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Comparison of Neural Network Methods for Classification of Banana Varieties (Musa paradiasaca) Zilvanhisna Emka Fitri; Wildan Bakti Nugroho; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Jurnal Rekayasa Elektrika Vol 17, No 2 (2021)
Publisher : Universitas Syiah Kuala

Every region in Indonesia has a very large diversity of banana species, but no system records information about the characteristics of banana varieties. The purpose of this research is to make an encyclopedia of banana types that can be used for learning by classifying banana varieties using banana images. This banana variety classification system uses image processing techniques and artificial neural network methods as classification methods.The varieties of bananas used are pisang merah, pisang pisang mas kirana, pisang klutuk, pisang raja and pisang cavendis. The parameters used are color features (Red, Green, and Blue) and shape features (area, perimeter, diameter, and length of fruit). The intelligent system used is the Backpropagation method and the Radial Basis Function Neural Network. The results showed that both methods were able to classify banana varieties with an accuracy rate of 98% for Backpropagation and 100% for the Radial Basis Function Neural Network.

Perintah Kontrol Gerak Kursi Roda Elektrik Menggunakan Sensor Elektromiograf Arizal Mujibtamala Nanda Imron; Wahyu Muldayani; Sumardi Sumardi
Jurnal Rekayasa Elektrika Vol 15, No 1 (2019)
Publisher : Universitas Syiah Kuala

Paralysis is a disease that can limit the mobility of the sufferer. One solution that can help people with paralysis in carrying out their mobility is the use of an electric wheelchair. In this study, an electric wheelchair with specifications where the wheelchair motion control uses muscles on both arms, so that the electric wheelchair is very suitable for patients with paralysis in the legs and weak hand strength in turning the wheels from the wheelchair. The input of motion control commands is carried out through an electromyograph sensor mounted on the flexor muscle in both patients’ arms. The output of each sensor is given a threshold of 2 volts to distinguish control commands or not. When the sensor output is more than the same as the threshold, it is considered logic one and the other is considered logic zero. The method is used to interpret the output as a control command by impulse detection. The electric wheelchair movement that can be done is forward, turn right, and turn left.

Penerapan Neural Network untuk Klasifkasi Kerusakan Mutu Tomat Zilvanhisna Emka Fitri; Rizkiyah Rizkiyah; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Jurnal Rekayasa Elektrika Vol 16, No 1 (2020)
Publisher : Universitas Syiah Kuala

The decrease in quality and productivity of tomatoes is caused by high rainfall, bad weather and cultivation so that the tomatoes become rotten, cracked, and spotting occurs. The government is trying to provide training to improve the quality of tomatoes for farmers. However, the training was not effective so the researchers helped create a system that was able to educate farmers in the classification of damage to tomato quality. This system serves to facilitate farmers in recognizing tomato damage thereby reducing the risk of crop failure. In this study, the classification method used is backpropagation with 7 input parameters. The input consists of morphological and texture features. The output of this classification system consists of 3 classes are blossom end rot, fruit cracking and fruit spots caused by bacterial specks. The best accuracy level of the system in classifying tomato quality damage in the training process is 89.04% and testing is 81.11%.

Perintah Kontrol Gerak Kursi Roda Elektrik Menggunakan Sensor Elektromiograf Arizal Mujibtamala Nanda Imron; Wahyu Muldayani; Sumardi Sumardi
Jurnal Rekayasa Elektrika Vol 15, No 1 (2019)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v15i1.12744

Paralysis is a disease that can limit the mobility of the sufferer. One solution that can help people with paralysis in carrying out their mobility is the use of an electric wheelchair. In this study, an electric wheelchair with specifications where the wheelchair motion control uses muscles on both arms, so that the electric wheelchair is very suitable for patients with paralysis in the legs and weak hand strength in turning the wheels from the wheelchair. The input of motion control commands is carried out through an electromyograph sensor mounted on the flexor muscle in both patients’ arms. The output of each sensor is given a threshold of 2 volts to distinguish control commands or not. When the sensor output is more than the same as the threshold, it is considered logic one and the other is considered logic zero. The method is used to interpret the output as a control command by impulse detection. The electric wheelchair movement that can be done is forward, turn right, and turn left.

Comparison of Neural Network Methods for Classification of Banana Varieties (Musa paradiasaca) Zilvanhisna Emka Fitri; Wildan Bakti Nugroho; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Jurnal Rekayasa Elektrika Vol 17, No 2 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v17i2.20806

Every region in Indonesia has a very large diversity of banana species, but no system records information about the characteristics of banana varieties. The purpose of this research is to make an encyclopedia of banana types that can be used for learning by classifying banana varieties using banana images. This banana variety classification system uses image processing techniques and artificial neural network methods as classification methods.The varieties of bananas used are pisang merah, pisang pisang mas kirana, pisang klutuk, pisang raja and pisang cavendis. The parameters used are color features (Red, Green, and Blue) and shape features (area, perimeter, diameter, and length of fruit). The intelligent system used is the Backpropagation method and the Radial Basis Function Neural Network. The results showed that both methods were able to classify banana varieties with an accuracy rate of 98% for Backpropagation and 100% for the Radial Basis Function Neural Network.

Penerapan Neural Network untuk Klasifkasi Kerusakan Mutu Tomat Zilvanhisna Emka Fitri; Rizkiyah Rizkiyah; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Jurnal Rekayasa Elektrika Vol 16, No 1 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v16i1.15535

The decrease in quality and productivity of tomatoes is caused by high rainfall, bad weather and cultivation so that the tomatoes become rotten, cracked, and spotting occurs. The government is trying to provide training to improve the quality of tomatoes for farmers. However, the training was not effective so the researchers helped create a system that was able to educate farmers in the classification of damage to tomato quality. This system serves to facilitate farmers in recognizing tomato damage thereby reducing the risk of crop failure. In this study, the classification method used is backpropagation with 7 input parameters. The input consists of morphological and texture features. The output of this classification system consists of 3 classes are blossom end rot, fruit cracking and fruit spots caused by bacterial specks. The best accuracy level of the system in classifying tomato quality damage in the training process is 89.04% and testing is 81.11%.

Implementing K-Nearest Neighbor to Classify Wild Plant Leaf as a Medicinal Plants Zilvanhisna Emka Fitri; Lalitya Nindita Sahenda; Sulton Mubarok; Abdul Madjid; Arizal Mujibtamala Nanda Imron
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 1 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.2220

in leaf shape. Therefore, this study aimed to create a system to help increase public knowledge about wild plant leaves that also function as medicinal plants by the KNN method. Leaves of wild plants, namely Rumput Minjangan, Sambung Rambat, Rambusa, Brotowali, and Zehneria japonica, are also medicinal plants in comparison. Image processing techniques used were preprocessing, image segmentation, and morphological feature extraction. Preprocessing consists of scaling and splitting the RGB components and using an RGB component decomposition process to find the color component that best describes the leaf shape and generate the blue component image. The segmentation process used a thresholding technique with a gray threshold value (T) of less than 150, which best separates objects and backgrounds. Some morphological feature extraction used are area, perimeter, metric, eccentricity, and aspect ratio. Based on the results of this research, the KNN method with variations in K values, namely 13, 15, and 17, obtained a system accuracy of 94.44% with a total of 90% training data and 10% test data. This comparison also affected the increase in system accuracy.

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