Alfiani, Faradillah Siska
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PERBANDINGAN TRANSFORMASI WAVELET DISKRIT UNTUK DENOISING CITRA MENGGUNAKAN VISUSHRINK, BAYESSHRINK, DAN NORMALSHRINK Umam, Ahmad Khairul; Ngastiti, Pukky Tetralian Bantining; Isro'il, Ahmad; Wahyuni, Zuanita May Tri; Alfiani, Faradillah Siska
MATHunesa: Jurnal Ilmiah Matematika Vol. 12 No. 2 (2024)
Publisher : Universitas Negeri Surabaya

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Abstract

Wavelet topics can be applied for image denoising. Wavelet transform is divided into discrete wavelet transform and continuous wavelet transform. Discrete wavelet transform is useful for image denoising, image compression, etc. In this research, we discuss image denoising using wavelets with hard thresholding and soft thresholding. We will also compare results of image denoising using VisuShrink, BayesShrink, and NormalShrink in determining threshold value. In this research, we use grayscale test image with size pixels. To measure algorithm, peak signal to noise ratio (PSNR) value is used. Image denoising uses hard thresholding: PSNR values of Haar, Daubechies 7, and biorthogonal 3.5 wavelets are same for three methods (VisuShrink, BayesShrink, NormalShrink). PSNR value of symlets 11 wavelet is highest for VisuShrink and NormalShrink methods. Whereas, PSNR value of coiflets 3 wavelet is highest for VisuShrink method. Fastest computing times of Haar, Daubechies 7, biorthogonal 3.5, and symlets 11 wavelets are for VisuShrink method. Whereas, fastest computing time of coiflets 3 wavelet is for NormalShrink method. Image denoising using soft threshoding: PSNR values of Haar, Daubechies 7, biorthogonal 3.5, and coiflets 3 wavelets are same for three methods (VisuShrink, BayesShrink, NormalShrink). Whereas, PSNR value of symlets 11 wavelet is highest for VisuShrink and BayesShrink methods. Fastest computing times of all wavelets (Haar, Daubechies 7, biorthogonal 3.5, symlets 11, and coiflets 3) are for NormalShrink method.
METODE HOLT DOUBLE EXPONENTIAL SMOOTHING UNTUK PERAMALAN PRODUKSI DAGING AYAM KAMPUNG Intahaya, Ari; Salamah, Nur; Ummah, Hidayatul; Alfiani, Faradillah Siska; Fadhlia, Zahrotul Wardah; Isroil, Ahmad
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

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Abstract

This study aims to forecast the production of free-range chicken in East Java using the Holt-Double Exponential Smoothing method. By utilizing production data from the past several years, this method was chosen for its ability to capture existing trend patterns. The analysis results indicate that the method provides a high level of accuracy in forecasting. In general, the production of free-range chicken is expected to increase in most regions, although some areas may experience a decline. It is expected that farmers and local governments would use the study's conclusions as a foundation for their decision-making in order to preserve the stability and sustainability of free-range chicken production.
FORECASTING RICE PRODUCTION WITH THE HOLT-WINTERS EXPONENTIAL SMOOTHING METHOD Salamah, Nur; Intahaya, Ari Maulidah; Alfiani, Faradillah Siska; Ummah, Hidayatul; Fadhlia, Zahrotul Wardah; Isro'il, Ahmad
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 1 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i1pp141-152

Abstract

This study aims to forecast rice production in East Java using the Holt-Winters Exponential Smoothing method which can be used to view data with seasonal and trend patterns. The rice production data used in this study comes from the Central Bureau of Statistics (BPS) which includes production data for the last five years. The results of the analysis show that this method provides a sufficient level of accuracy in forecasting and is also effective in providing estimates of rice production as well as assisting in strategic decision-making on the management of the agricultural sector and food security.