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Jurnal INFOTEL
Published by Institut Teknologi Telkom Purwokerto
ISSN : 20853688     EISSN : 24600997     DOI : https://doi.org/10.20895/infotel.v15i2
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal INFOTEL is a scientific journal published by Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) of Institut Teknologi Telkom Purwokerto, Indonesia. Jurnal INFOTEL covers the field of informatics, telecommunication, and electronics. First published in 2009 for a printed version and published online in 2012. The aims of Jurnal INFOTEL are to disseminate research results and to improve the productivity of scientific publications. Jurnal INFOTEL is published quarterly in February, May, August, and November. Starting in 2018, Jurnal INFOTEL uses English as the primary language.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 11 No 2 (2019): May 2019" : 5 Documents clear
Stateless Authentication with JSON Web Tokens using RSA-512 Algorithm Alam Rahmatullo; Aldy Putra Aldya; Muhammad Nur Arifin
JURNAL INFOTEL Vol 11 No 2 (2019): May 2019
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v11i2.427

Abstract

Today's technology needs are getting higher, one of the technologies that continues to grow now is Web Service (WS). WS can increase service flexibility on a system. However, security at WS is one of the things that needs attention. One effort to overcome this problem is JWT (JSON Web Token). JWT is one of the authentication mechanisms in WS, with a standard signature algorithm, HMAC SHA256, RSA-256 or ECDSA. In this research we will discuss the performance of JWT RSA-512 which is implemented on SOAP and RESTful. Because based on previous research the speed performance of the 512-bit algorithm is better, but it is not yet known if applied to JWT. The test results show that the speed of the JWT RSA-512 token on the RESTful process is superior to 24.69% compared to SOAP. Then the speed of the authentication of JWT RSA-512 tokens, RESTful is superior to 11.64% compared to SOAP. Whereas in testing the size of JWT RSA-512 generated tokens, RESTful is only 1.25% superior to SOAP.
Maize Leaf Disease Image Classification Using Bag of Features Wahyudi Setiawan; Mohammad Syarief; Novi Prastiti
JURNAL INFOTEL Vol 11 No 2 (2019): May 2019
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v11i2.428

Abstract

Image classification is an image grouping based on similarities features. The features extraction stage is a crucial factor for classifying an image. In the conventional image classification, the features commonly used are morphology, color, and texture with various derivative features. The type and number of appropriate features will affect the classification results. In this study, image classification by using the Bag of Features (BOF) method which can generate features automatically. It consists of 4 stages: feature point location by using grid method, feature extraction by using Speed Up Robust Feature (SURF), clustering word-visual vocabularies by using k-means, and classification by using Support Vector Machine (SVM). The classification data are maize leaf images from the PlantVillage-Dataset. The data consists of 3 types of images: RGB, grayscale, and segmentation images. Each type includes four classes: healthy, Cercospora, common rust, and northern leaf blight. There are 50 images for each class. We used two scenarios of testing for each type of data: training and validation, 70% and 80% images for training, and the rest for validation. Experimental results showed that the validation accuracies of RGB, grayscale, and segmentation images were 82%, 77%, and 85%.
Optimal Control Design of Active Suspension System Based on Quarter Car Model Nur Uddin
JURNAL INFOTEL Vol 11 No 2 (2019): May 2019
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v11i2.429

Abstract

The optimal control design of the ground-vehicle active suspension system is presented. The active suspension system is to improve the vehicle ride comfort by isolating vibrations induced by the road profile and vehicle velocity. The vehicle suspension system is approached by a quarter car model. Dynamic equations of the system are derived by applying Newton’s second law. The control law of the active suspension system is designed using linear quadratic regulator (LQR) method. Performance evaluation is done by benchmarking the active suspension system to a passive suspension system. Both suspension systems are simulated in computer. The simulation results show that the active suspension system significantly improves the vehicle ride comfort of the passive suspension system by reducing 50.37% RMS of vertical displacement, 45.29% RMS of vertical velocity, and 1.77% RMS of vertical acceleration.
Breast Cancer Detection using Residual Convolutional Neural Network and Weighted Loss Samuel Aji Sena; Panca Mudjirahardjo; Sholeh Hadi Pramono
JURNAL INFOTEL Vol 11 No 2 (2019): May 2019
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v11i2.430

Abstract

This research presents a breast cancer detection system using deep learning method. Breast cancer detection in a large slide of biopsy image is a hard task because it needs manual observation by a pathologist to find the malignant region. The deep learning model used in this research is made up of multiple layers of the residual convolutional neural network, and instead of using another type of classifier, a multilayer neural network was used as the classifier and stacked together and trained using end-to-end training approach. The system is trained using invasive ductal carcinoma dataset from the Hospital of the University of Pennsylvania and The Cancer Institute of New Jersey. From this dataset, 80% and 20% were randomly sampled and used as training and testing data respectively. Training a neural network on an imbalanced dataset is quite challenging. Weighted loss function was used as the objective function to tackle this problem. We achieve 78.26% and 78.03% for Recall and F1-Score metrics, respectively which are an improvement compared to the previous approach.
Investigation of Human Emotion Pattern Based on EEG Signal Using Wavelet Families and Correlation Feature Selection Dwi Utari Surya; Ponco Siwindarto; Erni Yudaningtyas
JURNAL INFOTEL Vol 11 No 2 (2019): May 2019
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v11i2.431

Abstract

Emotions is one of the advantages given by God to human beings compared to other living creatures. Emotions have an important role in human life. Many studies have been conducted to recognize human emotions using physiological measurements, one of which is Electroencephalograph (EEG). However, the previous researches have not discussed the types of wavelet families that have the best performance and canals that are optimal in the introduction of human emotions. In this paper, the power features of several types of wavelet families, namely Daubechies, symlets, and coiflets with the Correlation Feature Selection (CFS) method to select the best features of alpha, beta, gamma and theta frequencies. According to the results, coiflet is a method of the wavelet family that has the best accuracy value in emotional recognition. The use of the CFS feature selection can improve the accuracy of the results from 81% to 93%, and the five most dominant channels in the power features of alpha and gamma band on T8, T7, C5, CP5, and TP7. Hence, it can be concluded that the temporal of the left brain is more dominant in recognition of human emotions.

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