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All Journal Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Faurika, F
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Computer Science & IT Decision Sciences, Operations Research & Management

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Business Intelligence Dashboard Lokasi Rawan Bencana Alam Di Indonesia Menggunakan Tableau Faurika, F; Haris, M Syauqi
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 1 (2024): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i1.721

Abstract

Natural disasters cause socio-economic damage in a country. Indonesia is one of the countries prone to being affected by natural disasters. This disaster was triggered by various factors, both natural factors and human factors themselves that did not protect the natural ecosystem. This research aims to visualize data on natural disaster cases in Indonesia that occurred in the period 2020 to 2023. This visualization of natural disasters in Indonesia applies Business Intelligence (BI) using Tableau Public to produce information from a dataset of natural disaster cases in Indonesia for the period 2020 to 2023 in the form of visuals to facilitate the process of analyzing natural disaster cases in Indonesia and with an attractive visual appearance. The results of the visualizations obtained in this research are combined to form an attractive dashboard and contains visual information on the distribution of disasters in various provinces in Indonesia, the number of disasters that occurred, the number of victims, the amount of damage, and disaster trends from 2020 to 2023. 
Implementasi Decision tree Untuk Prediksi Kanker Paru-Paru Faurika, F; Khudori, Ahsanun Naseh; Haris, M Syauqi
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 1 (2024): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i1.717

Abstract

Lung cancer is a disorder of the lungs due to changes in respiratory tract epithelial cells which cause uncontrolled cell division and growth. Lung cancer is caused by several factors such as radiation exposure, smoking, heredity, gender, air pollution, and unhealthy lifestyles. Lung cancer can be detected when the cancer has entered an advanced stage. The large amount of lung cancer diagnosis data currently available can be used to predict lung cancer based on patterns in the data. One of the results of technological advances that can learn patterns in data is machine learning, which has currently made many positive contributions in the health sector. This research aims to predict lung cancer using a decision tree algorithm. This research produces rules based on decision trees which are built and then evaluated to produce the same accuracy, precision, recall, and F1-Score of 100%.
Co-Authors Ahsanun Naseh Khudori M. Syauqi Haris