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Fachruddin Fachruddin
Pascasarjana Program Studi Teknik Sipil dan Lingkungan, Fakultas Teknologi Pertanian, SPs Institut Pertanian Bogor.
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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Increasing the Accuracy of Brain Stroke Classification using Random Forest Algorithm with Mutual Information Feature Selection Fachruddin, Fachruddin; Rasywir , Errissya; Pratama, Yovi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5795

Abstract

Brain stroke stands out as a leading cause of death, distinguishing it from common illnesses and highlighting the critical need to utilize machine learning techniques to identify symptoms. Among these techniques, the Random Forest (RF) algorithm emerged as the main candidate because of its optimal accuracy values. RF was chosen for its ensemble learning properties that optimize accuracy while simultaneously, bagging all outputs (DT), thus increasing its efficacy. Feature Selection, an important data analysis step, which is mainly achieved through pre-processing, aims to identify influential features and ignore less impactful features. Mutual Information serves as an important feature selection method. Specifically, the highest level of accuracy was achieved by cross-validating the test data - 10, resulting in 0.7760 without feature selection and 0.7790 with mutual information. Most of the attributes in the brain stroke dataset show relevance to the stroke disease class, but the resulting decision tree shows age as a particularly important node. So, the research results show that the selection feature (Mutual Information) can increase the accuracy of brain stroke classification, although it is not significant, namely an increase of 0.0030%. With an increase, where there is no significant difference, it can be said that almost all the attributes contained in the brain stroke dataset used have an influence on their relevance to the stroke disease class.
Co-Authors AA Sudharmawan, AA Abadi, Cecep Slamet Abidin, Dodo Zaenal Adi Syuriadi Agusmaniza, Roni Ahmad Zaki Aldri Frinaldi Alkhayr, Sabila Mujahidah Alvisyahri, Alvisyahri Andini, Mirna Ria Arbeni, Wawan Ariska, Nana Arisma Siregar, Mawaddah Putri Astuti, Theresia Widji Aulia Rahman Auliya, Nurul Azwardi Azwardi Belyamin Belyamin Beni Irawan Bulan, Ramayanty Candra Damis Widiawaty, Candra Damis Chablullah Wibisono Dahlan Dahlan Desi Kisbianty, Desi Devianti, Devianti Dewi Purnama Sari DIAN FEBRIANTI Dinda, Raina Parmitalia Diswandi Nurba, Diswandi Errissya Rasywir Fahrijal, Farkhan Farizal, Teuku fathushahib, fathushahib Fazlina, Rita Februssi, Aditya Handriyani, Cut Hasibuan, Mulkan Hibatullah, Iqmal Ibrahim, Mikhail Claudio Idris, Fadli Indera Sakti Nasution Isfanda, Isfanda Jasmir, Jasmir Juliwardi, Ilham Kusumawati, Andi Malia, Rezqi Masganti Sit MASRURA, DELFIAN Mauliza, Rahma Meylis Safriani Muhammad Ardiansyah Muhammad Hidayat Tullah, Muhammad Hidayat Muhammad Ismail Muhammad Riza Pahlevi Muhammad Usman, Muhammad Muhammad Yasar Mustaqimah Mustaqimah Novianita, Lizza Nuraini, Assyifah Nurfitriani, Antie Nurhadi Pramulya, Rahmad Pramulya, Rahmat Putra, Nobby Darly Dwi Putri, Shinta Buana Rahman, Farhan Maulana Raina Parmitalia Dinda Rasywir , Errissya Rinaldy Rinaldy Rizki, Gebrina Robin Sirait Saefuddin, Reskiana Sahkholid Nasution Sanusi Sanusi Smarajaya, I Dewa Made Suharmanto, Billy Suhendra, Rivansyah Sulindawati, Rishi Suryadi Suryadi Susanta, Agus Syamsuddin Syamsuddin Utama, Melda Utari, Selfia Dela Utomo, Machfud Priyo Veranita, Veranita WILLY RIYADI Yovi Pratama Yuli Mafendro Dedet Eka Saputra Yusra, Andi yusuf, Ahmad Maulana Zainuddin, Zainuddin Zulyaden, Zulyaden