Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal

Hakim, Lutfi; Kristanto, Sepyan Purnama; Khoirunnisaa, Alfi Zuhriya; Wibawa, Adhi Dharma

doi:10.22219/kinetik.v5i1.896

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…An increase in accuracy scores using combined features in classification was also shown in the study conducted by Hakim et al in recognizing or detecting emotions based on Pulse Rate and SpO2 signals [15] using the Support Vector Machine method. This finding indicated that using combination features is recommended for classification to develop precise object detection or identification systems.…”

Section: Feature Extraction Resultssupporting

confidence: 55%

Disease Detection of Dragon Fruit Stem Based on The Combined Features of Color and Texture

et al. 2021

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Dragon fruit is one of the favorite commodities in Banyuwangi Regency's agriculture. In 2019, this commodity had the fourth largest harvest area among other fruit commodities in Banyuwangi until it was exported to China. However, disease attacks often appeared in several dragon fruit plantations in Banyuwangi, and the identification system was still conventional. Many farmers did not know the types of disease and how to handle it, causing the quality and quantity of their crops to decline. Therefore, this study implemented two feature extraction methods. Both methods include color feature extraction using the color moments method and texture feature extraction using gray level co-occurrence matrices (GLCM). The methods used to develop a system that recognized or detected the three types of dragon fruit stem based on digital image processing using Support Vector Machine and k-Nearest Neighbors methods as comparison methods. The results obtained from this study indicated that the combination of the two proposed feature extraction methods could distinguish between stem rot, smallpox, and insect stings with an optimal accuracy score of 87.5% obtained by using Support Vector Machine as a classification method.

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Section: Feature Extraction Resultssupporting

confidence: 55%

Disease Detection of Dragon Fruit Stem Based on The Combined Features of Color and Texture

et al. 2021

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“…Ada banyak rumus jarak yang digunakan dalam algoritma kNN. Pada penelitian ini menggunakan rumus jarak Euclidean dalam mencari jarak terdekat karena berdasarkan beberapa literatur menyebutkan rumus jarak ini paling powerful dibandingkan dengan rumus jarak lainnya [9]- [12]. Rumus jarak Euclidean didapatkan dengan menggunakan persamaan berikut:…”

Section: Klasifikasiunclassified

Pengenalan Motif Batik Banyuwangi Berdasarkan Fitur Grey Level Co-Occurrence Matrix

Hakim

Kristanto

Yusuf

et al. 2022

JTI

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Batik is one of Indonesia's cultural heritages that is recognized around the world and has existed since the colonial era. Indonesia has a variety of different batik pattern in every Indonesia's region. It causes many ordinary people and tourists to become harder to identify and recognize the existing patterns. Banyuwangi regency itself has more than 10 batik patterns, including the Gajah Oling pattern which in the oldest batik pattern. For preserving the culture and supporting the growing tourism aspect in Banyuwangi, this study developed a system for recognizing Banyuwangi batik patterns based on digital image processing. This system is built using python language and is able to recognize three classes of Banyuwangi batik patterns, such as Gajah Oling, Kopi Pecah and other Banyuwangi batik patterns. This system proposes Gray Level Co-occurrence Matrix (GLCM) as feature extraction method and k-Nearest Neighbors (kNN) as classification method. Based on the experiments that have been carried out, the optimal accuracy is 87,5% with the K parameter of kNN is 9.

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Classification of Dragon Fruit Stem Diseases Using Convolutional Neural Network

Hakim,

Asyhari,

Kristanto

et al. 2023

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A holticulture plant known as dragon fruit (pitaya) is a fruit that has many benefits and is widely cultivated by farmers in several areas of Banyuwangi. In dragon fruit plants there are various kinds of diseases that attack including red spot, stem rot, black rot, scab, and mosaic. Farmers still recognize diseases on dragon fruit stems manually so that sometimes there are errors in disease recognition. In this research, a system was developed to identify the types of diseases on dragon fruit stems. This system was built by proposing the Convolutional neural network method with the proposed architecture using the Python programming language with the Tensorflow, Keras, and Scikit-Learn libraries. The proposed system is tested using k-fold cross validation with tunning parameters fold = 5 and epoch = 5. The training results show that the highest accuracy performance value is 85.06% with the data used as test data as many as 191 images producing 147 correct data and 44 data wrong, while the average overall accuracy score was 76.43%.

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Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal

Cited by 3 publications

References 23 publications

Disease Detection of Dragon Fruit Stem Based on The Combined Features of Color and Texture

Disease Detection of Dragon Fruit Stem Based on The Combined Features of Color and Texture

Pengenalan Motif Batik Banyuwangi Berdasarkan Fitur Grey Level Co-Occurrence Matrix

Classification of Dragon Fruit Stem Diseases Using Convolutional Neural Network

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