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[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[Tatapudi, Gopikrishna Vasista]]>
<![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[Siregar, Victor Marudut Mulia]]>;
<![CDATA[Sinaga, Kalvin]]>;
<![CDATA[Sirait, Erwin]]>;
<![CDATA[Manalu, Andi Setiadi]]>;
<![CDATA[Yunus, Muhammad]]>
<![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.]]>
<![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.]]>
<![CDATA[Transformation of Binjai Police Presence Application: UI/UX Design with Design Thinking Method to Improve Efficiency and User Experience ]]>
<![CDATA[Algifahri, Muhammad Dzar]]>;
<![CDATA[Putra, Donny Dwi]]>;
<![CDATA[Zulfi, Tio Fahreza Zulfi]]>;
<![CDATA[Lubis, Aidil Halim]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 4 No. 1 (2024): Volume 4 Issue 1, 2024 [February] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v4i1.690
<![CDATA[Attendance systems have become integral to attendance management and employee supervision in various organizations. Binjai Resort Police, as a law enforcement agency in the region, is now using an access control system for all its employees. The system is expected to be a new solution for attendance management in the agency, providing efficiency and accuracy in monitoring employee attendance. In today's digital era, attention to user interface (UI/UX) is essential in product development, especially mobile applications. The ultimate goal of this study is to create an attendance mobile application prototype that meets the company's needs. Design Thinking methodology was used to focus on problem-solving by prioritizing end-user needs. The design process consists of five steps: Empathize, Define, Ideate, Prototype and Testing, and Testing. The test results show that the design is already running well, following the needs, and has the potential for further development.]]>
<![CDATA[Library Book Recommendation System Using Content-Based Filtering ]]>
<![CDATA[Rosidah, Lailatul]]>;
<![CDATA[Dellia , Prita]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 4 No. 1 (2024): Volume 4 Issue 1, 2024 [February] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v4i1.693
<![CDATA[In today's digital era, libraries are developed to adapt to student needs. Students can easily search for books on digital library services. However, the large number of books sometimes makes it difficult for students to find the books they want. Overcoming this problem can be done by using a recommendation system. A recommendation system is a system used to provide suggestions to users. This research aims to develop a library book recommendation system at Darul Mustofa Bangkalan Vocational School using content-based filtering. The content-based filtering method offers recommendations based on user preferences according to the item description. The algorithms used are Term Frequency Inverse Document Frequency (TF-IDF) and Cosine similarity. The method used in this research is research and development with a waterfall model. The researcher's testing stage used black box testing. Black box testing results were obtained from validation by system experts, website experts, and user tests with the qualification "Very Eligible." ]]>
<![CDATA[A Review and Management of the 'Anugerah' Computer Shop Application Based on Android and iOS Mobile ]]>
<![CDATA[Irmayani, Deci]]>;
<![CDATA[Siregar, Zakia El Husna]]>;
<![CDATA[Sari, Mila Nirmala]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 4 No. 1 (2024): Volume 4 Issue 1, 2024 [February] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v4i1.702
<![CDATA[The need for e-commerce is primarily for business actors such as Anugerah PC, which is engaged in PC repair and sales services. Since the COVID-19 pandemic devastated the industrial world, more time has passed, and ideas have emerged to build digital-based applications, even mobile-based ones. Digital-based, for example, is the existence of an application website that is very user-friendly but needs development, such as the development of an Android mobile or iOS mobile application, depending on the operating system used by the mobile device. This research is one of the answers to this problem. Anugerah PC built a mobile-based application to answer the challenges in the current 5.0 industrial revolution era. So, with this application, Anugerah stores can get a lot of followers, especially customers who are ready to buy at Anugerah stores. Anugerah PC shop can be the best in sales through e-commerce with a very flexible Android Mobile system.]]>
