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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 16 Documents
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Search results for , issue "Vol. 12 No. 1: February 2025" : 16 Documents clear
Sentiment Analysis of Public Opinion on BAWASLU Using Random Forest and Particle Swarm Optimization Untoro, Meida Cahyo; Farhan, Muhammad
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.22234

Abstract

Purpose: Sentiment analysis, commonly referred to as opinion mining, involves the study of people's opinions, emotions, and attitudes toward various subjects. While the Random Forest algorithm is frequently employed in sentiment classification tasks, its integration with Particle Swarm Optimization (PSO) for feature selection remains relatively underexplored. This study investigates whether PSO-based feature selection can enhance the predictive performance of Random Forest by optimizing the selection of relevant textual features, ultimately leading to more accurate sentiment classification. Methods: The research adopts a structured text preprocessing approach that includes data cleansing, case folding, normalization, stop-word removal, and stemming to refine the input text. Term Frequency-Inverse Document Frequency (TF-IDF) is applied to extract features, followed by PSO-driven feature selection to refine the input set for the Random Forest classifier. The proposed model is evaluated using a Twitter sentiment dataset related to “Bawaslu”, with performance measured based on Out-of-Bag (OOB) error and accuracy metrics. Result: Empirical results demonstrate that incorporating PSO-based feature selection into the Random Forest model substantially lowers the OOB error to 20.42%, compared to 28.72% in the baseline Random Forest model. Furthermore, the optimized model achieves an accuracy of 78.35%, outperforming the standard approach. However, the introduction of PSO-based feature selection increases computational demands, indicating a trade-off between classification accuracy and processing efficiency. Novelty: This study introduces the novel integration of PSO-driven feature selection with Random Forest classification for sentiment analysis, addressing challenges in imbalanced text data. By optimizing feature selection through a metaheuristic approach, it enhances model accuracy and efficiency. The novelty lies in applying PSO to refine feature selection in text classification, offering new insights into improving machine learning models for imbalanced datasets. Future research could explore reducing computational overhead and investigating hybrid selection techniques to further enhance scalability and performance.
Evaluating User Acceptance of Virtual Class on Bali Melajah Portal Using Technology Acceptance Model and Importance Performance Analysis Purnawan, I Putu Abdi; Sudarma, Made; Indra ER, Ngurah
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.22833

Abstract

Purpose: The purpose of this study was to evaluate the acceptance and performance of users of the Virtual Class application in the Bali Melajah Portal using the Technology Acceptance Model (TAM) and Importance-Performance Analysis (IPA). This study found the main components of adoption, evaluated performance disparities, and suggested ways to improve LMS adoption in secondary schools. Methods: This study used a quantitative approach through an online survey conducted to collect data from teachers and students. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to evaluate the relationship between the variables in the research model. IPA was used to evaluate system performance and determine areas for improvement. Results: The study showed that perceived ease of use significantly influenced perceived usefulness, which in turn affected attitudes and behavioral intentions. Meanwhile, behavioral intentions positively affected actual system use. Performance mismatch was found, with the lowest percentage of conformity of 97.76% for actual system use. Quadrant analysis highlighted that, although the system was perceived as useful and easy to use, involvement, accessibility, and institutional support were necessary for effective system use. Novelty: This study offers a two-pronged assessment of LMS adoption in secondary schools by combining TAM and IPA. Unlike earlier studies that only evaluated user perceptions or system performance separately, this paper provides a complete approach to maximizing government-supported digital learning platforms. These findings give policymakers and application developers knowledge to improve the adoption of Virtual Classroom LMS.
Information System Governance Evaluation at Diskominfo Central Java Using COBIT 2019 Framework Zaini, Ahmad; Widodo, Aris Puji; Nugraheni, Dinar Mutiara Kusumo
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.22883

Abstract

Purpose: To evaluate the governance of the LaporGub Central Java service portal information system using the COBIT 2019 framework to obtain a comprehensive overview of the implemented capability levels. Methods: This study employs a qualitative method using the COBIT 2019 framework to assess capability levels, which is further combined with CMMI to provide improvement recommendations. The method produces valid and accurate results as respondents are selected using the RACI model, and the assessment is conducted at the subdomain level. Consequently, each domain yields valid outcomes, enabling precise and well-targeted recommendations. Results: This study successfully determined the level of LaporGub service capability using COBIT 2019 and showed that most domains have achieved, and in some cases even exceeded, the set targets. However, several areas still require improvement, especially in services security and business process control, as well as problem management. These findings indicate that the Central Java Provincial Communication and Information Office has a strong foundation in operational management and service sustainability. With proper improvements in IT governance, LaporGub is expected to serve as a model for adaptive and accountable digital public services that can be replicated in other regions across Indonesia. Novelty: This study introduces innovation by integrating COBIT 2019 with CMMI, an uncommon combination in capability level assessments within government institutions. Innovative techniques such as text development, respondent questionnaires based on the RACI model, and interviews are used to avoid redundancy in the research findings. By utilizing questionnaires, interviews, and document reviews, this approach enhances the validity of the study results.
Implementation of ANN Optimization with SMOTE and Backward Elimination for PCOS Prediction Ilmiyah, Miftakhul; Barata, Mula Agung; Yuwita, Pelangi Eka
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.22886

Abstract

by women, making it potentially fatal owing to delayed diagnosis and treatment. With the advent of current technology, machine learning and medical care may become associated with disease prediction. The purpose of the study is to predict PCOS using an Artificial Neural Network (ANN) Deep Learning algorithm combined with Synthetic Minority Oversampling Technique (SMOTE) for data balancing and backward elimination for feature selection, aiming to provide a more accurate diagnosis of PCOS with high accuracy from thoose combination. Methods: ANN algorithm structure with three hidden layers, each with a ReLU activation function of 128, 64, and 32 neurons, a Dropout layer, an output layer with a sigmoid activation function, and an Adam learning rate. Result: Using the SMOTE approach for data balance and backward elimination feature selection, the research attributes are reduced to 18. And ANN algorithm predicts PCOS disease achieve an accuracy of 92%. Novelty: This study uses an ANN algorithm model combined with the SMOTE data balancing technique and a feature selection method using backward elimination. These methods and techniques have proven to have high accuracy. The results of this study are expected to be used as a more accurate diagnosis by medical professionals in predicting PCOS disease.
Rice Price Forecasting for All Provinces in Indonesia Using The Time Series Clustering Approach and Ensemble Empirical Mode Decomposition Ilmani, Erdanisa Aghnia; Sumertajaya, I Made; Fitrianto, Anwar
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.23536

Abstract

Purpose: Accurate forecasting of rice prices is essential to ensure food security and a healthy economy for a country like Indonesia. Problems regarding time-series phenomena, such as trends or seasonality, are problematic for traditional approaches like ARIMA (Autoregressive Integrated Moving Average). This study analyzes the effect of EEMD (Ensemble Empirical Mode Decomposition) combined with time-series data clustering on forecasting accuracy. Methods: From 2009 until 2023, the thirty-two Indonesian provincial rice prices were grouped monthly into time-series clusters using hierarchical clustering, average linkage, and DTW (Dynamic Time Warping). After clusterization, the time series were decomposed using the ensemble EEMD method to extract their IMFs (Intrinsic Mode Functions) and residual components. Each IMF was assigned an ARIMA model. The model forecast was generated by adding all individual estimates. MAPE (Mean Absolute Percentage Error) was used to measure the model's performance. Result: The prices were divided into three clusters with an optimized region. Price changes are well captured through EEMD, where the residual components contributed predominantly to the long-term trends. The validation of the prediction showed MAPE values under 10% for the majority of the provinces, which indicates a relatively accurate prediction. On the other hand, some regions had inaccuracies that were higher than others due to uncontrollable fluctuations. Novelty: This study integrates clustering with EEMD decomposition for monthly rice price forecasting using data from 32 Indonesian provinces from 2009 - 2023, offering a novel approach that improves traditional techniques. The model can capture distinct regional price patterns and provide essential information to policymakers to manage rice supply and price stabilization. Further studies can develop external hybrid models with economic variables.
Analysis of Service Package Improvement Factors by Users of Fingerprint Analysis Applications Using TAM and EUCS Methods Kusuma, Teja; Widodo, Aris Puji; Nugraheni, Dinar Mutiara Kusumo
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.23779

Abstract

Purpose: The research aims to determine the factors that encourage users to upgrade service packages, provide actual recommendations for application developers to improve service quality, and provide insights for the development of similar applications in the future. Future Link is an application that offers efficient online fingerprint analysis with the support of artificial intelligence (AI). Despite these advantages, user acceptance and satisfaction remain challenges that hinder service package upgrades. Methods: This study combines the TAM and EUCS models to identify the variables that influence users' intentions to upgrade their service packages in a fingerprint analysis application. TAM includes key constructs such as perceived usefulness and attitude toward using, while EUCS encompasses dimensions like content, accuracy, format, timeliness, and ease of use, which collectively represent users’ satisfaction with the quality of information provided by the application. Additionally, two external variables curiosity and buying intention were added. A quantitative approach was employed using a survey method, with data collected from 151 respondents who are active users of the application. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through Smart-PLS 3.29. The analysis process included instrument development based on validated indicators, testing for construct reliability and validity, followed by evaluation of the structural model to assess the significance and explanatory power of the hypothesized relationships among variables. Result: The findings show that eight out of nine hypotheses were accepted. Significant variables influencing attitude toward use include curiosity, content, format, accuracy, perceived usefulness, and ease of use. User attitude significantly influences satisfaction, which in turn impacts service package upgrade intentions. Novelty: The combination of two theoretical models the TAM and EUCS model is used to analyze user behavior in the context of service package upgrades in fingerprint analysis applications. While previous studies have applied TAM and EUCS separately to evaluate general technology adoption or information satisfaction, this study integrates both models to provide a more comprehensive framework that considers both perceptual and experiential factors influencing user decisions. Furthermore, this study introduces two additional variables curiosity and purchase intention to reflect emerging user motivations in the use of digital services. The results are expected to support the development and improvement of applications in the future.

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