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COMPARING GAUSSIAN AND EPANECHNIKOV KERNEL OF NONPARAMETRIC REGRESSION IN FORECASTING ISSI (INDONESIA SHARIA STOCK INDEX)

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 16 No. 1 (2022) · hlm. 323–332

DOI
10.30598/barekengvol16iss1pp321-330
ISSN
2615-3017, 1978-7227
Terbit
2022-03-21
Penerbit
PATTIMURA UNIVERSITY
Bahasa
eng

Abstrak

ISSI reflects the movement of sharia stock prices as a whole. It is necessary to forecast the share price to help investors determine whether the shares should be sold, bought, or retained. This study aims to predict the value of ISSI using nonparametric kernel regression. The kernel regression method is one of the nonparametric regression methods used to estimate conditional expectations using kernel functions. Kernel functions used in this study are gaussian and Epanechnikov kernel functions. The estimator used is the estimator Nadaraya-Watson. This study aims to compare the two kernel functions to predict the value of ISSI in the period from January 2016 to October 2019. The analysis results obtained the best method in predicting ISSI values, namely nonparametric kernel regression using Nadaraya-Watson estimator and Gaussian kernel function with the MAPE value of 15% and the coefficient of determination of 85%. Independent variables that significantly affect ISSI are interest rates, exchange rates, and inflation. Curve smoothing is done using bandwidth value (h) searched by the Silverman rule. The calculation result with the Silverman rule obtained a bandwidth value of 101832.7431.

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  • UIN Sunan Ampel Surabaya
  • Politeknik Elektronika Negeri Surabaya
  • Institut Agama Islam Negeri Kendari
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Rekan penulisOrganisasiArtikel bersama
Hani KhaulasariUIN Sunan Ampel Surabaya4
Moh. HafiyusholehUIN Sunan Ampel Surabaya4
Achmad Teguh WibowoUIN Sunan Ampel Surabaya3
Silvia Kartika SariUIN Sunan Ampel Surabaya2
Latifatun Nadya DesinainiUIN Sunan Ampel Surabaya2
Putroue Keumala IntanUIN Sunan Ampel Surabaya2
Aris FananiUIN Sunan Ampel Surabaya2
Luluk WulandariUIN Sunan Ampel Surabaya2
Nanida Jenahara ZaenUIN Sunan Ampel Surabaya2
Ghina Salsabila FirdausUIN Sunan Ampel Surabaya1
Ahmad Teguh WibowoAfiliasi tidak diketahui1
Dian Candra Rini NovitasariUIN Sunan Ampel Surabaya1
Diah Devi PramestiUIN Sunan Ampel Surabaya1
Halimatus Sa’dyahPoliteknik Elektronika Negeri Surabaya1
Nilna AlmumtazahUIN Sunan Ampel Surabaya1
Muhammad Sahrul KiromiUIN Sunan Ampel Surabaya1
Husna Nur LailiUIN Sunan Ampel Surabaya1
Dian YuliatiUIN Sunan Ampel Surabaya1
Suyesti YusiUIN Sunan Ampel Surabaya1
Mayandah FarmitaUIN Sunan Ampel Surabaya1
Wika Dianita UtamiUIN Sunan Ampel Surabaya1
Shafa Fitria Aqilah KhansaUIN Sunan Ampel Surabaya1
Fery FirmansyahAfiliasi tidak diketahui1
Yahya Vigo Tri SaputraUIN Sunan Ampel Surabaya1
Yanuwar ReinaldiUIN Sunan Ampel Surabaya1
Yayan Luthfi KhoirinaUIN Sunan Ampel Surabaya1
Ery Fuji RisnawatiUIN Sunan Ampel Surabaya1
Abdulloh HamidUIN Sunan Ampel Surabaya1
Siti Aulia AisyahInstitut Agama Islam Negeri Kendari1
Lutfi HakimUIN Sunan Ampel Surabaya1
Elisa SyafaqohUIN Sunan Ampel Surabaya1
Jiphie Gilia IndriyaniUIN Sunan Ampel Surabaya1
Firda Yunita SariUIN Sunan Ampel Surabaya1

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Sumber metadata: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">COMPARING GAUSSIAN AND EPANECHNIKOV KERNEL OF NONPARAMETRIC REGRESSION IN FORECASTING ISSI (INDONESIA SHARIA STOCK INDEX)</dc:title>
	<dc:creator>Farida, Yuniar</dc:creator>
	<dc:creator>Purwanti, Ida</dc:creator>
	<dc:creator>Ulinnuha, Nurissaidah</dc:creator>
	<dc:description xml:lang="en-US">ISSI reflects the movement of sharia stock prices as a whole. It is necessary to forecast the share price to help investors determine whether the shares should be sold, bought, or retained. This study aims to predict the value of ISSI using nonparametric kernel regression. The kernel regression method is one of the nonparametric regression methods used to estimate conditional expectations using kernel functions. Kernel functions used in this study are gaussian and Epanechnikov kernel functions. The estimator used is the estimator Nadaraya-Watson. This study aims to compare the two kernel functions to predict the value of ISSI in the period from January 2016 to October 2019. The analysis results obtained the best method in predicting ISSI values, namely nonparametric kernel regression using Nadaraya-Watson estimator and Gaussian kernel function with the MAPE value of 15% and the coefficient of determination of 85%. Independent variables that significantly affect ISSI are interest rates, exchange rates, and inflation. Curve smoothing is done using bandwidth value (h) searched by the Silverman rule. The calculation result with the Silverman rule obtained a bandwidth value of 101832.7431.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2022-03-21</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/5127</dc:identifier>
	<dc:identifier>10.30598/barekengvol16iss1pp321-330</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan; 323-332</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol16iss1year2022</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/5127/4034</dc:relation>
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