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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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Identification of Factors Influencing the Use of QRIS Using TAM and UTAUT 2 Methods Nuswantoro, Setio Ardy; Muhammad Ulfi; Miftahurrizqi; Muhammad Rafli
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.3562

Abstract

Purpose: This research aims to analyze user behavior towards Quick Response Code Indonesian Standard (QRIS) usage, employing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Technology Acceptance Model (TAM) methods. Methods: Online surveys were conducted among QRIS users, focusing on factors influencing adoption intention and usage behavior in Indonesian society. Sampling was random, with a sample size determined for a 95% confidence level and 5% margin of error. Data analysis employed Confirmatory Factor Analysis (CFA) for validity and reliability testing, followed by Structural Equation Modeling (SEM) to assess model fit. Result: The research results indicate the validity and reliability of the structural model, with Performance Expectancy significantly impacted by Effort Expectancy. However, Effort Expectancy insignificantly affects Behavioral Intention and Use Behavior, consistent with previous research. But when Effort Expectancy and Performance are used as mediating variables between Perceived Trust and Behavioral Intention, they have a significant impact on this relationship. Social Influence also insignificantly affects Behavioral Intention and Use Behavior. Facilitating Condition, Perceived Trust, Hedonic Motivation, Price Value, and Habit demonstrate significant impacts on Behavioral Intention and Use Behavior, reaffirming prior findings. Some variables also have a big influence on other variables, such as Perceived Trust has major impact to Effort Expectancy, and Habit which has a major impact on Behavioral Intention and Use Behavior as a whole. Additionally, Behavioral Intention significantly affects Use Behavior. Novelty: Effort Expectancy insignificantly affects Behavioral Intention and Use Behavior. However, when Effort Expectancy and Performance are used as mediating variables between Perceived Trust and Behavioral Intention, they have a significant impact on this relationship. Some variables also have a big influence on other variables, such as Perceived Trust has major impact to Effort Expectancy, and Habit which has a major impact on Behavioral Intention and Use Behavior as a whole. This study contributes to understanding QRIS adoption and usage behavior, offering insights for policymakers and practitioners in the digital payment sector.
An Exploration of TensorFlow-Enabled Convolutional Neural Network Model Development for Facial Recognition: Advancements in Student Attendance System Irawati, Anie Rose; Kurniawan, Didik; Utami, Yohana Tri; Taufik, Rahman
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.3585

Abstract

Purpose: Face recognition has become an increasingly intriguing field in artificial intelligence research. In this study,   This study aims to explore the application of CNNs, implemented through TensorFlow, to develop a robust model for enhancing facial recognition accuracy in student attendance systems. The focus of this research is the development of a model capable of recognizing student faces under various lighting conditions and poses in an academic environment, using a multi-class dataset of student images collected from internship attendance records at the Computer Science Department. Methods: The dataset, comprising facial images from 19 students, served as the foundation for training and validating the CNN model. The dataset, sourced from the computer science department's internship attendance records, included a total of 231 images for training and 59 images for validation. The preprocessing phase included facial area detection and categorization, resulting in a well-organized dataset for training and validation. The CNN architecture, consisting of seven layers, was meticulously designed to achieve optimal performance. Result: The model exhibited exceptional accuracy, reaching 93% on the validation dataset after 300 training epochs. Precision, recall, and F1-score metrics were employed for a detailed evaluation across diverse classes, highlighting the model's proficiency in accurately categorizing facial images. Comparative analyses with a VGG-16-based model showcased the superiority of the proposed CNN architecture. Moreover, the implementation of a web service demonstrated the practical applicability of the model, providing accurate predictions with a remarkable response time of less than 0.3 seconds. Novelty: This comprehensive study not only advances face recognition technology but also presents a model applicable to real-world scenarios, particularly in student attendance systems.
The Impact of Balanced Data Techniques on Classification Model Performance Pardede, Jasman; Dika Prasetia Pamungkas
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.3649

Abstract

Purpose: The aim of this study is to examine the impact of balanced data techniques on the performance of classification models. Methods: To balance the imbalanced dataset, several resampling techniques are employed: The Synthetic Minority Oversampling Technique (SMOTE), Borderline-SMOTE (B-SMOTE), and SMOTE and Edited Nearest Neighbors (SMOTE-ENN). Classification is then performed using both balanced and unbalanced datasets to evaluate the impact of resampling techniques on classification model performance. Result: This study proposes the SMOTE, B-SMOTE, and SMOTE-ENN techniques for generating synthetic data. Experimental results showed that re-sampling can improve model performance on KNN, Naive Bayes, and Decision Tree. The best-balanced data technique is the SMOTE-ENN. The second best is B-SMOTE, and the last is SMOTE. If compared to the unbalanced dataset, the SMOTE technique encourages increasing the performance of Accuracy, Precision, Recall, F1-Score, G-mean, and Curve-ROC respectively by 4.79%, 35.89%, 35.32%, 35.63%, 46.94%, and 34.89%, respectively on DT method. The B-SMOTE technique on the DT method improves the performance of Accuracy, Precision, Recall, F1-Score, G-mean, and Curve-ROC respectively by 5.62%, 36.45%, 35.88%, 36.19%, 47.40%, and 35.46% if compared to the unbalanced dataset. The SMOTE-ENN technique improves the performance of Accuracy, Precision, Recall, F1-Score, G-mean, and Curve-ROC respectively by 8.11%, 34.53%, 43.25%, 41.63%, 62.85%, and 42.91% if compared to the unbalanced dataset. Novelty: Based on the experiment results, the best-balanced data technique is the SMOTE-ENN. The SMOTE-ENN technique improves the performance of Accuracy, Precision, Recall, F1-Score, G-mean, and Curve-ROC.
Optimizing Fall Detection System as an Early Warning System for the Elderly to Enhance Quality of Life Syamsul Hidayat, Sidiq; Fadhil; Mujahidin, Irfan; Mohammad Faizin Zaini; Rahmalisa Suhartina
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.3716

Abstract

Purpose: The main objective of this research is to develop a fall detection system and improve rapid emergency response or early warning systems for falls in the elderly. Methods: In this research, the waterfall method was used for image analysis to detect falls with high accuracy. We also used Raspberry Pi 3, and OpenCV3 to set up a server to receive fall detection alerts and forward them to email. Results: This system integrated a camera mounted on a Raspberry Pi 3 to continuously monitor the area captured by the camera. In the fall detection system, the results of testing with data showed that the system accuracy was 72.22%, sensitivity 72.72%, and error 27.77%. Novelty: The approach this research adopted can be used in a variety of settings, including home healthcare, elderly care facilities, or places that require safety monitoring. With this system, we hope to improve rapid response in emergency situations, thereby protecting and improving the quality of life for people in need.
Village Potential Mapping: Comprehensive Cluster Analysis of Continuous and Categorical Variables with Missing Values and Outliers Dataset in Bogor, West Java, Indonesia Pratiwi, Nafisa Berliana Indah; Indahwati; Anwar Fitrianto
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.3903

Abstract

Purpose: This research emphasizes the need to map villages' conditions and identify village potentials, evaluate the effectiveness of development capability, and address the rural-urban development gap with clustering algorithms. The study employs the village development index (IPD) indicators obtained from the village potential dataset, with various numerical and categorical indicators, to capture both tangible and intangible aspects of village potential. Challenges such as missing data and outliers in IPD data collection can be found. The study aims to evaluate the effectiveness of clustering algorithms, with integrated and separated imputation processes, in handling these data issues and to track the development of villages in the Bogor Regency, West Java, Indonesia, based on the village’s potential (PODES) dataset. Methods: Three clustering algorithms, such as k-prototype, simple k-medoids, and Clustering of Mixed Numerical and Categorical Data with Missing Values (k-CMM) are compared. The pre-processing data, which is the imputation process for the first two algorithms, is conducted separately, while the k-CMM has an integrated imputation process. Both imputation stages are tree-based algorithms. Cluster evaluation is based on internal criteria and external criteria. Clusters resulting from the k-prototype and simple k-medoids are selected by internal validity indices and compared to k-CMM using external validity indices for several numbers of clusters (k = 3,4,5). Result: According to data exploration, the IPD of Bogor Regency, West Java, Indonesia dataset contains ± 5% of outliers and six missing values in some chosen variables. Tree-based imputation methods are applied separately in k-prototype and simple k-medoids, jointly in k-CMM. Based on the elbow and gap statistics methods, this research aims to determine the optimum number of clusters k = 3. The internal validity indices performed on k-prototype and simple k-medoids resulting in three clusters (k = 3) are optimum. Trials on several clusters (k = 3,4,5) for three algorithms show that the k-prototype with k = 3 performs the best and is most stable among the two other algorithms with IPD datasets containing many outliers; external validity indices evaluate cluster results. Novelty: This research addresses issues commonly found in mixed datasets, including outliers and missing values, and how to treat problems before and during cluster analysis. An improvement of Gower distance is applied in the medoid-based clustering algorithm, and the k-CMM algorithm is the first algorithm to integrate the imputation process and clustering analysis, which is interesting to explore this algorithm’s performance in clustering analysis.
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.
Impact of NEW SAKPOLE on Original Local Government Revenue Post-COVID-19: A Case Study of Central Java Province BAPENDA Saputri, Rivi; Mutiarin, Dyah
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.4427

Abstract

Purpose: This research scrutinizes the impact of NEW SAKPOLE on increasing Original Local Government Revenue (PAD) post-pandemic in Central Java Province BAPENDA. Methods: This research employed qualitative methods with a descriptive research approach. The types and sources of data used were primary and secondary data. Data collection techniques were conducted by looking for references from various sources, such as reports, previous research journal articles, and news sources related to research. Since the data analysis techniques utilized in NEW SAKPOLE application research must be appropriate to the data collection type, qualitative analysis was performed to analyze data collected from interviews, observations, and FGDs. Result: The research results uncovered that NEW SAKPOLE is a public service innovation by the Central Java Government that encourages people to be obedient and disciplined when paying motor vehicle taxes. Implementing this application service is highly effective, as it can save people's time and energy and does not cause crowds, especially during and after the pandemic. Other governments can imitate this innovation to increase revenue from the tax sector, especially the motor vehicle tax sector, which potentially increases Original Local Government Revenue in Central Java Province. Novelty: This research enriches literature by discovering the impact of implementing NEW SAKPOLE on increasing Original Local Government Revenue. This research provides new insights and sound examples for related parties regarding implementing the E-SAMSAT application in facilitating Motor Vehicle Tax (PKB) payments by considering post-COVID-19 as the current condition when this research was conducted. This study also completes a sector-by-sector analysis that has yet to be carried out in previous studies regarding the implementation of NEW SAKPOLE.
Evaluation of Travel App’s Usability Using the System Usability Scale Method Putra, Revi Raka; Yasirandi, Rahmat; Qusyairi, Muhammad Mirza
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.4509

Abstract

Purpose: The objective of this study is to assess the usability of the Baraya Travel application, highlighting areas for enhancement to improve the online ticket booking experience for users. By concentrating on the Baraya Travel app as the research subject, the study intends to offer valuable insights for its developers. It emphasizes user feedback and recommendations to improve interface navigation, application speed, and the addition of new features, ultimately increasing convenience and ease of use. This study aims to provide an extensive examination of the app's performance, identifying precise areas where improvements can be made. Methods: Using the Usability Testing methodology, particularly emphasizing the System Usability Scale (SUS), this study employs a quantitative research strategy. Surveys were administered to 409 Baraya Travel application users to gather primary data concerning usability aspects including user interface navigation, application responsiveness, and user convenience. The statistical analysis utilized both descriptive statistics, encompassing measures of central tendency and variability, to summarize the data, and potentially inferential tests like correlation analysis or t-tests, to investigate relationships and disparities between variables. The selection of statistical analysis aimed to offer a thorough interpretation of the data and extract meaningful insights into the usability of the Baraya Travel application. Furthermore, the utilization of the SUS evaluation metric provided a standardized measure to evaluate usability and facilitate comparisons across studies, ensuring consistency and reliability throughout the evaluation process. Result: The examination of the SUS questionnaire indicated that users of the Baraya Travel application provided an average score of 75.26. This score suggests that the application demonstrates a commendable level of usability, earning a "Good" rating and a Letter Grade of "B". Nonetheless, there remains potential for enhancement to reach the desired standard of perfection. Users offered invaluable feedback and suggestions, highlighting the necessity for enhanced user interface navigation, improved application speed, and the implementation of additional features to augment convenience and user-friendliness. Novelty: This research provides valuable insights for Baraya Travel application developers, emphasizing areas for improvement based on user feedback and suggestions. It contributes to the progression of online ticket booking systems, with the goal of enriching the overall travel experience for users. Highlighting the significance of user-friendly interfaces, efficient application performance, and convenient features is crucial for enhancing user satisfaction and fostering loyalty. Subsequent research endeavors could explore further factors influencing user satisfaction and loyalty in the context of digital travel applications.
Comparison Model Optimal Machine Learning Model With Feature Extraction for Heart Attack Disease Classification Salsa Desmalia; Amril Mutoi Siregar; Kiki Ahmad Baihaqi; Tatang Rohana
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.4561

Abstract

Purpose: The purpose of this study is to classify the number of people affected by heart disease and those not affected by heart disease based on various categories of heart attack causes. This study aims to urge people to take better care of their health and to serve as a reference for doctors to educate patients about the dangers of heart attacks. Methods: The model will be constructed via a machine learning methodology. The algorithms utilized in its development encompass the Support Vector Machine (SVM) algorithm, the K-Nearest Neighbor (k-NN) algorithm, and the Random Forest (RF) algorithm.  This study utilizes principal component analysis (PCA) as a means of extracting optimized features from the dataset, employing techniques for dimension reduction prior to modeling the data. Result: Cumulative explication of the concept of variance constitutes a foundational aspect of PCA (principal component analysis) within the scope of the current research, namely a dimensionality reduction technique employed in multivariate data analysis to facilitate model development, thereby enabling the creation of more optimal and comprehensive models. In this research, the dimensions of training data are incorporated during the process of model creation.   The results show KNN model exhibits the highest performance, with an accuracy of 86%, precision of 86%, recall of 91%, and F1-score of 88%. Furthermore, evaluation using the ROC metric also provides a relatively favorable value, 0.85. Novelty: Researchers used 1190 patient data sourced from Kaggle. Before modeling the algorithm, researchers conducted EDA & Preprocessing which includes missing values to find data that does not have information, then duplicate data to find duplicated data, there are 270 duplicated data, then the duplicated data is deleted so that the data becomes 737, then PCA implementation is carried out.  PCA is reducing features automatically without changing the data.

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