ANALYSIS TO PREDICTE THE NUMBER OF NEW STUDENTS AT UNU PASURUAN USING ARIMA METHOD
New student admission is an important aspect in higher education management, including Nahdlatul Ulama University (UNU) Pasuruan. Relevant prediction of total new students is needed to support resource planning such as teaching staff, facilities, and budget. This study aims to evaluate the historica...
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LPPM ISB Atma Luhur
2025-02-01
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| Series: | Jurnal Sisfokom |
| Online Access: | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2374 |
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| author | Fachri Ayudi Fitrony Laksmita Dewi Supraba Tessa Rantung |
| author_facet | Fachri Ayudi Fitrony Laksmita Dewi Supraba Tessa Rantung |
| author_sort | Fachri Ayudi Fitrony |
| collection | DOAJ |
| description | New student admission is an important aspect in higher education management, including Nahdlatul Ulama University (UNU) Pasuruan. Relevant prediction of total new students is needed to support resource planning such as teaching staff, facilities, and budget. This study aims to evaluate the historical pattern of new student admissions at UNU Pasuruan and predict the number of new students in the coming years using the ARIMA (Auto Regressive Integrated Moving Average) method. The study approach includes several main steps, namely collecting historical data on the number of new students, testing stationarity using the Augmented Dickey-Fuller (ADF) test, identifying model parameters through ACF and PACF graphs, and estimating ARIMA model parameters. The resulting model is evaluated using prediction error metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The study findings describe that the ARIMA model (6.0.1) produces an RMSE value of 21.88 and a MAPE of 0.2%. In addition to having the smallest error score, the ARIMA model (6.0.1) also has the smallest AIC score of the various models that can be used for predictions, which is 447.44 and the largest log likelihood value, which is -214.72. The largest prediction of the number of new students is in July, which is 92.72 and the smallest in February, which is 24.43. This prediction is expected to help university management in optimizing resource planning, increasing management efficiency, and anticipating fluctuations in the number of new students in the future. |
| format | Article |
| id | doaj-art-125b0057e8c84f12897849d4f5d95cdd |
| institution | Kabale University |
| issn | 2301-7988 2581-0588 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | LPPM ISB Atma Luhur |
| record_format | Article |
| series | Jurnal Sisfokom |
| spelling | doaj-art-125b0057e8c84f12897849d4f5d95cdd2025-02-12T07:27:38ZengLPPM ISB Atma LuhurJurnal Sisfokom2301-79882581-05882025-02-0114110.32736/sisfokom.v14i1.23742037ANALYSIS TO PREDICTE THE NUMBER OF NEW STUDENTS AT UNU PASURUAN USING ARIMA METHODFachri Ayudi Fitrony0Laksmita Dewi Supraba1Tessa Rantung2AMIKOM YOGYAAMIKOM YOGYAAMIKOM YOGYANew student admission is an important aspect in higher education management, including Nahdlatul Ulama University (UNU) Pasuruan. Relevant prediction of total new students is needed to support resource planning such as teaching staff, facilities, and budget. This study aims to evaluate the historical pattern of new student admissions at UNU Pasuruan and predict the number of new students in the coming years using the ARIMA (Auto Regressive Integrated Moving Average) method. The study approach includes several main steps, namely collecting historical data on the number of new students, testing stationarity using the Augmented Dickey-Fuller (ADF) test, identifying model parameters through ACF and PACF graphs, and estimating ARIMA model parameters. The resulting model is evaluated using prediction error metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The study findings describe that the ARIMA model (6.0.1) produces an RMSE value of 21.88 and a MAPE of 0.2%. In addition to having the smallest error score, the ARIMA model (6.0.1) also has the smallest AIC score of the various models that can be used for predictions, which is 447.44 and the largest log likelihood value, which is -214.72. The largest prediction of the number of new students is in July, which is 92.72 and the smallest in February, which is 24.43. This prediction is expected to help university management in optimizing resource planning, increasing management efficiency, and anticipating fluctuations in the number of new students in the future.https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2374 |
| spellingShingle | Fachri Ayudi Fitrony Laksmita Dewi Supraba Tessa Rantung ANALYSIS TO PREDICTE THE NUMBER OF NEW STUDENTS AT UNU PASURUAN USING ARIMA METHOD Jurnal Sisfokom |
| title | ANALYSIS TO PREDICTE THE NUMBER OF NEW STUDENTS AT UNU PASURUAN USING ARIMA METHOD |
| title_full | ANALYSIS TO PREDICTE THE NUMBER OF NEW STUDENTS AT UNU PASURUAN USING ARIMA METHOD |
| title_fullStr | ANALYSIS TO PREDICTE THE NUMBER OF NEW STUDENTS AT UNU PASURUAN USING ARIMA METHOD |
| title_full_unstemmed | ANALYSIS TO PREDICTE THE NUMBER OF NEW STUDENTS AT UNU PASURUAN USING ARIMA METHOD |
| title_short | ANALYSIS TO PREDICTE THE NUMBER OF NEW STUDENTS AT UNU PASURUAN USING ARIMA METHOD |
| title_sort | analysis to predicte the number of new students at unu pasuruan using arima method |
| url | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2374 |
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