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All Journal JTIM : Jurnal Teknologi Informasi dan Multimedia Journal of Student Research Exploration
Tsabita, Khansa
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Comparison of KNN, naive bayes, and decision tree methods in predicting the accuracy of classification of immunotherapy dataset Reska, Nadhifa; Tsabita, Khansa
Journal of Student Research Exploration Vol. 1 No. 2: July 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v1i2.170

Abstract

Health is crucial for humans to carry out daily activities, and cancer is the second leading cause of death worldwide. Maintaining health is essential in minimizing factors associated with cancer. Immunotherapy is a new cancer treatment technique that has s shown a bigger success rate compared with conventional techniques. However, the effectiveness of this method depends on accurate diagnosis, which requires deeper analysis and research on classification methods. This study compares the accuracy of KNN, Naive Bayes, and Decision Tree classification methods in predicting the accuracy of immunotherapy treatment. The goal is to find the most effective classification techniques that can provide more accurate predictive results in treating diseases using immunotherapy. Based on the test results of Naive Bayes, Decision Tree, and K-Nearest Neighbor, the result obtained of accuracy rates are 81.11%, 80.00%, and 74.44%. From the accuracy comparison, it is known that the Naive Bayes algorithm is the most effective algorithm with the highest accuracy value of 81.11%.
Perancangan Augmented Reality Book Sebagai Media Pembelajaran Monumen Bersejarah Menggunakan Metode Marker Based Tracking Tsabita, Khansa; Nurrizqa, Nurrizqa
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 4 (2025): November
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i4.819

Abstract

The significant advancements in information technology today have brought numerous benefits across various fields, particularly in education. In the educational sector, technological advancements can be implemented as learning media. Historical monuments are one of the most interesting topics to teach students; however, the limited availability of access and information regarding historical monuments has become a major obstacle in introducing local and international cultural heritage to students. The information currently available is still general in nature, such as books or online sources, which are less visually appealing and not well-supported by interactive media.In addition, not all schools have the facilities that allow students to directly experience historical sites, either through physical or virtual visits. This research aims to design a historical monument learning media based on augmented reality (AR) using the marker-based tracking method. The research method applied is the Multimedia Development Life Cycle (MDLC) method, with implementation stages that can be carried out non-sequentially. The stages include concept, design, material collecting, assembly, testing, and distribution.The research output is an augmented reality book containing materials about historical monuments, barcodes/QR codes, and AR markers. Validation was conducted through black box testing, which was successfully completed. In addition, the augmented reality book was evaluated by a subject matter expert, obtaining a score of 92 (average 9.2, percentage 92%) and a media expert, obtaining a score of 93 (average 9.3, percentage 93%), both categorized as “very good.” User testing involving 20 eighth-grade students from one Islamic junior high school (MTSs) in Aceh Besar resulted in a score of 891, or 89.1%, also categorized as “very good.” Thus, the augmented reality book was successfully designed, and is feasible and suitable for use as a learning medium to deliver material on historical monuments.
Co-Authors Nurrizqa, Nurrizqa Reska, Nadhifa