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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) PROSIDING SEMINAR NASIONAL Jurnal Kedokteran Diponegoro Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Wahana Didaktika Prosiding SNATIF Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Pengabdian Masyarakat Asia Alsina : Journal of Arabic Studies Jurnal Visionary : Penelitian Dan Pengembangan Di Bidang Administrasi Pendidikan El-Jaudah : Jurnal Pendidikan Bahasa dan Sastra Arab International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration (IJEBAS) Journal Transformation of Mandalika SIGEH ELT : Journal of Literature and Linguistics Jurnal Ilmiah Teknik (JUIT) Jurnal Teknik dan Science (JTS) Jurnal Sosial Humaniora dan Pendidikan (JUSHPEN) Kajian Linguistik dan Sastra Jurnal Hukum, Politik dan Ilmu Sosial (JHPIS) English Language, Linguistics, Literature, and Education Journal: ELLTURE JOURNAL Jurnal Kajian Islam dan Sosial Keagamaan Journal Educational Research and Development
Rani Puspita
Jurusan Sistem Informasi, Fakultas Ilmu Komputer Dan Teknologi Informasi, Universitas Gunadarma Jl. Margonda Raya No.1, Depok.
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Journal : International Journal of Electrical and Computer Engineering

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Implementing brain tumor detection using various machine learning techniques Puspita, Rani; Rahayu, Cindy
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3309-3318

Abstract

The brain is a very complex organ of the human body. One of the brain diseases is a tumor. Brain tumors are caused by uncontrolled cell growth. Early recognition, classification and analysis of brain tumors is very important to find out whether there is a tumor in a person's brain so it is important for us to do this in order to treat the tumor thoroughly. Machine learning (ML) techniques that have the highest accuracy in detecting the health sector are extreme gradient boosting (XGBoost), logistic regression, random forest, k-nearest neighbor (KNN), naive Bayes, and support vector machine (SVM). In this research, data collection and exploration were carried out, data training using six methods, and evaluation using a confusion matrix. After conducting the experiment, it was obtained that random forest had the highest accuracy, namely 98.41%. Where XGBoost obtained an accuracy of 98.14%, logistic regression obtained an accuracy of 97.34%, KNN and naive Bayes of 97.34%, and SVM of 97.88%.
Co-Authors Abd. Rasyid Syamsuri Ade Nandang Ahmad Syauqy Aisyah Siregar Alrajafi, Gali Anwar, Ridwan Ayu Atikhah Adelia Cai Cen, Cia Dina Anggraini DINA ANGGRAINI Doddi Yuniardi Eryon, Eryon Ferdinand Simanjuntak Gusmaneli Gusmaneli Gusmaneli Habibah Maulida Handayani, Sri Irfan Humaini Irham Lubis Izdihar, Zahra Nabila Juningsih, Juningsih Maretha , Carla Maretha, Carla MARTHA ARDIARIA Maulana Yusup, Maulana Melza Stefani Muchtar, Hendi S Nainggolan, Annisa E. Onny Marleen Parisya Arundati, Nadia Putri, Sabila Tamimi Rahayu, Cindy Ramadhani, Khairunisa Rheza Andika, Rheza Ridwan, Anggraeni Ridwan, Ridwan Rina Astriani Rina Astriani Samanik, Samanik Silvina Waroh Siti Chodijah Sugianto Sugianto Suharni Suharni Widiastuti Widiastuti Witta Listiya Ningrum