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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Pilar Nusa Mandiri INTI Nusa Mandiri Jurnal Informatika dan Rekayasa Elektronik Jurnal Teknik Informatika (JUTIF) Jurnal Sistem Informasi SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Teknologi Sistem Informasi Jurnal Aruna Mengabdi (ARMI)
Ganda Wijaya, Ganda
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Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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THE RIGHT STEPS TOWARDS GRADUATION: NB-PSO SMART COMBINATION FOR STUDENT GRADUATION PREDICTION Kahfi, Ahmad Hafidzul; Prihatin, Titin; Yudhistira, Yudhistira; Sudradjat, Adjat; Wijaya, Ganda
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1889

Abstract

The current digital era demands a more innovative approach in predicting student campuses considering that campuses are not only important for students but also for lecturers, student guardians and higher education institutions. Previous studies have used various machine learning methods such as Decision Trees, Neural Networks, Support Vector Machines, etc. in these predictions. The problem that occurs is that even though various machine learning methods have been used, there are still limitations in the accuracy and efficiency of predicting student admissions, The problem in question can be given a real example of a case that occurred. So with this problem the aim is to develop a more effective methodology in predicting student permits, with recommendations from an intelligent combination of two computational techniques Naive Bayes (NB) and Particle Swarm Optimization (PSO). This research methodology includes data collection, NB model development and model partnership with PSO. Student graduation data is used in model testing with evaluation based on metrics such as accuracy and Area Under the Curve (AUC). The results showed a significant increase in accuracy to 86.94% from 83.30% and AUC value from 0.860 to 0.884 when using the combination of NB and PSO compared to NB without either. The integration of NB and PSO has been proven to increase effectiveness in classifying student graduation prediction cases. This research opens up opportunities for the practical application of technology in the education sector and emphasizes the importance of using effective optimization and feature selection techniques in improving prediction results.
Co-Authors Achmad Maezar Bayu Aji Adjat Sudradjat Agung Baitul Hikmah Ali Mustopa, Ali Arief Rusman Desvia, Yessica Fara Henny Destiana Herlina, Maria Jajang Jaya Purnama Kahfi, Ahmad Hafidzul Kholifah, Desiana Nur Mahmud Nawawi, Hendri Mochamad Wahyudi Olivia, Shinta Pratama, Febryawan Yuda Rachman Komarudin Ridwansyah Ridwansyah Ristiyani, Lina Ariska Rudianto, Biktra Sugiono, Sugiono Suhardjono S Sumanto, Sumanto Sumarna Suryadithia, Rachmat Tussyifa, Zaqia Verry Riyanto Wawan Setiawan WITRIANA ENDAH PANGESTI Yudhistira Yudhistira Yudhistira