BibTex Citation Data :
@article{JSINBIS12030, author = {Ica Admirani and Rachmat Gernowo and Suryono Suryono}, title = {Model Heuristic Time Invariant Fuzzy Time Series dan Regresi Untuk Prediksi Laba dan Analisis Variabel yang Mempengaruhi}, journal = {JSINBIS (Jurnal Sistem Informasi Bisnis)}, volume = {6}, number = {2}, year = {2016}, keywords = {Profit forecast; heuristic time invariant fuzzy time series; multiple linear regression}, 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. }, issn = {2502-2377}, pages = {144--153} doi = {10.21456/vol6iss2pp144-153}, url = {https://ejournal.undip.ac.id/index.php/jsinbis/article/view/12030} }
Refworks Citation Data :
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.
Article Metrics:
Last update:
Penulis yang mengirimkan naskah harus memahami dan menyetujui bahwa jika diterima untuk dipublikasikan, hak cipta dari artikel adalah milik JSINBIS dan Universitas Diponegoro sebagai penerbit jurnal.Hak cipta (copyright) meliputi hak eksklusif untuk mereproduksi dan memberikan artikel dalam semua bentuk dan media, termasuk cetak ulang, foto, mikrofilm dan setiap reproduksi lain yang sejenis, serta terjemahan. Penulis mempunyai hak untuk hal-hal berikut:
JSINBIS dan Universitas Diponegoro serta Editor melakukan segala upaya untuk memastikan bahwa tidak ada data, pendapat atau pernyataan yang salah atau menyesatkan yang dipublikasikan di jurnal ini. Isi artikel yang diterbitkan di JSINBIS adalah tanggung jawab tunggal dan eksklusif dari masing-masing penulis.
View My Stats This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.