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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 15 Documents
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Search results for , issue "Vol. 12 No. 3: August 2025" : 15 Documents clear
Optimization of Random Forest Algorithm with SMOTE Method to Improve the Accuracy of Early Diabetes Prediction Nisa, Siti Khoirun; Barata, Mula Agung; Yuwita, Pelangi Eka
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.22986

Abstract

Purpose: This research aims to examine the performance of the random forest algorithm in diabetes risk classification with data balancing using the Synthetic Minority Oversampling Technique (SMOTE) method to improve the representation of minority classes and increase the prediction accuracy value. Methods: The study used the Behavioral Risk Factor Surveillance System (BRFSS) dataset, obtained from Kaggle, which contains health-related survey data used to identify individuals at risk of diabetes. The Random Forest algorithm was applied to classify diabetes. To balance the data, the SMOTE method was used. The model’s performance was evaluated using 10-fold cross-validation by comparing result before and after SMOTE. Result: The results showed that the application of the SMOTE method improved the performance of the Random Forest classification model, especially in minority classes. Model performance in minority classes without SMOTE had poor evaluation metrics with precision of 49%, recall of 18%, and F1-score of 26%. After applying SMOTE, these values increased to precision of 96%, recall of 88%, and F1-score of 92%. Representing improvements of 47 percentage points in precision, 70 points in recall, and 66 points F1-score. The overall accuracy of the Random Forest model also increased from 86% to 92%, showing a 6 percentage point improvement. Novelty: This study use integrating the Random Forest algorithm with the SMOTE technique and validating the results using 10-fold cross-validation. The combination significantly improves minority class prediction performance in early diabetes detection, addressing the common limitations of previous studies in handling imbalanced datasets effectively.
ATM Network Infrastructure Migration Strategy from Bank to PT XYZ Dharmawan, Arief; Hilman, Muhammad Hafizhuddin Hilman
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.24713

Abstract

Purpose: This study addresses PT XYZ’s technical challenges in optimizing the Automatic Teller Machine (ATM) network infrastructure, which caused service disruptions and customer complaints during the initial pilot migration. The research aims to solve the network availability problem by designing a reliable migration strategy, thereby improving service quality and strengthening partnerships with banks. Methods: A qualitative case study analysis was conducted, combining stakeholder interviews with technical evaluations. The Data Center WAN zone deployment followed the waterfall methodology for structured network and security appliance implementation, while remote ATM migrations used an incremental approach. Service Level Agreement (SLA) metrics were monitored to measure performance. Result: The solution improved the ATM network average SLA from 98.31% to 99.53%. Stakeholder feedback confirmed the effectiveness of the phased migration and enhanced operational reliability. This demonstrates the benefit of direct ATM WAN network re-engineering in eliminating legacy infrastructure complexity. Novelty: The study provides actionable benchmarks for ATM network implementation, including phased migration steps combined with high availability WAN topology. The implementation and migration method presents a replicable model for similar financial institutions in emerging markets.
Implementation of IndoBERT for Sentiment Analysis of the Constitutional Court's Decision Regarding the Minimum Age of Vice Presidential Candidates Setiawan, Very Dwi; Iswavigra, Dwi Utari; Anggiratih, Endang
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.26320

Abstract

Purpose: This study aims to analyze the effectiveness of the IndoBERT model for sentiment analysis of Indonesian anguage YouTube comments related to the legal Court’s ruling on the minimum age of vice presidential candidates for 2024. While previous research applied conventional machine learning methods, this study addresses the challenge of understanding nuanced public opinion using a language-specific transformer model. Methods: A dataset of 23,796 YouTube comments was collected using the YouTube Data API in January 2025. The comments underwent extensive preprocessing including normalization, case folding, text cleansing, symbol removal, stopword elimination, and stemming. Sentiment labels (positive, negative, neutral) were assigned through a lexicon based approach. Three models IndoBERT, BERT, Support Vector Machine (SVM), and Random Forest were trained and tested using an 80% and 20% split. Model result was evaluated with accuracy, precision, recall, and F1-score metrics. Result: IndoBERT achieved the maximum result with 95% accuracy, outperforming BERT 92%, SVM 88%, and Random Forest 85%. This confirms IndoBERT’s superior ability to capture contextual nuances in Indonesian sentiment analysis compared to other models. Novelty: This research demonstrates the advantage of transformer based models, particularly IndoBERT, in analyzing complex Indonesian social media texts. The findings support the use of IndoBERT for automated sentiment monitoring to inform government and media responses. Future work could extend to broader discourse analysis across diverse public sectors.
Development of Employee Management Information System UI/UX Using a User Centered Design Approach Saputra, Deva Dimastawan; Sukadarmika, Gede; Purnama, Fajar
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.27979

Abstract

Purpose: This study aims to develop a user interface and user experience (UI/UX) prototype for an Employee Management Information System (EMIS) at PT. Galsoft. The research focuses on addressing the manual administrative processes that are still widely used in the company's daily operations. The primary objective is to produce an interface design that aligns with user needs and improves the efficiency of work processes. Methods: This research adopts a User Centered Design (UCD) approach, consisting of four main stages: understanding the context of use, specifying user requirements, designing solutions, and evaluating the design. The prototype developed focuses solely on the UI/UX aspects without involving full system implementation. Usability evaluation was conducted using the System Usability Scale (SUS), involving 38 respondents from various divisions within the company. Result: The evaluation results show that the developed UI/UX prototype achieved an average SUS score of 89.4. This score indicates that the design has a high level of usability, is easy for users to operate, and supports the administrative workflows required by the organization. These findings demonstrate that the UCD approach effectively contributed to creating a design that is both functional and responsive to user needs. Novelty: This study contributes to the field of administrative information system prototyping in workplace environments that have yet to adopt digital solutions. The novelty of this research lies in the comprehensive application of the UCD approach, combined with SUS based usability evaluation, to produce a relevant and functional design that is ready to be further developed into a fully implemented system.
Biclustering-Based Analysis to Identify Fruit Production Potential in Indonesia Using Plaid Model Algorithm Alwani, Nadira Nisa; Sumertajaya, I Made; Wigena, Aji Hamim
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.25054

Abstract

Purpose: The application of biclustering using the plaid model aims to simultaneously identify mapping or grouping patterns of provinces and fruit type in Indonesia. The performance evaluation of the plaid model algorithm is used to assess its capability to discover and generate optimal biclusters, thereby representing the relationship between regions and fruit types with similar production characteristics. Methods: The plaid model algorithm produces optimal biclusters by configuring parameter scenarios such as model selection, managing the number of layers, and determining threshold values for rows and columns. The Average Mean Square Residue (MSR) value and the number of biclusters that can provide the most relevant data are used to determine the optimal parameter selection. Result: The plaid model algorithm effectively grouped provinces and fruit varieties into multiple biclusters. The row-constant model was choosen based on the average MSR value of 2.0537, which formed five overlapping biclusters across provinces and fruit types. Several provinces, such as Central Java and West Java, demonstrated a high potential for rose apples, breadfruit, and salak. Other provinces showed comparatively moderate levels of production. Novelty: This study presents a novel way to apply the plaid model biclustering algorithm to data on fruit varieties in various Indonesian provinces. Rarely used in horticulture, this method offers an alternative perspective on structured commodity mapping, especially when identifying specific patterns between fruit varieties and geographic distribution.
Deep Learning-Based Eye Disorder Classification: A K-Fold Evaluation of EfficientNetB and VGG16 Models Paramita, Cinantya; Rakasiwi, Sindhu; Andono, Pulung Nurtantio; Shidik, Guruh Fajar; Shier Nee Saw; Rafsanjani, Muhammad Ivan
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.26257

Abstract

Purpose: The study evaluates EfficientNetB3 and VGG16 deep learning architectures for image classification, focusing on stability, accuracy, and interpretability. It uses Gradient-weighted Class Activation Mapping to improve transparency and robustness. The research aims to create reliable AI-based diagnostic tools. Methods: The study used a dataset of 4,217 color retinal fundus images divided into four classes: cataract, diabetic retinopathy, glaucoma, and normal. The dataset was divided into 70% for training, 10% for validation, and 20% for testing. The researchers used a transfer learning approach with EfficientNetB3 and VGG16 models, pretrained on ImageNet. Real-time augmentation was applied to prevent overfitting and improve generalization. The models were compiled with the Adam optimizer and trained with categorical cross-entropy loss. Early stopping was implemented to allocate computational resources efficiently and reduce overfitting. A learning rate scheduler (ReduceLROnPlateau) was added to adjust the learning rate if no significant improvement was made concerning validation loss. EfficientNetB3 was more efficient in model size, possessing only 12 million parameters compared to VGG16's 138 million, making it suitable for resource-constrained mobile or embedded systems. The final evaluation was done on the held-out test set. Result: The EfficientNetB3 architecture outperforms VGG16 in classification accuracy and loss value stability, with an average accuracy of 93%. It also exhibits better transparency and predicted accuracy, making it a reliable model for medical image categorization. Novelty: This work introduces a novel framework integrating EfficientNetB3 architecture, stratified cross-valuation, L2 regularization, and Grad-CAM-based interpretability, focusing on openness and explainability in model evaluation.
Ambidextrous Blockchain Governance Approach for Advancing SmartCo's Digital Transformation Using COBIT 2019 Traditional and DevOps Khairiyah, Izzah; Mulyana, Rahmat; Kusumasari, Tien Fabrianti
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.26974

Abstract

Purpose: This study aims to design an ambidextrous governance model based on COBIT 2019 Traditional and DevOps Focus Areas, focusing on DSS05 (Managed Security Services), to enhance SmartCo’s security readiness for blockchain adoption. Methods: Adopting the Design Science Research (DSR) and case study methodology, data were gathered through semi-structured interviews with six key stakeholders at SmartCo and triangulated with internal documents until data saturation was achieved. Governance and Management Objectives (GMOs) were prioritized using organizational design factors, the relevance of COBIT 2019 DevOps guidance, national regulations (ICT Minister No.5/2021 and SOE Ministerial No. PER-2/MBU/03/2023), and insights from prior research. Results: The study presents an ambidextrous design of the seven governance components for DSS05, addressing people, process, and technology dimensions. Recommendations include formalizing dedicated security roles, standardizing procedures, issuing new policies, and adopting a Security Information and Event Management (SIEM) system. Implementation is projected to improve the DSS05 process capability from 3.29 to 3.86. Novelty: This research contributes to the IT governance body of knowledge by proposing a practical pre-implementation governance model for blockchain security in technology-focused enterprises. Its originality lies in the application of the ambidextrous COBIT 2019 framework to the DSS05 objective and the use of a comprehensive multi-criteria prioritization method to guide governance of emerging technologies.
Comparative Analysis of High School Student and AI-Generated Essays Using IndoBERT and Linguistic Features Adani, Muhammad Harits Shofwan; Rausanfita, Alqis; Mustaqim, Tanzilal
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.27732

Abstract

Purpose: The purpose of this study is to address the growing challenge of distinguishing between essays written by humans and essays generated by AI, particularly in the context of high school education in Indonesia. This study aims to analyze the semantic and linguistic differences between student-written and ChatGPT-generated in Indonesian language. Methods: The study employs an IndoBERT-based semantic model trained with triplet loss to generate paragraph-level embeddings, allowing the measurement of semantic similarity within and between essay classes. Additionally, linguistic features such as lexical diversity, word count, modal usage, and stopword ratio were extracted to capture stylistic and structural differences. These three key features are combined and used as input to a neural network classifier. Result: The IndoBERT-based semantic model successfully grouped student-written and ChatGPT-generated essays into distinct clusters. The similarity scores within student essays ranged from 0.7 to 0.9, while the similarity between classes was mostly negative with a few outliers, reflecting the cosine similarity metric used in this study, which has a range of -1 to 1. The classification model showed a 90.55% accuracy and an AUC of 0.9999 when evaluated on the independent test set defined in the Data Preparation stage. These results suggest that student-written and ChatGPT-generated essays form distinct semantic clusters. Students’ essays show more linguistic diversity, while ChatGPT essays show consistency in the coherence and formality aspects of the essays. Novelty: This study provides empirical insights of semantic similarities and linguistic features to differentiate between human and AI-generated essays in the Indonesian language. It contributes to supporting academic integrity efforts and highlighting the need for further research across different writing models and contexts.
Comparative Analysis: Accuracy of Certainty Factor and Dempster Shafer Methods in Expert Systems for Tropical Disease Diagnosis Yanti, Novi; Insani, Fitri; Okfalisa, Okfalisa; Zain, Ruri Hartika; Setiawan, Adil
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.28047

Abstract

Purpose: This study aims to diagnose Neglected Tropical Diseases early by applying the concept of an expert system as a tool that works by mimicking the thought patterns of an expert (doctor). The methods applied in this expert system are Certainty Factor and Dempster Shafer. Both methods work by combining a number of pieces of evidence (symptoms) to produce a confidence value for a disease. Methods: The study began with discussions and interviews with experts to collect information and data about Neglected Tropical Diseases. Conducting a literature review study to enrich knowledge about Neglected Tropical Diseases. Two main inference methods are used to detect diseases based on patient symptoms. The Certainty Factor method uses expert value weighting parameters and patient input value weighting as a basis for knowledge. The Dempster Shafer method only uses expert value weighting in analyzing the probability of symptoms to produce a level of diagnostic accuracy. Result: The Certainty Factor method works by integrating patient and expert weight values into its calculations. Meanwhile, the Dempster Shafer method considers expert weight values without involving patient weight values. Expert system searches using the Forward Chaining inference engine show that the Certainty Factor method has an accuracy probability value of up to 90%. Meanwhile, the Dempster Shafer method has an accuracy value of 70%. Novelty: The results of the study show that expert systems can be applied in the health sector, especially in diagnosing Neglected Tropical Diseases. Of the two methods used, the Certainty Factor method shows a high accuracy value, so it can help detect Neglected Tropical Diseases early and provide treatment solutions to improve health.
Estimation Model of Nutritional Content Based on Broiler Feed Images Using Convolutional Neural Network and Random Forest Mufti, Abdul; Sitanggang, Imas Sukaesih; Neyman, Shelvie Nidya; Abdullah, Luki
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.28682

Abstract

Purpose: This research aims to develop an intelligent model to estimate the nutritional content of broiler chicken feed based on feed images to assist farmers in selecting the best broiler feed and quickly verifying its quality to meet requirements. Methods: The methodology of this research includes literature study, data collection, data preprocessing, image classification, model evaluation, integration of CNN and random forest models, and estimation of nutritional content based on feed images. We collected 99 samples of broiler chicken feed from online stores in various regions of Indonesia, particularly Java. Next, we took pictures with a smartphone and analyzed the nutritional content using near-infrared spectroscopy. Preprocess the data by enhancing the dataset (color space and data augmentation). We use Convolutional Neural Network (CNN) for the classification of broiler feed images. The performance of the CNN model is evaluated using a confusion matrix. We integrate CNN and Random Forest Regressor (RFR) to estimate nutritional content from the features of broiler feed images. Result: The performance evaluation shows that the CNN (VGG-16) model is 0.9744% accurate and the RFR model has the highest R2 value of 0.8018. The benefits of this research include faster, more efficient, and automated feed quality measurement compared to traditional methods; maintaining feed quality standards; and avoiding health risks for livestock. Novelty: This research introduces an intelligent model to estimates the nutritional content of broiler feed images by integrating a CNN model with an RFR.

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