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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 302 Documents
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A Combination of Inverted LSB, RSA, and Arnold Transformation to get Secure and Imperceptible Image Steganography Edi Jaya Kusuma; Christy Atika Sari; Eko Hari Rachmawanto; De Rosal Ignatius Moses Setiadi
Journal of ICT Research and Applications Vol. 12 No. 2 (2018)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2018.12.2.1

Abstract

Securing images can be achieved using cryptography and steganography. Combining both techniques can improve the security of images. Usually, Arnold's transformation (ACM) is used to encrypt an image by randomizing the image pixels. However, applying only a transformation algorithm is not secure enough to protect the image. In this study, ACM was combined with RSA, another encryption technique, which has an exponential process that uses large numbers. This can confuse attackers when they try to decrypt the cipher images. Furthermore, this paper also proposes combing ACM with RSA and subsequently embedding the result in a cover image with inverted two-bit LSB steganography, which replaces two bits in the bit plane of the cover image with message bits. This modified steganography technique can provide twice the capacity of the previous method. The experimental result was evaluated using PSNR and entropy as the parameters to obtain the quality of the stego images and the cipher images. The proposed method produced a highest PSNR of 57.8493 dB and entropy equal to 7.9948.
Word Embedding for Rhetorical Sentence Categorization on Scientific Articles Ghoziyah Haitan Rachman; Masayu Leylia Khodra; Dwi Hendratmo Widyantoro
Journal of ICT Research and Applications Vol. 12 No. 2 (2018)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2018.12.2.5

Abstract

A common task in summarizing scientific articles is employing the rhetorical structure of sentences. Determining rhetorical sentences itself passes through the process of text categorization. In order to get good performance, some works in text categorization have been done by employing word embedding. This paper presents rhetorical sentence categorization of scientific articles by using word embedding to capture semantically similar words. A comparison of employing Word2Vec and GloVe is shown. First, two experiments are evaluated using five classifiers, namely Naïve Bayes, Linear SVM, IBK, J48, and Maximum Entropy. Then, the best classifier from the first two experiments was employed. This research showed that Word2Vec CBOW performed better than Skip-Gram and GloVe. The best experimental result was from Word2Vec CBOW for 20,155 resource papers from ACL-ARC, features from Teufel and the previous label feature. In this experiment, Linear SVM produced the highest F-measure performance at 43.44%.
Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas) Irwandi Hipiny; Hamimah Ujir; Aazani Mujahid; Nurhartini Kamalia Yahya
Journal of ICT Research and Applications Vol. 12 No. 3 (2018)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2018.12.3.4

Abstract

Passive biometric identification enables wildlife monitoring with minimal disturbance. Using a motion-activated camera placed at an elevated position and facing downwards, images of sea turtle carapaces were collected, each belonging to one of sixteen Chelonia mydas juveniles. Then, co-variant and robust image descriptors from these images were learned, enabling indexing and retrieval. In this paper, several classification results of sea turtle carapaces using the learned image descriptors are presented. It was found that a template-based descriptor, i.e. Histogram of Oriented Gradients (HOG) performed much better during classification than keypoint-based descriptors. For our dataset, a high-dimensional descriptor is a must because of the minimal gradient and color information in the carapace images. Using HOG, we obtained an average classification accuracy of 65%. 
Performance Analysis of BigDecimal Arithmetic Operation in Java Jos Timanta Tarigan; Elviawaty M. Zamzami; Cindy Laurent Ginting
Journal of ICT Research and Applications Vol. 12 No. 3 (2018)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2018.12.3.5

Abstract

The Java programming language provides binary floating-point primitive data types such as float and double to represent decimal numbers. However, these data types cannot represent decimal numbers with complete accuracy, which may cause precision errors while performing calculations. To achieve better precision, Java provides the BigDecimal class. Unlike float and double, which use approximation, this class is able to represent the exact value of a decimal number. However, it comes with a drawback: BigDecimal is treated as an object and requires additional CPU and memory usage to operate with. In this paper, statistical data are presented of performance impact on using BigDecimal compared to the double data type. As test cases, common mathematical processes were used, such as calculating mean value, sorting, and multiplying matrices.
Using Graph Pattern Association Rules on Yago Knowledge Base Wahyudi Wahyudi; Masayu Leylia Khodra; Ary Setijadi Prihatmanto; Carmadi Machbub
Journal of ICT Research and Applications Vol. 13 No. 2 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.2.6

Abstract

The use of graph pattern association rules (GPARs) on the Yago knowledge base is proposed. Extending association rules for itemsets, GPARS can help to discover regularities between entities in a knowledge base. A rule-generated graph pattern (RGGP) algorithm was used for extracting rules from the Yago knowledge base and a GPAR algorithm for creating the association rules. Our research resulted in 1114 association rules, with the value of standard confidence at 50.18% better than partial completeness assumption (PCA) confidence at 49.82%. Besides that the computation time for standard confidence was also better than for PCA confidence.
Accessibility Degradation Prediction on LTE/SAE Network Using Discrete Time Markov Chain (DTMC) Model Hendrawan Hendrawan
Journal of ICT Research and Applications Vol. 13 No. 1 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.1.1

Abstract

In this paper, an algorithm for predicting accessibility performance on an LTE/SAE network based on relevant historical key performance indicator (KPI) data is proposed. Since there are three KPIs related to accessibility, each representing different segments, a method to map these three KPI values onto the status of accessibility performance is proposed. The network conditions are categorized as high, acceptable or low for each time interval of observation. The first state shows that the system is running optimally, while the second state shows that the system has deteriorated and needs full attention, and the third state indicates that the system has gone into degraded conditions that cannot be tolerated. After the state sequence has been obtained, a transition probability matrix can be derived, which can be used to predict future conditions using a DTMC model. The results obtained are system predictions in terms of probability values for each state for a specific future time. These prediction values are required for proactive health monitoring and fault management. Accessibility degradation prediction is then conducted by using measurement data derived from an eNodeB in the LTE network for a period of one month.
Modeling of Decision-making Processes to Ensure Sustainable Operation of Multiservice Communication Network Alevtina Aleksandrovna Muradova
Journal of ICT Research and Applications Vol. 13 No. 1 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.1.4

Abstract

This paper shows the modeling of decision-making processes to ensure stable operation of multiservice communication networks (MCNs) using the mathematical apparatus of fuzzy logic models. A classification of the main factors affecting the stability of an MCN is given. The main factors affecting the structural stability of MCNs are external factors, internal factors, energy factors, and maintenance factors. A decision-making strategy (DM) was chosen. The main factors that affect the stability of the functioning of an MCN are characterized by heterogeneity. Therefore, the task of the DM to ensure stability of the functioning of the MCN was reduced to producing a sequential solution of the following interrelated tasks: identification of the MCN by a systematic analysis of the main factors affecting the stability of the MCN, ranking the states of the MCN, and definition of the decision-making criteria. The first point is implemented by setting up a complex model of the MCN based on integration of the principles of fuzzy set theory (FST). A promising method for choosing a rational alternative is the method of non-dominated alternatives (MNDA), based on the aggregation of fuzzy information to characterize the relationship between the alternatives according to certain criteria.
Using Customer Emotional Experience from E-Commerce for Generating Natural Language Evaluation and Advice Reports on Game Products Hamdan Gani; Kiyoshi Tomimatsu
Journal of ICT Research and Applications Vol. 13 No. 2 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.2.5

Abstract

Investigating customer emotional experience using natural language processing (NLP) is an example of a way to obtain product insight. However, it relies on interpreting and representing the results understandably. Currently, the results of NLP are presented in numerical or graphical form, and human experts still need to provide an explanation in natural language. It is desirable to develop a computational system that can automatically transform NLP results into a descriptive report in natural language. The goal of this study was to develop a computational linguistic description method to generate evaluation and advice reports on game products. This study used NLP to extract emotional experiences (emotions and sentiments) from e-commerce customer reviews in the form of numerical information. This paper also presents a linguistic description method to generate evaluation and advice reports, adopting the Granular Linguistic Model of a Phenomenon (GLMP) method for analyzing the results of the NLP method. The test result showed that the proposed method could successfully generate evaluation and advice reports assessing the quality of 5 game products based on the emotional experience of customers.
Identifying Fake Facebook Profiles Using Data Mining Techniques Mohammed Basil Albayati; Ahmad Mousa Altamimi
Journal of ICT Research and Applications Vol. 13 No. 2 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.2.2

Abstract

Facebook, the popular online social network, has changed our lives. Users can create a customized profile to share information about themselves with others that have agreed to be their 'friend'. However, this gigantic social network can be misused for carrying out malicious activities. Facebook faces the problem of fake accounts that enable scammers to violate users' privacy by creating fake profiles to infiltrate personal social networks. Many techniques have been proposed to address this issue. Most of them are based on detecting fake profiles/accounts, considering the characteristics of the user profile. However, the limited profile data made publicly available by Facebook makes it ineligible for applying the existing approaches in fake profile identification. Therefore, this research utilized data mining techniques to detect fake profiles. A set of supervised (ID3 decision tree, k-NN, and SVM) and unsupervised (k-Means and k-medoids) algorithms were applied to 12 behavioral and non-behavioral discriminative profile attributes from a dataset of 982 profiles. The results showed that ID3 had the highest accuracy in the detection process while k-medoids had the lowest accuracy.
Big Data Assisted CRAN Enabled 5G SON Architecture Kiran Khurshid; Adnan Ahmed Khan; Haroon Siddiqui; Imran Rashid; M. Usman Hadi
Journal of ICT Research and Applications Vol. 13 No. 2 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.2.1

Abstract

The recent development of Big Data, Internet of Things (IoT) and 5G network technology offers a plethora of opportunities to the IT industry and mobile network operators. 5G cellular technology promises to offer connectivity to massive numbers of IoT devices while meeting low-latency data transmission requirements. A deficiency of the current 4G networks is that the data from IoT devices and mobile nodes are merely passed on to the cloud and the communication infrastructure does not play a part in data analysis. Instead of only passing data on to the cloud, the system could also contribute to data analysis and decision-making. In this work, a Big Data driven self-optimized 5G network design is proposed using the knowledge of emerging technologies CRAN, NVF and SDN. Also, some technical impediments in 5G network optimization are discussed. A case study is presented to demonstrate the assistance of Big Data in solving the resource allocation problem.

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