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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.
Website-Based Employee Attendance Information System (Case Study: PT. Excelindo Karya Abadi) Shabrina Husna Batubara; Willy Pramudia Ananta; Zulfahmi Indra
Journal of Computer Science Advancements Vol. 2 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i3.1106

Abstract

This research aims to develop a web-based employee attendance information system at PT. Excelindo Karya Abadi, overcoming inefficiencies in the manual attendance process. Using the Waterfall method, this research includes the stages of needs analysis, system design, implementation, testing, and maintenance. Data collection is carried out through interviews and direct observation so that it becomes the basis of a system that includes the function of recording attendance via selfie photos, managing employee data, and making reports. The system architecture is designed with front-end and back-end components, using technologies such as HTML, CSS, JavaScript, PHP, and MySQL. Testing involves black box techniques to ensure functionality and user feedback for system improvement. The implemented system demonstrated significant improvements in the accuracy and efficiency of attendance tracking, reducing the potential for data manipulation and errors. The transition to a web-based system allows for greater accessibility and integration with an organization's existing systems, thereby contributing to increased operational efficiency. The findings show that digital attendance systems can simplify administrative processes substantially, offer reliable solutions for employee attendance management, and align with technological advances to support company growth.