Prediksi Hasil Ujian Kompetensi Mahasiswa Program Diploma Keperawatan

R Rayendra(1*),

(1) Program Studi D3 Keperawatan Akademi Keperawatan Nabila
(*) Corresponding Author

Abstract


Nursing Diploma Degree Student Competency Test which has been done since 2013 is still found by students who have not passed. This makes the nursing college conducting the competency test have to think of corrective steps so that in each period of the competency test there are no more students who do not pass. One way to do this is to make predictions on the results of the competency test using the Adaptive Neuro Fuzzy Inference System (ANFIS) Method. The data used are past data from the results of the Nabila Nursing Academy competency test results from 2015 to 2019 totaling 146 data. The variables used are gender, participation status, academic achievement index (GPA), and follow the tryout. For ANFIS training data were used 50 data and 96 data for testing data. Obtained a minimum error of 0.00% and a maximum error of 0.94% and a MAPE value of 0.11%. With a small MAPE value it can be concluded that ANFIS can predict the results of competency tests in nursing diploma degree students.

Full Text:

PDF

References


Kemristekdikti, Peraturan Menteri Riset, Teknologi, dan Pendidikan Tinggi Nomor 12 Tahun 2016, 2016.

S. B. Alos, L. C. Caranto, and J. J. T. David, “Factors Affecting the Academic Performance of the Student Nurses of BSU,” Int. J. Nurs. Sci., doi: 10.5923/j.nursing.20150502.04, 2015.

D. Z. R. Syah, “Hubungan Prestasi Akademik Dan Faktor Eksternal Dengan Kelulusan Uji Kompetensi Mahasiswa Keperawatan Stikes Jenderal Achmad Yani Yogyakarta,” J. Keperawatan Muhammadiyah, doi: 10.30651/jkm.v2i2.985, 2018.

A. Hartina, T. Tahir, N. Nurdin, and M. Djafar, “Faktor Yang Berhubungan Dengan Kelulusan Uji Kompetensi Ners Indonesia (UKNI) Di Regional Sulawesi,” J. Persat. Perawat Nas. Indones., doi: 10.32419/jppni.v2i2.84, 2018.

F. Nhita, Adiwijaya, S. Annisa, and S. Kinasih, “Comparative study of grammatical evolution and adaptive neuro-fuzzy inference system on rainfall forecasting in Bandung,” doi: 10.1109/ICoICT.2015.7231388, 2015.

A. Akhirson and B. Heruseto, “Pendekatan Adaptive Neuro Fuzzy Sebagai Alternatif Bagi Bank Indonesia Dalam Menentukan Tingkat Inflasi Di Indonesia,” J. Ekon. dan Bisnis, doi: 10.24914/jeb.v19i2.463, 2016.

R. Roem and T. Nurmala, “Simulation and Prediction of Rainfall and Crop Yield in West Java Using ANFIS,” J. Mat. Integr., doi: 10.24198/jmi.v13.n2.11844.83-94, 2017.

A. Khosravi, L. Machado, and R. O. Nunes, “Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil,” Appl. Energy, doi: 10.1016/j.apenergy.2018.05.043, 2018.

A. A. M. Ahmed and S. M. A. Shah, “Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River,” J. King Saud Univ. - Eng. Sci., doi: 10.1016/j.jksues.2015.02.001, 2017.

S. Kusumadewi and S. Hartati, Neuro-Fuzzy Integrasi Sistem Fuzzy dan Jaringan Syaraf. Graha Ilmu, Yogyakarta, 2006.

N. Walia, H. Singh, and A. Sharma, “ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey,” Int. J. Comput. Appl., doi: 10.5120/ijca2015905635, 2015.

N. Talpur, M. N. M. Salleh, and K. Hussain, “An investigation of membership functions on performance of ANFIS for solving classification problems,” doi: 10.1088/1757-899X/226/1/012103, 2017.




DOI: http://dx.doi.org/10.30645/j-sakti.v4i1.199

Refbacks

  • There are currently no refbacks.


J-SAKTI (Jurnal Sains Komputer & Informatika)
Published Papers Indexed/Abstracted By:

Jumlah Kunjungan :

View My Stats