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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
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.

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