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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 131 Documents
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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.
The Digital Escape: Examining the Impact of Role Stressors on Cyberloafing Saragi, Meysi Putri Cristine; Mas'ud, Fuad; Sari, Intan Permata; Setyaningrum, Diana Ayu
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.v12i3.28982

Abstract

Objective: Cyberloafing Behavior (CB) has emerged as a major concern in the workplace. Apart from causing decreased performance, cyberloafing also carries the risk of hacking or data breaches. This study seeks to explore how role stressors influence Cyberloafing Behavior among healthcare professionals. Methods: This study employed a quantitative research design, utilizing accidental sampling to collect data from 109 respondents at Sinar Kasih Hospital, Purwokerto. Data analysis was conducted using Structural Equation Modeling (SEM) with the assistance of SmartPLS software. Result: The results of this study reveal that role stressors, which include Role Ambiguity (RA), Role Conflict (RC), and Role Overload (RO), have a statistically significant and positive impact on Cyberloafing Behavior (CB) among healthcare employees. High stress from unclear roles, conflicting duties, and heavy workloads increases the likelihood of non-work internet use. Addressing these stressors can help reduce counterproductive behavior and improve focus in healthcare environments. Novelty: This study recommends that healthcare organizations provide clear work guidelines to prevent role ambiguity, monitor workloads to reduce stress, and address role conflicts among employees. These strategies can help to reduce role stressor factors to prevent cyberloafing behavior among employees in order to avoid the risks arising from such behavior. The novelty of this study lies in its application of varied research subjects and a distinct methodological approach, setting it apart from previous studies. By focusing on the healthcare sector and employing SEM analysis, it offers new insights into the relationship between role stressors and Cyberloafing Behavior.
MoLLe: A Hybrid Model for Classifying Diseases in Chili Plants Using Leaf Images Khoirunnisa, Itsnaini Irvina; Fadlil, Abdul; Yuliansyah, Herman
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.v12i3.29071

Abstract

Purpose: Leaf diseases are often early indicators of problems in plants. More detailed image information with feature extraction on leaves can improve accuracy. However, MobileNetV2 tends to be less than optimal in capturing the fine texture characteristics of leaves. This research aims to propose a classification model for diseases in chili plants based on leaf images using MobileNetV2 with Local Binary Pattern (LBP), with three fully connected layers (220-120-60 neurons) using the ReLU activation function, referred to as MoLLe. Methods: This research consists of six stages. It begins with a dataset collected from chili farms comprising 900 images, which are then preprocessed into 3,600 images. Next, LBP feature extraction is performed. After that, a comparison between the benchmark architecture and the proposed architecture is conducted. A softmax layer is used to perform three-class classification. The MoLLe model was tested with the MobileNetV2 and MobileNetV2+LBP benchmark architectures and evaluated using a confusion matrix. Result: Based on the evaluation conducted, using batch size 32, learning rate 0.001, and 20 epochs, the MoLLe model experienced early stopping at epoch 11, achieving an accuracy of 0.97 training data, 0.84 validation data, and 0.91 testing data. The evaluation results showed consistent precision, recall, and F1-score values of 0.91, indicating the model's balanced ability to identify the three disease classes. Novelty: The novelty of this research lies in the integration of MobileNetV2 and LBP with modifications to three fully connected layers, which not only reduces the number of training parameters but also accelerates the detection process. This research makes an essential contribution to the development of more efficient and effective plant disease detection systems, with experimental results showing that MoLLe outperforms the benchmark architecture.
SOCA-YOLO: Smart Optic with Coordinate Attention Model for Vision System-Based Eye Disease Detection Rianto, Rianto; Purwayoga, Vega; Aradea; Mikail, Ali Astra; Yumna, Irsalina
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.v12i3.29293

Abstract

Purpose: The purpose of this research is to identify eye diseases using a modified YOLOv9. In particular, we modified YOLOv9 with the addition of Coordinate Attention (CA) for better eye disease detection performance, the use of Programmable Gradient Information (PGI), and Generalized Efficient Layer Aggregation Network (GELAN) for higher computational efficiency and accuracy. Methods: This study consists of several stages, including the acquisition of eye disease data obtained from the Roboflow website, data annotation, image augmentation, modeling using a modified YOLOv9, and model evaluation. Result: SOCA-YOLO model achieved an F1 score of 87,2% and mAP50 of 92,9%, outperforming YOLOv9-e by 1,7%. It also surpassed YOLOv6-L6 by 11,1%, YOLOv10-X by 0,8% in mAP50, and YOLOv8-X by 1,1% in recall, showcasing its superior detection accuracy and recall performance. Novelty: This research contributes by introducing the SOCA-YOLO model in improving the performance of the YOLOv9 by modifying the addition of Coordinate Attention (CA) for better eye disease detection performance, alongside Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) for better computational efficiency and accuracy.
Performance Analysis of Machine Learning Models using RFE Feature Selection and Bayesian Optimization in Imbalanced Data Classification with Shap-Based Explanations Aqmar, Nurzatil; Wijayanto, Hari; Mochamad Afendi, Farit
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.v12i3.31459

Abstract

Purpose: This research aims to evaluates the performance of Random Forest (RF) and Light Gradient Boosting Machine (LightGBM) models integrated with Recursive Feature Elimination (RFE) for feature selection, Bayesian Optimization (BO) for hyperparameter tuning, and three imbalanced data handling techniques Random Undersampling (RUS), Random Oversampling (ROS), and SMOTENC. Identifying key determinants of household food insecurity in Papua using SHAP for transparent feature interpretation. Methods: The research used 2022 SUSENAS data from Papua Province. Exploring data composition and variable characteristics, and aggregating individual data into household data. Data were split using random sampling (80% training, 20% testing). Eighteen experimental scenarios were created by combining feature selection or no feature selection, three imbalance handling methods, and default or hyperparameter tuning. RF and LightGBM were evaluated over 50 iterations using accuracy, sensitivity, specificity, and G-Mean, with SHAP applied to the best-performing models for interpretability. Result: LightGBM achieved the highest accuracy and stability, particularly when combined with SMOTENC and RFE+BO. RF showed better performance in maintaining G-Mean when paired with RUS, with the highest G-Mean (0.756) obtained by RF + BO + RUS. Three-way ANOVA proved that model type, imbalance handling, feature selection, and their interaction significantly affected the G-Mean value. SHAP analysis shows that health, financial, and educational limitations can increase the risk of food insecurity. Novelty: This research offers a new integration between feature selection, hyperparameter tuning, and imbalanced data handling within an interpretable machine learning framework, thereby providing a robust solution for food vulnerability classification on imbalanced datasets.
Sensor Integration and ARIMA-Based Forecasting in WAQMS for Environmental Monitoring in Riau Province, Indonesia Warnia Nengsih; Cyntia Widiasari; Putri Madhona; Helmi Chazali Lubis; Indra Agus Lukman; T.Marlina Cahyani; Elnovrian Purnama Saghita; Muhammad Saputra; Felix Gary; Eki Haiyal'ulya; Irwan Chandra; Aulia Gusri Pratama; Eka Ariefyanto Putra; Rama Yoedha Satria; Shinta Utiya Syah
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.v12i3.24742

Abstract

Purpose: This study aims to develop an integrated solution for real-time environmental monitoring in Riau Province, Indonesia, where air and water quality are increasingly impacted by industrial, agricultural, and climatic factors. Existing monitoring systems are often limited by their lack of real-time capabilities and predictive analytics. Methods: To address this, we designed the Water and Air Quality Monitoring System (WAQMS), which integrates sensor-based data acquisition with the Autoregressive Integrated Moving Average (ARIMA) model for forecasting. Sensor units were deployed across three pilot locations—Kampar, Siak, and Pekanbaru—to continuously collect environmental data. The ARIMA model was applied to historical datasets to predict future trends in air and water quality, while a web-based dashboard was developed to visualize real-time data and forecasts. Result: Calibration results showed a system accuracy of 85%, surpassing the national threshold of 75% set by the Indonesian Ministry of Environment and Forestry. This validates the use of WAQMS for Air Pollution Standard Index (ISPU) classification. Novelty: The novelty of this study lies in the seamless integration of AQMS and WQMS within a unified predictive monitoring system, combined with a user-friendly interface for stakeholders. The results demonstrate the system's potential as a decision-support tool for local governments, offering timely insights and enabling more effective and sustainable environmental management.
Optimizing Inventory Management: Data-Driven Insights from K-Means Clustering Analysis of Prescription Patterns Dermawan, Aulia Agung; Ansarullah Lawi; Putera, Dimas Akmarul; Kurniawan, Dwi Ely; Ummatin, Kuntum Khoiro; Jorvick Steve
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.8690

Abstract

Purpose: The goal is to improve how inventory is managed in healthcare by using K-Means clustering to analyze prescription trends. This approach helps ensure better stock availability, streamlines operations, and ultimately increases sales opportunities. Methods: This research applied the K-Means clustering algorithm to analyze a comprehensive dataset of prescription behaviors from XYZ Clinic. By grouping similar prescriptions into clusters, this method highlighted patterns within the data. These insights led to the identification of unique prescription categories, enabling the creation of tailored recommendations for improving inventory management. Result: The analysis showed that Cluster 1 should be prioritized for inventory management due to its high sales potential and consistent prescription patterns. It is recommended to increase stock for the medications in Cluster 1 to improve inventory turnover and streamline clinical operations. These findings underscore the value of K-Means clustering in healthcare, especially for enhancing inventory management and operational efficiency. Novelty: This research presents a novel application of K-Means clustering in healthcare, focusing on prescription patterns and inventory management. While previous studies have primarily used K-Means clustering for areas such as risk assessment and logistics, this study provides valuable data-driven insights to improve inventory management strategies in healthcare. The results highlight how clustering methods can support better decision-making and resource allocation, ultimately leading to greater operational efficiency and improved patient care.

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