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All Journal Ceddi Journal of Information System and Technology (JST) Jurnal Abdimas Cendekiawan Indonesia
Omita Abigail
Universitas Teknologi Akba Makassar
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Health Professions Library & Information Science Nursing Public Health Social Sciences Other

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Utilization of Learning Animation Technology in Assisting the Creation of Interactive Teaching Materials for Teachers at SMAS Kristen Soleman Nurhajar Anugraha; Rivanky Valensius Bara; Omita Abigail; Nur Azizah Muchtar; Nurlinda Aprialliani; Angelin Marici B; Andryanto Aman; Muhammad Qadri; Dyah Utari Yusa Wardhani
Jurnal Abdimas Cendekiawan Indonesia Vol. 1 No. 1 (2024): January
Publisher : Yayasan Cendekiawan Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jaci.v1i1.75

Abstract

The teacher's job is to increase student interest in learning with the aim of improving student achievement in the learning process. One of the problems faced by teachers at SMAS Kristen Soleman is the lack of knowledge and skills in the use of animation learning technology, so that the teaching materials produced are less attractive and become one of the factors in decreasing student interest in the learning process. The purpose of this community service is to improve knowledge and skills through training and mentoring in making interactive teaching materials using animated learning technologies such as Canva, Kahoot, and Powtoon to teachers at SMAS Kristen Soleman. The implementation method is carried out starting from the preparation stage (field observation, literature study, and partner identification), the implementation stage (training and mentoring), to the evaluation stage using pre-test and post-test before and after training and mentoring is carried out. Based on the evaluation results of training and mentoring teachers in making interactive teaching materials using animated learning technology, it shows an increase in knowledge from 41.7% to 100%, where teachers already knew interactive teaching materials using animation learning technology. Meanwhile, teachers' skills increased from 12.5% to 91.7%.
Application of the Adaptive Boosting Method to Increase the Accuracy of Classification of Type Two Diabetes Mellitus Patients Using the Decision Tree Algorithm Hao Chieh Chiua; Robbi Rahim; Mahmud Mustapa; Kamaruddin; Akbar Hendra; Asnimar; Omita Abigail
Ceddi Journal of Information System and Technology Vol. 2 No. 2 (2023): December
Publisher : Yayasan Cendekiawan Inovasi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v2i2.47

Abstract

One of the data mining processes that is often used in machine learning is the data classification process. A decision tree is a classification algorithm that has the advantage of being easy to visualize because of its simple structure. However, the decision tree algorithm is quite susceptible to incorrect classification calculations due to the presence of noise in the data or imbalance in the data, which can reduce the overall level of accuracy. Therefore, the decision tree algorithm should be combined with other methods that can increase the accuracy of classification performance. Machine Learning is used through an artificial intelligence approach to solve problems or carry out optimization. Adaptive Boosting is used to optimize classification calculations. This study aims to examine the performance of Adaptive Boosting in the process of classifying second-degree diabetes mellitus patients using the Decision Tree algorithm. Diabetes mellitus is known as a chronic condition of the human body, the cause of which is an increase in the body's blood sugar levels because the body is unable to produce insulin or is unable to utilize insulin effectively, which is usually referred to as hyperglycemia.. By using a 60:40 data split, the Decision Tree algorithm produces an accuracy value of 95.71%, while the Adaptive Boosting-based Decision Tree results reach a value of 98.99%.
Co-Authors Akbar Hendra Andryanto A Angelin Marici B Asnimar Dyah Utari Yusa Wardhani Hao Chieh Chiua Kamaruddin Mahmud Mustapa Muhammad Qadri Nur Azizah Muchtar Nurhajar Anugraha Nurlinda Aprialliani Rivanky Valensius Bara Robbi Rahim