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All Journal CESS (Journal of Computer Engineering, System and Science) Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Safari : Jurnal Pengabdian Masyarakat Indonesia Journal of Vocational Education and Information Technology (JVEIT) SAINTEK
Eko Budiarto
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Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Other

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Analisa Sistem Informasi Perpustakaan Berbasis Website Menggunakan Metode OOAD Pada SDS IT Asy-Syakur Cikarang Eko Budiarto; Regina Febrianti
Prosiding Sains dan Teknologi Vol. 2 No. 1 (2023): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 2 - Februari 2023
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

The school library is an important facility that supports the learning process and enhances students’ knowledge. The SDS IT Asy-Syakur library still uses a manual system for borrowing and returning books, which often causes recording errors and difficulties in report preparation. Additionally, there is no information system capable of processing transactions quickly, generating reports efficiently, and storing data securely. This study aims to develop a web-based library information system using PHP with the Laravel framework and a MySQL database, developed using the OOAD method. The results show that the implemented system facilitates library monitoring, improves the speed and accuracy of borrowing and returning processes, enables faster report generation, and ensures secure data storage through database integration. Therefore, the implementation of this web-based library information system improves the efficiency of library management at SDS IT Asy-Syakur.
Identifikasi Kanker Payudara Dengan Pendekatan Algoritma Support Vector Machine Eko Budiarto; Mega Sholihat
Prosiding Sains dan Teknologi Vol. 4 No. 1 (2025): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 4 - Februari 2025
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

Breast cancer is a malignant tumor that develops in breast cells and is one of the leading causes of cancer-related deaths in Indonesia, with the highest number of patients among all cancer types. According to Globocan 2020, there were 68,858 new cases of breast cancer, representing 16.6% of 396,914 total new cancer cases in the country. The number of cases continues to rise, with 1,194 cases reported in 2021 and 1,427 cases in 2022. The increasing incidence and high mortality highlight the need for accurate early detection and classification methods. This study investigates the use of the Support Vector Machine (SVM) algorithm for classifying breast cancer. SVM was chosen due to its effectiveness in handling high-dimensional data and providing clear separation between classes. The model was evaluated using accuracy, precision, and recall metrics. The results show that SVM achieved 98% accuracy, 100% precision, and 95% recall, demonstrating strong performance in classifying breast cancer cases, minimizing false positives, and detecting the majority of positive cases. These findings indicate that SVM is a reliable method for breast cancer classification and can support medical diagnosis, facilitate early detection, and potentially reduce mortality rates. Furthermore, this approach provides a basis for developing machine learning-based clinical decision support systems in healthcare.
Co-Authors Gunawan Ali, Gunawan Mega Sholihat Nurhadi Surojudin Regina Febrianti Revita, Elinda Rima Puji Lestari Sindik Widati Syach, Ridwan Taufik Hidayat Wahyu Hadikristanto