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Journal of ICT Research and Applications
Published by Institut Teknologi Bandung
ISSN : 23375787     EISSN : 23385499     DOI : https://doi.org/10.5614/itbj.ict.res.appl.
Core Subject : Science,
Computer Science & IT
Journal of ICT Research and Applications 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 & Mobile Computing, Internet Technology, Multimedia, Software Engineering, Computer Science, Information System and Knowledge Management.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 7 Documents
 1 
Search results for , issue "Vol. 15 No. 2 (2021)" : 7 Documents clear
A Scheme Towards Automatic Word Indexation System for Balinese Palm Leaf Manuscripts Made Windu Antara Kesiman; Gede Aditra Pradnyana
Journal of ICT Research and Applications Vol. 15 No. 2 (2021)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2021.15.2.1

Abstract

This paper proposes an initial scheme towards the development of an automatic word indexation system for Balinese lontar (palm leaf manuscript) collections. The word indexation system scheme consists of a sub module for patch image extraction of text areas in lontars and a sub module for word image transliteration. This is the first word indexation system for lontar collections to be proposed. To detect parts of a lontar image that contain text, a Gabor filter is used to provide initial information about the presence of text texture in the image. An adaptive sliding patch algorithm for the extraction of patch images in lontars is also proposed. The word image transliteration sub module was built using the long short-term memory (LSTM) model. The results showed that the image patch extraction of text areas process succeeded in optimally detecting text areas in lontars and extracting the patch image in a suitable position. The proposed scheme successfully extracted between 20% to 40% of the keywords in lontars and thus can at least provide an initial description for prospective lontar readers of the content contained in a lontar collection or to find in which lontar collection certain keywords can be found.
Design and Implementation of Triple Band Half Mode Substrate Integrated Waveguide (HMSIW) Antenna with Compact Size Zahraa Taha; Hafsa Jassim; Anas Ahmed; Ikhlas Farhan
Journal of ICT Research and Applications Vol. 15 No. 2 (2021)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2021.15.2.2

Abstract

This study investigated structure strategies and exploratory scenarios for a half mode substrate integrated waveguide (HMSIW) antenna. The proposed antenna consists of three Hilbert cells, which are simulated by using CST programming. The antenna was manufactured with the realities of minor imperfections and high incorporation. The proposed structure offers a suitable substrate integrated waveguide (SIW) with about a decrease in size by half. In addition, Hilbert cells were added to realize the triple-band characteristics with good impedance matching, radiation patterns, and radiation performance. The antenna was fabricated on h = 1 mm thick dielectric substrate with dielectric constant (???????? = 4.3). The Hilbert cells were drilled on the top plane of the antenna substrate and fed using a microstrip transmission line. The proposed antenna is small, with a slot side length of approximately half of the guided wavelength. The three developed Hilbert cell HMSIW antenna resonates at 3.25, 5.94 and 6.5 GHz with a bandwidth of 2.97, 2.25 and 2.29% within a return loss of ‑38.77, ‑35.82 -23.35 dB, respectively. The results showed enhancements in antenna gain of 3.56, 4.97 and 6.43 dBi, with a radiation efficiency of -1.253, -0.493 and -0.586 dB, respectively.
Reducing Power Consumption in Hexagonal Wireless Sensor Networks Using Efficient Routing Protocols Razan Khalid Alhatimi; Omar Saad Almousa; Firas Ali Albalas
Journal of ICT Research and Applications Vol. 15 No. 2 (2021)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2021.15.2.5

Abstract

Power consumption and network lifetime are vital issues in wireless sensor network (WSN) design. This motivated us to find innovative mechanisms that help in reducing energy consumption and prolonging the lifetime of such networks. In this paper, we propose a hexagonal model for WSNs to reduce power consumption when sending data from sensor nodes to cluster heads or the sink. Four models are proposed for cluster head positioning and the results were compared with well-known models such as Power Efficient Gathering In Sensor Information Systems (PEGASIS) and Low-Energy Adaptive Clustering Hierarchy (LEACH). The results showed that the proposed models reduced WSN power consumption and network lifetime.
A New Term Frequency with Gaussian Technique for Text Classification and Sentiment Analysis Vuttichai Vichianchai; Sumonta Kasemvilas
Journal of ICT Research and Applications Vol. 15 No. 2 (2021)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2021.15.2.4

Abstract

This paper proposes a new term frequency with a Gaussian technique (TF-G) to classify the risk of suicide from Thai clinical notes and to perform sentiment analysis based on Thai customer reviews and English tweets of travelers that use US airline services. This research compared TF-G with term weighting techniques based on Thai text classification methods from previous researches, including the bag-of-words (BoW), term frequency (TF), term frequency-inverse document frequency (TF-IDF), and term frequency-inverse corpus document frequency (TF-ICF) techniques. Suicide risk classification and sentiment analysis were performed with the decision tree (DT), naïve Bayes (NB), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) techniques. The experimental results showed that TF-G is appropriate for feature extraction to classify the risk of suicide and to analyze the sentiments of customer reviews and tweets of travelers. The TF-G technique was more accurate than BoW, TF, TF-IDF and TF-ICF for term weighting in Thai suicide risk classification, for term weighting in sentiment analysis of Thai customer reviews for Burger King, Pizza Hut, and Sizzler restaurants, and for the sentiment analysis of English tweets of travelers using US airline services.
Automated Detection and Classification of Breast Cancer Nuclei with Deep Convolutional Neural Network Shanmugham Balasundaram; Revathi Balasundaram; Ganesan Rasuthevar; Christeena Joseph; Annie Grace Vimala; Nanmaran Rajendiran; Baskaran Kaliyamurthy
Journal of ICT Research and Applications Vol. 15 No. 2 (2021)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2021.15.2.3

Abstract

Heterogeneous regions present in tissue with respect to cancer cells are of various types. This study aimed to analyze and classify the morphological features of the nucleus and cytoplasm regions of tumor cells. This tissue morphology study was established through invasive ductal breast cancer histopathology images accessed from the Databiox public dataset. Automatic detection and classification was carried out by means of the computer analytical tool of deep learning algorithm. Residual blocks with short skip were employed with hidden layers of preserved spatial information. A ResNet-based convolutional neural network was adapted to perform end-to-end segmentation of breast cancer nuclei. Nuclei regions were identified through color and tubular structure morphological features. Based on the segmented and extracted images, classification of benign and malignant breast cancer cells was done to identify tumors. The results indicated that the proposed method could successfully segment and classify breast tumors with an average Dice score of 90.68%, sensitivity = 98.64, specificity = 98.68, and accuracy = 98.82.
Convolution and Recurrent Hybrid Neural Network for Hevea Yield Prediction Lince Rachel Varghese; Vanitha Kandasamy
Journal of ICT Research and Applications Vol. 15 No. 2 (2021)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2021.15.2.6

Abstract

Deep learning techniques have been used effectively for rubber crop yield prediction. A hybrid of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) is the best technique for crop yield prediction because it can effectively handle uncertainty of features. Hence, in this paper, a hybrid CNN-RNN method is proposed to forecast Hevea yields based on environmental data in Kerala state, India. The proposed hybrid CNN-RNN method reduces the internal covariate shift of CNN by batch normalization and solves the gradient vanishing or exploding problem of RNN using LSTM with a cell activation mechanism. The proposed method has three essential characteristics: (i) it captures the time dependency of environmental factors and improves the inherent computational time; (ii) it is capable of generalizing the yield prediction under uncertain conditions without loss of prediction accuracy; (iii) combined with the back propagation and feed forward  method it can reveal the extent to which samples of weather conditions and soil data conditions are suitable to provide a clear boundary between rubber yield variations.
Cover JICTRA Vol. 15 No. 2, 2021 Journal of ICT Research and Applications
Journal of ICT Research and Applications Vol. 15 No. 2 (2021)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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