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All Journal Jiko (Jurnal Informatika dan komputer) Nusantara Science and Technology Proceedings
Putri, Irma Amanda
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology

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Modelling of Return of S&P 500 Using the Non Linear Generalized Autoregressive Conditional Heteroscedasticity (NGARCH) Model Trimono, Trimono; Damaliana, Aviolla Terza; Putri, Irma Amanda
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4110

Abstract

ARIMA Box-Jenkins is one of the most popular forecasting methods. ARIMA modeling requires a non-heteroskedastic care that shows constant residual variants. Awake, meaning residual residue from heteroscedastic ARIMA modeling (not constant). To overcome the problem of residual heteroskedasticity ARIMA used modeling volatility that is Generalized Autoregressive Conditional Heteroscedasticity (GARCH). GARCH is used to model the ARIMA residual variant which means symmetric. Some financial data has an asymmetric nature caused by the influence of good news and bad news. To accommodate these asymmetric properties, we use the Non-Linear Generalized Autoregressive Conditional Heteroscedasticity (NGARCH) volatility model which is the development of the GARCH model. This research applies NGARCH model using S & P 500 share price index data from January 1, 2019, until July 31, 2023 during active day (Monday-Friday). The purpose of this study, to find the best model NGARCH. The best model generated for S & P 500 stock price index data is ARIMA (1,0,1) NGARCH (1,1) because it has small AIC.
IMAGE CLASSIFICATION OF VINE LEAF DISEASES USING COMPLEX-VALUED NEURAL NETWORK Putri, Irma Amanda; Prasetya, Dwi Arman; Fahrudin, Tresna Maulana
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7809

Abstract

Leaf diseases are a serious challenge in the agricultural industry affecting crop quality and yield especially in grapevines. Early recognition and classification of grape leaf diseases is crucial to enable farmers to take appropriate preventive measures in maintaining the health of their crops. The research utilized an innovative approach based on Complex-Valued Neural Network (CVNN) to address the problem. Using Complex-Valued Neural Network (CVNN) this research seeks to identify and classify grape leaf diseases through a series of experiments. A total of 100 images divided into 4 classes namely Black Rot, ESCA, Leaf Blight, and Healthy were collected to train the model. The results show that the trained CVNN model successfully achieved a training accuracy of 100% and a testing accuracy of 97%, demonstrating excellent performance in classifying grape leaf diseases. This states that the proposed approach has great potential to be an effective tool in helping growers manage their vineyards more efficiently and effectively. The developed image processing method is expected to be applied in designing a system to perform image classification of diseases on grape leaves.
IMAGE CLASSIFICATION OF VINE LEAF DISEASES USING COMPLEX-VALUED NEURAL NETWORK Putri, Irma Amanda; Prasetya, Dwi Arman; Fahrudin, Tresna Maulana
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7809

Abstract

Leaf diseases are a serious challenge in the agricultural industry affecting crop quality and yield especially in grapevines. Early recognition and classification of grape leaf diseases is crucial to enable farmers to take appropriate preventive measures in maintaining the health of their crops. The research utilized an innovative approach based on Complex-Valued Neural Network (CVNN) to address the problem. Using Complex-Valued Neural Network (CVNN) this research seeks to identify and classify grape leaf diseases through a series of experiments. A total of 100 images divided into 4 classes namely Black Rot, ESCA, Leaf Blight, and Healthy were collected to train the model. The results show that the trained CVNN model successfully achieved a training accuracy of 100% and a testing accuracy of 97%, demonstrating excellent performance in classifying grape leaf diseases. This states that the proposed approach has great potential to be an effective tool in helping growers manage their vineyards more efficiently and effectively. The developed image processing method is expected to be applied in designing a system to perform image classification of diseases on grape leaves.
Co-Authors Damaliana, Aviolla Terza Dwi Arman Prasetya Fahrudin, Tresna Maulana Trimono, Trimono