Abstract
<p>Cash flow is a form of financial report that is used as a measure of the company success in the investment world. So that companies need to forecast the cash flow to manage their finances. Statistics can be applied for the forecasting of cash flow using the <em>Support Vector Machine </em>(SVM) method on the time series data. The aim of this research is to determine the optimal parameter pair model of the <em>Radial Basic Function</em> kernel and to obtain the forecasting results of cash flow using the SVM method on the time series data. The independent variable is needed the data on cash flow from operating income, expenditure and investment expenditure, sum of all cash flow. While the dependent variable is the financial condition based on the <em>Free Cash Flow</em>. The result of this research is a model with the best parameter pairs of the SVM tuning results with the greatest accuracy that is 75%, 82%, 88%, 64% and the forecasting financial condition of PT Cakrawala for the next 16 months.</p><p><strong>Keywords: </strong>cash flow, forecasting, time series, support vector machine.</p>
Highlights
Arus kas adalah bentuk laporan keuangan yang dijadikan tolok ukur keberhasilan perusahaan di dunia investasi, sehingga perusahaan perlu melakukan peramalan arus kas untuk mengatur keuangannya
Cash flow is a form of financial report
that is used as a measure of the company success
Summary
Tabel 1 menunjukkan bahwa semua variabel data stasioner karena nilai statistik-t < nilai kritis ADF sehingga keputusan hipotesis yaitu tolak H 0 dan terima. Data dengan variabel pemasukan_operasi, pengeluaran_investasi, arus_kas dan data_out stasioner pada taraf signifikan 1%, 5%, dan 10%. Peramalan SVM pada data training menggunakan paket “e1071” dan metode k-fold cross validation dengan nilai k sebesar 5 dan 10. Hasil perhitungan peramalan data pemasukan operasi menghasilkan tingkat akurasi sebesar 85% untuk 40 data training dan 84,44% untuk 45 data training. Sedangkan hasil perhitungan peramalan data pengeluaran operasi menghasilkan tingkat akurasi sebesar 85% untuk 40 data training dan 82,22% untuk. Hasil perhitungan peramalan data pengeluaran investasi menghasilkan tingkat akurasi sebesar 82,5% untuk 40 data training dan 84,44% untuk. Dan hasil perhitungan jumlah arus kas menghasilkan tingkat akurasi sebesar 80% untuk 40 data training dan 86,67% untuk 45 data training. Penentuan nilai parameter pada penelitian ini menggunakan perpaduan metode grid search dan k-fold cross validation yang disebut juga tuning SVM. Pasangan parameter terbaik pada setiap data dapat dilihat pada Tabel 2
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