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All Journal JISKa (Jurnal Informatika Sunan Kalijaga) Innovation in Research of Informatics (INNOVATICS)
Qohar, Bagus Al
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Library & Information Science

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Predicting Olympic Medal Trends for Southeast Asian Countries Using the Facebook Prophet Model Qohar, Bagus Al; Tanga , Yulizchia Malica Pinkan; Utami, Putri; Ningsih, Maylinna Rahayu; Muslim, Much Aziz
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 1 (2025): January 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.1.16-32

Abstract

The Olympics are a world-class sporting event held every four years, serving as a meeting place for all athletes worldwide. The Olympics are held alternately in different countries. The Olympics were first held in Athens in 1896 and have now reached the 33rd Olympics, which will be held in Paris in 2024. Significant work has been conducted to develop prediction models, with a primary focus on enhancing the accuracy of predicting Olympic outcomes. However, low-performance regression algorithms are the main problem with prediction. By integrating custom seasonality with the Facebook Prophet prediction model, this study aims to enhance the accuracy of Olympic predictions. The proposed new model involves several steps, including preparing the data and initializing and fitting the Facebook-Prophet model with several parameters such as seasonal mode, annual seasonality, and prior scale. The model is tested using the Olympic dataset (1994–2024). The evaluation results indicate that this prediction model provides a reliable estimate of the total medals earned. On the Olympic Games (1994-2024) dataset, the model exhibits a very low error, as indicated by its MAE, MSE, and RMSE, and achieves an R² score of 0.99, which is close to perfect. This research shows that the model is effective in improving prediction accuracy.
Melanoma Skin Cancer Classification Using EfficientNetB7 for Deep Feature Extraction and Ensemble Learning Approach Darmawan, Aditya Yoga; Dullah, Ahmad Ubai; Qohar, Bagus Al; Unjung, Jumanto; Muslim, Much Aziz
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.12764

Abstract

Cancer is one of the deadliest diseases in the world. cancer is caused by the presence of cancer cells due to abnormal conditions during the cell turnover process. One of the dangerous types of cancer is melanoma skin cancer, this cancer attacks the outer skin of humans because skin cells are prone to damage. However, diagnosis for this disease is mostly done manually while there are previous studies that use deep learning approaches with the accuracy that can be improved. The purpose of this study is to find an effective and efficient method for melanoma cancer recognition so that it can be treated more quickly. We propose several methods that we have compared to be able to classify melanoma skin cancer with EfficientNetB7 Feature Extractor and Ensemble Learning. The results of this research model get the highest accuracy of 91.2%. When EfficientNetB7 together with ensemble learning. This research model has better and efficient results when compared to previous research.
Co-Authors Darmawan, Aditya Yoga Dullah, Ahmad Ubai Jumanto Unjung Much Aziz Muslim Ningsih, Maylinna Rahayu Putri Utami, Putri Tanga , Yulizchia Malica Pinkan