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Asriyanik .
Universitas Muhammadiyah Sukabumi, Sukabumi
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Journal : PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic

 1 
Machine Learning-Based Classification for Scholarship Selection Asriyanik Asriyanik; Agung Pambudi
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 2 (2023): September 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i2.7393

Abstract

University of Muhammadiyah Sukabumi (UMMI) is a university that accepts KIP scholarship every year. However, KIP student applicants always exceed the quota, so it requires a re-selection process to determine KIP Shcolarship Awardee. UMMI does not have a clear method to support decisions in the selection process for KIP Shcholarship Awardee. To solve this problem, a classification modeling process will be carried out from previous data using machine learning algorithms, namely with Decision Tree (DT) and Support Vector Machine (SVM) algorithms. The general method for its development uses the SEMMA method (Sample, Explore, Modify, Model, Assess). Starting with collecting a dataset of KIP recipients studying at UMMI from 2021-2022 which amounted to 519 data with 16 attributes. From the results of exploration, the main attributes that became features for modeling were DTKS Status, P3KE Status, Combined income of father and mother and achievement. These attributes are converted into numeric data for easy data modeling. The results of K-Fold Cross-Validation for the DT model in the case of classification of KIP Kuliah recipients resulted in an accuracy of 78.44% of the entire test dataset, a precision of 0.73107 indicating that 73.11% of the model's predictions were correct, recall (sensitivity level) of 78.45% and an F1 score of 73.20%. The results of modeling and validation with SVM are 80.17% accuracy, 84.44% precision and 80.17% recall. The SVM model shows slightly better in terms of accuracy and precision, both models show competitive performance in classifying KIP scholarship recipients studying at UMMI.
Co-Authors Abhista Hibatullah, Akbar Adi Sunarto, Asril Adiwijaya, Fahmi Adzkia, Hawarizmi Ummul Afiansyah, Rifan Agung Pambudi Agung Pambudi Akyas Hifdzi Rahman, Rifqi Alifatih, Auriel Haiqal Asep Budiman Kusdinar Asep M.Ramdan Asep Muhamad Ramdan Asril Adi Sunarto Azhilla Margiani Saraswati Budhy Adzy, Luthfy Budiman Kusdinar, Asep Dafa Satria Sidik, Muhamad Dang Kurniawan, Dito DANNY RAMADHAN Daris Riyadi Didik Indrayana Din Azwar Uswatun Edward, MA Algifari Eka Fitriah, Tika Elwanda Putra, Isra Fadhil Faizal Akbar Fahmi Nurfalah Fajar Hikmal Gunawan Fathia Frazna Az-Zahra Fathia Frazna Azzahra Frananda Adiezwara Ramadhan, Mohamad Frazna Azzahra, Fathia Frazzna Az-zahra, Fathia Ilmi Barokah Indra Griha Tofik Isa iqbal setiawan Isa, Indra Griha Tofik Iwan Rizal Setiawan Jamaludin, Firdaus kania, euis Kokom Komariah Kokom Komariah Larasati Mayan Pramesti Lelah Lelah Lelah Lelah Leonita Siwiyanti lucky valiant M. Rizky Suherlan M.Ramdan, Asep MA Algifari Edward Maulana Muhammad Rizky Mohamad Nurizki Mohamad Ridwan Mokhamad Hendayun Mubharak, Gilang Fauzul Muhammad Drajat Ramdhani muhammad musyfik Muhammad Zaynurroyhan Mulud Muchamad, Reski musyfik, muhammad Nesta Suandana, Ilham Nur Asiah Ramdani Nuraeni, Fika Nurmilah, Risma Nurmillah, Risma Prajoko . Prajoko Prajoko Putra, Muhammad Rafli Afandi Rahmawati, Verra Sri Yulia Ramadhan, Vito Rambe, Sarah Syakira Ramdan, Adam Rijal Agus Rusmana Risma Nurmilah Riyadi, Daris Rustiandi, Ryan Santiastry, Sany Sarah Novia Hermawanti Soebandi, Andry Subhan, Roby Azhari Suhendar Syafira Zahara Syah Rizal Fauzy Syahputra, M Ramdhan widi aulia rohmah Winda Apriandari Winda Apriyandari Zahra, Fathia Frazna Az