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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 25 Documents
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Search results for , issue "Vol. 11 No. 3: August 2024" : 25 Documents clear
Classification Modeling with RNN-based, Random Forest, and XGBoost for Imbalanced Data: A Case of Early Crash Detection in ASEAN-5 Stock Markets Siswara, Deri; M. Soleh, Agus; Hamim Wigena, Aji
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.v11i3.4067

Abstract

Purpose: This research aims to evaluate the performance of several Recurrent Neural Network (RNN) architectures, including Simple RNN, Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM), compared to classic algorithms such as Random Forest and XGBoost, in building classification models for early crash detection in the ASEAN-5 stock markets. Methods: The study examines imbalanced data, which is expected due to the rarity of market crashes. It analyzes daily data from 2010 to 2023 across the major stock markets of the ASEAN-5 countries: Indonesia, Malaysia, Singapore, Thailand, and the Philippines. A market crash is the target variable when the primary stock price indices fall below the Value at Risk (VaR) thresholds of 5%, 2.5%, and 1%. Predictors include technical indicators from major local and global markets and commodity markets. The study incorporates 213 predictors with their respective lags (5, 10, 15, 22, 50, 200) and uses a time step of 7, expanding the total number of predictors to 1,491. The challenge of data imbalance is addressed with SMOTE-ENN. Model performance is evaluated using the false alarm rate, hit rate, balanced accuracy, and the precision-recall curve (PRC) score. Result: The results indicate that all RNN-based architectures outperform Random Forest and XGBoost. Among the various RNN architectures, Simple RNN is the most superior, primarily due to its simple data characteristics and focus on short-term information. Novelty: This study enhances and extends the range of phenomena observed in previous studies by incorporating variables such as different geographical zones and periods and methodological adjustments.
Public Satisfaction with Online Services: (Case Study on the JEPIN Application) Anggita, Irva; Pribadi, Ulung
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.v11i3.4144

Abstract

Purpose: This study aims to analyze the use of the JEPIN application for users, namely the community in Pontianak City. JEPIN stands for Jendela Pontianak Integration This application was launched by the Pontianak City Communication and Information Office. Measurement uses variables consisting of infrastructure, ICT, human resources, budgeting, and digital government implementation. Methods: The method used is a quantitative type, primary data in the form of a survey of 100 respondents who come from JEPIN application users in Pontianak City. Using the Likert Scale (1: strongly disagree, 2: disagree, 3: neutral, 4: agree, and 5: strongly agree). The analysis technique for this study uses SmartPLS 4. Result: The results of the study show that the infrastructure variables have a P-Value of (0.000), an ICT P-Value of (0.000), and a P-Value Budgeting of (0.002), respectively, which shows a positive and significant influence on the implementation of digital government in the implementation of the JEPIN application. On the other hand, the human resources variable has a P-Value of (0.998), which shows a positive and significant influence on the implementation of digital government in the implementation of the JEPIN application in Pontianak City. Novelty: This study is unique because it looks at users who use the JEPIN application with a measurement indicator, namely the online services index (OSI). Most previous research on this theory has focused only on measuring how satisfied people are with online services, but this study offers a new perspective on the use of OSI in the context of application-based online services.
Analysis of Student Graduation Prediction Using Machine Learning Techniques on an Imbalanced Dataset: An Approach to Address Class Imbalance Hermanto, Dedy; Desy Iba Ricoida; Desi Pibriana; Rusbandi; Muhammad Rizky Pribadi
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.v11i3.5528

Abstract

Purpose: Machine learning is a key area of artificial intelligence, applicable in various fields, including the prediction of timely graduation. One method within machine learning is supervised learning. However, the results are influenced by the distribution of data, particularly in the case of imbalanced classes, where the minority class is significantly smaller than the majority class, affecting classification performance. Timely graduation from a university is crucial for its sustainability and accreditation. This research aims to identify a suitable method to address the issue of predicting timely graduation by managing class imbalance using SMOTE (Synthetic Minority Oversampling Technique). Methods: This study uses a five-year dataset with 26 attributes and 1328 records, including status labels. The preprocessing stages involve applying five classification algorithms: Decision Tree (DT), Naive Bayes (NB), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Random Forest (RF). Each algorithm is used both with and without SMOTE to handle the class imbalance. The dataset indicates that 60.84% of the cases represent timely graduations. To mitigate the imbalance, over/under-sampling methods are employed to balance the data. The evaluation metric used is the confusion matrix, which assesses the classification performance. Result: Without SMOTE, the accuracies were 89.12% for DT, 79.65% for NB, 89.47% for LR, 87.72% for KNN, and 90.88% for RF. With SMOTE, the accuracies were 88.89% for DT, 81.48% for NB, 91.05% for LR, 92.59% for KNN, and 89.81% for RF. The algorithms NB, LR, and KNN showed improvement with SMOTE, with KNN yielding the best results. Novelty: Based on the comparison results, a comparison of five algorithms with and without SMOTE can reasonably classify several of the algorithms being compared.
Prediction-based Stock Portfolio Optimization Using Bidirectional Long Short-Term Memory (BiLSTM) and LSTM Putra, Raditya Amanta; Nurmawati, Erna
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.v11i3.5941

Abstract

Purpose: Investment is the allocation of funds with the aim of obtaining profits in the future. An example of the investment instruments with high returns and high risks are stocks. The risks associated with the investment can be reduced by forming a portfolio of quality stocks optimized through mean-variance (MV). This is necessary because successful selection of high-quality stocks depends on the future performance which can be determined through accurate price prediction. Methods: Stock price can be predicted through the adoption of different forms of deep learning methods. Therefore, BiLSTM and LSTM models were applied in this research using the stocks listed on the LQ45 index as case study. Result: The utilization of LSTM and BiLSTM models for stock price prediction produced favorable outcomes. It was observed that BiLSTM outperformed LSTM by achieving an average MAPE value of 2.1765, MAE of 104.05, and RMSE of 139.04. The model was subsequently applied to predict a set of stocks with the most promising returns which were later incorporated into the portfolio and further optimized using the Mean-Variance (MV). The results from the optimization and evaluation of the portfolio showed that the BiLSTM+MV strategy proposed had the highest Sharpe Ratio value at k=4 compared to the other models. The stocks found in the optimal portfolio were BRPT with a weight of 19.7%, ACES had 16.9%, MAPI 11.8%, and BMRI at 51.6%. Novelty: This research conducted a novel comparison of LSTM and BiLSTM models for the prediction of stock prices of companies listed in the LQ45 index which were further used to construct a portfolio. Past research showed that the development of portfolios based on predictions was not popular.
Recognition of Organic Waste Objects Based on Vision Systems Using Attention Convolutional Neural Networks Models Aradea; Rianto; Mubarok, Husni
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.v11i3.6494

Abstract

Purpose: High population growth and increasing consumption patterns have resulted in significant organic waste production. The public often does not understand the correct way to deal with the problem of organic waste, including public awareness regarding the need for its management. Therefore, a system is needed to recognize waste objects based on various types. Currently, much research in this field has been studying object recognition, for example, the implementation of the Convolutional Neural Networks (CNN) model. However, there are still various challenges that must be addressed, including objects with diverse visual characteristics such as form, size, color, and physical condition. This research focuses on developing a system that enhances object recognition of waste, specifically organic waste, using an Attention Convolutional Neural Network (ACNN). By integrating attention mechanisms into the CNN model, this study addresses the challenges of recognizing waste objects with diverse visual characteristics. The proposed system seeks to improve the accuracy and efficiency of organic waste identification, which is crucial for advancing waste management practices and reducing environmental impact. Methods: This research combines a CNN architecture with an attention mechanism to create a better object detection environment called Attention-CNN (ACNN). The ACNN architecture employed consists of one layer input, three convoluted layers, three max-pooling layers, one attention layer, one flattened layer, four dropout layers, and two dense layers arranged in a certain way. Result: The research result shows that the model CNN with attention mechanism (ACNN) was slightly better at 86.93% than the standard model of CNN, which accounted for 86.70% in accuracy. Novelty: In general, the current use of CNN architecture to address waste object recognition problems typically employs standard architectures, resulting in lower accuracy for complex waste objects. In contrast, our research integrates attention mechanisms into the CNN architecture (ACNN), enhancing the model's ability to focus on relevant features of waste objects. This leads to improved recognition accuracy and robustness against visual variability. This distinction is important as it overcomes the limitations of standard CNN models in handling visually diverse and complex waste objects, thereby highlighting the novelty and contribution of our research.
Feature Expansion with GloVe and Particle Swarm Optimization for Detecting the Credibility of Information on Social Media X with Long Short-Term Memory (LSTM) Raffly, Famardi Putra Muhammad Raffly; Setiawan, Erwin Budi Setiawan
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.v11i3.7839

Abstract

Purpose: This research aims to develop a system for detecting the credibility of information on social media X by classifying tweets as credible or non-credible. Additionally, it seeks to improve the accuracy of classification and prediction of information credibility using feature extraction methods, semantic features, feature expansion, and optimization. Methods: The system is built using a deep learning approach with Long Short-Term Memory (LSTM), Term Frequency-Inverse Document Frequency (TF-IDF), Robustly optimized BERT Approach (RoBERTa), Global Vector (GloVe), and Particle Swarm Optimization (PSO). The dataset consists of 54,766 Indonesian tweets from social media X, focusing on the 2024 General Election and using several keywords such as ‘Pemilu 2024’, ‘Pilpres 2024’, ‘anies baswedan’, ‘Prabowo’, ‘#GanjarPranowo’, and ‘#debatCapres’. Result: The results of this study show that the highest accuracy achieved is 89.09% using LSTM with an 80:20 data split, baseline unigram, RoBERTa, Top1 corpus IndoNews, and PSO of the LSTM model’s hyperparameters, resulting in a highly significant statistical improvement of 0.96% over the baseline model. Novelty: This research contributes to information credibility classification research using RoBERTa to add semantic features and GloVe to expand features by utilizing a built corpus and finding similar words to connect with these expanded features. Additionally, PSO is applied to find the optimal hyperparameters, thereby improving the performance and accuracy of the LSTM classification model.
Comparison of Extremely Randomized Survival Trees and Random Survival Forests: A Simulation Study Zaenal, Mohamad Solehudin; Fitrianto, Anwar; Wijayanto, Hari
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.v11i3.8464

Abstract

Abstract. Purpose: This simulation study investigates the Extremely Randomized Survival Trees (EST) model, a machine learning technique expected to handle survival analysis, particularly in large survival datasets, effectively. The study compares the performance of the EST model with that of the Random Survival Forest (RSF) model, focusing on the C-index value to determine which model performs better. Methods: The analysis begins with the generation of 540 simulated datasets, created by combining three levels of sample sizes, two levels of censoring proportions, three types of hazard functions, and 30 repetitions for each scenario. The simulation data were split into 80% training and 20% testing data. The training data were used to build the EST and RSF models, while the test data were used to evaluate their performance. The model with the highest C-index value was deemed the best performer, as a higher C-index indicates superior model performance. Result: The results indicate that the sample size, type of hazard function, and the method used influence that model performance. The EST model significantly outperformed the RSF model when the sample size was large, though no significant difference was observed when the sample size was small or medium. Additionally, the EST model consistently demonstrated faster computation times across all simulation scenarios. Novelty: This study provides a pioneering exploration into applying decision tree algorithms, specifically EST and RSF, in survival analysis. While these methods have been extensively studied in regression and classification contexts, their application in survival analysis remains relatively unexplored.
Effect of Business Digitalization and Social Media on MSME Performance with Digital Competence as a Mediating Variable Titin; Sutrisno; Mahmudah, Henny; Muhtarom, Abid; Syamsuri
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.v11i3.9942

Abstract

Abstract. Purpose: The advancement of digital technology has fundamentally transformed the business landscape. Business digitalization and social media utilization have emerged as key drivers in SME strategies to enhance performance. This research aims to assess the effect of business digitalization and social media utilization on SME performance and to examine the role of digital competence as a mediator in this relationship. Method: This study employed a quantitative approach utilizing SEM-PLS methodology to explore the interrelationships among relevant variables. The study was conducted on 51 SMEs in Lamongan Regency, Indonesia, using an online questionnaire as the data collection tool. Result: The study findings revealed that business digitalization, social media utilization, and digital competence have a significant and positive effect on SME performance in Lamongan Regency. Moreover, digital competence is a significant mediator between business digitalization, social media utilization, and SME performance. These findings underscore that digital competence enables SMEs to optimize the benefits of business digitalization and social media, thereby enhancing operational efficiency, expanding market reach, and adapting to market changes swiftly and effectively. Novelty: In particular, the implementation of digital technology in production processes, integration of digital systems in business management, social media utilization for marketing and customer interaction, and the development of digital competence are key factors in enhancing operational efficiency, reducing costs, increasing sales and revenue, and improving customer satisfaction and loyalty among MSMEs.
Information System for Managing Village-Owned Enterprises (BUMDes) in Bogoharjo Village with DevOps Method Buchori, Achmad; Muthoharoh, Nurul Afifah; Wijayanto
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.v11i3.6627

Abstract

Purpose: The purpose of this research is to build a Bogoharjo Village-Owned Enterprises (BUMDes) Business Unit Management Information System to facilitate BUMDes managers in managing business units and providing available services to the community online. Methods: The method used in this research is the DevOps method with the stages of plan, code, build, test, release, deploy, operate, and monitor. System design uses UML (Unifed Modeling Language), namely flowchart, use case diagram, activity diagram, class diagram, and sequence diagram. System development using the Laravel framework. Results: The results showed that expert validation with an average percentage by content expert validation of 89% and media expert validation of 84% so that the BUMDes Management Information System (Village-Owned Enterprises) in Bogoharjo Village with the DevOps Method was very feasible to use. While the results of the practicality trial using 25 respondents produced an average percentage of 89% with a very practical category. Thus, the BUMDes Management Information System (Village-Owned Enterprises) in Bogoharjo Village with the DevOps Method is very practical to use. Novelty: The novelty of this research is used to assist BUMDes managers in managing data or providing information related to the BUMDes itself. In addition, BUMDes management becomes more organized and structured so that services to the community become more effective and well managed.
Community Acceptance of SIKESAL: An UTAUT Model Approach in E-Government Services in Jambi City Bulya, Bulya; Ulung Pribadi
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.v11i3.7511

Abstract

Purpose: This research examines the factors influencing public acceptance of e-government services, particularly the Sikesal system in Jambi City. The urgency of this research lies in its potential to improve the quality of public services, increase community engagement, and support digital transformation goals in Jambi City. This study contributes to understanding how local communities perceive and utilize e-government services, providing insights to improve service design and Using the Unified Theory of Acceptance and Use of Technology (UTAUT) model, we evaluated variables including Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions. The study utilized a quantitative approach, collecting data through questionnaires distributed to 100 respondents, selected using the Slovin formula. Result: Results indicate that Effort Expectancy significantly impacts the use of e-government services (T value of 18.339, P value of 0.000), highlighting the importance of user-friendliness. However, performance expectancy, social influence, and facilitating conditions did not significantly impact. Novelty: The novelty of this study lies in its localized examination of e-government acceptance, providing insights for targeted improvements in service design and implementation.

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