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[Decision Support System For Student Activity Unit Selection Using Certainty Factor Method ]]>
<![CDATA[Manurung, Kiki Hariani]]>;
<![CDATA[Hayati, Nova]]>;
<![CDATA[Shofia, Alima]]>;
<![CDATA[Syaputra, Aldo Eko]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v4i3.794
<![CDATA[In various fields, including the selection of Student Activity Units on campus, Decision Support Systems (DSS) have become an important tool to assist the decision-making process. SPK provides information and analysis that is structured and easy to understand, thereby helping decision-makers to choose SMEs that best suit their interests, talents, and goals. Choosing the right Student Activity Units for students can contribute to the development of their personal qualities and help develop a variety of social and professional skills. Using the Certainty Factor method in creating a Decision Support System to assist students in choosing Student Activity Units that are most relevant to their desired interests and talents. The Certainty Factor method is an artificial intelligence technique that can overcome uncertainty in data and provide a level of confidence in every decision. Based on trials carried out on several interest and talent characteristics using the Certainty Factor method, percentage results were obtained with a confidence level of 80.26%. Based on the test results, it can be concluded that the expert system created can make it easier to determine talent interests that match student desires.]]>
<![CDATA[Augmented Reality-Based Car Showroom Application as a Promotional Media at Alya Motor Car Showroom ]]>
<![CDATA[Kandiaz, Muhammad]]>;
<![CDATA[Ekawati , Nia]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 4 No. 4 (2024): Volume 4 Issue 4, 2024 [November] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v4i4.795
<![CDATA[In today's fast-paced world, transportation is crucial in facilitating the movement of people and goods. As a popular means of land transportation, automobiles have continuously evolved with advanced technologies to enhance fuel efficiency and driving safety. Established in 2014, Alya Motor has served hundreds of customers using social media platforms for marketing. However, innovative marketing strategies are essential to enhance the shopping experience, especially amidst declining purchasing power. This research aims to design, implement, and test a car showroom application based on Augmented Reality (AR) as a promotional medium for Alya Motor. An Android application was developed using Blender, Unity 3D, and Vuforia to offer an interactive car viewing experience. The research followed the Multimedia Development Life Cycle (MDLC) methodology, ensuring a user-centric approach. The application was successfully tested, achieving an 86% acceptance rate in User Acceptance Testing (UAT). The AR-based application significantly improves the promotional strategy by providing potential buyers with an engaging and effective shopping experience.]]>
<![CDATA[Implementation of AES-256 Algorithm for Encryption on Chatting Platforms ]]>
<![CDATA[Nirwan, Saepudin]]>;
<![CDATA[Hamidin, Dini]]>;
<![CDATA[Azzalea, Shifa Eldita]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 4 No. 4 (2024): Volume 4 Issue 4, 2024 [November] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v4i4.804
<![CDATA[This research aims to design and build a web-based corporate chat media using prototype methodology and AES (Advanced et al.) algorithm for data encryption. The platform has been developed with MERN technology (MongoDB et al.), which allows the application to be dynamic and responsive. Prototype methodology is used for iterative development based on user feedback, ensuring the application meets user needs. The AES algorithm is applied to maintain the confidentiality and security of each message sent and received. The results show that the application effectively provides efficient and secure communication for the company, with an intuitive and easy-to-use interface. Implementing MERN technology provides flexibility in the development and maintenance of the application, making it the right solution for corporate communication needs.]]>
<![CDATA[Performance Comparison Analysis on Weather Prediction using LSTM and TKAN ]]>
<![CDATA[Wardhana, Ajie Kusuma]]>;
<![CDATA[Riwanto, Yudha]]>;
<![CDATA[Rauf, Budi Wijaya]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v4i3.808
<![CDATA[The development of machine learning methods in the last few decades has shown great potential in various predictive applications, including in domains such as financial prediction, medical diagnosis, and big data analysis. One of the most widely used methods in prediction tasks is Long Short-Term Memory (LSTM). LSTM has become popular because of its ability to handle time series data by retaining relevant information in the long term and the ability to forget irrelevant information through the forget-gate mechanism. However, along with the development of technology and the need to improve accuracy and efficiency, new methods such as the Kolmogorov Arnold Network (KAN) have emerged. KAN was then developed into the Temporal Kolmogorov Arnold Network (TKAN), which was designed to match or even surpass the performance of LSTM. The TKAN architecture has produced significant improvements in the management of both new and historical information. Because of this capability, TKAN can excel in multi-step predictions, demonstrating a clear advantage over conventional models such as LSTM and GRU, particularly in the context of long-term forecasting. This research goal is to give insight into the comparison of both the TKAN and LSTM models for weather prediction using model loss and mean absolute error evaluation (MAE). The model for both LSTM and TKAN achieved 0.09 and 0.11 for model loss and 0.08 and 0.96 for MAE.]]>
<![CDATA[Implementation of Load Balancing Using the PCC (Per Connection Classifier) Method on Computer Networks to Improve Responsiveness ]]>
<![CDATA[Arfianita, Arfianita]]>;
<![CDATA[Wahid, Abdul]]>;
<![CDATA[Zain, Satria Gunawan]]>;
<![CDATA[Parenreng , Jumadi M]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 4 No. 4 (2024): Volume 4 Issue 4, 2024 [November] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v4i4.810
<![CDATA[Dependence on a single ISP can lead to the risk of internet disconnection, necessitating a solution to enhance network performance and responsiveness. This research explores the implementation of load balancing using the Per Connection Classifier (PCC) method to improve the responsiveness of computer networks utilizing three ISPs (Internet Service Providers). The test results indicate that the PCC method across the three ISPs can increase throughput by 9.32 Mbps, reduce delay by 43.54 ms, and decrease jitter by 74.17 ms while packet loss remains stable at 0.0%. Additionally, the recursive gateway failover technique in PCC load balancing demonstrates a significant increase in throughput up to 83.3 Mbps, a reduction in delay by 0.27 ms, and a reduction in jitter by 1.15 ms. Packet loss remains stable at 0.0% under varying ISP conditions. Moreover, a comparison between the PCC and PBR methods with the least connection algorithm shows that both methods effectively enhance network responsiveness, with the PCC method exhibiting superior performance in throughput.]]>
<![CDATA[Sentiment Analysis of Patient Reviews of Az-Zainiyah Clinic Services Using Neural Language Processing with the Naïve Bayes Method ]]>
<![CDATA[Gufairoh, Siti Gufairoh]]>;
<![CDATA[Nadhiroh, Anis Yusrotun]]>;
<![CDATA[Pawening, Ratri Enggar]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 5 No. 1 (2025): Volume 5 Issue 1, 2025 [February] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v5i1.770
<![CDATA[In the research, the researcher evaluates analysis sentiment from review patients about services at the Az-Zainiyah clinic with the use Naïve Bayes method in Natural Language Processing (NLP). The dataset used consists of from review grouped patients become three categories of sentiment: positive, neutral, and negative. The Naïve Bayes model was trained and tested. To test its performance in classifying sentiment review patients. Research results show that the Naïve Bayes model achieves accuracy by 96%, Good macro average or weighted average shows high precision, recall, and f1-score values, around 0.97 and 0.96, respectively. These results show the effectiveness of the model in identifying sentiment review patients with high accuracy. Findings This gives valuable insights for increased quality services at the Az-Zainiyah clinic based on bait come back from patients, who in turn can increase satisfaction and experience patient.]]>
<![CDATA[Classification of Cow's Milk Freshness Based on Color and Homogeneity Using the Support Vector Machines (SVM) Method ]]>
<![CDATA[Fitri, Fitri Aulia Huzaini]]>;
<![CDATA[anis, Anis Yusrotun Nadhiroh]]>;
<![CDATA[wali, Wali Ja'far Shudiq]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 5 No. 1 (2025): Volume 5 Issue 1, 2025 [February] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v5i1.771
<![CDATA[Cow's milk is an important food ingredient in meeting human health needs, because cow's milk has high nutritional benefits and an overall healthy structure with very good nutritional proportions, so it has very important value for the younger generation, especially those who are still in school, who need protein. Animal origin from milk. Classifying milk that has various levels of suitability for consumption requires a method that has maximum accuracy so that accurate results are obtained so that we can distinguish between types of milk that can be consumed and those that cannot. This research proposes a Support Vector Machine (SVM) processing technique for classifying milk. The color and homogeneity of various kinds of milk in different positions and conditions of light contrast are used as data to classify types of milk. The results obtained by the SVM algorithm are efficient in classifying the color and homogeneity of milk. The resulting accuracy of applications using the SVM algorithm is 84.44%.]]>
<![CDATA[Smart Hotel Security: Integrating AI for Advanced Safety Solutions ]]>
<![CDATA[Rahmawati, Noni]]>;
<![CDATA[Djauhari, Teuku]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 5 No. 1 (2025): Volume 5 Issue 1, 2025 [February] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v5i1.787
<![CDATA[Security systems are essential for hotels and lodging services, considering that this lodging service is a service that is intended for all people from various circles and from anyone who comes from various places, this is very common and dangerous, in terms of security and comfort. Several security systems are built by hotels, such as CCTV and security systems on doors and various security system techniques such as retina, fingerprint, RFID, and various security techniques. Not only at the door but safety boxes are made with various locking techniques from a combination of numbers. In addition to hardware, software with enhanced capabilities in Artificial Intelligence can also be applied such as face detection, predictive analysis, and automated surveillance. The combination of hardware such as Jetson Nano and software can be improved to build AI-based security systems in creating security systems in hotels that can be built professionally and smartly. Some methods developed in the face detection system of a person trying to enter a hotel room to commit theft, for example, are using Convolutional Neural Network (CNN). The developed system can detect predictions of age, gender, and other parameters.]]>
<![CDATA[Implementation of E-Commerce for Andika Jeans with PHP, MySQL, and SDLC for Web-based Applications ]]>
<![CDATA[Handayani, Indah Tri]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 4 No. 4 (2024): Volume 4 Issue 4, 2024 [November] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v4i4.811
<![CDATA[The website has information about the product's name, price, and specifications, which aims to make it easier for customers to choose the product they want to buy. This website is exclusively for men's jeans. This men's jeans E-Commerce Website contains information about products for men's needs. Andika Jeans previously used conventional methods, where customers had to come to the store to see the product. With these problems, the Andika Jeans website was created. The method used in this writing is SWLDC, which has several stages: analysis, design, implementation, and trial. The conclusion that can be drawn from this website is that it aims to get information about the available products so that visitors can easily choose products that meet their needs. This website has been hosted so that customers can access it and can also place and confirm orders. This website has been tested in several browsers and devices, and the results of this website function very well; this website can be accessed via http://widi-system.co/andikajeans/.]]>
<![CDATA[Wireless Network Service Quality Analysis at Kefamenanu 1 State Vocational School using QoS Methods ]]>
<![CDATA[Silla , Intan Nubriyanti]]>;
<![CDATA[Rema, Yasinta Oktaviana Legu]]>;
<![CDATA[Fallo, Kristoforus]]>
<![CDATA[ Internet of Things and Artificial Intelligence Journal Vol. 4 No. 4 (2024): Volume 4 Issue 4, 2024 [November] ]]>
Publisher : <![CDATA[Association for Scientific Computing, Electronics, and Engineering (ASCEE) ]]>
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DOI: 10.31763/iota.v4i4.812
<![CDATA[Indonesia is one of the largest archipelagic countries in the world, one of the challenges is that the existing internet network is not optimal and should be evenly distributed throughout Indonesia. This article is one of the efforts to increase the capacity and use of the internet, especially to support the education process which should be evenly distributed in various regions in Indonesia, not only in Java but also in areas outside Java. especially in East Nusa Tenggara. NTT is one of the areas discussed in this article, specifically at Kefamenanu 1 State Vocational School. This article puts forward some essential aspects in the development and installation of the internet in this school, including using the right measurements of Quality of Service (QoS). QoS includes many things such as Throughput, Bandwidth, Packet Loss, and other parameters that are essential in building and analyzing internet networks that have a wide scope, especially for the Education level. Vocational High Schools are not only expected to be able to use the internet but also build, design, install, and perform detailed analysis on the internet network they build. Two essential networks must be able to be installed, i.e., a Wireless Network and Local Area Network.]]>
