Journal of ICT Research and Applications 2022-04-30T23:44:08+07:00 Dr. Eng. Achmad Munir Open Journal Systems <p><img class="imgdesc" src="" alt="" /></p> <p style="text-align: justify;"><em>Journal of ICT Research and Applications</em> welcomes full research articles in the area of Information and Communication Technology from the following subject areas: Information Theory, Signal Processing, Electronics, Computer Network, Telecommunication, Wireless &amp; Mobile Computing, Internet Technology, Multimedia, Software Engineering, Computer Science, Information System and Knowledge Management.</p> <p style="text-align: justify;">Abstracts and articles published on Journal of ICT Research and Applications are available online at ITB Journal and indexed by <a href="">Scopus</a>, <a href=";hl=id">Google Scholar</a>, <a href="">Directory of Open Access Journals</a>, <a href=";colors=7&amp;lang=en&amp;jour_id=167017">Electronic Library University of Regensburg</a>, <a href=";Find=journal+of+ICT+RESEARCH+AND+APPLICATIONS&amp;GetResourcesBy=QuickSearch&amp;resourceTypeName=allTitles&amp;resourceType=&amp;radioButtonChanged=">EBSCO Open Science Directory</a>, <a href="">International Association for Media and Communication Research (IAMCR)</a>, <a href="">MIAR: Information Matrix for the Analysis of Journals Universitat de Barcelona</a>, Cabells Directories, <a href="">Zurich Open Repository and Archive Journal Database</a>, <a href="">Open Academic Journals Index</a>, Indonesian Publication Index and ISJD-Indonesian Institute of Sciences. The journal is under reviewed by Compendex, Engineering Village.</p> <p>ISSN: 2337-5787<br />E-ISSN: 2338-5499</p> <p>Reg. No. 691-SIC-UPPGT-SIT-1963, <a title="Accreditation Decree" href="">Accreditation No. 30/E/KPT/ 2018</a></p> <p>Published by the Institute for Research and Community Services, Institut Teknologi Bandung, in collaboration with Indonesian Engineering Association (<em>Persatuan Insinyur Indonesia-PII</em>).</p> <p><span style="text-decoration: underline;"><strong>Publication History</strong></span></p> <p><strong>Formerly known as:</strong></p> <ul> <li>ITB Journal of Information and Communication Technology (2007 - 2012)</li> </ul> <p>Back issues can be read online at:</p> <p><span style="text-decoration: underline;"><strong>Scimago Journal Ranking</strong></span></p> <p><a title="SCImago Journal &amp; Country Rank" href=";tip=sid&amp;exact=no"><img src=";title=true" alt="SCImago Journal &amp; Country Rank" border="0" /></a></p> Mobile Robot Path Planning Optimization Based on Integration of Firefly Algorithm and Cubic Polynomial Equation 2021-10-27T13:24:10+07:00 Sura Mazin Ali Janan Farag Yonan Omar Alniemi Amjed Abbas Ahmed <p>Mobile Robot is an extremely essential technology in the industrial world. Optimal path planning is essential for the navigation of mobile robots. The firefly algorithm is a very promising tool of Swarm Intelligence, which is used in various optimization areas. This study used the firefly algorithm to solve the mobile robot path-planning problem and achieve optimal trajectory planning. The objective of the proposed method is to find the free-collision-free points in the mobile robot environment and then generate the optimal path based on the firefly algorithm. It uses the A∗ algorithm to find the shortest path. The essential function of use the firefly algorithm is applied to specify the optimal control points for the corresponding shortest smooth trajectory of the mobile robot. Cubic Polynomial equation is applied to generate a smooth path from the initial point to the goal point during a specified period. The results of computer simulation demonstrate the efficiency of the firefly algorithm in generating optimal trajectory of mobile robot in a variable degree of mobile robot environment complexity.</p> 2022-04-30T00:00:00+07:00 Copyright (c) 2022 Journal of ICT Research and Applications A CNN-ELM Classification Model for Automated Tomato Maturity Grading 2021-11-22T13:10:34+07:00 John Paul Tan Yusiong <p>Tomatoes are popular around the world due to their high nutritional value. Tomatoes are also one of the world’s most widely cultivated and profitable crops. The distribution and marketing of tomatoes depend highly on their quality. Estimating tomato ripeness is an essential step in determining shelf life and quality. With the abundant supply of tomatoes on the market, it is exceedingly difficult to estimate tomato ripeness using human graders. To address this issue and improve tomato quality inspection and sorting, automated tomato maturity classification models based on different features have been developed. However, current methods heavily rely on human-engineered or handcrafted features. Convolutional neural networks have emerged as the preferred technique for general object recognition problems because they can automatically detect and extract valuable features by directly working on input images. This paper proposes a CNN-ELM classification model for automated tomato maturity grading that combines CNNs’ automated feature learning capabilities with the efficiency of extreme learning machines to perform fast and accurate classification even with limited training data. The results showed that the proposed CNN-ELM model had a classification accuracy of 96.67% and an F1-score of 96.67% in identifying six maturity stages from the test data.</p> 2022-04-30T00:00:00+07:00 Copyright (c) 2022 Journal of ICT Research and Applications Wireless Vibration Monitoring System for Milling Process 2022-03-09T12:38:10+07:00 Muhamad Rausyan Fikri Kushendarsyah Saptaji Fijai Naja Azmi <p>The implementation of industrial revolution 4.0 in manufacturing industries is necessary to adapt to the rapid changes of technologies. The milling process is one of the common manufacturing processes applied in the industries to produce engineering products. The vibration that occurs in the milling process can disturb the continuity of the process. The wired vibration monitoring system implemented in the manufacturing process needs to be replaced with the wireless monitoring system. Hence wireless vibration monitoring system is developed to solve the problem with wired monitoring systems where tucked cable and high cost are the main challenges of the wired monitoring system. The wireless monitoring system setup is built using three components: sensor node, monitoring node, and base station. Milling experiments with various depths of cut, feed rate, and spindle speed were conducted to examine the performance of the wireless monitoring system. The results indicate the wireless system shows similar data recorded by the wired system. The wireless vibration monitoring system can identify the effect of milling parameters such as depth of cut, feed rate, and spindle speed on the vibrations level. The effect of cut depth is more significant than spindle speed and feed rate in the defined parameters.</p> 2022-04-30T00:00:00+07:00 Copyright (c) 2022 Journal of ICT Research and Applications Predicting the Extent of Sidoarjo Mud Flow Using Remote Sensing 2022-02-14T16:43:04+07:00 Wishnumurti Wicaksono Sani M. Isa <p>The Sidoarjo mud flow in East Java is the result of a natural phenomenon in which hot mudflow occurs due to volcanic activity. The Sidoarjo mud flow resulted in a considerable ecological disaster in the area. In this study, by using the Modification of Normalized Difference Water Index (MNDWI) technique we measured the extension of the mudflow area from 2013 to 2020 using Landsat 8 satellite data imagery. This study is meant to predict the extension of the mud flow area in the research site by comparing regression and neural network techniques in order to find the best approach. The RPROP MLP neural network technique was used to predict the Sidoarjo mud-flowing area in 2021 to 2025. Surprisingly the results of these calculations showed that the RPROP MLP neural network with three hidden layers and 20 neurons performed the best, with an R square value for training of 0.77915565 and for testing of 0.78321550.</p> 2022-04-30T00:00:00+07:00 Copyright (c) 2022 Journal of ICT Research and Applications Automatically Detect Software Security Vulnerabilities Based on Natural Language Processing Techniques and Machine Learning Algorithms 2021-11-08T09:44:11+07:00 Do Xuan Cho Vu Ngoc Son Duong Duc <p>Nowadays, software vulnerabilities pose a serious problem, because cyber-attackers often find ways to attack a system by exploiting software vulnerabilities. Detecting software vulnerabilities can be done using two main methods: i) signature-based detection, i.e. methods based on a list of known security vulnerabilities as a basis for contrasting and comparing; ii) behavior analysis-based detection using classification algorithms, i.e., methods based on analyzing the software code. In order to improve the ability to accurately detect software security vulnerabilities, this study proposes a new approach based on a technique of analyzing and standardizing software code and the random forest (RF) classification algorithm. The novelty and advantages of our proposed method are that to determine abnormal behavior of functions in the software, instead of trying to define behaviors of functions, this study uses the Word2vec natural language processing model to normalize and extract features of functions. Finally, to detect security vulnerabilities in the functions, this study proposes to use a popular and effective supervised machine learning algorithm.</p> 2022-05-11T00:00:00+07:00 Copyright (c) 2022 Journal of ICT Research and Applications A Classifier to Detect Profit and Non Profit Websites Upon Textual Metrics for Security Purposes 2022-04-06T11:28:01+07:00 Yahya Tashtoush Dirar Darweesh Omar Darwish Belal Alsinglawi Rasha Obeidat <p class="Abstract">Currently, most organizations have a defense system to protect their digital communication network against cyberattacks. However, these defense systems deal with all network traffic regardless if it is from profit or non-profit websites. This leads to enforcing more security policies, which negatively affects network speed. Since most dangerous cyberattacks are aimed at commercial websites, because they contain more critical data such as credit card numbers, it is better to set up the defense system priorities towards actual attacks that come from profit websites. This study evaluated the effect of textual website metrics in determining the type of website as profit or nonprofit for security purposes. Classifiers were built to predict the type of website as profit or non-profit by applying machine learning techniques on a dataset. The corpus used for this research included profit and non-profit websites. Both traditional and deep machine learning techniques were applied. The results showed that J48 performed best in terms of accuracy according to its outcomes in all cases. The newly built models can be a significant tool for defense systems of organizations, as they will help them to implement the necessary security policies associated with attacks that come from both profit and non-profit websites. This will have a positive impact on the security and efficiency of the network.</p> 2022-05-17T00:00:00+07:00 Copyright (c) 2022 Journal of ICT Research and Applications