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 Polynomial Regression on Coconut Charcoal Making System Integrated with IoT and Cloud in Real Time ]]>
<![CDATA[Chairunisa, Difa]]>;
<![CDATA[Ardhiah, Aulia]]>;
<![CDATA[Prasetyo, Muhammad]]>;
<![CDATA[Santoso, Iman Hedi]]>;
<![CDATA[Budiman, Gelar]]>
<![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.v4i3.797
<![CDATA[Polynomial regression is an analytical method often used to model non-linear relationships between independent and dependent variables. This method is effective in various fields of application, such as prediction, estimation, and analysis. In this study, polynomial regression was applied to facilitate the coconut charcoal manufacturing process to predict the duration of drying time based on the measured temperature. Polynomial regression is implemented with Internet of Things (IoT) technology, where temperature data obtained from sensors is sent in real-time to a mobile application. This application provides convenience for users in monitoring and managing the coconut charcoal drying process, thereby enhancing the efficiency and quality of the final product. This integration shows excellent potential in optimizing the production process using data-driven innovative technology.]]>
<![CDATA[Application of Location-Based Service (LBS) in The Information System for Determining The Location of Craft Shops in The City of Tasikmalaya Based on Android ]]>
<![CDATA[Cahyadi, Cepi]]>;
<![CDATA[Jaelani, Rusani]]>
<![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.v4i3.801
<![CDATA[Information is data that is necessary and essential in the era of technological development. One of the information needs is to find the location of craft shops in Tasikmalaya City, especially for foreigners who need information to buy craft products who do not know the location. Currently, information is only created manually through official documents in the form of paper posted on walls or in public places where people gather. Based on the description of the problems obtained, it is necessary to improve services in the information sector, designing a manual craft shop location search information system which will be developed into a technology-based digital information system using the Android Studio application and MySQL database. With the Location Service (LBS) and Global Positioning System (GPS) methods, with this system, users will be given location information and routes to their destination points easily to craft shop locations in Tasikmalaya City. All of these systems provide convenience in conveying information on searching for the location of craft shops in the city of Tasikmalaya.]]>
<![CDATA[Selection of Head of Study Program using Weighted Aggregated Sum Product Assessment (WASPAS) method ]]>
<![CDATA[Ramadani, Ramadani]]>;
<![CDATA[Fadillah, Riszki]]>;
<![CDATA[Fitriyani, Intan Nur]]>
<![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.v4i3.803
<![CDATA[Selecting a Head of Study Program is a crucial strategic decision in education, particularly in Vocational High Schools. At the Software Engineering Study Program Vocational School Sitibanun Sigambal, Labuhanbatu, Rantau Prapat, this process becomes highly complex due to the involvement of various criteria, such as Psychotest Scores, IQ Tests, Communication Skills, Cognitive Tests, and Teaching Experience. The Weighted Aggregated Sum Product Assessment (WASPAS) method, which combines the Weighted Sum Model (WSM) and Weighted Product Model (WPM), is utilized to enhance the accuracy and efficiency of decision-making. This method enables a more objective and structured selection process by leveraging information technology. Based on implementing the Decision Support System (DSS) using the WASPAS method, it can be concluded that it is highly effective in determining the best Head of Study Program rankings, considering the complex criteria and the need for accurate decisions. This DSS facilitates the selection process with results that are more objective, transparent, and aligned with the School's needs and priorities, thus aiding in achieving the School's mission of providing high-quality education.]]>
<![CDATA[Implementation of Webservice Security: A Case Study on HTTPS Usage and ARP Spoofing Attack Threats ]]>
<![CDATA[Fitri, Novi Aryani]]>;
<![CDATA[Maulana, Okta]]>
<![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.v4i3.805
<![CDATA[This study explores network security within a simulated environment built using VirtualBox, focusing on comparing the HTTP and HTTPS protocols in protecting data from eavesdropping. The research follows the PPDIOO model (Prepare, Plan, Design, Implement, Operate, and Optimize), including system requirements mapping and the setup of a virtual network environment that supports VLAN and data security. Two main scenarios were tested: an authorized user securely accesses a server using HTTPS, and another where an attacker attempts to intercept communication between the client and server using HTTP. The results indicate that HTTPS effectively protects data from eavesdropping attempts by attackers, while HTTP leaves security vulnerabilities that can be exploited to steal sensitive information. This study underscores the importance of using secure protocols like HTTPS in VLAN-based networks to protect data from eavesdropping and other threats. Additionally, the research paves the way for developing further security measures in network management, such as firewalls, intrusion detection systems (IDS), and more advanced encryption.]]>
<![CDATA[Optimization of K-Means Clustering Method by Using Elbow Method in Predicting Blood Requirement of Pelamonia Hospital Makassar ]]>
<![CDATA[Anggreani, Desi]]>;
<![CDATA[Nurmisba, Nurmisba]]>;
<![CDATA[Setiawan, Dedi]]>;
<![CDATA[Lukman, Lukman]]>
<![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.v4i3.755
<![CDATA[Hospitals require an adequate supply of blood to meet patient needs. Accurate prediction of blood demand is essential to optimize inventory management and avoid shortages or overstocks. This study aims to predict blood demand at Pelamonia Hospital using K-Means Clustering and Elbow methods. Historical data on blood demand at Pelamonia Hospital was collected and processed. The Elbow method is used to determine the optimal number of clusters in the K-Means Clustering algorithm. Sum of Squared Errors (SSE) or Within-Cluster Sum of Squares (WCSS) values were calculated for various clusters, and the elbow point on the graph of SSE/WCSS vs. number of clusters was identified as the optimal number of clusters. Once the optimal number of clusters is determined, the K-Means Clustering algorithm is applied to the blood demand data, resulting in grouping the data into specific clusters. Each cluster is analyzed to find interesting patterns or characteristics, such as clusters with high or low blood demand. From the results of the SSE calculation process on 1057 blood demand data, the result that has the biggest decrease is at k = 4 with a difference value of 2754.90. The clustering results and patterns found are used to predict future blood demand by identifying which cluster best fits the current or expected conditions. The characteristics of the clusters are used to estimate the likely blood demand. This approach provides valuable insights into blood demand patterns and enables hospitals to better anticipate blood demand, thereby optimizing inventory management and improving the quality of healthcare services.]]>
<![CDATA[Monitoring and Detection System of Level and Turbidity in Embankment Water in Real Time Based on IoT ]]>
<![CDATA[Alamsyah, Hendri]]>;
<![CDATA[Sawaludin, Prengki]]>;
<![CDATA[Kalsum, Toibah Umi]]>
<![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.v4i3.760
<![CDATA[In 2020 there were floods in the Air Hitam and Tanjung Alam villages, Bengkulu Province, and surrounding areas. These floods have caused a lot of losses to the community, especially resulting in damage to houses and agricultural land. Flooding in the Air Hitam and Tanjung Alam village areas, Bengkulu Province, occurred due to heavy rain that fell for 3 consecutive days, resulting in the overflow of the Musi Hydroelectric Power Plant (Hydroelectric Power Plant) dam which was unable to withstand the water discharge and eventually overflowed. So in this research, a prototype system for monitoring and detecting height and turbidity in embankment water in real time based on IoT will be designed. The method used is a waterfall, where the ultrasonic sensor and turbidity sensor will be connected to the ESP-8266 NodeMCU so that data on the height and turbidity of the embankment water is obtained in real-time via the user interface on the Thinger io Platform. Apart from that, the system uses LEDs in red, yellow, and green as indicators of the embankment water level. These results show that the implementation of a real-time IoT-based monitoring and detection system for height and turbidity in embankment water has good capabilities. This is indicated by the user being able to see data on the prototype system for monitoring and detecting height and turbidity in embankment water in real-time based on IoT with the display of numbers and LED indicator lights. The seawater height scale used in the system on the red LED indicator lights is 16 - 20 cm, yellow is 10 - 15 cm, and green is 0 - 9 cm.]]>
<![CDATA[Performance Analysis of Genetic Algorithms and KNN Using Several Different Datasets ]]>
<![CDATA[Riwanto, Yudha]]>;
<![CDATA[Atika, Enda Putri]]>
<![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.v4i3.767
<![CDATA[This research aims to increase the accuracy of the classification of mango, corn, and potato leaf types using an approach involving feature selection with a genetic algorithm (Genetic Algorithm), classification with K-Nearest Neighbors (KNN), and image processing in the HSV color space (Hue, Saturation). , Value). The dataset used consists of more than 1500 image samples for each type of leaf, with a total of 10 tests carried out. The research process begins with processing leaf images in HSV color space to extract more representative color information. Next, a genetic algorithm is applied to select the most relevant features from the processed image. The selected features are then used as input for the KNN model in the classification process. The test results show that the proposed method can achieve a classification accuracy of 87,9%. This shows that the combination of image processing in the HSV color space, feature selection using a genetic algorithm, and classification with KNN can improve performance in recognizing leaf types. This research makes significant contributions to the field of image processing and classification and shows the potential of using genetic algorithms for feature selection in pattern recognition applications.]]>
<![CDATA[Expert System to Determine the Fermentation Quality of Sheep Feed Using Forward Chaining Method ]]>
<![CDATA[Yunita, Farida]]>;
<![CDATA[Sadya, Siwi Bi'arfina]]>;
<![CDATA[Mubarrok, Fatih Syariful]]>;
<![CDATA[Widiarto, Ragil]]>;
<![CDATA[Pamungkas , Setyo]]>
<![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.v4i3.780
<![CDATA[Livestock productivity is strongly influenced by three main pillars: breeding, feeding, and management. The quality of animal feed, especially for sheep, is highly dependent on the raw materials used, which must have a balanced nutritional content and quality. Grass as the main feed is becoming increasingly difficult to obtain due to land conversion, so the use of concentrates and feed fermentation techniques is a promising solution. This research focuses on the development of an expert system to determine the quality of fermented sheep diets using the forward chaining method. The forward chaining method is used to organize the facts and data collected to arrive at the optimal solution. Feed quality is classified into three grades (G1, G2, G3) based on the ingredients used and certain criteria. Through observation, interviews, problem identification, understanding, analysis, and literature study, this system is designed to assist farmers in choosing the right and efficient feed ingredients. The results show that this expert system is effective in identifying and classifying the quality of fermented sheep diets and provides clear guidance to feed manufacturers in selecting suitable ingredients. The system is also expected to be developed into a mobile application to help users obtain information quickly and accurately, thereby increasing the efficiency and effectiveness of sheep feed management.]]>
<![CDATA[Sentiment Analysis of Twitter Users Ahead of the 2024 Election Using the Naive Bayes Method ]]>
<![CDATA[Subasar, Andi]]>
<![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.v4i3.784
<![CDATA[Elections are pivotal moments in a democratic nation, where citizens can express their opinions and political preferences. In today's digital era, social media, particularly Twitter, has become a crucial platform for expressing sentiments related to elections. This research aims to analyze Twitter users' sentiments towards the 2024 election using the Naive Bayes method, Knowing the public's views, especially on the Twitter platform, regarding the 2024 election and implementing the Naive Bayes method to classify sentiment. The research method itself consists of data collection from Twitter using the Twitter API, data preprocessing including data cleaning, removal of URLs, hashtags, duplicate words, normalization of words, tokenization, and removing meaningless words using the Rapid Miner application, then testing using training data and testing data in the Naive Bayes method., the data obtained from the keyword "2024 election" on Twitter for the initial data amounted to 2991 data. After going through the cleaning process, clean data amounting to 1069 data was obtained. From the tested data, the results obtained are as follows: The precision class produces an average percentage of true positives of 100.00% while negatives of 81.48%. Class recall produces a percentage of 98.95%, and the accuracy of testing the model is 99.00%. The research results show that the Naive Bayes method has been successfully applied to analyze Twitter user sentiment.]]>
<![CDATA[Usability Analysis of The Website of The Housing and Settlement Area Office of The District of South Bengkulu ]]>
<![CDATA[heriadi, wiwin]]>
<![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.v4i3.793
<![CDATA[The Bengkulu Selatan Regency Government has developed e-government by providing various information channels through websites throughout the Regional Apparatus Organizations (OPD), in this case, the Housing and Settlement Service. Usability assessment is needed to support further development and determine the value of the benefits of e-government to meet the community's needs. There are 4 aspects evaluated in this study, namely 4 aspects: usability, System Usefulness, Information Quality, Interface Quality, and Overall. This study uses the Post Study System Usability Questionnaire (PSSUQ) method, namely a questionnaire used to assess a system or to determine the extent of user experience to get a comprehensive impression of the user experience or end user of a system with a case study of the website of the Housing and Settlement Service of Bengkulu Selatan Regency. The results of the website usability research obtained the Usefulness aspect value (System Usefulness) with an average value of 2.26, the Information Quality aspect (Information Quality) with a value of 2.03, and Interface Quality (Interface Quality) with an average value of 1.91 and Overall (Overall) with a value of 2.11 where the lower limit of the PSSUQ norm is 2.62.]]>
