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JUTI: Jurnal Ilmiah Teknologi Informasi
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : 24068535     EISSN : 14126389     DOI : http://dx.doi.org/10.12962/j24068535
Core Subject : Health, Science, Education,
Computer Science & IT Dentistry Education Library & Information Science
JUTI (Jurnal Ilmiah Teknologi Informasi) is a scientific journal managed by Department of Informatics, ITS.
Arjuna Subject : -
Articles 399 Documents
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IMPROVED LIP-READING LANGUAGE USING GATED RECURRENT UNITS Nafa Zulfa; Nanik Suciati; Shintami Chusnul Hidayati
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 2, Juli 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i2.a1080

Abstract

Lip-reading is one of the most challenging studies in computer vision. This is because lip-reading requires a large amount of training data, high computation time and power, and word length variation. Currently, the previous methods, such as Mel Frequency Cepstrum Coefficients (MFCC) with Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) with LSTM, still obtain low accuracy or long-time consumption because they use LSTM. In this study, we solve this problem using a novel approach with high accuracy and low time consumption. In particular, we propose to develop lip language reading by utilizing face detection, lip detection, filtering the amount of data to avoid overfitting due to data imbalance, image extraction based on CNN, voice extraction based on MFCC, and training model using LSTM and Gated Recurrent Units (GRU). Experiments on the Lip Reading Sentences dataset show that our proposed framework obtained higher accuracy when the input array dimension is deep and lower time consumption compared to the state-of-the-art.
MAPPING POTENTIAL ATTACKERS AGAINST NETWORK SECURITY USING LOCATION AWARE REACHABILITY QUERIES ON GEO SOCIAL DATA Hafara Firdausi; Bagus Jati Santoso; Rohana Qudus; Henning Titi Ciptaningtyas
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 2, Juli 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i2.a1071

Abstract

Attacks on network security can happen anywhere. Using Geo-Social Networks (GSN), i.e., a graph that combines social network data and spatial information, we can find the potential attackers based on the given location. In answering the graph-based problems, Reachability Queries are utilized. It verifies the reachability between two nodes in the graph. This paper addresses a problem defined as follows: Given a geo-social graph and a location area as a query point, we map potential attackers against network security using location-aware reachability queries. We employ the concepts of Reachability Minimum Bounding Rectangle (RMBR) and graph traversal algorithm, i.e., Depth-First Search (DFS), to answer the location-aware reachability queries. There are two kinds of the proposed solution, i.e., (1) RMBR-based solution map potential attackers by looking for intersecting RMBR values, and (2) Graph traversal-based solution map potential attackers by traversing the graph. We evaluate the performance of both proposed solutions using synthetic datasets. Based on the experimental result, the RMBR-based solution has much lower execution time and memory usage than the graph traversal-based solution.
INSTRUMENTATION-BASED MONITORING TECHNIQUES SURVEY ON HOST, PLATFORM, AND SERVICE LEVEL IN MICROSERVICE ARCHITECTURE Achmadaniar Anindya Rhosady; Fuad Dary Rosyadi; Lucas Susanto
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 2, Juli 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i2.a994

Abstract

Microservice is an application architecture that separates one big application into smaller ones. The architecture simplifies development, deployment, and management process. However, the architecture is quite complex thus the monitoring process becomes much more challenging. Classifications for the instrumentations that are used in the monitoring process is needed to achieve better practicality for the administrators. We surveyed the monitoring technique classification method in microservice architecture. The method is divided into three levels. They are host level, platform level, and service level. In this paper, we present the latest instruments that are being used in the monitoring process in each level. Correlation between the goals, needs, and stakeholder is also presented.
PREDICTION OF MULTIVARIATE TIME SERIES DATA USING ECHO STATE NETWORK AND HARMONY SEARCH Muhammad Muharrom Al Haromainy; Chastine Fatichah; Ahmad Saikhu
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 2, Juli 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i2.a1051

Abstract

Multivariate time series data prediction is widely applied in various fields such as industry, health, and economics. Several methods can form prediction models, such as Artificial Neural Network (ANN) and Recurrent Neural Network (RNN). However, this method has an error value more significant than the development method of RNN, namely the Echo State Network (ESN). The ESN method has several global parameters, such as the number of reservoirs and the leaking rate. The determination of parameter values dramatically affects the performance of the resulting prediction model. The Harmony Search (HS) optimization method is proposed to provide a solution for determining the parameters of the ESN method. The HS method was chosen because it is easier to implement, and based on other research, the HS method gets the optimum value better than other meta-heuristic methods. The methods compared in this study are RNN, ESN, and ESN-HS. Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) are used to measure the error rate of forecasting results. ESN got a smaller error value than RNN, and ESN-HS produced a minor error value among the other trials, namely 0.782e-5 for RMSE and 0.28% for MAPE. The HS optimization method has successfully obtained the appropriate global parameters for the ESN prediction model.
AUTOMATIC TESTING FRAMEWORK BASED ON SERENITY AND JENKINS AUTOMATED BUILD Umi Sa'adah; Jauari Akhmad Nur Hasim; Andhik Ampuh Yunanto; Desy Intan Permatasari; Fadilah Fahrul Hardiansyah; Irma Wulandari; Hazna At Thooriqoh
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 2, Juli 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i2.a1017

Abstract

Software Testing plays an important role in making high quality products and the right time. The process of testing done manually is often inaccurate, unreliable, and needed more than automatic testing. This research proposes a new framework for automation testing. This framework will help developers to create applications with better quality and shorten testing time. This framework offers a solution for developers so that the testing process is carried out easily and quickly. Our proposed concept consists of an automated test script based on Serenity Framework and can be done as a background process using Jenkins. Input of the system is a testing scenario, then mapped into Java Programming Language. Output of this system are test reports that represent the scenario that has been carried out. the results of implementation system prove that developers are helped by this framework in the software testing process. So that in this study it can be concluded that the automated testing framework that has been developed can improve the quality of application products through effective and efficient work methods.
EMERGENCY PROCESSES HANDLING IN URBAN AREA USING MODIFIED DIJKSTRA METHOD Prima Wiratama; Ary Mazharuddin Shiddiqi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 2, Juli 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i2.a1047

Abstract

Emergency Aid has a very vital role in saving the patient's life. The emergency process involves two stages, namely the pre-hospital and the hospital stage. Striving for the entire emergency process is to have the fastest response time. The initial part of emergency treatment (pre-hospital) is determining the shortest and fastest route to the hospital. In addition, the availability of the targeted hospital must also be considered. We modified Dijkstra's Algorithm to produce the shortest route and the fastest time by considering the availability of the targeted hospital to support the handling of the emergency process. The modification made to the Dijkstra algorithm replaces the weight of Dijkstra's distance with a quantity representing the congestion rate and distance. Besides, the event time is estimated to determine the status of the intended hospital. As a result, Dijkstra's modification method can produce a more efficient and faster route.
DETECTION AND CLASSIFICATION OF RED BLOOD CELLS ABNORMALITY USING FASTER R-CNN AND GRAPH CONVOLUTIONAL NETWORKS Amirullah Andi Bramantya; Chastine Fatichah; Nanik Suciati
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 1, January 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i3.a1118

Abstract

Research in medical imagery field such as analysis of Red Blood Cells (RBCs) abnormalities can be used to assist laboratory’s in determining further medical actions. Convolutional Neural Networks (CNN) is a commonly used method for the classification of RBCs abnormalities in blood cells images. However, CNN requires large number of labeled training data. A classification of RBCs abnormalities in limited data is a challenge. In this research we explore a semi-supervised learning using Graph Convolutional Networks (GCN) to classify RBCs abnormalities with limited number of labeled sample images. The proposed method consists of 3 stages, i.e., extraction of Region of Interest (ROI) of RBCs from blood images using Faster R-CNN, abnormality labeling and abnormality classification using GCN. The experiment was conducted on a publicly accessible blood sample image dataset to compare classification performance of pretrained CNN models (Resnet-101 and VGG-16) and GCN models (Resnet-101 + GCN and VGG-16 + GCN). The experiment showed that the GCN model build on VGG-16 features (VGG-16  + GCN) produced the best accuracy of 95%.
THE DEVELOPMENT OF QR CODE BASED MOBILE ATTENDANCE INFORMATION SYSTEM USING SCRUM FRAMEWORK Muhammad Aqil Maulana; Sri Rahayu Natasia; Dwi Arief Prambudi; Tegar Palyus Fiqar
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 1, January 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i3.a1015

Abstract

As one of State ‘s Higher Education Institutions, the Kalimantan Institute of Technology (ITK) must perform the education and teaching function as mandated by the tri dharma perguruan tinggi, then the function is regulated in academic regulations and implemented in business processes of attendance. Currently, the attendances data are recapitulated manually at week 15 by Academic Staff. The attendance process that has been running has several problems, namely prone to violations of the actual meeting realization and attendance count, recapitulation time that takes a long time, risk of data input errors and loss of presence sheet. Based on these problems, the attendance information system is developed (SIAP ITK). This research was conducted with the agile software development methodology with the scrum framework. The results of this research is an android  application following the business processes of attendance in ITK. Based on the testing result which was carried out during this research, SIAP ITK is considered capable of optimizing the attendance process that has been running at ITK.
MALICIOUS TRAFFIC DETECTION IN DNS INFRASTRUCTURE USING DECISION TREE ALGORITHM Hazna At Thooriqoh; M. Naufal Azzmi; Yoga Ari Tofan; Ary Mazharuddin Shiddiqi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 1, January 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i3.a1054

Abstract

Domain Name System (DNS) is an essential component in internet infrastructure to direct domains to IP addresses or conversely. Despite its important role in delivering internet services, attackers often use DNS as a bridge to breach a system. A DNS traffic analysis system is needed for early detection of attacks. However, the available security tools still have many shortcomings, for example broken authentication, sensitive data exposure, injection, etc. This research uses DNS analysis to develop anomaly-based techniques to detect malicious traffic on the DNS infrastructure. To do this, We look for network features that characterize DNS traffic. Features obtained will then be processed using the Decision Tree algorithm to classifyincoming DNS traffic. We experimented with 2.291.024 data traffic data matches the characteristics of BotNet and normal traffic. By dividing the data into 80% training and 20% testing data, our experimental results showed high detection aacuracy (96.36%) indicating the robustness of our method.
FRECOMTWEET: PRODUCT RECOMMENDATION APPLICATION USING FRIENDSHIP CLOSENESS ON TWITTER Ratih Nur Esti Anggraini; Ainatul Maulida; Daniel Oranova Siahaan
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 1, January 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i3.a1104

Abstract

The information and communication technology development makes someone interact with each other easier. This convenience is used to exchange ideas, like using social media Twitter for product recommendations before buying it. It brings up a trend that consumers seek product recommendations through other people on social media. Social media, especially Twitter, has several features such as tweets, ReTweet and mentions to interact with other people. Users can describe the product, attach a link, and give a positive or negative rating in a tweet. These types of tweets can be used as an alternative to product recommendations. FrecomTweet is an Android-based product recommendation application that can detect close friendships based on the user’s ReTweet and mentions. This application also detects a product recommendation that appears in a conversation between users. This detection uses the keyword filtering method, which matches the conversation content with the markers in the database. If the conversation has a positive rating, it will recommend the user’s closest friends. This research uses a crawling method with the Twitter API streaming filter built using the CodeIgniter framework. The results of the black box test show that Twitter user conversations can be used as a product recommendation with a precision and recall value of 0.94 and 0.81, respectively.

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