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INDONESIA
IJoICT (International Journal on Information and Communication Technology)
Published by Universitas Telkom
ISSN : -     EISSN : 23565462     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal on Information and Communication Technology (IJoICT) is a peer-reviewed journal in the field of computing that published twice a year; scheduled in December and June.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 1 (2015): December 2015" : 5 Documents clear
Usability Improvement based on Hierarchical Task Analysis (Case Study on i-Caring) Muhammad Zafif Muttaqy; Angelina Prima Kurniati; Yanuar Firdaus A.W
International Journal on Information and Communication Technology (IJoICT) Vol. 1 No. 1 (2015): December 2015
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/IJOICT.2015.11.5

Abstract

i-Caring (IT Telkom i-Gracias Collaboration and e-learning) is an LMS (Learning Management System)-based e-learning integrated in i-Gracias own by IT Telkom Bandung. This application is being used to facilitate the implementation of learning activities supporting the face-to-face discussion in class. I-Caring supports some functionalities, such as instructional material storage, assignment, quizzes, and communication through chats and forum. But the great functionalities are not supported by an interactive interface, which is not comply to QUIM (Quality in Use Integrated Measurement) standard. This paper explains our research results of evaluation and recommendation process. This research is using Hierarchical Task Analysis (HTA) which has some indicators to analyze the behavior of a sample lecturers and students which are randomly selected. Based on the questionnaires answers, we analyze the relations of each points on HTA principles using SPSS Statistics 20 tools. The recommendations are then formulated to improve i-Gracias user interface quality.
Indonesian Vehicles Number Plates Recognition System Using Multi Layer Perceptron Neural Network and Connected Component Labelling Andre Sitompul; Mahmud Dwi Sulistiyo; Bedy Purnama
International Journal on Information and Communication Technology (IJoICT) Vol. 1 No. 1 (2015): December 2015
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/IJOICT.2015.11.1

Abstract

In recent years, the amount of vehicle in Indonesia has been increasing rapidly. This surely, if it is conducted conventionally, challenges the upholder in recognizing and detecting the lawbreakers vehicle. The objective of this research aims to create the system which can automatically recognize vehicles number plates. This is also expected to be able to assist the upholder to take an action against the lawbreaker. The method used are sliding concentric windows and connected component for detecting and segmenting each of character on vehicles number plates. Further, multi-layer perceptron neural network classification model is used to identify each of character on it.The system has been tested using variety of vehicles number plate images and succesfully recognize 180 of 224 characters images (80.35%). Based on the computation of each character, the accuracy of the system, throughout tested vehicles number plate images, can reach 95.69% (1509 of 1577 characters can be identified).The tested system has shown prospective results, thus the technique used on this research can be implemented through vehicles number plate recognition system in Indonesia.
Brain Tumor Detection and Classification in Magnetic Resonance Imaging (MRI) using Region Growing, Fuzzy Symmetric Measure, and Artificial Neural Network Backpropagation Lugina Muhammad; Retno Novi Dayawanti; Rita Rismala
International Journal on Information and Communication Technology (IJoICT) Vol. 1 No. 1 (2015): December 2015
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/IJOICT.2015.11.2

Abstract

Brain tumor is one type of malignant tumors that occurs because there is an abnormal and uncontrolled cell division activity. There are several ways to diagnose brain tumors, for example use MRI images. Through the MRI images, the radiologist can see the brain anatomy without performing surgery. However, this process is still done manually and could lead to misdiagnose. In addition, the different characteristics of brain tumor makes the diagnose more difficult. Therefore, we need a system of Computer-Aided Diagnostic (CAD) that will help radiologist in identifying brain tumors. In general, the CAD system consists of two major processes, namely image segmentation and feature extraction and classification. One example of segmentation is Region Growing that will classify the pixels based on certain criteria. However, the manual selection of seed point is a drawback of this method. The examples of feature extraction methods are Fuzzy Symmetric Measure (FSM), and First and Second Order Statistics. FSM values can be used to calculate the symmetry of the image brain, while the first and second order to represent feature in the image. As for the classification process, Artificial Neural Network Backpropagation method is widely used for its ability to resolve nonlinear dan complex problems.This research implements CAD system that uses Region Growing, Symmetric Fuzzy Measure, and Backpropagation Neural Network for detecting and classifying the brain tumors. In addition, the modification of converging square is conducted to select a seed point automatically. After testing, the system generates a 100% accuracy and BER is 0 in the case of distinguishing between normal and tumor brain. Besides, the average accuracy in classifying the types of brain tumors achieved 89.72% , the BER 0.1 for training data, and the average accuracy of 84.44%, BER 0.16 for the testing data.
Analysis and Implementation of Metode Collaborative Analysis Methods of Requirement and Design (CARD) on E-commerce Website in Indonesia Oktariantoro Anggit K; Angelina Prima K; Erda Guslinar P
International Journal on Information and Communication Technology (IJoICT) Vol. 1 No. 1 (2015): December 2015
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/IJOICT.2015.11.3

Abstract

The development of internet in recent years caused a major prospect in online business. One of the businesses is e-commerce. E-commerce is an activity of selling and buying information through computer network. Website is a medium that can be used in e-commerce implementation. E-commerce website becomes an important part in supporting the companys success. Yet, there are many e-commerce websites that are not understandable by the user since the user cannot find out what they want. In addition, e-commerce website is still difficult to use. The users who cannot find what they want in e-commerce website will surely decrease the usability of the website. As a result, there must be a design planning which can be comprehended by the users to find the expected product easily. The Collaborative Analysis of Requirement and Design method can be used to solve the design problem. The method is one of the techniques in user centered design. The method focuses on users because in its process it involves the users, and the users are the the information source of this method. The information that is gained from the users can be taken form interview, questionairre and experimentation with component order. The result of this research gives contribution to the websites, that is advise to improve the website design so that the users can understand the working system and the design more comprehensively.
Electronic Product Feature-Based Sentiment Analysis Using Nu-SVM Method J. Ratna Juita S; Hetti Hidayati; Alfian Akbar Gozali
International Journal on Information and Communication Technology (IJoICT) Vol. 1 No. 1 (2015): December 2015
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/IJOICT.2015.11.4

Abstract

Sentiment in a product online review is useful and influence decision-making a person may take in buying any product as well as that of organization in determining the number of product to produce. In an opinion, reviewer may provide positive and negative reviews at the same time that can be ambiguous. This is because opinion targets are often not the product as a whole; instead they are only part of a product called as feature, which have advantages and disadvantages based on the reviewers point of view. In this paper, the goal is to produce sentiment of a mobile phone opinion based on its feature. Opinion data used in this thesis are in English taken from www.cnet.com. Feature extraction is conducted by searching for phrases that match the dependency relation template, which is followed by feature filtering. The sentiment identification, positive and negative probability value, as well as target class label of the data preparation become the Nu SVM classifier input parameters. In the study of NU SVM, some data are treated as unlabeled data. The evaluation towards sentiment identification obtained from the study shows F1 Measure of 86.25% for positive class and 77.71% for negative class. The accuracy for feature identification, however, is 82%.

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