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All Journal Jurnal Informatika dan Teknologi Komputer ( J-ICOM) The Indonesian Journal of Computer Science
Zuhri, Burhanudin
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Studi Literatur: Optimasi Algoritma Machine Learning Untuk Prediksi Penerimaan Mahasiswa Pascasarjana Zuhri, Burhanudin; Harani, Nisa Hanum
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 5 No 1 (2024): Jurnal Informatika dan Teknologi Komputer ( JICOM)
Publisher : Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v5i1.8074

Abstract

Machine learning algorithms are mathematical procedures used to find complex and hidden patterns in data with a high degree of accuracy and have brought major advances in various fields for fast and precise decision making. One of these fields is the field of education, which is to predict the admissions process for postgraduate students. The purpose of admitting postgraduate students is to select prospective students who are qualified and meet the academic requirements set by the institution concerned based on GRE (Graduate Record Examination) scores, TOEFL (Test of English as a Foreign Language) scores, university rankings, letters of recommendation, GPA bachelor degree, and research experience. Success in postgraduate admissions can open opportunities to earn advanced degrees and acquire more in-depth knowledge and skills in areas of interest. In this study, an analysis was carried out on various machine learning algorithm optimizations used to optimize topics or trends in previous studies. In this case, the researcher compares performance and selects the best algorithm optimization to be applied to the topic of graduate student admissions. The results of this review show that the hybrid algorithm has the best performance in optimizing predictions for most of the data in previous studies. The results of this study indicate that the CNN-LSTM (Convolutional Neural Network - Long Short-Term Memory) hybrid model is expected to be an appropriate alternative in optimizing predictions of postgraduate student admissions. Therefore, further research is needed to develop this algorithm and expand its application to the topic of graduate student admissions.
Probability Prediction for Graduate Admission Using CNN-LSTM Hybrid Algorithm Zuhri, Burhanudin; Harani, Nisa Hanum; Prianto, Cahyo
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3248

Abstract

Currently, the prediction of student admissions still uses conventional machine learning algorithms where there is no algorithm for optimization. This study aims to produce a model that can predict student acceptance of ownership more optimally by using an optimization hybrid learning algorithm, namely the Convolutional Neural Network Long Short Term Memory (CNN-LSTM). This study uses the Microsoft Team Data Science Process method which consists of business understanding, data acquisition & understanding, modeling, and implementation as well as using the acceptance dataset obtained from the kaggle.com website as much as 500 data. The results showed that the CNN-LSTM hybrid learning model could optimize the prediction of students' chances of success in exposure as evidenced by the evaluation results of RMSE of 6.31%, MAE of 4.4%, and R2 of 80.52%. This model is implemented in a website application using the Python language, the Django framework, and the MySQL database.
Co-Authors Harani, Nisa Hanum Prianto, Cahyo