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All Journal Jurnal Ilmiah Kursor Jurnal Elkolind : Jurnal Elektronika dan Otomasi Industri
M. Taqijuddin Alawiy
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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DEEP LEARNING ARCHITECTURE BASED ON CONVOLUTIONAL NEURAL NETWORK (CNN) IN IMAGE CLASSIFICATION Fawaidul Badri; M. Taqijuddin Alawiy; Eko Mulyanto Yuniarno
Jurnal Ilmiah Kursor Vol. 12 No. 2 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i2.349

Abstract

In current technological developments, Deep Learning is one of the most popular studies today, especially in the fields of machine learning and computer vision, GPU Acceration Technology is one of the reasons for the development of Deep Learning. Deep Learning has a very good ability to solve classic problems in the field of computer vision, one of which is in the case of object classification in images. one of the deep learning methods that is often used in image processing is the Convolution Neural Network (CNN) which is a development of the Multi Layer Perceptron method. This study uses the CNN architecture which consists of a convolution layer, as well as a fully connected layer, and will also determine the appropriate Optimizer and Loss function for CNN. The implementation of this method uses Google Colab (Tensorflow and Keras) with the Python programming language. In the training process using CNN, setting the number of epochs is done to improve accuracy in image classification, in the first scenario using epoch 20 produces an average accuracy of 99.45 with a loss value of 1.66. In the second scenario using epoch 15 produces an average accuracy value of 99.00 with a loss value of 2.92. then in the third scenario with a number of epochs 10 it produces an average accuracy value of 95.55 with a loss value of 95.55, while in the last scenario with a number of epochs 5 it produces an average accuracy value of 73.6 with a loss value of 51.92. From the 4 trial scenarios using the CNN method gives effective results and produces a fairly good accuracy value with an average accuracy and loss value of 99.99%. As well as the results of an average loss of 4.
Sistem Speed Sensorless Vertical Axis Wind Turbine dengan Fuzzy Time Series Model Chen Melfazen, Oktriza; Dewatama, Denda; M. Taqijuddin Alawiy
Jurnal Elektronika dan Otomasi Industri Vol. 11 No. 3 (2024): Jurnal Elkolind Vol. 11 No. 3 (September 2024)
Publisher : Program Studi Teknik Elektronika Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/elkolind.v11i3.6821

Abstract

Pemanfaatan energi angin sebagai Pembangkit Listrik Tenaga Bayu di wilayah Indonesia perlu pengolahan dengan cermat, karena kecepatan angin rata-rata harian yang berkisar antara 2,5 – 6 m/s merupakan kategori kecepatan angin kelas rendah hingga menengah. Penelitian tentang prediksi kecepatan angin pada Vertical-Axis Wind Turbine (VAWT) menggunakan klasifikasi fuzzy time series ini dimaksudkan untuk menggantikan sensor kecepatan dalam mendeteksi dan mendapatkan potensi kecepatan angin terbaik untuk menghasilkan tegangan listrik maksimal sepanjang catur wulan pertama dalam satu tahun. Algoritma fuzzy time series Chen mampu melakukan prediksi kecepatan angin untuk menghasilkan tegangan listrik pada sistem VAWT 800 Watt sehingga sistem dapat beroperasi dengan mode tanpa sensor namun tetap dapat mengukur kecepatan angin dengan akurasi hingga 70%.
Co-Authors Dewatama, Denda Eko Mulyanto Yuniarno Fawaidul Badri Oktriza Melfazen