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Decision-Making System for KIP IAIN Bukittinggi Scholarship Recipients Using the SAW and TOPSIS Methods Uqwatul Alma Wizsa; Yulifda Elin Yuspita; Wikasanti Dwi Rahayu
Knowbase : International Journal of Knowledge in Database Vol 2, No 1 (2022): June 2022
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

The KIP scholarship is one of the scholarships available at IAIN Bukittinggi, and prospective recipients will be chosen based on the number of quotas available. Thus far, the selection process has been carried out by calculating the total value based on the sum of the percentages of each criterion arranged according to the level of importance. The procedure does not include a decision-making system for determining whether or not to accept the KIP scholarship. As a result, a decision support system is required to quickly and accurately determine which students are eligible for scholarships. In this research, the decision-making system compares the SAW and TOPSIS methods, with the latter using normalized weights in calculating the preference value as a determining value for alternative scholarship recipients to be selected. The SAW method was found to be more sensitive than the TOPSIS method in the data for the KIP scholarship 2020 recipients at IAIN Bukittinggi, with a sensitivity value of 96.87 compared to 81.96 for the TOPSIS method. Based on these findings, the SAW method can be recommended as a decision return system for KIP scholarship recipients to study at IAIN Bukittinggi the following year.

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

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

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

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

Decision-Making System for KIP IAIN Bukittinggi Scholarship Recipients Using the SAW and TOPSIS Methods Wizsa, Uqwatul Alma; Yuspita, Yulifda Elin; Rahayu, Wikasanti Dwi
Knowbase : International Journal of Knowledge in Database Vol. 2 No. 1 (2022): June 2022
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/ijokid.v2i1.5188

The KIP scholarship is one of the scholarships available at IAIN Bukittinggi, and prospective recipients will be chosen based on the number of quotas available. Thus far, the selection process has been carried out by calculating the total value based on the sum of the percentages of each criterion arranged according to the level of importance. The procedure does not include a decision-making system for determining whether or not to accept the KIP scholarship. As a result, a decision support system is required to quickly and accurately determine which students are eligible for scholarships. In this research, the decision-making system compares the SAW and TOPSIS methods, with the latter using normalized weights in calculating the preference value as a determining value for alternative scholarship recipients to be selected. The SAW method was found to be more sensitive than the TOPSIS method in the data for the KIP scholarship 2020 recipients at IAIN Bukittinggi, with a sensitivity value of 96.87 compared to 81.96 for the TOPSIS method. Based on these findings, the SAW method can be recommended as a decision return system for KIP scholarship recipients to study at IAIN Bukittinggi the following year.

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

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

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

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

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