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All Journal Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika
Rahmawati, Verra Sri Yulia
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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ARIMA (AUTOREGRESSIVE INTEGRATED MOVING AVERAGE) SEBAGAI ALGORITMA PREDIKSI TINGKAT PEMBATALAN HEREGISTRASI MAHASISWA BARU DI UNIVERSITAS X Rahmawati, Verra Sri Yulia; Setiawan, Iwan Rizal; Asriyanik, Asriyanik
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.14913

Abstract

The digitalization era has transformed education, impacting new student admissions in Indonesia, which has various universities: State, Official, Religious, and Private. These universities share common procedures for admitting new students: file selection, test selection, and re-registration. However, many new students cancel their re-registration due to financial constraints, distance, or choosing another campus. Some students neglect the re-registration process until the deadline passes, affecting the accreditation of study programs and the reputation of the campus. To address this issue, a prediction model for re-registration cancellation rates can evaluate campus performance in attracting new students. The ARIMA algorithm (AutoRegressive Integrated Moving Average) is proposed as a suitable prediction model for time series data. This model can help universities identify and address factors leading to re-registration cancellations, thereby improving their performance and reputation. Using the SEMMA (Sample, Explore, Modify, Model, Assess) data mining methodology, the research produced an evaluation matrix with RMSE (Root Mean Square Error) values for various features: "non_registered" (145.77), "parents_income" (0.84), "parents_job" (4.07), and "entrance" (0.16). Additionally, the correlation matrix revealed two variables with a high influence on the target: "entrance" (0.85) and "parents_income" (0.68).
Co-Authors Asriyanik . Iwan Rizal Setiawan