Journal of ICT Research and Applications <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> LPPM ITB en-US Journal of ICT Research and Applications 2337-5787 Strengthening INORMALS Using Context-based Natural Language Generation <p>The noiseless steganography method that has been proposed by Wibowo can embed up to six characters in the provided cover text, but more than 59% of Indonesian words have a length of more than six characters, so there is room to improve Wibowo’s method. This paper proposes an improvement of Wibowo’s method by modifying the shifting codes and using context-based language generation. Based on 300 test messages, 99% of messages with more than six characters could be embedded by the proposed method, while using Wibowo’s method this was only 34%. Wibowo’s method can embed more than six characters only if the number of shifting codes is less than three, while the proposed method can embed more than six characters even if there are more than three shifting codes. Furthermore, the security for representing the number of code digits is increased by introducing a private key with the probability of guessing less than 1, while in Wibowo’s method this is 1. The naturalness of the cover sentences generated by the proposed method was maintained, which was about 99% when using the proposed method, while it was 98.61% when using Wibowo’s method.</p> Soni Yora Ari Moesriami Barmawi Copyright (c) 2022 Journal of ICT Research and Applications 2022-08-31 2022-08-31 16 2 101 122 10.5614/itbj.ict.res.appl.2022.16.2.1 Medium Access Control Protocol for High Altitude Platform Based Massive Machine Type Communication <p>Massive Machine Type Communication (mMTC) can be used to connect a large number of sensors over a wide coverage area. One of the places where mMTC can be applied is in wireless sensor networks (WSNs). A WSN consists of several sensor nodes that send their sensing information to the cluster head (CH), which can then be forwarded to a high altitude platform (HAP) station. Sensing information can be sent by the sensor nodes at the same time through the same medium, which means collision can occur. When this happens, the sensor node must re-send the sensing information, which causes energy wastage in the WSN. In this paper, we propose a Medium Access Control (MAC) protocol to control access from several sensor nodes during data transmission to avoid collision. The sensor nodes send Round Robin, Interrupt and Query data every eight hours. The initial slot for transmission of the Round Robin data can be either randomized or reserved. Analysis performance was done to see the efficiency of the network with the proposed MAC protocol. Based on the series of simulations that was conducted, the proposed MAC protocol can support a WSN system-based HAP for monitoring every eight hours. The proposed MAC protocol with an initial slot that is reserved for transmission of Round Robin data has greater network efficiency than a randomized slot.</p> Veronica Windha Mahyastuty Iskandar Iskandar Hendrawan Hendrawan Mohammad Sigit Arifianto Copyright (c) 2022 Journal of ICT Research and Applications 2022-08-31 2022-08-31 16 2 123 137 10.5614/itbj.ict.res.appl.2022.16.2.2 Context-Aware Sentiment Analysis using Tweet Expansion Method <p>The large source of information space produced by the plethora of social media platforms in general and microblogging in particular has spawned a slew of new applications and prompted the rise and expansion of sentiment analysis research. We propose a sentiment analysis technique that identifies the main parts to describe tweet intent and also enriches them with relevant words, phrases, or even inferred variables. We followed a state-of-the-art hybrid deep learning model to combine Convolutional Neural Network (CNN) and the Long Short-Term Memory network (LSTM) to classify tweet data based on their polarity. To preserve the latent relationships between tweet terms and their expanded representation, sentence encoding and contextualized word embeddings are utilized. To investigate the performance of tweet embeddings on the sentiment analysis task, we tested several context-free models (Word2Vec, Sentence2Vec, Glove, and FastText), a dynamic embedding model (BERT), deep contextualized word representations (ELMo), and an entity-based model (Wikipedia). The proposed method and results prove that text enrichment improves the accuracy of sentiment polarity classification with a notable percentage.</p> Bashar Tahayna Ramesh Ayyasamy Rehan Akbar Copyright (c) 2022 Journal of ICT Research and Applications 2022-08-31 2022-08-31 16 2 138 151 10.5614/itbj.ict.res.appl.2022.16.2.3 Breast Cancer Diagnosis in Women Using Neural Networks and Deep Learning <p>Breast cancer is a deadly disease affecting women around the world. It can spread rapidly into other parts of the body, causing untimely death when undetected due to rapid growth and division of cells in the breast. Early diagnosis of this disease tends to increase the survival rate of women suffering from the disease. The use of technology to detect breast cancer in women has been explored over the years. A major drawback of most research in this area is low accuracy in the detection rate of breast cancer in women. This is partly due to the availability of few data sets to train classifiers and the lack of efficient algorithms that achieve optimal results. This research aimed to develop a model that uses a machine learning approach (convolution neural network) to detect breast cancer in women with significantly high accuracy. In this paper, a model was developed using 569 mammograms of various breasts diagnosed with benign and maligned cancers. The model achieved an accuracy of 98.25% and sensitivity of 99.5% after 80 iterations. </p> Ojo Fagbuagun Olaiya Folorunsho Lawrence Adewole Titilayo Akin-Olayemi Copyright (c) 2022 Journal of ICT Research and Applications 2022-09-09 2022-09-09 16 2 152 166 10.5614/itbj.ict.res.appl.2022.16.2.4 Towards Enhancing Keyframe Extraction Strategy for Summarizing Surveillance Video: An Implementation Study <p>The large amounts of surveillance video data are recorded, containing many redundant video frames, which makes video browsing and retrieval difficult, thus increasing bandwidth utilization, storage capacity, and time consumed. To ensure the reduction in bandwidth utilization and storage capacity to the barest minimum, keyframe extraction strategies have been developed. These strategies are implemented to extract unique keyframes whilst removing redundancies. Despite the achieved improvement in keyframe extraction processes, there still exist a significant number of redundant frames in summarized videos. With a view to addressing this issue, the current paper proposes an enhanced keyframe extraction strategy using k-means clustering and a statistical approach. Surveillance footage, movie clips, advertisements, and sports videos from a benchmark database as well as Compeng IP surveillance videos were used to evaluate the performance of the proposed method. In terms of compression ratio, the results showed that the proposed scheme outperformed existing schemes by 2.82%. This implies that the proposed scheme further removed redundant frames whiles retaining video quality. In terms of video playtime, there was an average reduction of 27.32%, thus making video content retrieval less cumbersome when compared with existing schemes. Implementation was done using MATLAB R2020b.</p> Bashir Olaniyi Sadiq Habeeb Bello-Salau Latifat Abduraheem-Olaniyi Bilyaminu Muhammed Sikiru Olayinka Zakariyya Copyright (c) 2022 Journal of ICT Research and Applications 2022-09-23 2022-09-23 16 2 167 183 10.5614/itbj.ict.res.appl.2022.16.2.5