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Firdaus Annas
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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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Decision Support System for Selecting Marching Band Field Commanders Using Profile Matching Alza, Nurul Hafizhah; Suryamen, Haris
Knowbase : International Journal of Knowledge in Database Vol. 3 No. 1 (2023): June 2023
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v3i1.7561

Abstract

The selection process for Marching Band field commanders often relies on subjective evaluations such as the coach's feelings or other perceptions that lack objectivity. Therefore, a more systematic and objective approach is required to choose the most suitable marching band field commander. This research aims to establish a Decision Support System using the Profile Matching method. The development methodology employed was the Waterfall model, encompassing five steps: requirements, design, development, testing, and deployment. The research findings revealed three crucial aspects in determining the marching band field commander: body posture, field ability, and personality. Various criteria were utilized, including body language, preparedness, knowledge of marching rules, vocal skills, general understanding of marching bands, accuracy, experience, attitude, and presence. To facilitate the selection process, a decision support system application was developed using the PHP programming language. This system utilizes a database to store processed data and generates output in the form of rankings. The implementation of this decision support system resolves the challenge of determining the best marching band field commander, providing marching bands with a more objective means of identifying the ideal candidate.
Prototype Motion Sensor And Automatic Locking Based On The Internet Of Things (IOT): Prototype Motion Sensor And Automatic Locking Based On The Internet Of Things (IOT) Choiriah, Wirdah; Putra Pane, Eddissyah; Roki Hardianto; Eva Tri Ningsih
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.v4i1.8122

Abstract

The application of IoT in remote control systems to home security is becoming a necessity. This research designed 2 parts of the application of IoT, namely the automatic locking control system and motion sensor detection. With IoT, it can help reduce the risk of laziness in households. The equipment used in the IoT circuit is the nodemcu esp8266 internet module, 5vdc relay module, electrical sockets and pitting and cellphone chargers. The way this electrical IoT works is by controlling it from a smartphone that is connected to a device using applications and the internet. With a note on the IoT tool at home, there is already a living internet network. If the internet network has a problem, the tool will not work properly. The output of this research is a Control System IoT product in the form of a prototype that can be used for the motion detection process and an IOT-based door locking system that can be controlled from a smartphone
Application of Graph Colouring Algorithm in Course Scheduling Process Borotan, Nella Lestari; Yuspita, Yulifda Elin; Annas, Firdaus; Darmawati, Gusnita
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.v4i1.8136

Abstract

Scheduling is a crucial aspect in every occurrence, ensuring that all processes are orderly. Gema Nusantara Bukittinggi Health Vocational School currently utilizes Microsoft Excel for managing subject scheduling, which often leads to scheduling conflicts. The objective of this research is to develop a web-based subject scheduling system for Gema Nusantara Bukittinggi Health Vocational School. The outcome of this research is a web-based subject scheduling system that is valid, practical, and effective, thereby serving as a useful tool for subject scheduling. This research is classified as research and development (R&D). The system development follows an incremental model with four stages: analysis, design, coding, and testing. The product was evaluated through three types of tests: validity, practicality, and effectiveness. The validity test, conducted with three experts, yielded a value of 0.80, indicating validity. The practicality test, carried out with three practitioners, resulted in a value of 0.97, signifying high practicality. The effectiveness test, involving fifteen teachers, achieved a value of 0.95, demonstrating high effectiveness. Based on the product testing results, it can be concluded that the research product, which is a web-based scheduling system, is suitable for use in the subject scheduling process at Gema Nusantara Bukittinggi Health Vocational School
Design of a Predictive Model for Prospective New Students Using Monte Carlo Simulation Paramita, Dela; Efriyanti, Liza; Zakir, Supratman; Imamuddin, M.
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.v4i1.8144

Abstract

The competition between universities in the admission of new students is becoming increasingly intense, making it more difficult for the public, especially prospective students, to choose a study program. The Department of Hadith Sciences at UIN Syech M. Djamil Djambek Bukittinggi has fewer applicants compared to other departments due to the limited interest of prospective students in enrolling. In response to this issue, the author developed a Monte Carlo simulation predictive model that allows prospective students to estimate the number of applicants to the Department of Hadith Sciences for the upcoming academic year. The research technique employed in this study uses Monte Carlo simulation to apply this research. The number of students in the Department of Hadith Sciences who enrolled from 2019 to 2023 serves as the data used to predict the number of new student registrations. The accuracy of the simulation in estimating the number of new students who will enroll in the Department of Hadith Sciences is 218 students, with an average annual accuracy of 65.1%. By applying the Monte Carlo method, it is possible to predict the number of students who will enroll in the Department of Hadith Sciences with a relatively high level of accuracy in its application
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.
Application of the Naïve Bayes Algorithm in Classifying the Reading Interests of Regional Library Visitors Murlena, Murlena; Syahindra, Wandi
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.v4i1.8680

Abstract

Reading interest is a key indicator in assessing the success of library services. However, manually understanding visitors' preferences poses a challenge for library managers. This study aims to classify the reading interests of regional library visitors by employing the Naïve Bayes algorithm, a widely-used classification method in data mining. The research data includes visit records and book borrowing data from a regional library. Through a quantitative approach, this study analyzes reading interest patterns and evaluates the performance of the Naïve Bayes algorithm in classifying these interests. The analysis results show that the algorithm achieves an accuracy of 65%, with a precision of 62%, recall of 63%, and F1-score of 63%. These findings are expected to assist libraries in formulating better-targeted collection management and service policies, contributing to the overall improvement of reading interest in the community. This study contributes to the field by providing a practical, data-driven solution for libraries to enhance service quality through a better understanding of visitor preferences. Furthermore, it demonstrates the applicability of the Naïve Bayes algorithm in a non-commercial context, encouraging future research on data-driven approaches in library management to support literacy and educational development
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.

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