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INDONESIA
Journal of Information Technology and Computer Science
Published by Universitas Brawijaya
ISSN : 25409433     EISSN : 25409824     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
The Journal of Information Technology and Computer Science (JITeCS) is a peer-reviewed open access journal published by Faculty of Computer Science, Universitas Brawijaya (UB), Indonesia. The journal is an archival journal serving the scientist and engineer involved in all aspects of information technology, computer science, computer engineering, information systems, software engineering and education of information technology. JITeCS publishes original research findings and high quality scientific articles that present cutting-edge approaches including methods, techniques, tools, implementations and applications.
Arjuna Subject : -
Articles 245 Documents
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Simple and Cost-Effective Detection of Carbon Monoxide Gas Setyawati, Onny; Iswanjaya, Septian; Abidin, Zainul; Bahr, Andreas
Journal of Information Technology and Computer Science Vol. 9 No. 1: April 2024
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202491567

Abstract

In several major cities throughout Indonesia, the air pollution represents a significant issue. The escalation of motorized vehicle usage yields increased concentrations of carbon monoxide gas, as one of the primary sources of gas pollution. This study introduced a tool designed and implemented for detecting levels of carbon monoxide gas and providing accurate indications. The tool used an MQ-7 gas sensor in combination with a dot matrix display for this purpose.  The detection apparatus was comprised of an IC 74HC595, an ATMEGA16 microcontroller, a BC557 PNP transistor, and a LED dot matrix. The ATMEGA16 microcontroller served as the primary control device of the system. It received input signals from the MQ-7 gas sensor and subsequently converted them into digital format for display on the dot matrix. The IC 74HC595 and transistor BC557 were utilized as the column controller and line controller, respectively, in the 5x8 LED dot matrix.  The gas level measurement at 0 cm exhibited the lowest error of approximately 0.6 %, measuring 300 ppm CO gas levels. On the other hand, at 10 cm, the result showed approximately an error of 6.7 % for a CO gas level of 200 ppm.
HiroPoseEstimation: A Dataset of Pose Estimation for Kid-Size Humanoid Robot Rafly Azmi Ulya, Amik; Hutama Harsono, Nathanael; Mulyanto Yuniarno, Eko; Hery Purnomo, Mauridhi
Journal of Information Technology and Computer Science Vol. 8 No. 3: December 2023
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202383568

Abstract

Pose estimation is a field of computer vision research that involves detecting, associating, and tracking data points on body parts. It is used for health monitoring, sign language understanding, human gesture control, elderly activities, sports, and humanoid robot pose estimation. The anatomy of a humanoid robot is similar to a human, which forms the basis for utilizing humanoid robot pose estimation. The Humanoid League is a major domain of the RoboCup competition, featuring soccer matches between humanoid robots. Pose estimation is used to measure the robot’s performance. Nevertheless, there have not been many research done on this subject. A new dataset model needs to be developed to solve the proposed problem. This work introduces HiroPoseEstimation, a kid-size humanoid robot dataset with several types of robots used in various poses based on movements in a soccer game. It is evaluated with both bottomup and top-down approaches using keypoint mask R-CNN and single-stage encoder-decoder model. Both methods demonstrate good performance on the proposed dataset.
Performance Evaluation of Tilt-based Vehicle Accident Report System Using Triple Modular Redundancy Primananda, Rakhmadhany; Bagus Prabowo, Bayu; Budi, Agung Setia; Tri Ananta, Mahardeka; Abidin, Zainul
Journal of Information Technology and Computer Science Vol. 8 No. 3: December 2023
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202383569

Abstract

A vehicle accident report system was designed to detect accidents accurately and send message to related person. However, after accident, some components or modules can be damaged and it may cause failure of accident detection. Therefore, Triple Modular Redundancy (TMR) method was implemented in the system to mask faults within the modules and ensure the system to continue working. TMR is one of passive hardware redundancy methods. The system includes three MPU-6050 sensors functioning as vehicle tilt sensors to provide hardware redundancy, a TC9548A module serving as an I2C multiplexer, an Arduino Nano acting as a microcontroller and voter, and a NodeMCU ESP8266 responsible for transmitting message. Based on experiment results, the TMR system has a reliability value of 0.9928, a failure rate value of 0.0004/cycle, and accuracy of 3 sensor value of 85%. While, the non-redundancy system has a reliability value of 0.9286, a failure rate value of 0.0053/cycle, and accuracy of single sensor value of 57%. The results of the system process were sent to and displayed via an e-mail. It can be concluded that the TMR system has better performance than the non-redundancy one.
Data Delivery Implementation on River Flow Control System Prototype Using HTTP Ichsan, Mochammad Hannats Hanafi; Akbar, Sabriansyah Rizqika; Aji, Hamdan Malik Satriyo; Setiawan, Eko; Batubara, Othman Mirizi
Journal of Information Technology and Computer Science Vol. 8 No. 3: December 2023
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202383570

Abstract

Data transmission is one of the most essential components in a critical system. One of them is to control the river water canal. Previous research related to water canal monitoring has been carried out, but the data is still local. This research implements a water canal with one main river with three tributaries/river branches where the three tributaries have different area conditions. In this research, the process of sending data from the sensor node (Control Flow System) to the master node (Control station) to the cloud server to be displayed on a website. The microcontroller used for the main processor and as a data-sending device is the ESP8266 and the cloud server used is ThingSpeak. This research succeeded in implementing HTTP for the process of sending data. The HTTP protocol is suitable for the process of data delivery. Sending data on the Control Flow System to the Control Station was successfully sent optimally at a distance of 3 meters with the "Good" classification according to the TIPHON standard. Meanwhile, the average data is sent from the Control Station to the cloud server in about 44 seconds.
Implementation of Lora and CoAP Protocol in Hydroponic Plant Water Pump Control Nurwarsito, Heru; Pradana, Kevin Dion Andre
Journal of Information Technology and Computer Science Vol. 8 No. 3: December 2023
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202383571

Abstract

Hydroponics is the cultivation of plants without using soil media, but using water media that contains nutrients and minerals. To observe the growth parameters of hydroponic plants, it is still necessary to come directly to the hydroponic site and provide the nutrients needed manually. In time efficiency, the use of Internet of Things (IoT) is needed so that there is no need to come directly to the hydroponic site. LoRa is a Low Power Wide Area Network (LPWAN) communication system. This research uses sensor nodes, middleware, and client nodes as well as the Constrained Application Protocol (CoAP) protocol for clients to access the value of sensors to control automatic pumps so that nutrients for plants can be given directly without the need to come to the hydroponic site. The result of the implementation is that the water pump control can run properly until the plants can be harvested. The performance testing is done so that LoRa can transmit data at a distance of 1 km with an RSSI value of -114 dBm.
Measurement of User Satisfaction for Gamification-based Programming E-Learning Platform using End-user Computing Satisfaction Method Pradana, Fajar; Setyosari, Punaji; Ulfa, Saida; Hirashima, Tsukasa; Saputra, Mochamad Chandra
Journal of Information Technology and Computer Science Vol. 9 No. 2: August 2024
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.92572

Abstract

HSS Learning is a learning media innovation in the form of a gamification-based e-learning platform to support the learning process in programming. HSS Learning has been applied to web design and programming courses, primarily on HTML and CSS topics. This study uses a quantitative model using a descriptive approach to measure the satisfaction level of HSS Learning users—measurement of user satisfaction using End-user Computing Satisfaction (EUCS). The variables used in this research are content, format, ease of use, timeliness, and accuracy. Data collection used a questionnaire consisting of 12 questions, with the number of participant data being 264 participants. The results show that the overall level of user satisfaction reaches 4.30 at level 5 (very strong). The results of this study can be used as a material evaluation for the application of instructional media in the programming field.
Development of Moodle-based Plugin for Automated Essay-Type Grading Pradana, Fajar; Purnomo, Welly; Adillah, Atthoriq; Hanggara, Buce Trias
Journal of Information Technology and Computer Science Vol. 9 No. 1: April 2024
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202491573

Abstract

The essay is an exam requiring a more profound understanding of answering and evaluating the answers. However, if the number of questions and participants increases, this will result in a decrease in the quality of the lecturer's assessment. This prompted the development of a software based on the Moodle plugin named Essay Similarity to assess essay answers based on the similarity between the two documents, namely the answers given and the answer key provided. Moodle was chosen because, after the Covid-19 pandemic, many sectors have shifted to working remotely, including the education sector. This resulted in an LMS like Moodle experiencing an increase in users. As of 2020 yesterday, Moodle users have exceeded 190 million users on more than 145,000 websites. In developing this plugin, the method used is the waterfall method using the PHP programming language. The algorithm used to find similarities between the two documents is cosine similarity. Testing the level of similarity between manual and automatic grading was carried out on four models of essay questions. Based on the test results, the average similarity between automatic grading compared to manual grading is 45.44%.
Designing an Intuitive Android Based m-Learning to Support Students in Learning Fundamental Java Programming Brata, Komang Candra; Ahmad Afif Supianto; Fadhyl Farhan Alghifari
Journal of Information Technology and Computer Science Vol. 9 No. 2: August 2024
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.92574

Abstract

In the context of a Computer Science Major in Indonesian higher education, Java programming language is not only one of the most essential programming languages courses but is also considered to be challenging to learn. This fact has led to the development of numerous mobile learning (m-learning) applications to assist learners in acquiring Java programming skills independently. Most existing Java learning assistants are presented on websites or computer platforms which can only be accessed when students open their computer or Laptop and it mostly focuses on enhancing coding abilities, and practical hands-on. While these aspects are crucial, understanding the programming concepts and guided practice are equally essential in fundamental Java programming learning. This paper presents the concept of practical coding exercises in an m-learning application, providing a new interactive approach for learners as an alternative tool for learning Java programming so they can learn Java programming concepts more frequently from their smartphones. The primary objective is to investigate the feasibility of the proposed m-learning prototype. The proposed application is expected to offer intuitive coding exercises and case study opportunities. Experimental results demonstrate that the overall functionality of the proposed application is feasible for future implementation, with a usability score of 84.
Identifying The Influence of Consumer Purchase Intention Through Live Streaming Shopping: A Systematic Literature Review Mindiasari, Irtiyah Izzaty; Priharsari, Diah; Setiawan, Budi Darma; Purnomo, Welly
Journal of Information Technology and Computer Science Vol. 9 No. 1: April 2024
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202491576

Abstract

The rapid development of technology influences some changes in e-commerce. One of them is the emergence of live-streaming shopping, which combines live-streaming technology with e-commerce, social networking, and entertainment. This shopping format allows viewers to interact with the streamer (seller) and instantly make a purchase with just one touch. Consumers who watch live streaming shopping generally are those who initially have an interest in the offered product. According to prior studies, the presence of live shopping can enhance both customer desire to buy and business sales. To investigate the factors influencing purchase intention in live-streaming shopping, a systematic literature review was conducted. A total of 40 factors were found from 13 selected articles containing live-streaming shopping and purchase intention. Based on these factors, 34 had a positive impact, 2 had a negative impact, and 4 had no significant impact on buyer purchase intention.
Comparative Analysis of Machine Learning Techniques for Hand Movement Prediction Using Electromyographic Signals adani, M. Syakhisk N.; Widasari, Edita Rosana; Setiawan, Eko
Journal of Information Technology and Computer Science Vol. 9 No. 1: April 2024
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202491577

Abstract

The analysis of electromyography (EMG) signals plays a vital role in diverse applications such as medical diagnostics and prosthetic device control. This study focuses on evaluating machine learning methods for EMG signal analysis, specifically in predicting hand movements and controlling prosthetic hands. In contrast to many existing studies that solely employ a limited set of feature extraction methods, we employ a comprehensive comparison technique that encompasses nine machine learning techniques K-Nearest Neighbor (KNN) , State Vector Machine (SVM ) , Decision Tree, Random Forest, Linear Discriminant Analysis (LDA), XGBoost, Naïve Bayes, Gradient Boosting, and Quadratic Discriminant Analysis (QDA)  and five combination of feature extraction methods (Mean Absolute Value (MAV), Root Mean Square (RMS), Waveform Length, Willison Amplitude, and Skewness). The experimental results demonstrate promising accuracy levels, with the best result method being KNN achieving 96.66% accuracy, SVM achieving 95.83% accuracy, and RF achieving 92.5% accuracy. These findings contribute to advancing the understanding of effective machine learning approaches for EMG signal analysis and provide valuable insights for guiding future research in this field. The study also compares the results with previous studies and showcases the effectiveness of the proposed approach.

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