DOI: https://doi.org/10.21456/vol6iss2pp144-153

Model Heuristic Time Invariant Fuzzy Time Series dan Regresi Untuk Prediksi Laba dan Analisis Variabel yang Mempengaruhi

*Ica Admirani  -  Universitas Diponegoro
Rachmat Gernowo  -  Universitas Diponegoro
Suryono Suryono  -  Universitas Diponegoro
Open Access Copyright (c) 2016 JURNAL SISTEM INFORMASI BISNIS

Citation Format:
Abstract

Model of prediction with fuzzy time series method has ability to capture the pattern of past data to predict the fu ture of data does not need a complicated system, making it easier to use. The research aims to built prediction system using model of  heuristic time invariant fuzzy time series and multiple linear regression to predict profit and analysis of variables that affect profit. Profit forecasting aims to determine the company's prospects in the future in order to remain exist in doing its business. The variables that use in the modelling are profit as the dependent variable, and sales, cost of goods sold, general and administrative expenses, selling and marketing expenses and interest income as the indepent variables. Profit forecasting modelling begins by defining universe of discourse and interval actual data of profit, then determine fuzzy set and actual data fuzzified. Furthermore, fuzzy logical relationship and fuzzy logical relationships group to fuzzified data. The prediction process consist of two prediction phase there are training phase aimed to determine trend predictor and testing phase to determine prediction results. By using 24 profit data samples resulted prediction error by using Mean Absolute Percentage Error is 11,64% and added 13 data for testing obtained prediction error is 22,27%.  In analysis of variables that affect profit is known that sales variable most effect on profit than other variables with a regression coefficient 0.976.

Fulltext View|Download
Keywords: Profit forecast; heuristic time invariant fuzzy time series; multiple linear regression

Article Metrics:

Article Info
Section: Research Articles
Language : EN
Recent articles
Sistem Informasi Manajemen Rantai Pasok Pariwisata Untuk Pembuatan Produk Wisata Pada Agen Tour dan Travel dengan Analisis Swot dan Metode Analytic Network Process ) Diagnosa Kerusakan Bearing Menggunakan Principal Component Analysis (PCA) dan Naïve Bayes Classifier Algoritma K-Means Clustering Untuk Pengelompokan Ayat Al Quran Pada Terjemahan Bahasa Indonesia Penerapan Green Supply Chain Management Untuk Peningkatan Kinerja Keuangan Perusahaan More recent articles
Most cited articles
Sistem Gesture Accelerometer dengan Metode Fast Dynamic Time Warping (FastDTW) Penerapan Metode AHP dan Fuzzy Topsis Untuk Sistem Pendukung Keputusan Promosi Jabatan Sistem Informasi Geografis Berbasis Web untuk Pemetaan Sebaran Alumni Menggunakan Metode K-Means Support Vector Machine Untuk Klasifikasi Citra Jenis Daging Berdasarkan Tekstur Menggunakan Ekstraksi Ciri Gray Level Co-Occurrence Matrices (GLCM) Kajian Data Mining Customer Relationship Management pada Lembaga Keuangan Mikro More cited articles
  1. Alonso, C.R.G., Jimenez, M.T., dan Martinez, C.H., 2010. Income prediction in the agrarian sector using product unit neural networks, European Journal of Operational Research 204, 355–365
  2. Bai, E., Wong, W.K., Chu, W.C., Xia, M., dan Pan, F., 2011. A heuristic time-invariant model for fuzzy time series forecasting, Expert Systems with Applications 38, 2701–2707
  3. Chen, S.M., 1996. Forecasting Enrollments Based On Fuzzy Time Series, Fuzzy Sets and Systems 81, 311–319
  4. Chen, S.M., 2002. Forecasting Enrollments Based On High-Order Fuzzy Time Series, Cybernetics and Systems: An International Journal 33, 1–16
  5. Higgins, H., 2013. Can securities analysts forecast intangible firms’ earnings?, International Journal of Forecasting 29, 155–174
  6. Huarng, K., 2001. Effective lengths of intervals to improve forecasting in fuzzy time series, Fuzzy Sets and Systems 123, 387–394
  7. Jilani, T.A., dan Burney, S.M.A, 2008. A refined fuzzy time series model for stock market forecasting, Physica A 387, 2857–2862
  8. Nany, M., 2013. Analisis Kemampuan Prediksi Arus Kas Operasi (Studi Pada Bursa Efek Indonesia), Jurnal Dinamika Akuntansi Vol. 5, No. 1, pp. 35-46
  9. Robandi, I., 2006. Desain Sistem Tenaga Modern, Optimasi, Logika Fuzzy dan Algoritma Genetika, Andi, Yogyakarta
  10. Rustami, P., Kirya, I.K., dan Cipta, W., 2014. Pengaruh Biaya Produksi, Biaya Promosi, Dan Volume Penjualan Terhadap Laba Pada Perusahaan Kopi Bubuk Banyuatis, e-Journal Bisma Universitas Pendidikan Ganesha Jurusan Manajemen Volume 2
  11. Siregar, S., 2013. Metode Penelitian Kuantitatif Dilenkapi dengan Perbandingan Perhitungan Manual & SPSS, Kencana
  12. Siregar, S., 2015. Statistik Parametrik untuk Penelitian Kuantitatif Dilengkapi dengan Perhitungan Manual dan Aplikasi SPSS Versi 17, Bumi Aksara, Jakarta
  13. Song, Q., dan Chissom, B., 1993. Forecasting Enrollments with Fuzzy Time Series part 1. Fuzzy Sets and System 54, 1-9
  14. Steel, R.G.D., dan Torrie, J.H., 1991. Prinsip dan Prosedur Statistika (Suatu Pendekatan Biometrik), Gramedia, Jakarta
  15. Sugiyono, 20014. Statistika Untuk Penelitian, Alfabeta, Bandung
  16. Sungkawa, I., 2013. Penerapan Analisis Regresi Dan Korelasi Dalam Menentukan Arah Hubungan Antara Dua Faktor Kualitatif Pada Tabel Kontingensi, Jurnal Mat Stat, Vol. 13 No. 1, 33-41
  17. Syamsudin dan Primayuta, C., 2009. Rasio Keuangan Dan Prediksi Perubahan Laba Perusahaan Manufaktur Yang Terdaftar Di Bursa Efek Indonesia, BENEFIT Jurnal Manajemen dan Bisnis Volume 13, Nomor 1, hlm.61-69
  18. Zhu, S.J., Sun, A.H., Zhang, Z.X., dan Wang, B., 2012. Multivariable Linear Regression Equation for Rice Water Requirement based on Meteorological Influence, Procedia Engineering 28, 516 – 521

Last update:

No citation recorded.

Last update: 2025-05-15 23:25:36

No citation recorded.