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 System Analysis Using the HTTP Protocol Against Packet Sniffing Attacks ]]>
<![CDATA[Manguling, Inez sri wahyuningsi]]>;
<![CDATA[Parenreng, Jumadi Mabe]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 3 No. 4 (2023): Vol. 3 No. 4 (2023): Volume 3 Issue 4, 2023 [November] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v3i4.612
<![CDATA[The security level of the SIM MBKM website information system needs to be analyzed because it is accessed by many students who provide essential data. The security testing process for the SIM MBKM system uses the software Ettercap and Wireshark to test the system's security level and network data against cybercrime attacks. The results of these experiments showed the same essential data, but Wireshark displayed more personal information. The difference lies in the system architecture using different methods or stages, namely ARP Poisoning and Filtering HTTP. The third difference is the estimated time taken during the experiment. Ettercap and Wireshark applications enable eavesdropping on confidential and essential data and information. Through security testing using Ettercap and Wireshark, more critical data is displayed. solutions and preventive actions can be implemented by encrypting confidential data and improving website security using the HTTPS (Hypertext Transfer Protocol Secure) protocol.]]>
<![CDATA[Network Security Analysis Using Switch Port Security CPT ]]>
<![CDATA[Istiqamah, Nurfadilah]]>;
<![CDATA[Parenreng, Jumadi Mabe]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 3 No. 4 (2023): Vol. 3 No. 4 (2023): Volume 3 Issue 4, 2023 [November] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v3i4.614
<![CDATA[The gradual advancement of information network technology has rapidly increased due to the growing demand for efficient, stable, and fast network connectivity, as well as reliable information security. One factor that influences network quality is network security, which encompasses various techniques to enhance network security, such as building firewalls, layer seven protocols, and port security. Port security utilizes existing ports to enable secure switching access. The switch can protect the local area network (LAN). Several types of switch port security are commonly used, including default/static port security, dynamic learning port security, and sticky port security.]]>
<![CDATA[Adaptive Algorithm to Improve the Image Quality of Vehicle License Plates based on Lighting Parameters ]]>
<![CDATA[Suhartono, Suhartono]]>;
<![CDATA[Suhardi, Iwan]]>;
<![CDATA[Angraini, Indah]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 3 No. 4 (2023): Vol. 3 No. 4 (2023): Volume 3 Issue 4, 2023 [November] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v3i4.646
<![CDATA[This study aims to develop an Adaptive Algorithm to Improve the Image Quality of Vehicle Number Plates Based on Lighting Parameters. The type of research used by the Author is Research and Development. This research was conducted at the Computer Engineering Laboratory for six months. This research consists of several stages, from the potential and problem stages, needs analysis, literacy studies, building prototypes, system design, and system testing. The collected datasets were taken using smartphone cameras and webcams, with 207 image datasets divided into two categories: training data and validation. The training dataset of 207 objects was 100% successful. System testing was carried out in two conditions, namely during the day and at night, for each two-wheeled and four-wheeled vehicle object. The results of adaptive algorithm testing to improve the quality of vehicle license plate images based on the light parameter experienced a change in the average MSE and PSNR values between the original image and the quality-improved image, although not too much of a difference. Based on this, it can be interpreted that the adaptive algorithm can produce better photos than before. ]]>
<![CDATA[Decision Support System for Selecting the Best Internship Students using SAW Method ]]>
<![CDATA[Siringo-Ringo, Eko Deswin]]>;
<![CDATA[Sugara, Heru]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 3 No. 4 (2023): Vol. 3 No. 4 (2023): Volume 3 Issue 4, 2023 [November] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v3i4.660
<![CDATA[The goal of this study is to find a solution to the issue of choosing the best student internship for the BPKD office in Pematangsiantar city. This office chooses the best interns candidates in order to hire candidates who are competent, skilled, able to improve their adaptability and perception in improving their performance and carrying out the tasks assigned. A Decision Support System (DSS) powered by computers is used to carry out this selection. The SAW approach is applied by the decision support system. Responsibility, Delay, Attitudes/Ethics, Presence, and Cooperation are the criteria employed in this strategy. The results of the study include recommendations for the top interns, with Mona Rachel Sitohang coming in at number one with an alternative value of 0.7500, followed by Irviana Soneta Manalu at number two with a value of 0.7042, and Elsa Paulina Simanjuntak at number three with a value of 0.6042. This office may find it simpler to choose the top students internship candidates with the use of this decision assistance system.]]>
<![CDATA[Decision Support System for Selecting Social Assistance Recipients using The Preference Selection Index Method ]]>
<![CDATA[Parapat, Eka Pratiwi Septania]]>;
<![CDATA[Sinaga, Kalvin]]>;
<![CDATA[Sirait, Erwin]]>;
<![CDATA[Manalu, Andi Setiadi]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 3 No. 4 (2023): Vol. 3 No. 4 (2023): Volume 3 Issue 4, 2023 [November] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v3i4.662
<![CDATA[This research aims to solve the problem of selecting social assistance recipients in the Nagori Moho area, Java Marajah Bah Subdistrict, Jambi, Simalungun District; in order to obtain the right targeted recipients of social assistance, the Nagori office carries out the selection of its residents, this selection is carried out by implementing a computer-based decision support system (DSS). The decision support system uses the PSI method. The criteria used in this method consist of economic condition, income, jobs, age, and dependents of the school children. The results obtained from this research are recommendations for the population receiving aid with results consisting of rank 1 with the alternative value S_Purba with a value of 0.9286, then rank two with the alternative F_Azhar with a value of 0.7599, and rank 3 is Jumiati with a value of 0.7163. This decision support system can make it easier for the Nagori office to select residents worthy of assistance.]]>
<![CDATA[Smart System for Stabilizing Water Flow Output on Android-Based Taps ]]>
<![CDATA[Parenreng, Jumadi M]]>;
<![CDATA[Zain, Satria Gunawan]]>;
<![CDATA[Yusuf, Zulfatni]]>;
<![CDATA[Suhardi, Iwan]]>;
<![CDATA[Kaswar, Andi Baso]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 3 No. 4 (2023): Vol. 3 No. 4 (2023): Volume 3 Issue 4, 2023 [November] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v3i4.668
<![CDATA[This study aims to produce and find out the results of the Smart System Stabilization Water Discharge Output Test on Android-Based Faucets based on the results of water flow sensor readings whose data is used as a reference for the rotation of the Adj or Adjustable Water Pressure Reducing Regulator Valve. The tests were carried out in the form of measuring water flow without and with Adj and measuring water discharge without and with Adj. Based on the research results, the water flow without Adj is 7 L/min for tap 1.9 L/min for tap 2. The water flow with Adj for both taps is 8 L/min. The flow of water from both taps is more stable with Adj than without Adj because the flow of both taps is 8 L/min. The measurement results of the water discharge without Adj are 0.1216 L/s for tap 1 and 0.1470 L/s for tap 2; the difference in water discharge is 0.0254 L/s. Water debit with Adj 0.135 L/s tap 1 and 0.14125 L/s tap 2, the difference in water discharge is 0.00625 L/s. The water debit is more stable with Adj than without Adj because the difference in water discharge is smaller.]]>
<![CDATA[The Relationship of Teacher Activity in the Teaching and Learning Process to Elementary Student Learning Outcomes Using Bootstrap Machine Learning ]]>
<![CDATA[Hia, Faomaha]]>;
<![CDATA[Sihombing, Volvo]]>;
<![CDATA[Juledi, Angga Putra]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 3 No. 4 (2023): Vol. 3 No. 4 (2023): Volume 3 Issue 4, 2023 [November] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v3i4.669
<![CDATA[Often, after the learning and teaching process is over, students will be tested with quizzes, midterm exams, and even end-of-semester exams, but these exams still take time after the teacher has taught several weeks or months that have passed; what if after teaching, for example, a math lesson, and students immediately understand or do not understand at all, and this can be detected using Machine Learning. The variable that can be raised is the value or quiz grade of a particular subject; for example, mathematics is one of the disliked subjects for most elementary school students, but how to find out that the student is able or unable to solve math problems and predict the end of semester grades for mathematics, this can be determined using Machine Learning, using the KNN Algorithm or K-MEANS method, or other methods that are deemed appropriate to the existing case study. In this case study, it is predicted whether a variable affects each other or affects other variables; this is done by doing or drawing relationships between variables. This research successfully concluded from the performance of machine learning in predicting students' understanding of math lessons after teaching and learning activities ended. The parameters that will be used for testing are population and sampling, and then data analysis, validity, and reliability tests are carried out.]]>
