Perancangan Pengenalan Karakter Alfabet menggunakan Metode Hybrid Jaringan Syaraf Tiruan

Salim, Rin Rin Meilani; Jauhari, Andrew Sagitta

doi:10.55601/jsm.v17i1.269

J. Sifo Mikrosk.

2016

DOI: 10.55601/jsm.v17i1.269

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Perancangan Pengenalan Karakter Alfabet menggunakan Metode Hybrid Jaringan Syaraf Tiruan

Rin Rin Meilani Salim¹,

Andrew Sagitta Jauhari²

Abstract: Self Organizing Maps adalah salah satu metode dalam jaringan syaraf tiruan yang menggunakan pembelajaran tanpa supervisi yang digunakan untuk meng-cluster neuron-neuron berdasarkan kelompok tertentu. Backpropagation adalah salah satu metode dalam jaringan syaraf tiruan yang menggunakan pembelajaran dengan supervisi yang populer dan memiliki keunggulan dalam kemampuan pembelajarannya. Algoritma hybrid dari self organizing maps dan backpropagation dapat meningkatkan performansi dan akurasi dari pembelajaran, ter… Show more

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Cited by 2 publications

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Classification of rice leaf diseases using artificial neural network

Gunawan¹,

Kencana

Sari³

2021

J. Phys.: Conf. Ser.

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Rice is one of the important food crops for humans. The yield of this crop is affected by several factors, one of them is climate change. The irregularities of climate can cause the crops were infected by various diseases. By knowing the diseases through its unusual characteristics, fast remedial actions could be expected. The purpose of this essay is to find the best artificial neural network (ANN) architecture in classifying brown spot and leaf smut diseases on rice crops, two common types of diseases attack rice plant, based on the leaf images. Two features of rice leaf images i.e. shape and color were extracted to classify both diseases. The result of this research demonstrated the best ANN architecture to classify the diseases is the architecture with 4 neurons in the input layer, 5 neurons in one single hidden layer, and one neuron in the output layer. This architecture has accuracy as much as 66.3 percent.

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Classification of rice leaf diseases using artificial neural network

Gunawan¹,

Kencana

Sari³

2021

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

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Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World

Setti

Wanto

2019

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The Internet today has become a primary need for its users. According to market research company e-Marketer, there are 25 countries with the largest internet users in the world. Indonesia is in the sixth position with a total of 112.6 million internet users. With the increasing number of internet users are expected to help improve the economy and also education in a country. To be able to increase the number of internet users, especially in Indonesia, it is necessary to predict for the coming years so that the government can provide adequate facilities and pre-facilities in order to balance the growth of internet users and as a precautionary step when there is a decrease in the number of internet users. The data used in this study focus on data on the number of internet users in 25 countries in 2013-2017. The algorithm used is Artificial Neural Network Backpropagation. Data analysis was processed by Artificial Neural Network using Matlab R2011b (7.13). This study uses 5 architectural models. The best network architecture generated is 3-50-1 with an accuracy of 92% and the Mean Squared Error (MSE) is 0.00151674.Abstrak-Internet saat ini sudah menjadi kebutuhan primer untuk para penggunanya. Menurut lembaga riset pasar e-Marketer, ada 25 negara teratas dengan pengguna internet terbanyak di dunia. Indonesia berada pada posisi keenam dengan jumlah pengguna internet sebanyak 112,6 juta jiwa. Dengan semakin meningkatnya jumlah pengguna internet diharapkan dapat ikut memajukan perekonomian dan juga pendidikan di suatu negara. Untuk dapat meningkatankan jumlah pengguna internet, khususnya di Indonesia maka perlu dilakukan prediksi untuk tahun-tahun mendatang sehingga pemerintah dapat menyediakan sarana dan pra-sarana yang memadai guna untuk mengimbangi pertumbuhan jumlah pengguna internet dan sebagai langkah antisipasi saat terjadi penurunan jumlah pengguna internet. Data yang digunakan pada penelitian ini fokus pada data jumlah pengguna internet di 25 negara tahun 2013-2017. Algoritma yang digunakan yaitu Jaringan Syaraf Tiruan Backpropagation. Analisa data dilakukan dengan metode Jaringan Syaraf Tiruan menggunakan software Matlab R2011b (7.13). Penelitian ini menggunakan 5 model arsitektur. Arsitektur jaringan terbaik yang dihasilkan adalah 3-50-1 dengan tingkat akurasi 92% dan nilai Mean Squared Error (MSE) adalah 0,00151674.

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Perancangan Pengenalan Karakter Alfabet menggunakan Metode Hybrid Jaringan Syaraf Tiruan

Cited by 2 publications

References 3 publications

Classification of rice leaf diseases using artificial neural network

Classification of rice leaf diseases using artificial neural network

Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World

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