PREDICTION FOR TOTAL NUMBER OF LAB PARTICIPANTS BY FUZZY TIME SERIES METHOD (CASE STUDY: INFORMATION ENGINEERING OF BHAYANGKARA SURABAYA UNIVERSITY )

FEBRIARDI MAHENDRA, RIFKI FAHRIAL ZAINAL, SYARIFUL ALIM

Abstract


Forecasting is a way to estimate a future value with using past data. One method of forecasting is the fuzzy method time series. The purpose of this study is to predict the number of students practitioners follow Department of Informatics University Bhayangkara Surabaya by using fuzzy method time series. The created app can be used to predict the next 1 year. If the actual data in the year predicted inputted, the application can predict the next year again. The prediction error rate is calculated using Mean Absolute Percentage Error (MAPE). From the test results in predicting the number of students followers 7 courses Practicum Informatics Engineering Bhayangkara University of Surabaya in 2010-2012 using the method proposed in this thesis for practicum PTI obtained MAPE value of 20.50%, Practical ANP obtained MAPE value of 0.50%, practicum Jarkom obtained MAPE value at 8.50%, practicum Database obtained MAPE value of 0.50%, practicum Manjarkom obtained MAPE value of 14.50%, practicum PKG obtained MAPE value of 0.84% and practicum PBO obtained MAPE value of 0.21%. Based on the results of testing the data it can be concluded that the fuzzy time series method when used on more data many, it will get the accuracy of better and precise forecasting values.
Keywords: Forecasting, Fuzzy Time series, Mean Absolute Percentage Error (MAPE)


References


Chen, S. M. 1996. Forecasting enrollments based on fuzzy time series - Fuzzy Sets and Systems. International

Journal of Applied Science and Engineering.

Chen S. M., Hsu C.-C. 2004. A new method to forecasting enrollments using fuzzy time serie. International

Journal of Applied Science and Engineering.

Hsu LY, Horng SJ, Kao TW, Chen YH, Run RS, Chen RJ, Lai JL, Kuo IH. 2010. Temperature prediction and

TAIFEX forecasting based on fuzzy relationships and MTPSO techniques. Expert Systems with Applications

: 2756-2770.

Kusumadewi, Sri, Hari Purnomo. 2004. Fuzzy Logic Applications for Supporters Decision. Graha Science.

Yogyakarta.

Makridakis, S., Wheelright, S.C., and McGee, V.E. 1992. Methods and Applications Forecasting - 2nd edition,

volume I. Transfer Language: Andriyanto, U.S., and Basith, A. Erlangga. Jakarta.

Prasetyo, Huda. (2008), Fuzzy Implementation Time Series for Sales Prediction Sarong At PT. NABATEX

Gresik, Thesis, Informatics Engineering, STIKOM, Surabaya.

Song Q, Chissom BS. 1993. Forecasting enrollments with fuzzy time series-Part I. Fuzzy Sets and Systems 54:

9.

Steven. (2013), Comparison of Fuzzy Methods Time Series and Holt Double Exponential Smoothing At

Forecasting Number of New Students Institute of Agriculture, Thesis, Mathematics, Bogor Agricultural

University, Bogor.

T. A. Jilani, S. M. A., Burney, C., Ardil. 2007. Fuzzy Metric Approach for Fuzzy Time Series Forecasting

based on Frequency Density Based Partitioning. Proceedings of World Academy of Science, Engineering and

Technology Vol. 23, pp.333-338

Icha Puspitasari, Suparti, Y. W. (2012). Analisis Indeks Harga Saham Gabungan (IHSG) dengan

Menggunakan Model Regresi Kernel. Jurnal Gaussian, 1(1), 93–102.

Rachmawansah, K. (2014). Average-Based Fuzzy Time Series untuk Peramalan Kurs Valuta Asing ( Studi

Kasus pada Nilai Tukar USD-IDR dan EUR-USD ). Jurnal Mahasiswa Statistik, 2(6), 413.

Solo, J., & Yogyakarta, D. I. (2011). Peramalan Penjualan dengan Metode Fuzzy Time Series Ruey Chin Tsaur

,2,3).

Peramalan jumlah pendaftar calon mahasiswa stmik duta bangsa menggunakan metode. (2016),8(April 2015).

Pratama, M. (2014). Penerapan Fuzzy Time Series dengan Prinsip Gelombang Elliott untuk Peramalan Harga

Saham, 2(6), 417–420.


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