularly under single-subject data collection settings. In BISINDO alphabet recognition, this limitation often leads to significant performance degradation when models are applied to unseen users or different acquisition devices. This study aims to improve cross-domain generalization of BISINDO alphabet recognition models by introducing realistic feature-level augmentation applied directly to 3D hand keypoints. Methods: A realistic 3D keypoint augmentation framework was proposed, consisting of Gaussian Jitter, Anisotropic Scale, Bone Length Scale, and Depth & Tilt Jitter to simulate sensor noise, anatomical variability, and viewpoint changes. Hand keypoints were extracted using MediaPipe Hands and classified using a multilayer perception (MLP). Model performance was evaluated through k-fold cross-validation on a single-subject internal dataset and cross-domain testing on an external dataset involving unseen subjects and different acquisition devices. Results: The experimental results indicate that the proposed augmentation strategy substantially improves generalization performance without degrading in-domain accuracy. The cross-domain F1-score increased from 67.20% in the baseline model to 89.55% after applying realistic 3D keypoint augmentation, while performance variability across validation folds was also reduced, indicating more stable learning behavior. Novelty: This work highlights that controlled geometric manipulation at the 3D keypoint level provides an effective and computationally efficient approach to mitigating overfitting in low-resource BISINDO recognition scenarios. By focusing on feature-level augmentation rather than image-based transformations or algorithm replacement, this study offers a practical strategy for enhancing robustness in real-world sign language recognition systems.

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