cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Jumanto
Contact Email
jumanto@mail.unnes.ac.id
Phone
+628164243462
Journal Mail Official
sji@mail.unnes.ac.id
Editorial Address
Ruang 114 Gedung D2 Lamtai 1, Jurusan Ilmu Komputer Universitas Negeri Semarang, Indonesia
Location
Kota semarang,
Jawa tengah
INDONESIA
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
 8   9   10   11   12 
Music Genre Classification Using Mel Frequency Cepstral Coefficients and Artificial Neural Networks: A Novel Approach Alamsyah, Alamsyah; Ardiansyah, Fahmi; Kholiq, Abdul
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.13660

Abstract

Purpose: Music is an artistic expression with many categories in various genres and styles, characterized by its melodic and harmonic compositions. Music genre classification is crucial because genres serve as descriptors commonly used to organize large music collections, especially on the internet and in widely used applications like JOOX and Spotify. The aim of this research is to implement the Mel Frequency Cepstral Coefficients (MFCC) feature extraction method to generate numerical features from a set of specific music tracks. This collection of information will then be classified using machine learning. Methods: The method used in this study begins with combining the "GTZAN Dataset - Music Genre Classification" with additional data from TikTok and YouTube. The total dataset consists of 1,200 audio files, divided into 12 classes. The MFCC extraction process generates numerical representations of acoustic characteristics, which are then processed using Artificial Neural Networks. Result: The experiments demonstrate that increasing the amount of data is crucial, as it can enhance both variation and accuracy. The average accuracy achieved in this study is 91.42%, while the highest accuracy reaches 92.16%. These findings indicate that this study outperforms previous studies. Novelty: The novelty of this research lies in the integration of dynamic social media data (TikTok and YouTube) to enrich the standard GTZAN dataset, the repetition of the MFCC feature extraction process, and the combination of MFCC with Artificial Neural Networks (ANN).
Evaluation of User Experience in the Banjarbaru Disdukcapil Public Service Application Using User Experience Questionnaire and System Usability Scale Martalisa, Asri; Wahyu Saputro, Setyo; Turianto Nugrahadi, Dodon; Abadi, Friska; Budiman, Irwan
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.13780

Abstract

Purpose: Dukcapil Banjarbaru is an online-based government agency application used for various public services. According to the complaint report from Disdukcapil Banjarbaru, several users have reported similar problems and difficulties. The application has received a rating of 3.3 stars from approximately 24.000 users on the Google Play Store. Therefore, researchers conducted a user experience analysis using the UEQ methods and a usability evaluation using the SUS methods. Methods: This research analyzes user experience in applications using the UEQ to identify issues faced by users and evaluate usability through the System Usability Scale. The UEQ method is chosen for its efficiency and simplicity in assessing user experience within an application. The SUS method is employed because it is an effective approach for obtaining reliable statistical data and generating clear and accurate scores. Result: The UEQ benchmark results show that the scales for Attractiveness (1.59), Efficiency (1.68), Accuracy (1.66), and Stimulation (1.54) are categorized as "Good." The scales for clarity (1.37) and novelty (0.80) are classified as "Above Average." Meanwhile, the SUS score of 65 positions the application within the "acceptable" category for the acceptability range, the "D" category on the grade scale, and the "OK" category for adjective ratings. This indicates that while the Banjarbaru Dukcapil application has good usability, it requires improvements based on the total SUS score, which reveals several critical areas with scores below the average (258.4). Novelty: In this research, solutions for improvements are provided to Disdukcapil based on each aspect to improve the quality of the application, thereby offering better services to users.
Comparative Analysis of CNN Architectures in Siamese Networks with Test-Time Augmentation for Trademark Image Similarity Detection Suyahman; Sunardi; Murinto
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.13811

Abstract

Purpose: This study aims to enhance the detection of trademark image similarity by conducting a comparative analysis of various Convolutional Neural Network (CNN) architectures within Siamese networks, integrated with test-time augmentation techniques. Existing methods often face challenges in accurately capturing subtle visual similarities between trademarks due to limitations in feature extraction and generalization capabilities. The research seeks to identify the most effective CNN architecture for this task and to assess the impact of test-time augmentation on model performance. Methods: The study implements Siamese networks utilizing three distinct CNN architectures: VGG16, VGG19, and ResNet50. Each network is trained on a dataset of trademark images to learn deep feature representations that can discriminate between similar and dissimilar trademarks. During the evaluation phase, test-time augmentation (TTA) is applied to enhance model robustness by averaging predictions over multiple augmented versions of the input images. TTA includes transformations such as random rotations (up to 40%), width and height shifts (up to 20%), random shear transformations, zooming (up to 20%), horizontal and vertical flips, and random brightness adjustments. Result: Experimental findings reveal that the Siamese network based on VGG19 achieves the highest accuracy at 98.82%, outperforming the VGG16-based network with an accuracy of 97.07% and the ResNet50-based network with 50.00% accuracy. The application of TTA has improved performance across all models, with the VGG19 model receiving the highest improvement. The extremely low accuracy of ResNet50 can be attributed to its misinterpretation of original trademark images as close-forged ones, probably due to overfitting or lack of an efficient ability in generalizing very fine visual features. Novelty: The study conducted a comparative analysis of CNN architectures, namely VGG16, VGG19, and ResNet50 in Siamese networks for trademark image similarity detection.
A Performance Comparison of Data Balancing Model to Improve Credit Risk Prediction in P2P Lending Pertiwi, Dwika Ananda Agustina; Ahmad, Kamilah; Unjung, Jumanto; Muslim, Much Aziz
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.14018

Abstract

Purpose: The problem of imbalanced datasets often affects the performance of classification models for prediction, one of which is credit risk prediction in P2P lending. To overcome this problem, several data balancing models have been applied in the existing literature. However, existing research only evaluates performance based on classification model performance. Thus, in addition to measuring the performance of classification models, this study involves the contribution of the performance of data balancing models including Random Oversampling (ROS), Random Undersampling (RUS), and Synthetic Minority Oversampling (SMOTE). Methods: This research uses the Lending Club dataset with an imbalanced ratio (IR) of 4.098, and 2 classifiers such as LightGBM and XGBoost, as well as 10 cross-validation to assess the performance of the data balancing model including Random Oversampling (ROS), Random Undersampling (RUS), and Synthetic Minority Oversampling (SMOTE). Then the model is evaluated using the metrics of accuracy, recall, precision, and F1-score. Result: The research results show that SMOTE has superior performance as a data balancing model in P2P lending, with an accuracy of the LightGBM+SMOTE model of 92.56% and the XGBoost+SMOTE model of 92.32%, where this performance is better than other models. Novelty: This research concludes that SMOTE as a data balancing model to improve credit risk prediction in P2P lending has superior performance. Apart from that, in this case, we find that the larger the data size used as a model training sample, the superior performance obtained by the classification model in predicting credit risk in P2P lending.
Heart Disease Clustering Modeling Using a Combination of the K-Means Clustering Algorithm and the Elbow Method Wala, Jihan; Herman, Herman; Umar, Rusydi; Suwanti, Suwanti
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.14096

Abstract

Purpose: Heart disease is the leading cause of death throughout the world, especially in developing countries like Indonesia. Modern approaches for diagnosing and managing heart disease rely on machine learning due to the complexity of medical data. Among the biggest challenges in using machine learning is clustering heart disease patients. This study aims to develop a machine-learning model using K-means clustering to determine the severity and level of patient emergencies. The specific objective of the model is to find the optimal number of clusters using the Elbow Method. Methods/Study design/approach: Model development using a dataset from the Kaggle Repository consisting of 303 patient data. Each data point consists of the attributes age, gender, type of chest pain, blood pressure, serum cholesterol level, blood sugar, electrocardiography results, maximum heart rate, angina, ST depression, and segment slope ST. The combination of the K-means clustering algorithm and the Elbow Method is expected to find the optimal number of clusters in the developed model. Result/Findings: The results of building a machine learning model show that k-means clustering is quite effective in clustering heart disease patients. For the model built with 303 data points, the elbow method successfully identified the optimal number of clusters, resulting in two clusters (k=2), where the elbow point on the graph shows a significant decrease in the Sum of Squared Errors (SSE). Novelty/Originality/Value: This study combines the k-means clustering algorithm and the elbow method to determine the severity and level of patient emergencies. The clustering model produces specific risk clusters that help doctors determine more appropriate interventions.
Evaluation of Integrated Business Licensing System in Indonesia Using HOT-fit Model Masbudi, Handika; Husain, Husain; Lusa, Sofian; Sensuse, Dana Indra; Indriasari, Sofianti; Putro, Prasetyo Adi Wibowo
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.14431

Abstract

Purpose: In an increasingly interconnected global economy, there is a growing recognition among governments of the importance of fostering an environment for businesses to thrive. Indonesia has introduced an integrated business licensing system that centralizes and automates the licensing and permitting processes, simplifying the journey for entrepreneurs, business owners, and government agencies. To multiply the benefits and effectiveness of the system, the government must assess the system to establish a firm basis for its ongoing advancement and enhancement. Methods: The evaluation of integrated business licensing system was conducted using the HOT-fit framework with the perspective of government agencies. Their involvement is crucial for effective licensing and supervision processes. Data was gathered through questionnaires and analyzed using the PLS-SEM method. Result: The findings revealed that system quality and service quality significantly influenced user satisfaction, whereas information quality, user satisfaction, organizational structure, and organizational environment positively impacted system usage. The results also showed that system usage and user satisfaction influenced net benefit. These results can be used to improve the system to match the government agencies’ expectations. Novelty: The novelty of this study lies in its research object which is an integrated system. This research provides insights for targeted improvements in technological, organizational, and human aspects of integrated system implementation.
Web Design and Consumer Repurchase Intention: The Roles of E-Satisfaction Pangestika, Devi Wahyu; Rayhan, Raditya Valeri Aurelia; Mawardi, Wisnu
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.15367

Abstract

Objective: Repurchase Intention (RI) becomes one of the frame aspects of customer retention. This study aims to investigate the impact of Web Design (WD) on customers Repurchase Intention (RI) and to evaluate whether E-Satisfaction (ES) serves as a mediator in this relationship. Methods: The research employed a quantitative approach, utilizing purposive sampling to gather data sourced from 124 students from several colleges in Semarang. It conducted a Structural Equation Modeling (SEM) analysis using SmartPLS. Result: The findings of this research show that Web Design (WD) has a significant and positive effect on Repurchase Intention (RI). Additionally, E-Satisfaction (ES) positively influences Repurchase Intention (RI) and serves as a mediator for the beneficial impact of Web Design (WD) on Repurchase Intention (RI). Novelty: The findings of this study provide recommendations for Bukalapak to enhance its Web Design (WD) and ensure high levels of user E-Satisfaction (ES). These strategies can help boost user interest in making repeat purchases on the Bukalapak platform. The novelty in this study lies in the use of diverse range of objects and research methods that are different from previous studies.
Evaluating Ensemble Learning Techniques for Class Imbalance in Machine Learning: A Comparative Analysis of Balanced Random Forest, SMOTE-RF, SMOTEBoost, and RUSBoost Fulazzaky, Tahira; Saefuddin, Asep; Soleh, Agus Mohamad
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.15937

Abstract

Purpose: This research aims to identify the optimal ensemble learning method for mitigating class imbalance in datasets utilizing various advanced techniques which include balanced random forest (BRF), SMOTE-random forest (SMOTE-RF), RUSBoost, and SMOTEBoost. The methods were systematically evaluated against conventional algorithms, including random forest and AdaBoost, across heterogeneous datasets with varying class imbalance ratios. Methods: This study utilized 13 secondary datasets from diverse sources, each with binary class outputs. The datasets exhibited varying degrees of class imbalance, offering scenarios to assess the effectiveness of ensemble learning techniques and traditional machine learning approaches in managing class imbalance issues. Study data were split into training (80%) and testing (20%), with stratified sampling applied to maintain consistent class proportions across both sets. Each method underwent hyperparameter optimization with distinct settings with repetition over 10 iterations. The optimal method was evaluated based on balanced accuracy, recall, and computation time. Result: Based on the evaluation, the BRF method exhibited the highest performance in balanced accuracy and recall when compared to SMOTE-RF, RUSBoost, SMOTEBoost, random forest, and AdaBoost. Conversely, the classical random forest method outperformed other techniques in terms of computational efficiency. Novelty: This study presents an innovative analysis of advanced ensemble learning techniques, including BRF, SMOTE-random forest, SMOTEBoost, and RUSBoost, which demonstrate significant effectiveness in addressing class imbalance across various datasets. By systematically optimizing hyperparameters and applying stratified sampling, this research produces findings that redefine the benchmarks of balanced accuracy, recall and computational efficiency in machine learning.
A Hybrid Sampling Approach for Handling Data Imbalance in Ensemble Learning Algorithms Astari, Reka Agustia; Sumertajaya, I Made; Soleh, Agus Mohamad
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.19163

Abstract

Purpose: This research aims to address the methodological challenges posed by imbalanced data in classification tasks, where minority classes are severely underrepresented, often leading to biased model performance. It evaluates the effectiveness of hybrid sampling techniques specifically, the Synthetic Minority Oversampling Technique combined with Neighborhood Cleaning Rule (SMOTE-NCL) and with Edited Nearest Neighbors (SMOTE-ENN) in improving the predictive performance of ensemble classifiers, namely Double Random Forest (DRF) and Extremely Randomized Trees (ET), with a focus on enhancing minority class detection. Methods: A total of eighteen simulated scenarios were developed by varying class imbalance ratios, sample sizes, and feature correlation levels. In addition, empirical data from the 2023 National Socioeconomic Survey (SUSENAS) in Riau Province were employed. The data were partitioned using stratified random sampling (80% training, 20% testing). Models were trained with and without hybrid sampling and optimized through grid search. Their performance was evaluated over 100 iterations using balanced accuracy, sensitivity, and G-mean. Feature importance was interpreted using Shapley Additive Explanations (SHAP). Results: DRF combined with SMOTE-NCL consistently outperformed all other models, achieving 87.56% balanced accuracy, 82.17% sensitivity, and 86.75% G-mean in the most extreme simulation scenario. On the empirical dataset, the model achieved 76.37% balanced accuracy and 75.49% G-mean. Novelty: This study introduces a novel integration of hybrid sampling techniques and ensemble learning within an interpretable machine learning framework, providing a robust solution for poverty classification in imbalanced datasets.
Audit of Governance and Service Management of Unisnu Jepara's Library Information System (SIPERPUS) Using ITIL V4 Based on Website Nuradira, Afrida Hilda; Azizah, Noor; Minardi, Joko
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.v12i1.19168

Abstract

Purpose: This research aims to assess the maturity level of governance and service management in the Library Information System (SIPERPUS) at UNISNU Jepara. The objective is to identify strengths and gaps, ensuring better alignment with ITIL V4 best practices. Methods: The research was conducted using a qualitative methodology that incorporated a literature review, user questionnaires, and the creation of a web-based audit application designed specifically for this study. Service practices such as Service Request Management, Service Desk, Change Control, and Service Catalog Management were evaluated using the ITIL V4 framework. Result: The findings indicate that SIPERPUS has independently attained a managed maturity level with well-defined Service Request Management and Service Desk practices. Additional work is required to improve Change Control and Service Catalog Management to increase process integration and make information clearer. The new web-based audit application described in this study demonstrates its capability to systematically evaluate, produce data-based recommendations, and continuously improve digital library services. Novelty: This study introduces a new web-based audit application that conforms to ITIL V4, providing an efficient and reliable alternative to manual audit methodologies. The tool significantly reduces audit time, enhances operational efficiency, and optimizes user satisfaction while improving overall service quality. The study adds to the literature on IT governance audits, specifically in higher education, and presents a scalable approach for institutions striving to meet the standards of Society 5.0.

Page 10 of 14 | Total Record : 131

 8   9   10   11   12 

Filter by Year

2024 2025


Reset
Filter By Issues
All Issue Vol. 12 No. 4: November 2025 Vol. 12 No. 3: August 2025 Vol. 12 No. 2: May 2025 Vol. 12 No. 1: February 2025 Vol. 11 No. 4: November 2024 Vol. 11 No. 3: August 2024 Vol. 11 No. 2: May 2024