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All Journal Mutiara: Jurnal Penelitian dan Karya Ilmiah Student Research Journal
Wahyu Ardiantito S
Universitas Negeri Medan
Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Klasifikasi Tumor Otak Menggunakan Local Binary Pattern dan SVM Classifier Wahyu Ardiantito S; Stacyana Jesika Surianto; Suci Ramadhani; Willy Pramudia Ananta
Student Research Journal Vol. 1 No. 6 (2023): Desember : Student Research Journal
Publisher : Sekolah Tinggi Ilmu Administrasi (STIA) Yappi Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/srjyappi.v1i6.823

Abstract

Brain tumors are abnormal cell growths in brain tissue that can be life-threatening. This study aims to classify brain tumors to help early diagnosis. The method used is to extract features from brain MRI images using Local Binary Pattern (LBP) and then classified with Support Vector Machine (SVM). The data used were 2044 brain MRI images consisting of 3 classes namely meningioma, no tumor, and pituitary. The best results were obtained using LBP with a radius of 1 and the number of neighbors 8, while the best SVM model used the RBF kernel with a C value of 50, resulting in 88% accuracy, 86% precision, and 87% recall. It can be concluded that the combination of LBP and SVM methods is effective enough to classify brain tumor types to support early diagnosis.
Komparasi Algoritma Machine Learning dalam Memprediksi Penyakit Gagal Ginjal Wahyu Ardiantito S; Rizki Agung Ramadhan; Richard Steven Immanuel S
Mutiara : Jurnal Penelitian dan Karya Ilmiah Vol. 1 No. 6 (2023): Desember: Mutiara : Jurnal Penelitian dan Karya Ilmiah
Publisher : STAI YPIQ BAUBAU, SULAWESI TENGGARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59059/mutiara.v1i6.781

Abstract

Chronic Kidney Disease (CKD) is a serious health problem, with significant impact on patients' quality of life and healthcare costs. In an effort to improve early diagnosis, a comparison was made between several Machine Learning algorithms used for analysis of patient clinical data. This clinical data contains the medical history or health records of patients. The Machine Learning algorithms used in this study include K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression. By searching for the best algorithm through the calculation of Accuracy, Precision, and Recall with comparasion when using SMOTE (Synthetic Minority Oversampling) to balancing the class attribute.
Co-Authors Richard Steven Immanuel S Rizki Agung Ramadhan Stacyana Jesika Surianto Suci Ramadhani Willy Pramudia Ananta