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Abstract

This research is an application/applied research, namely by taking or collecting data and analyzing it using a binary logistic regression model to determine the factors that influence the accuracy of graduating students at UIN Alauddin Makassar.  The type of data used in this research is secondary data. These data originally from undergraduate students data 0f 8 faculties obtained from the PUSTIPAD Information System of UIN Alauddin Makassar Rector Class of 2016. Undergraduate/D-IV program students are declared to graduate on time if they complete their studies at tertiary institutions for less than or equal to 8 semesters or you could say 4 years, with a minimum number of credits of 144 credits.  To determine the binary logistic regression model, parameter significance tests were carried out simultaneously using the G test and partially using the Wald test.  Then test the fit of the model by measuring the chi-square value and the Hosmer and Lowshow test at a significant level of 5%.  The results showed that there were three factors that influenced the timeliness of graduation accuracy, namely gender (X1), IPK (X3) and educational background (X4)

Keywords

Binary Logistic Regression, Graduation time accuracy, G Test, Wald Test

Article Details

References

  1. D. W. Hosmer, S. Lemeshow, and R. X. Sturdivant, Applied Logistic Regression: Third Edition. 2013.
  2. I. Z. Muflihah, "ANALISIS FINANCIAL DISTRESS PERUSAHAAN MANUFAKTUR DI INDONESIA dengan REGRESI LOGISTIK," Majalah Ekonomi , vol. XXII , no. 2, pp. 254-269, 2017.
  3. J. E. Helmreich, “Regression Modeling Strategies with Applications to Linear Models, Logistic and Ordinal Regression and Survival Analysis (2nd Edition),” J. Stat. Softw., 2016.
  4. N. I. Putri and B. , "PENERAPAN REGRESI LOGISTIK ORDINAL DENGAN PROPORTIONAL ODDS MODEL PADA DETERMINAN TINGKAT STRES AKADEMIK MAHASISWA ((Studi Kasus pada Mahasiswa Tingkat I Politeknik Statistika STIS Tahun Akademik 2018/2019)," in Seminar Nasional Official Statistics 2019: Pengembangan Official Statistics dalam mendukung Implementasi SDG’s, 2019
  5. S. A. Czepiel, “Maximum Likelihood Estimation of Logistic Regression Models: Theory and Implementation,” Cl. Notes, 2012.
  6. S. ChatteFuee and A. S. Hadi, Regression Analysis By Example 4th ed, Canada: John Wiley & Sons, Inc., 2006.
  7. S. Imaslihkah, M. Ratna and V. Ratnasari, "Analisis Regresi Logistik Ordinal terhadap Faktor-faktor yang Mempengaruhi Predikat Kelulusan Mahasiswa S1 di ITS Surabaya," Jurnal Sains dan Seni ITS, vol. 2, no. 2, pp. 177-182, 2013.
  8. W. Agwil, H. Fransiska and N. Hidayati, "ANALISIS KETEPATAN WAKTU LULUS MAHASISWA DENGAN MENGGUNAKAN BAGGING CART," FIBONACCI : Jurnal Pendidikan Matematika dan Matematika, vol. 6, no. 2, 2020.
  9. Y. Tampil, H. Komaliq and Y. Langi, "Analisis Regresi Logistik Untuk Menentukan Faktor-Faktor Yang Mempengaruhi Indeks Prestasi Kumulatif (IPK) Mahasiswa FMIPA Universitas Sam Ratulangi Manado," d'CARTESIAN:Jurnal Matematika dan Aplikasi, vol. 6, no. 2, pp. 56-62, 2017.