Internet of Things and Artificial Intelligence Journal
Internet of Things and Artificial Intelligence Journal (IOTA) is a journal that is officially under the auspices of the Association for Scientific Computing, Electronics, and Engineering (ASCEE), Internet of Things and Artificial Intelligence Journal is a journal that focuses on the Internet of Things (IoT), ISSN 2774-4353, publishing the latest papers in the IoT field and Artificial Intelligence (AI) i.e., Machine Learning (ML), and Deep Learning (DL)., etc., Topics can be included in this journal : IoT for various applications ( medical, sport, agriculture, smart city, smart home, smart environment, etc.) IoT communication and networking protocols ( LoRa, WiFi, Bluetooth Low Energy, etc.) IoT enabling technologies IoT system architecture IoT with a Recently Sensors Technology IoT with Wireless Sensor Network (WSNs) Technology Cloud-based IoT IoT data analytics IoT Security IoT Management Services IoT with Low Power and Energy Harvesting Future technologies for IoT Future Internet design for IoT Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) Drone or UAV, and IoT Analyzes IoT with a Financial Technology (FINTECH) Managemen approach IoT for Education Technology IoT for Industry Computers & Security :: computer security, audit, control and data integrity in all sectors - industry, commerce and academia Computer application for Economy, Finance, Business, Micro, Small & Medium Enterprises (MSMEs), Accounting, Management, and other sectors Review articles on international & national legal rules in the use of computer software, internet of things, frequency usage, etc. Internet of Things and Artificial Intelligence Journal has a frequency of being published 4 times a year or 4 issues every year (February, May, August, and November) with the Peer review process.
Articles
174 Documents
<![CDATA[Implementation of One-Time Password and SHA-3 Algorithm on the Lab Inventory Website of the Department of Informatics and Computer Engineering ]]>
<![CDATA[Sadiq, Muhammad Hakim]]>;
<![CDATA[Wahid, Abdul]]>;
<![CDATA[Lamada, Mustari S.]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 5 No. 2 (2025): Volume 5 Issue 2, 2025 [May] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v5i2.914
<![CDATA[This study evaluates the effectiveness of the One-Time Password (OTP) system on the Inventory Lab website of the Department of Informatics and Computer Engineering, focusing on OTP and user password security against Brute Force attacks. The objectives include testing OTP validation, analyzing OTP vulnerabilities to Brute Force attacks, and examining the resilience of user passwords under similar attacks. The study contributes to cyber security research by offering insights into implementing OTP and SHA-3 encryption algorithms on websites. Its findings aim to enhance the security measures of the Inventory Lab website. Results indicate that OTP delivery on the website is both successful and secure, with codes encrypted using SHA-3, rendering them unreadable in the database. OTP validation effectively distinguished correct and incorrect codes, including those that expired due to time limits. However, Brute Force trials on OTPs succeeded in some cases due to extended expiration times. Reducing the expiration period to one minute significantly minimized this risk. Similarly, trials on user passwords showed that passwords with complex character combinations resisted attacks more effectively than simpler ones. In summary, the OTP system and SHA-3 encrypted passwords demonstrate robust security but require adjustments to OTP expiration settings and stronger password policies to mitigate the risks of brute-force attacks. These improvements will further safeguard the website’s security infrastructure.]]>
<![CDATA[The Fabrique: A Pathfinding Algorithm in a Mobile Game Developed Using Construct 3* ]]>
<![CDATA[Kusumawardhani, Ruby]]>;
<![CDATA[Prastiningtiyas, Diah Arifah]]>;
<![CDATA[Alfianti Oktavia, chaulina]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 5 No. 2 (2025): Volume 5 Issue 2, 2025 [May] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v5i2.915
<![CDATA[The rapid growth of the digital game industry, particularly on mobile platforms, has driven the development of algorithms to enhance gameplay quality and player experience. The A* algorithm is a widely used pathfinding method for controlling the movement of non-playable characters (NPCs) in games. This study aims to evaluate the implementation of the A* algorithm in The Fabrique, a mobile game developed using Construct 3, a 2D game development engine. Testing was conducted across various path and obstacle scenarios. The results indicate that the A* algorithm delivers fast computation time and optimal pathfinding for short-distance navigation. In the medium to high obstacle scenarios, the algorithm maintained good performance with only minimal increases in processing time. The implementation of the A* algorithm in The Fabrique proved effective, contributing to a more dynamic and interactive gameplay experience. With an average user satisfaction rate of 81.94%, the algorithm demonstrates not only technical efficiency but also strong user acceptance.]]>
<![CDATA[Artificial-Based Pulse Learning Web Application Intelligence Using Convolutional Neural Networks ]]>
<![CDATA[Susanto, Chayadi Oktomy]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 5 No. 2 (2025): Volume 5 Issue 2, 2025 [May] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v5i2.917
<![CDATA[Studying pulse waveforms in healthcare is crucial as they aid in diagnosing and treating chronic diseases. However, the limited data on pulse waveforms makes it challenging for health education to teach this topic effectively. Practitioners of Traditional Chinese Medicine (TCM) require a significant amount of time to obtain pulse wave data accurately. Additionally, the pulse wave data collected by TCM practitioners exhibit various forms and characteristics. This study aims to integrate web-based pulse waveform learning with Artificial Intelligence (AI) using Convolutional Neural Network (CNN) to enhance effectiveness and efficiency. Pulse waveform data were obtained from Traditional Chinese Pulse Diagnosis and were redrawn to achieve diverse and accurate results. A total of 400 images were generated for each of the five types of pulse waveforms to improve data quality. The redrawn data were then tested to ensure accuracy. Once validated, a comparison of deep learning models using three CNN architectures—VGG16, VGG19, and ResNet50—was conducted, with VGG19 achieving the highest accuracy among the models. Consequently, the VGG19 model was implemented into a web-based pulse waveform learning application using JavaScript, HTML5, and TensorFlow. The results demonstrate that the VGG19 model outperformed other architectures in terms of accuracy. The successful integration of the VGG19 model into the web-based application shows that AI can be used to create an interactive learning platform for pulse waveform education. This study proves that the collaboration between web-based pulse waveform learning and AI can serve as an interactive educational tool for the future.]]>
<![CDATA[IoT-Based Real-Time Train Position Monitoring System Using GPS ]]>
<![CDATA[Andriana, Andriana]]>;
<![CDATA[Setia, Hipni]]>;
<![CDATA[Ramdani, Dani]]>;
<![CDATA[Zulkarnain, Zulkarnain]]>;
<![CDATA[Rahman, Sutisna Abdul]]>;
<![CDATA[Adiprabowo, Tjahjo]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 5 No. 2 (2025): Volume 5 Issue 2, 2025 [May] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v5i2.919
<![CDATA[In this modern era, the need for fast, efficient, and quality transportation is important, one of which is in the railway sector. Although trains have special tracks, accidents can still occur due to various factors, such as infrastructure damage and officer negligence. One of the accidents that occurred on January 5, 2024, between the Turangga train and the Bandung Commuter Line caused fatalities and injuries. To overcome this problem, a real-time train position monitoring system is needed. This research aims to design and develop a train position monitoring system using a GPS module. GPS (Global Positioning System) is widely used for tracking systems. This system monitors the position of trains and other information that can be accessed by station officers and the general public. By using the ESP32 microcontroller and NEO-6M GPS module, this system is expected to improve the safety of train travel, minimize the potential for accidents, and provide more accurate information about train arrival times at the station. This research also aims to test the performance of the monitoring system in real conditions. It is hoped that with this system, train accidents due to negligence can be minimized, and provide significant benefits for officers and the public who use train transportation.]]>
<![CDATA[Image quality enhancement by applying a combination of filtering between the median filter and CLAHE ]]>
<![CDATA[Hsb, Adinda Tarisyah]]>;
<![CDATA[Harahap, Lailan Sofinah]]>;
<![CDATA[Fadillah, Hasti]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 5 No. 2 (2025): Volume 5 Issue 2, 2025 [May] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v5i2.921
<![CDATA[Digital image quality is often degraded due to disturbances such as salt-and-pepper noise and uneven lighting. This can hinder further image analysis and processing. This research aims to improve image quality by applying a combination of two filtering methods, namely Median Filter and CLAHE (Contrast Limited Adaptive Histogram Equalization), using MATLAB. Median Filter is used to remove impulsive noise without obscuring important details, while CLAHE is applied to improve image contrast adaptively and locally. Tests were conducted on grayscale images with artificial noise added. The experimental results show that the combination of both methods provides a significant improvement in image quality compared to the use of either method alone. Thus, this approach is effectively used for image pre-processing that requires detail recovery and contrast enhancement.]]>
<![CDATA[Smart Room Based on ESP32 and Google Assistant: Automation Solution in Electrical Engineering Laboratory Environment ]]>
<![CDATA[Syamsuri, Wahyu]]>;
<![CDATA[Wijanarko, Yudi]]>;
<![CDATA[Maja, Ibnu]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 5 No. 2 (2025): Volume 5 Issue 2, 2025 [May] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v5i2.924
<![CDATA[The development of home automation technology and smart rooms is growing rapidly, especially through the utilization of voice commands to control electronic devices. This research develops a voice command-based smart room system using an ESP32 module connected with Google Assistant and the IFTTT platform for device control in an electrical engineering laboratory. The system allows users to turn on or off devices such as lights and TVs with just a voice, improving the efficiency and convenience of laboratory operations. The implementation using ESP32 as a Wi-Fi microcontroller, which activates relays to control devices, was tested in real laboratory conditions that have challenges in the form of electromagnetic interference and noise. The test results showed a fast and accurate system response of up to 90%, although there were some failures in high noise conditions. The system has the potential to be an effective automation solution to support learning and practical processes in electrical engineering laboratories, with further development needed to improve the system's robustness against noise interference.]]>
<![CDATA[Design and Development of Information System Security with Authentication Using One-Time Password Identification Based on SMS with MD5 Hash ]]>
<![CDATA[Meiditra, Irzon]]>;
<![CDATA[Gusti Alex Candra, Dori]]>;
<![CDATA[Rahmansyah, Rizky]]>;
<![CDATA[Yuda, Fitra]]>;
<![CDATA[Restu Selvanda, Alifia]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 5 No. 2 (2025): Volume 5 Issue 2, 2025 [May] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v5i2.926
<![CDATA[Login security to access WEB-based applications, in the form of security using OTP (One Time Password) which is generated using Hash MD5 and generates a code sent via SMS. The system will take the email field, password, and phone number. The result of the hash function will produce a 32-digit hexadecimal number. Furthermore, four digits of the hexadecimal number are taken. The four numbers are sent as OTP with Zenziva's Cloud SMS Gateway service and the OTP code will be temporarily stored in the database. The OTP sent to the user will be matched with the one stored in the database table to check its validity. If the OTP sent with the one stored in the table matches, then the user can access the WEB-based application. The OTP generated is for security authentication of the WEB user account after logging in by entering the username and password. Users who enter the wrong OTP 3 times will be blocked, the restriction is to narrow the hackers to intercept and infiltrate.]]>
<![CDATA[Controlling a Delivery Robot Based on Color Using Fuzzy Logic Method ]]>
<![CDATA[Sapriyanto, Hidayat]]>;
<![CDATA[Rasyad, Sabilal]]>;
<![CDATA[Damsi, Faisal]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 5 No. 2 (2025): Volume 5 Issue 2, 2025 [May] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v5i2.927
<![CDATA[This research presents the design and implementation of an IoT-based delivery robot equipped with a line navigation system and color classification using Mamdani fuzzy logic. The robot follows a black line using infrared sensors and identifies delivery zones via color markers (red, green, blue, yellow) detected by a TCS3200 sensor. Fuzzy logic processes RGB input to classify colors and trigger item placement using a servo mechanism. The system integrates hardware components such as Arduino Mega, ESP8266, and L298N motor drivers, and utilizes the Blynk application for remote control. Navigation decisions are enhanced through fuzzy inference, enabling adaptation to sensor uncertainty and lighting variations. Experimental results show a 94% success rate in path following, 92% color classification accuracy, and sub-2-second response time for remote commands. The combination of fuzzy logic and IoT enables flexible, real-time control, making the system suitable for dynamic indoor environments like offices or labs.]]>
<![CDATA[Content Blocking Method To Reduce False Positives Based On Machine Learning ]]>
<![CDATA[Arkam, Andi Iksan]]>;
<![CDATA[Yahya, Muhammad]]>;
<![CDATA[Wahid, Abdul]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 5 No. 2 (2025): Volume 5 Issue 2, 2025 [May] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v5i2.935
<![CDATA[This study presents an experimental approach to enhance content-blocking systems by integrating machine learning with domain classification and Pi-hole DNS server technology. While traditional blocking mechanisms often result in false positives—legitimate domains mistakenly blocked—this research aims to mitigate such issues. By implementing various testing scenarios, including TF-IDF and N-gram feature extraction with and without preprocessing, the study evaluates the classification performance using the Naive Bayes algorithm. The results reveal the highest accuracy of 84% achieved with the N-gram method without preprocessing. This integrated approach shows promise in improving the precision of ad and website blocking mechanisms.]]>
<![CDATA[Sentiment analysis of Faculty of Science and Technology students' satisfaction with the 2024 graduation using the Naïve Bayes method ]]>
<![CDATA[Siregar, Kalfida Eka Wati]]>;
<![CDATA[Ramadani, Wily Supi]]>;
<![CDATA[Sitepu, Anggi Jelita]]>;
<![CDATA[Fadil, Ulfi Muzayyanah]]>;
<![CDATA[Furqan, Mhd.]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 5 No. 2 (2025): Volume 5 Issue 2, 2025 [May] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v5i2.940
<![CDATA[Sentiment analysis of UINSU student graduation based on academic data is one of the efforts to understand the factors that affect the success of student studies. This research aims to analyze the sentiment of UINSU student graduation by utilizing academic data such as cumulative grade point average (GPA), number of credits taken, and other relevant attributes, using the Naive Bayes method. Naive Bayes was chosen because of its ability to classify data efficiently and accurately, even though the data used has noise or inconsistency. The research process begins with collecting student data from the university database, and then data cleaning is carried out to ensure the quality of the data used. Next, the data is processed and classified using the Naive Bayes algorithm in Weka software to predict graduation status based on academic parameters. The results show that the Naive Bayes method is able to produce quite high accuracy in predicting student graduation, with accuracy values ranging from 75% to more than 85% depending on parameter selection and data cleaning. GPA is the most influential attribute on the prediction results, while other attributes such as class activity and organizational experience also contribute, although not as much as GPA. These findings provide important insights for the campus in designing more effective academic coaching and planning programs and can be a reference in the development of data mining-based decision support systems to improve the quality of computer science graduates.]]>
