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Knowbase : International Journal of Knowledge in Database
Published by Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi
ISSN : 27980758     EISSN : 27977501     DOI : https://www.doi.org/10.30983/knowbase
Core Subject : Science,
Computer Science & IT
Knowbase : International Journal of Knowledge in Database is a peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia to focus on understanding Modern developments in this field, and establishing new collaborations in these areas. Authors are solicited to contribute to the journal by submitting articles that illustrate research results that describe significant advances in the areas of Database management systems.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 150 Documents
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Analysis of Acceptance and Use of the MyKopay Application Using the UTAUT 2 and EUCS Models Zahrati, Wenty; La Ode Muh. Rabil Saputra; Zahra Aqilah Dytihana
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 1 (2024): June 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v4i1.8366

Abstract

The government of Payakumbuh City introduced the MyKopay application through the Department of Communication and Information as a means to provide public services and information related to local government activities. This study aims to analyze the acceptance and use of the MyKopay application using two models: the Unified Theory of Acceptance and Use of Technology (UTAUT 2) and End-User Computing Satisfaction (EUCS). Additionally, it seeks to identify aspects of the application that need improvement and maintenance. The research method is descriptive quantitative. The research findings indicate that factors such as Content, Accuracy, Format, Timeliness, Ease of Use, Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, Habit, and User Satisfaction do not have a significant positive impact on User Satisfaction or Behavioral Intention. However, Behavioral Intention has been proven to have a significant positive effect on Use Behavior. Age and gender factors strengthen the Habit variable and have a significant positive impact on Use Behavior. The research results show that the MyKopay application has been accepted and used by the people of Payakumbuh City
Integration of Digital Public Services Mall Application with a Citizen Centric Government Services Approach Rina Wahyuni
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 1 (2024): June 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v4i1.8368

Abstract

The integration of government services represents a practical solution in the context of the numerous applications developed by both Central and Regional Governments, particularly those used to access public service applications. The Digital Public Service Mall application can be leveraged by Regional Governments through data integration, enhanced with Single Sign-On (SSO) capabilities. This will facilitate collaboration between Regional Apparatus Organizations (OPD), enabling them to work together as technical managers of public services. Consequently, this will simplify public access to these services, eliminating the need for repeated data entry processes. Additionally, this system can be developed using facial recognition (FR) technology, which can be integrated with the Digital Population Identity (IKD). The concept of Citizen-Centric Government Services has been widely adopted by governments in various countries to bring government services closer to their citizens. This research focuses on analyzing the integration of data and public service applications, specifically the Digital Public Service Mall (MPP) application in West Java Province. The data analysis technique employed is descriptive-analytical with a qualitative approach. The Citizen-Centric Government Services framework assists in analyzing the extent of data and application integration implementation in a government service. This framework outlines the dimensions within it based on achievement indicators aligned with expectations. Data collection includes semi-structured interviews, participatory observations, and documentation. Based on the analysis results using the dimensional approach within the Citizen-Centric Government Services Framework, it is evident that the Digital MPP application of West Java Province is optimally utilized by the Regency/City Governments and the people of West Java Province. The analysis using the Citizen-Centric Government Services Framework approach reveals that several achievement indicators within each dimension can be met through effective collaboration between the government and the community.
Implementation of Convolutional Neural Networks (CNN) in An Emotion Detection System for Measuring Learning Concentration Levels Chan, Fajri Rinaldi; Firdaus Annas; Yulifda Elin Yuspita; Gusnita Darmawati
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 1 (2024): June 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v4i1.8429

Abstract

Technological advancements have had a significant impact on the education sector, including the application of Convolutional Neural Networks (CNN) for facial image analysis. This research aims to implement CNN to measure students' learning concentration levels. The FER2013 dataset, which includes seven emotion classifications and comprises 28,709 images for training data, is used as the database. The data is processed through rescaling and augmentation to prepare the CNN model. The model consists of several convolutional layers, pooling layers, and fully connected layers designed to extract crucial features from facial images. Evaluation results demonstrate a very high accuracy of 94.95% on training data, indicating that the model effectively recognizes complex patterns within the data. Although there is a higher loss value of 157% and a decreased accuracy of 62.75% on validation data, this suggests that the model possesses a strong foundational capability and can still be improved through further adjustments. With high accuracy in training and promising validation results, the model shows substantial potential for real-world application, where it can assist teachers in understanding students' emotional responses in real-time. The implementation of CNN aids educators in comprehending students' emotional responses and adapting their teaching methods more effectively, thereby creating a more conducive learning environment and enhancing students' academic and social development. These findings also open opportunities for further research to improve the performance and generalization of the model on unseen data, making this technology an increasingly reliable tool in education
Association Rule Mining To Enhance Sata Bottle Sales slamet, slamet kacung; Rohmah, Farah Aqmarinar; Edi Prihartono
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 1 (2024): June 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v4i1.8555

Abstract

Sales of sata bottles are growing and increasing, However, the results of these sales transactions have not been maximally utilized by shop owners. In fact, by using data mining techniques, the collection of data can generate new information. Association rule mining can find interaction patterns between one or more items in a very large data set. This algorithm is widely used in transaction data for purchasing product items at the same time by customers. research objectives to improve sales strategy, by collecting sales patterns that help related parties make sales strategy decisions, recommend products to customers, and maintain product availability. The research method using apriori algorithm data mining system that aims to determine consumer purchasing patterns.  The association rule obtained results in 1 product that is often purchased simultaneously, namely Buy Rabbit Bottle, 420ml Clear Bottle, Buy Rabbit Bottle, Glass Straw, and Buy Rabbit Bottle, Nice Glass with a support value of 10% and a confidence of 80% in three frequent itemset and Rabbit Bottle, 420ml Clear Bottle, Rabbit Bottle, Glass Straw, and Nice Glass, 420ml Clear Bottle with a support value of 15% and a confidence of 83% in two frequent itemset.
Enhancing Stroke Diagnosis with Machine Learning and SHAP-Based Explainable AI Models Galih Hendro Martono; Neny Sulistianingsih
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v4i2.8720

Abstract

Stroke is a serious illness that needs to be treated quickly to enhance patient outcome. Machine Learning (ML) offers promising potential for automated stroke detection through precise neuroimaging analysis. Although existing research has explored ML applications in stroke medicine, challenges remain, such as validation concerns and limitations within available datasets. The study aims to compare ML models and SHapley Additive exPlanations (SHAP) algorithm insights for stroke detection optimization. The research evaluates classifiers' performance, including Deep Neural Networks (DNN), AdaBoost, Support Vector Machines (SVM), and XGBoost, using data from www.kaggle.com. Results demonstrate XGBoost's superior performance across various data splits, emphasizing its effectiveness for stroke prediction. Utilizing SHAP provides deeper insights into stroke risk factors, facilitating comprehensive risk assessment. Overall, the study contributes to advancing stroke detection methodologies and highlights ML's role in enhancing clinical practice in stroke medicine. Further research could explore additional datasets and advanced ML algorithms to enhance prediction accuracy and preventive measures.
Optimization Of Agricultural Production In South Sumatera Using Multiple Linear Regression Algorithm Setiadi, Dedi; Sasmita, Sasmita; Mukti, Yogi Isro
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v4i2.8754

Abstract

Rice is one of the agricultural commodities in South Sumatra whose productivity level still fluctuates. In 2000, rice production reached 1,863,643.00 kg, then increased to 3,272,451.00 kg, in 2010, but decreased again in 2020 to 2,696,877.46 kg. This instability is influenced by various factors such as land area, rainfall, pest attacks, and fertilizer use. This study aims to optimize rice production by applying machine learning using multiple linear regression algorithms, and the CRISP-DM method, with the stages being business understanding, data understanding, data preparation, modeling, evaluation, and implementation. Data of 1,000 records obtained from farmers were analyzed using Google Collaboratory, resulting in an intercept of -3836,2639, and coefficients for land area of 5,7336, rainfall of 1,2710, pests of 6,1153, urea of 1,6226, and phonska of 1,2581. To evaluate the accuracy of rice production predictions based on these independent variables, calculations were made on the RMSE value and analysis of the coefficient of determination. The results were that the RMSE value was recorded at 17065084,9641, and the coefficient of determination (R²) was 0,6487, indicating that around 64,87 % of the variability in rice production can be explained by independent variables such as land area, rainfall, pest attacks, use of urea fertilizer, and phonska, while the remaining 35,13 % was influenced by other factors.
Application of Data Mining for Ceramic Sales Data Association Using Apriori Algorithm Habibi, M. Ilham; Nazir, Alwis; Haerani, Elin; Budianita, Elvia
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v5i2.8757

Abstract

This research is conducted to provide an understanding of consumer purchasing patterns at CV. Sukses Bersama by applying data mining using the association rules method and the Apriori algorithm to identify the relationships between one item that influences other items within a ceramic sales dataset at CV. Sukses Bersama. This information is expected to serve as a foundation for improving sales strategies, optimizing customer satisfaction, and expanding the company's market share. The Apriori algorithm is a popular algorithm implemented to identify association rules in data mining. The Apriori algorithm was chosen due to its ability to efficiently identify association rules and its good scalability in handling large datasets. This research begins with the collection of ceramic sales data, followed by data preprocessing to clean and prepare the data. The Apriori algorithm is then applied to discover the association rules, which generate two matrices: support and confidence, and the results are subsequently evaluated. This research was conducted using Google Colaboratory, a web application that is a cloud-based platform provided by Google to run Python code. The results of the study show that the Apriori algorithm can depict significant association structures between different ceramic brand types in the sales data of CV. Sukses Bersama. The calculation results show that the rule has the maximum support and confidence value, namely 67% support value and 84% confidence value in the rule "if you buy the DIAMD brand, you will buy the TOTAL brand"
Performance Comparison of Naïve Bayes and SVM Algorithms in Sentiment Analysis on JKN Application Data Eka Apriyani, Meyti; Fikri Nur, Amiruddin; Setyo Astuti, Ely
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v4i2.8758

Abstract

In 2022, 67.88% of Indonesia's population owned mobile devices. BPJS Kesehatan responded to this trend by launching the Mobile JKN application to provide modern, accessible healthcare services. To drive continuous innovation, BPJS Kesehatan needs insights into user feedback regarding the Mobile JKN application. Given the large volume of reviews, sentiment analysis is employed to classify reviews into positive or negative categories. This study compares the performance of Naïve Bayes and SVM (Support Vector Machine) algorithms in sentiment classification using a dataset from the Mobile JKN application. The dataset consists of 200 reviews labeled by two different raters, yielding 110 positive and 90 negative reviews for the first set and 114 positive and 86 negative reviews for the second set. Testing was conducted using three data split scenarios for training and testing: 70:30, 80:20, and 90:10. Model performance was evaluated using a confusion matrix, with metrics including accuracy, precision, recall, and F1-score. The results show that the Naïve Bayes algorithm achieved its best performance with a 90:10 data split, yielding an accuracy of 85%, precision of 77%, recall of 100%, and F1-score of 87%. Conversely, the SVM algorithm performed best with an 80:20 data split, achieving 93% accuracy, 100% precision, 84% recall, and an F1-score of 91% for the first rater's dataset. For the second rater's dataset, SVM reached optimal performance with a 90:10 data split, yielding 90% accuracy, 100% precision, 80% recall, and an F1-score of 89%. Overall, the comparison highlights that SVM outperforms Naïve Bayes in terms of accuracy and precision, making it more effective for predicting positive sentiment in Mobile JKN application reviews.
Modelling Time Series Data for Stock Prices Prediction Using Bidirectional Long Short-Term Memory Syukriyah, Yenie; Purnama, Adi
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v4i2.8759

Abstract

The dynamic nature of stock markets, characterized by intricate patterns and sudden fluctuations, poses significant challenges to accurate price prediction. Traditional analytical methods are often unable to capture this complexity. This requires the use of advanced techniques capable of modelling non-linear dependencies. This study aims to build a model using recurrent neural network and predict the Indonesian stock prices. PT Gudang Garam Tbk.'s (GGRM.JK) stock was selected due to its significant role in the Indonesian stock market and its contribution to national revenue through excise tax. The method used in this research involves training the BiLSTM (Bidirectional Long Short-Term Memory) model using historical stock price data with training and test data ratios of 90:10, 80:20 and 70:30 to determine the optimal configuration. The evaluation results showed that the 90:10 data ratio gave the best performance with a MAPE of 1.51%, MAE of 343.55 IDR and RMSE of 522.30 IDR. These results indicate that the BiLSTM model has high accuracy and minimal prediction errors. Further analysis showed that the model performed optimally with a batch size of 32 and higher epochs, such as 200 and 250, providing greater stability and prediction accuracy. These results demonstrate the potential of the BiLSTM model as an effective predictive tool to support strategic investment decisions, particularly for high volatility stocks. Future research is recommended to test this model on other stock data and to consider external factors to improve its generalizability.
Business Intelligence Dashboard Human Resource Capacity to Increase the Capacity City of Bekasi Prio Pamungkas, R Wisnu; Rakhmi Khalida
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v4i2.8764

Abstract

Bekasi City with qualified and evenly distributed human resources will be better able to meet dynamic and complex development needs. Effective data visualization can simplify complex information related to HR capacity, such as education levels, skills distribution, and the number of workers in various sectors, making it easier for policy makers to design strategies including identifying the distribution of filling several positions based on gender and identifying areas of need for educational facilities, children's health, and other infrastructure that supports the growth and development of the younger generation, and developing more effective policies to improve the overall capacity of the city. This research aims to develop a human resource capacity data visualization model as a tool in improving city capacity. This research uses Google Looker Studio as a data visualization platform, data integration is done by Extract, Transform, Load (ETL) method, the data starts from Excel then cleaned, adjusted the format and loaded into Google Sheets. The data used includes key variables that describe the characteristics of human resources in the Bekasi city area, such as education, age group, gender, and demographic distribution. The results show that based on the dashboard visualization, the Bekasi City government can increase 10% representation of the number of women in supervisory and administrator positions in 2 years and the number of only 5% at the S2 or S3 education level requires an increase in education to support the optimization of HR for strategic positions

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