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.
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<![CDATA[Security Performance of LoRaWAN Servers using Advanced Encryption Standard ]]>
<![CDATA[Puput Dani Prasetyo adi]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 3 No. 3 (2023): Vol. 3 No. 3 (2023): Volume 3 Issue 3, 2023 [August] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v3i3.632
<![CDATA[One of the essential components in LoRaWAN is managing the server with all existing components, including Device Management, Data forwarding, Network security, and real-time network monitoring. It is necessary to manage the uplink and downlink of the LoRaWAN server and maintain the quality of data transmission from the LoRa end node to the LoRaWAN server. Depending on the service provider, the LoRaWAN Server provides space for payload, uplink, and downlink data. How many times in a day, week, month, or even year? This discussion depends on the LoRaWAN server provider. Some are open sources, and some provide free uplink and downlink services. Settings on the LoRaWAN Server will determine how much data will enter the server or the network size. Security is also an essential parameter, e.g., encryption or authentication, supported by the feature requirements used on the server, which will determine the type of communication that LoRa will apply, for example, multi-communication or analysis data. Centralized management, Security, scalability, and cost-effectiveness are essential parameters if LoRaWAN is managed well.]]>
<![CDATA[Network Security Analysis Based on Internet Protocol Security Using Virtual Private Network (VPN) ]]>
<![CDATA[aliyyah rosyidah]]>;
<![CDATA[Jumadi Mabe Parenreng]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 3 No. 3 (2023): Vol. 3 No. 3 (2023): Volume 3 Issue 3, 2023 [August] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v3i3.613
<![CDATA[The network security system is continuously advancing alongside technological developments. VPNs, which utilize open networks, aim to provide security by leveraging IPSec to transmit private data through L2TP tunneling strategy from the server to the branch computer/client and vice versa. Conversely, it can also lead to poor security practices. VPNs are implemented using the layer 2 IPSec tunneling protocol with two MikroTik devices. Testing is conducted to assess the security and speed of the network using the command line and MikroTik Winbox, where the server monitors packet delays to determine the improvement in network security quality. This research has identified several weaknesses in implementing this VPN protocol, namely the need for caution regarding the security of transmitted data to prevent misuse by the VPN provider]]>
<![CDATA[Detection of Bruteforce Attacks on the MQTT Protocol Using Random Forest Algorithm ]]>
<![CDATA[Galuh Muhammad Iman Akbar]]>;
<![CDATA[Mokhamad Amin Hariyadi]]>;
<![CDATA[Ajib Hanani]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 3 No. 3 (2023): Vol. 3 No. 3 (2023): Volume 3 Issue 3, 2023 [August] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v3i3.630
<![CDATA[Bruteforce is a hacking technique that launches an attack by guessing the username and password of the system that is the target of the attack. The Bruteforce attack on the MQTT protocol is an attack that often occurs on the IoT, so it is necessary to detect attacks on the MQTT protocol to find out normal traffic and brute force traffic. Random Forest was chosen because this method can classify a lot of data in a relatively short time, and the results from Random Forest can improve accuracy and prevent overfitting in the data classification process. This study uses two types of data: primary data from the hacking environment lab and secondary data from the IEEE Data Port MQTT-IOT-IDS2020 dataset. Trials on primary data and the results obtained are accuracy of 99.55%, precision of 100%, recall of 99.54%, and f-measure of 99.77%, the duration needed to get these results with 1796 data lines, i.e., for 0 seconds. As for the secondary data, the researcher obtained an accuracy of 99.77%, a precision of 100%, a recall of 99.43%, and an f-measure of 98.71%, the duration required to obtain these results with 85002 data lines, i.e., for 62 seconds.]]>
<![CDATA[Determining the feasibility of using the automated market basket analysis method to investigate a cause-and-effect pattern of construction accidents and its safety associations ]]>
<![CDATA[Gopikrishna Vasista Tatapudi]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 3 No. 3 (2023): Vol. 3 No. 3 (2023): Volume 3 Issue 3, 2023 [August] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v3i3.636
<![CDATA[Construction sites are complex and dangerous. Over the past decade, construction fatality rates have been high in most countries. Construction accidents cause harm to construction workers and financial loss to construction firms. Smart wearable jackets are among the most effective personal protection equipment (PPE) for India's most recent and future generations. Prevention is better than Cure. To prevent the occurrences of construction accidents and to provide better safety and health to construction workers, the sensor data has to be collected from the IoT environment and has to make it subjected to cloud-based big data analytics to provide better decision support to project managers and doctors. Further, the decision support system can be enhanced by adding semantic capabilities using Ontology, Semantic Web Services, and data mining and artificial intelligence techniques. This study highlights the feasibility of using the automated market basket analysis method to investigate the cause-and-effect pattern of construction accidents, especially when using the Apriori algorithm to extract frequent item set associations. Data File preparation is one of the most essential and significant modules of incorporating automation. Therefore the value of this research effort lies in preparing a sample database and how such a sample database can become helpful in construction safety and health management to prevent accidents, as well as the computations of measures of the Apriori algorithm that support decisions regarding construction safety and health provision to construction supervisors and managers are explained]]>
<![CDATA[Classification of Customer Satisfaction Through Machine Learning: An Artificial Neural Network Approach ]]>
<![CDATA[Victor Marudut Mulia Siregar]]>;
<![CDATA[Kalvin Sinaga]]>;
<![CDATA[Erwin Sirait]]>;
<![CDATA[Andi Setiadi Manalu]]>;
<![CDATA[Muhammad Yunus]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 3 No. 3 (2023): Vol. 3 No. 3 (2023): Volume 3 Issue 3, 2023 [August] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v3i3.643
<![CDATA[This study aims to classify customer satisfaction data from Café Alvina using Machine Learning, specifically by implementing the Backpropagation Artificial Neural Network. The data used in this study consists of 70 training data and 30 testing data, with the input layer of the Artificial Neural Network having 5 neurons and the output layer having 2 neurons. The tested Artificial Neural Network models include the 5-5-2 model, 5-10-8-8-2 model, 5-5-10-2 model, and 5-8-10-2 model. Among the four models used in the testing process of the Backpropagation Artificial Neural Network system using Matlab, the 5-10-8-8-2 architecture model performed the best, achieving an MSE (Mean Squared Error) of 0.000999932 during training with 2920 epochs and a testing MSE of 0.000997829. After conducting the testing, the performance of the Artificial Neural Network models was as follows: the 5-5-2 model achieved 81%, the 5-10-8-8-2 model achieved 100%, the 5-5-10-2 model achieved 98%, and the 5-8-10-2 model achieved 96%. Through the implementation of Backpropagation Artificial Neural Network, the classification of customer satisfaction can be effectively performed. The trained and tested data demonstrate that the Artificial Neural Network can accurately recognize the input data in the system.]]>
