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Delsi K Delsi K
Universitas PGRI Sumatera Barat
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Journal : Journal of Applied Computer Science and Technology (JACOST)

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Klasifikasi Metode Data Mining untuk Prediksi Kelulusan Tepat Waktu Mahasiswa dengan Algoritma Naïve Bayes, Random Forest, Support Vector Machine (SVM) dan Artificial Neural Nerwork (ANN) Satrio Junaidi; Valicia Anggela, Rani; Kariman, Delsi
Journal of Applied Computer Science and Technology Vol 5 No 1 (2024): Juni 2024
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v5i1.489

Abstract

Timely graduation of students is essential for determining the quality of college. Universities must know the percentage of students' ability to complete their studies on time. So, to deal with this problem, data mining classification is carried out to predict student graduation on time to find patterns for student on-time graduation predictions. This research can yield new information to help colleges anticipate student graduations that are not on time. The method used is a classification data mining method with 4 algorithms: naïve Bayes, random forest, support vector machine (SVM), and artificial neural network (ANN). The attributes used are gender, parental income, length of guidance, working student status or not, semester 1 to semester 8 grades, and GPA. This study used Python 3 programming language on jupyter notebooks in Anaconda to process datasets. The distribution of datasets is divided by 70% for training data and 30% for testing data. The results of this study were obtained with the best algorithm accuracy in the support vector machine (SVM) algorithm is 0.94. Based on the results of this study, the accuracy is good for predicting student graduation on time.
Co-Authors . NURFAIDA Agustini Mardelyni Ainil Mardiyah Alfi Yunita Ananda, Muthia Ananda, Muthia Anggia Murni Anna Cesaria Anny Sovia Anny Sovia Aprimadani Aprimadani Arisman Halomoan Armi Safitra Askina, Meutia Azra Triyana Yusuf Budi Yusdario Dea Amelia Putri Desi Marnis Dewi Yuliana Fitri Elvica Elva Winda Epin Maizelianto Fadhli, Irfan Fatdillah, Haezah Fefri Sanra Ginne, Yusri Grief Septiani Habibillah, Muhammad Hary iswadi Hengri Dian Putra Hera Dwi Triani I Ketut Suada Ida Yanti INTAN MUCHTADI-ALAMSYAH Intan Muchtadi-Alamsyah Irawati Irawati Irena Respita Jasril Jasril Jasril, Jasril Jesi Kurnia Amaliah Joneta, Nadya Indra Junios Junios Landra, Windra Agus Lara Mulya Putri Mai Yun i Putri Mardiyah, Ainil Mega Wahyuni Melisa Melisa Metri Welnita Minora Longgom Nasution Mukhni Mukhni Nia Febriyani Nilawasti ZA Nova Depita Pianti Nova Widia Arif Putri Salsabila, Kharisma Radhya Yusri, Radhya Rahima Rahima Rahma Yuneta Rahmi Rahmi Rani Raflesia Resi Novita Rezi Amelia Roza Rian febriana Rici Roliana Rina Febriana Rina Zahara RISNAWITA RISNAWITA Rizkina, Arfezsa Ulfa ROBI ALMI Rully Charitas Indra Prahmana Sari, Lisma Sastridani Sastridani Satriafani Yasnel Satrio Junaidi Sefna Rismen Sereming, Norbertus Sri Widiawati Susi Gusmawidia Syafwa Yande Syaputri, Nengsi Titin Mustika Utami Korza Putri Valicia Anggela, Rani Vinelza, Nala Wahyu Prima Fernando Widya Novita Sari Wira Anjelina Wiwing Nasra Widali Yanti, Gusri Yosi Susanti Yossi Fitrina Yulia Haryono Yulyanti Harisman Yunere, Falerisiska Yuri Fattriani Zakia Oktaviarla