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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Fuadi, Wahyu Sahputra, Ilham Fariadi, Dedi Agustinar Universitas Malikussaleh Informatics Engineering Study Program Faculty of Engineering Lhokseumawe Indonesia Universitas Malikussaleh Information Systems Study Program Faculty of Engineering Lhokseumawe Indonesia Universitas Malikussaleh Electrical Engineering Study Program Faculty of Engineering Lhokseumawe Indonesia

ISBN: (纸本)9798350382914

The importance of the K-Medoid model in viewing disaster-prone areas in each sub-district in North Aceh. The advantage of the k-medoid model results is that it has better cluster centers than other models. The novelty of this research is the cluster results are assessed by each decision maker using the Borda group model in providing flood disaster assistance or areas that are a priority for handling, which are included in the flood-prone cluster. Therefore, this research is important because it can be used to identify areas most severely affected by flooding. This research aims to analyze and identify the impact of flood hazards based on grouping and decision-making using the Borda group model in each sub-district in North Aceh. The results of the k-medoids model show 3 clusters that can see potential areas for flood disasters, namely very vulnerable. There are six sub-districts. For the k-medoids results, ten sub-districts are vulnerable. Lastly, the safe zones are 11 sub-districts. This research's k-medoid model analysis is more precise in seeing clusters of places that are potentially affected by flooding based on spatial maps of their nearest neighboring areas. The percentage of each cluster is as follows: Cluster 1 is 4%, Cluster 2 is 55%, and Cluster 3 is 41%. The k-medoid model provided this result after three iterations, with a closeness difference of 1.594510537. The results of the Borda model group research can be used as a recommendation for the areas that receive the highest priority in disaster management. © 2023 IEEE.

关键词： Floods

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Web-Based Incentive Calculations System at CV Karya Prima using Prototyping Method 7

Web-Based Incentive Calculations System at CV Karya Prima us...

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Cornelia, Monica Suryasari Universitas Multimedia Nusantara Information Systems Study Program Faculty of Engineering and Informatics Tangerang Indonesia

ISBN: (纸本)9798350307504

Consumption is an essential aspect of daily life, and fulfilling customer needs requires efficient distribution of goods. Therefore, distributors are needed and they frequently set targets for their employees to achieve, and motivation plays a significant role in employee performance. An organization that distributes goods called CV Karya Prima motivates its employees by offering incentives. However, manual incentive calculation sets out problems such as a lack of progress tracking, irregular payout dates, and probable inaccuracies. This study aims to develop a web-based incentive calculation system using the analytical hierarchy process method and a database for data retrieval. The system utilizes PHP, CodeIgniter, and MySQL and adopts the SDLC prototyping method. The developed system is focused on the sales division and variables like total sales, effective calls, calls, and outlets. The web-based system is capable of solving the issues using the features that were built based on user needs and have already been tested through user acceptance testing. © 2023 IEEE.

关键词： Websites

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Unraveling Game Addiction Behavior Among Digital Native Students: Leveraging Machine Learning for Insights 13

Unraveling Game Addiction Behavior Among Digital Native Stud...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Amriza, Rona Nisa Sofia Fadhilla, Cut Alna Telkom University Department of Information System Purwokerto Indonesia Universitas Samudra Informatics Study Program Faculty of Engineering Aceh Indonesia

ISBN: (纸本)9798350368086

The allure of gaming often leads to excessive engagement, which can result in addictive and problematic behaviors, negatively impacting academic performance and school achievements. While gaming becomes increasingly prevalent, concerns about game addiction have grown, particularly among students. The excessive and obsessive usage of video games that characterizes game addiction can have detrimental effects like academic deterioration, social isolation, and mental health problems. This study seeks to elucidate the complex patterns of game addiction among digital-native students by utilizing advanced machine learning techniques. We employed Random Forest, SVM, Decision Tree, and Naïve Bayes algorithms to classify game addiction data from students. To address the class imbalance, we applied Borderline-SMOTE. Our results demonstrate that decision tree and random forest outperformed other machine learning approaches, achieving an accuracy of 95% in classifying game addiction. These findings contribute to the expanding body of knowledge on game addiction. Furthermore, in order to provide real-time monitoring and intervention for students who are at risk of developing a game addiction, future research may investigate incorporating these findings into educational platforms or applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Smart electrical Devices Control with Intrusion Detection Alert

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第5期16卷 139-152页

作者： Achmad, Andani Muliadi Areni, Intan Sari Palantei, Elyas Hadis, Muhammad Sabirin Department of Electrical Engineering Faculty of Engineering Universitas Hasanuddin Gowa Indonesia Department of Electrical Engineering Education Faculty of Engineering Universitas Negeri Makassar Indonesia Study Program of Informatics STMIK AKBA Makassar Indonesia

The consumption profile of electrical energy always increases every year due to the highly consumptive and inappropriate lifestyles of modern society. A bad lifestyle such as forgetting to turn off the unused electronic devices when they are leaving home is one of the factors influenced the increasing consumption of electrical energy. This is off course contributing in wasting energy. In the paper, an IoT-based electronic device control system has been successfully constructed. Monitoring and control tasks could be performed remotely and in real time mode using an interface application installed on a smartphone. The system built is equipped with a device operational condition monitoring featured by placing light, temperature, thermal and current sensors, respectively. Through the utilization of the implemented system, users can monitor and control electronic devices easily and can figure out the condition of the device remotely in a real time mode. The control test on the system was successful with an average response time of approximately 3.32 seconds and the accuracy of sensor readings above 89%. With the implementation of this system, users can easily monitor and control electronic devices anywhere and anytime so that the opportunities for efficient and effective use of electrical energy could be further optimized. © 2022. All Rights Reserved.

关键词： Energy efficiency

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Aircraft Flight Schedule Announcement at Pionir Indonesian Airport Based on SMS Broadcast 1

Aircraft Flight Schedule Announcement at Pionir Indonesian A...

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1st International Conference of Adisutjipto on Aerospace electrical engineering and informatics, ICAAEEI 2024

作者： Kusumaningrum, Anggraini Wahyu, Septian Sahal, Ahmad Pujiastuti, Asih Ayuningtyas, Astika Pratiwi, Elizabeth Anna Adisutjipto Institute of Aerospace Technology Informatics Study Program Yogyakarta Indonesia Adisutjipto College of Technology Informatic Engineering Department Yogyakarta Indonesia Respati University D3 Information Technology Study Program Faculty of Science and Technology Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Engineering Study Program Yogyakarta Indonesia

ISBN: (纸本)9798331507930

The design and construction of delivering announcements using a web-based SMS gateway aims to help overcome problems that often occur at pioneer airports;this is because when passengers want to travel, many passengers still arrive late to check-in, even though the passenger ticket has a notification for checking. -in 1 hour before Departure. If a flight's Departure is delayed or accelerated, passengers will only receive this information when they arrive at the airport, whereas if at the time the departure notification is delayed or accelerated, the passenger is still outside the airport or still on their way to the airport, as a result, they will definitely not receive the information. Testing voice announcements on parking, arrival, departure, delay, check-in, and waiting room services, as well as testing SMS broadcasts (30 user numbers and one airline), shows that the announcement application provides information through voice data recording and broadcasts SMS to passengers and families. Testing was carried out at Indonesian Pioner airport for 30 passengers for a week. The percentage of Ease of obtaining test voice information was 81.98%, and the percentage of Ease of obtaining broadcast SMS information was 83.1%. Implementing the announcement is feasible at pioneer airports with a percentage of 82.54%. © 2024 IEEE.

关键词： Text messaging

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Citrus Limon L. Ripeness Identification Using Feature Extraction of Mean RGB, HSV, LBP and K-Nearest Neighbor

Citrus Limon L. Ripeness Identification Using Feature Extrac...

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2023 International Seminar on Application for Technology of information and Communication, iSemantic 2023

作者： Priyo Utomo, Ade Cahyo Sari, Christy Atika Rachmawanto, Eko Hari Hadi, Heru Pramono Mostafa, Salama A. Al-Dayyeni, Wisam Subhi Talib University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Ada University School of Information Technologies and Engineering Baku Azerbaijan

ISBN: (纸本)9798350339215

Citrus Limon L. (Lemon) is a type of fruit that is currently widely consumed, because it contains abundant vitamin C, fiber, and antioxidants. This fruit have high potential in the agribusiness sector and is widely cultivated by traditional farmers. However, during harvest time, traditional farmers generally still use manual methods using the human eye in distinguishing the maturity level of lemons which is less efficient because it has a low accuracy. Digital image processing is one solution to this problem. In the research on the classification of lemon maturity levels using digital image processing with the feature extraction method of the Mean RGB, HSV, and LBP methods and the K-Nearest Neighbor classification algorithm in this study, a total of 120 lemon images were used which were divided into 80 training image data and 40 image data. testing. The results of model performance measurements in the form of the highest accuracy level in the Mean RGB method of 100%, the highest accuracy in the HSV method of 98%, and the highest accuracy in the LBP method of 82.5%. © 2023 IEEE.

关键词： Citrus fruits

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Stunting Classification in Toddlers: Implementation and Evaluation of the Decision Tree Algorithm

Stunting Classification in Toddlers: Implementation and Eval...

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2024 International Conference on information Technology and Computing, ICITCOM 2024

作者： Alita, Debby Ahmad, Imam Jafar Rumandan, Rhaishudin Erkamim, Moh Widyasmoro Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Institut Agama Islam Negeri Ambon Islamic Education Management Study Program Ambon Indonesia Universitas Tunas Pembangunan Smart City Information Systems Study Program Surakarta Indonesia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350379839

Stunting in toddlers is a chronic nutritional issue that affects the physical and cognitive development of children, with serious long-term consequences such as reduced cognitive function and an increased risk of chronic diseases in adulthood. Therefore, early identification and prevention efforts for stunting are crucial. Classifying toddlers into categories of at-risk for stunting or not is essential to provide timely and appropriate interventions. This study employs data mining techniques using the decision tree algorithm to expedite the stunting detection process and improve the accuracy of nutritional status classification in children. The results indicate that the constructed decision tree model can classify children's nutritional status with an accuracy of 83.26%. The decision tree achieves high accuracy in classifying stunting in toddlers due to its ability to handle complex data and identify significant patterns within the data. © 2024 IEEE.

关键词： Classification Data Mining Decision Tree Stunting

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Offsets Reconstruction Method for Clock Skew Measurement Over High-Jitter Communication

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SN Computer Science 2025年第5期6卷 1-14页

作者： Sastra, Nyoman Putra Saputra, Komang Oka Teng, Wei-Chung Study Program of Electrical Engineering Faculty of Engineering Udayana University Jalan Raya Kampus Bukit Jimbaran Bali Badung 80361 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology No. 43 Section 4 Keelung Rd Taipei 106 China

Measuring clock skew of devices over a network fully relies on the offsets, the differences between sending and receiving times. Offsets that shape a thick line are the most ideal one as their slope is directly the clock skew sought. When some outliers separate from the thick line due to higher delay jitter, current advanced approaches use the lower bound or the majority of offsets to derive accurate clock skew. However, outliers in offsets that shape parallel dotted lines, reduce the availability of offsets in the lower region, and scatter widely the offsets from their majority zone. These conditions drive the current skew estimation approaches to lose their accuracy. This paper then developed a new method, which reconstructs back the outliers into their origin positions, in order to provide ideal offsets for clock skew measurement. The experimental evaluations have demonstrated that the clock skew accuracy of dotted-lines offsets tampered by outliers was improved from 2.1 to 0.9 ppm by the proposed reconstruction method. For a broader purpose, the reconstruction method has been adjusted to be implemented also on non-dotted-lines offsets. The robustness of the proposed method for this case has also been exhibited by the very ideal reconstructed offsets that shape like a thick line. © The Author(s) 2025.

关键词： Clock skew Delay jitter Dotted lines Hough transform

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Optimizing Indoor Vertical Farming with DDPG Reinforcement Learning: Enhancing Control of Nutrient Levels, Climate Conditions, and Plant Health 5

Optimizing Indoor Vertical Farming with DDPG Reinforcement L...

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5th IEEE International Conference on Technology, informatics, Management, engineering and Environment, TIME-E 2024

作者： Albert Panggabean, Good Fried Romadhon, Rahmat Lubis, Ramot Joelianto, Endra Institut Teknologi Bandung Doctoral Program in Engineering Physics Bandung Indonesia Institut Teknologi Del Faculty of Informatics and Electrical Engineering Toba Indonesia Institut Teknologi Bandung Engineering Physics Study Program Bandung Indonesia Institut Teknologi Bandung Instrumentation Control and Automation Research Group Bandung Indonesia

ISBN: (纸本)9798350364743

In indoor vertical farming systems, managing dynamic and complex environments poses significant challenges. Traditional control strategies often fall short in adapting to rapid changes in critical parameters such as nutrient levels, climate conditions, and plant health indicators. To address these challenges, this paper presents a novel approach for optimizing indoor vertical farming using Deep Deterministic Policy Gradient (DDPG), a reinforcement learning algorithm well-suited for environments with continuous action spaces. Our study focused on the control and monitoring of key parameters, including nutrient levels (pH and EC), climate conditions (temperature, humidity, light, and CO2 levels), and plant health indicators (biomass accumulation and leaf size structure). By defining a comprehensive state and action space, we simulated an indoor farming environment and trained the DDPG model to maximize crop yield and health. © 2024 IEEE.

关键词： DDPG indoor vertical farming precision agriculture reinforcement learning smart farming

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