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All Journal Scientific Journal of Informatics Riau Journal of Computer Science JIKO (Jurnal Informatika dan Komputer)
B. Herawan Hayadi
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Computer Science & IT

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Visualisasi Konsep Umum Sistem Pakar Berbasis Multimedia B. Herawan Hayadi
RJOCS (Riau Journal of Computer Science) Vol. 3 No. 1 (2017): Riau Journal of Computer Science
Publisher : RJOCS (Riau Journal of Computer Science)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (563.318 KB) | DOI: 10.30606/rjocs.v3i1.1169

Abstract

Pengetahuan dari suatu sistem pakar mungkin dapat direpresentasikan dalam sejumlah cara. Salah satu metode yang paling umum untuk merepresentasikan pengetahuanadalah dalam bentuk tipe aturan (Rule) IF .... THEN (Jika.... Maka). Turban 1995 menyatakan bahwa konsep dasar dari suatu sistem pakar mengandung beberapa unsur atau elemen, yaitu keahlian, ahli, pengalihan keahlian, inferensi, aturan, dan kemampuan menjelaskan. Dalam penulisan ilmiah ini dibantu dengan bentuk visualisasi untuk menyampaikan konsep umum sistem pakar menggunakan macromedia flash, dengan memilihnya macromedia flash ini sebagai medianya agar para pembejaran konsep umum sistem pakar muda untuk dipahaminya dengan adanya dalam bentuk animasi.
Komparasi Fungsi Aktivasi Relu Dan Tanh Pada Multilayer Perceptron Ichsan Firmansyah; B. Herawan Hayadi
JURNAL INFORMATIKA DAN KOMPUTER Vol 6, No 2 (2022): ReBorn -- September 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (272.079 KB) | DOI: 10.26798/jiko.v6i2.600

Abstract

Neural network is a popular method used in machine research, and activation functions, especially ReLu and Tanh, have a very important function in neural networks, to minimize the error value between the output layer and the target class. With variations in the number of hidden layers, as well as the number of neurons in each different hidden layer, this study analyzes 8 models to classify the Titanic's Survivor dataset. The result is that the ReLu function has a better performance than the Tanh function, seen from the average value of accuracy and precision which is higher than the Tanh activation function. The addition of the number of hidden layers has no effect on increasing the performance of the classification results, it can be seen from the decrease in the average accuracy and precision of the models that use 3 hidden layers and models that use 4 hidden layers. The highest accuracy value was obtained in the model using the ReLu activation function with 4 hidden layers and 50 neurons in each hidden layer, while the highest precision value was obtained in the model using the ReLu activation function with 4 hidden layers and 100 neurons in each hidden layer
Classification SARS-CoV-2 Disease based on CT-Scan Image Using Convolutional Neural Network Kohsasih, Kelvin Leonardi; Hayadi, B. Herawan
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.36583

Abstract

Purpose: Convolutional Neural Network (CNN) is one of the most popular and widely used deep learning algorithms. These algorithms are commonly used in various applications, including image processing in medical and digital forensics, speech recognition, and other academic disciplines. SARS-CoV-2 (COVID-19) is a disease that first appeared in Wuhan, China, and has symptoms similar to pneumonia. This study aims to classify the covid-19 virus by proposing a deep learning model to prevent infection rates.Methods: The dataset used in this study is a public dataset originating from a hospital in Sao Paulo, Brazil. The data images consisted of 1252 infected with covid and 1230 data classified as non-covid but have other lung diseases. The classification method proposed in this research is a CNN model based on Resnet 50.Result: The experimental results show that the proposed Resnet 50-based convolutional neural network model works well in classifying SARS-CoV-2 disease using CT-Scan images. Our proposed model obtains 95% accuracy, precision, recall, and f1 values on the Epoch 500.Novelty: In this experiment, we utilized the Resnet50-based CNN model to classify the SARS-CoV-2 (COVID-19) disease using CT-Scan images and got good performance.
Co-Authors Ichsan Firmansyah Kohsasih, Kelvin Leonardi