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arXiv 2024年

作者： Mujahidan, Muhammad Yahya Ayyashy Manullang, Martin Clinton Tosima Program Studi Teknik Informatika Fakultas Teknologi Informasi Institut Teknologi Sumatera Lampung Selatan Lampung Indonesia

Respiratory rate is a vital sign indicating various health conditions. Traditional contact-based measurement methods are often uncomfortable, and alternatives like respiratory belts and smartwatches have limitations in cost and operability. Therefore, a non-contact method based on Pixel Intensity Changes (PIC) with RGB camera images is proposed. Experiments involved 3 sizes of bounding boxes, 3 filter options (Laplacian, Sobel, and no filter), and 2 corner detection algorithms (ShiTomasi and Harris), with tracking using the Lukas-Kanade algorithm. Eighteen configurations were tested on 67 subjects in static and dynamic conditions. The best results in static conditions were achieved with the Medium Bounding box, Sobel Filter, and Harris Method (MAE: 0.85, RMSE: 1.49). In dynamic conditions, the Large Bounding box with no filter and ShiTomasi, and Medium Bounding box with no filter and Harris, produced the lowest MAE (0.81) and RMSE (1.35). © 2024, CC BY.

关键词： Edge detection

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Classification of Autism Using Feature Extraction Speed Up Robust Feature (SURF) with Boosting Algorithm

Classification of Autism Using Feature Extraction Speed Up R...

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Information Technology Research and Innovation (ICITRI), International Conference on

作者： Yorris Siagian Muhathir Maqhfirah D R Fakultas Teknik Universitas Medan Area Mahasiswa Program Studi Teknik Informatika Medan Indonesia Fakultas Teknik Universitas Medan Area Program Studi Teknik Informatika Medan Indonesia Fakultas Psikologi Universitas Medan Area Program Studi Psikologi Medan Indonesia

Autism is a complex disorder that affects an individual's neurobehavioral development. This disorder is characterized by restricted and repetitive communication patterns, difficulties in social interaction, and changes in sensory processing. This study aims to classify autistic and normal faces by using feature extraction using the SURF (Speeded Up Robust Features) method and the boosting algorithm. This study uses several variants of boosting algorithms, such as Adaboost, Gradient Boosting, and LightGBM. The dataset used in this study consisted of 100 samples of autistic faces obtained from Special Schools in Medan, as well as 100 samples of normal faces. Comparison of training data and data testing is 70%:30%. The results of this study indicate that the boosting algorithm provides good performance in classifying autistic and normal faces. Among the boosting algorithm variants used, Gradient Boosting achieved the highest accuracy of 91.67%, followed by LightGBM with 88.33% accuracy, and Adaboost with 81.67% accuracy. This finding demonstrates the effectiveness of the boosting algorithm in this classification task.

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A Comparative Study of Load Balancing Methods in Cloud-Based Village Information Systems

A Comparative Study of Load Balancing Methods in Cloud-Based...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Bagus Wibowo Abdul Haq Mochamad Taufiqurrochman Abdul Aziz Zein Program Studi Informatika UNU Al Ghazali Cilacap Indonesia Program Studi Sistem Informasi UNU Al Ghazali Cilacap Indonesia

Internet usage has been steadly increasing, with many needs or information that must be sought. The problem that is often experienced when many visitors access a website is that the server acting as a web server is unable to handle requests so services cannot be processed. This can be handled using the load balancing method using HAProxy. A web-based application certainly requires an adequate server computer so that the application can run properly. Cloud computing has emerged as a new trend that promotes efficient resource utilization and optimal server infrastructure utilization. Presently, numerous cloud computing platforms are offered, one of which is Amazon Web Services (AWS). In this research, several stages were carried out, starting from literature study, software design, testing stages and analysis of the results of testing data, drawing conclusions, and finishing. In designing cloud infrastructure, researchers created three services on AWS including EC2 (Elastic Cloud Compute), RDS (Relational Database Service), and VPC (Virtual Private Cloud) services. The village information system's load balancing design successfully applied to cloud technology, Amazon Web Service, and successfully implemented HAProxy as the load balancer and OpenSID as the village information system's website. the HA proxy employs various algorithms for a website, including round robin, least connections, source IP Hash, and Uniform Resource Identifier (URI). By using the parameters of standard deviation, throughput, average bytes and error in performance tests consisting of load tests and soak tests in this research, it has been determined that the URI algorithm is the most suitable for OpenSID.

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Augmented Reality Solutions by Utilizing Mobile Technology for Enhanced Skill Development

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第20期18卷 129-141页

作者： Arpan, Muhamad Ambiyar Ridwan Nasution, Auliana Rifdarmon Fakultas Teknik Universitas Negeri Padang Sumatra Barat Indonesia Program Studi Pendidikan Teknologi Informasi IKIP PGRI Pontianak Kalimantan Barat Indonesia Program Studi Informatika Universitas Battuta Medan Sumatra Utara Indonesia

This study aimed to identify the effectiveness of the peer tutorial project learning model assisted by augmented reality (AR) in improving students’ 21st-century skills. The focus of the study was to describe the improvement in students’ skills taught using the AR-assisted project and peer tutoring-based learning (PPTBL) model, with an emphasis on four main abilities: critical thinking, creativity, collaboration, and communication. This study involved a comparison between a control group and an experimental group to measure the effectiveness of the learning model. The study results showed that the average critical thinking skills reached 81%, creativity 77%, collaboration 73%, and communication 74%. Data analysis showed that the hypothesis stating that there was a significant difference in 4C skills between students who study with the AR-assisted PPTBL model and those who study with the conventional learning model was accepted at a significance level of 5% (0.05). These findings were expected to make a significant contribution to the development of innovative learning methods that can be widely applied in higher education institutions. © 2024 by the authors of this article.

关键词： Contrastive Learning

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Moving Image YOLOv3 Process Comparison with Multiple Resolutions and Frames-per-Second 23

Moving Image YOLOv3 Process Comparison with Multiple Resolut...

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23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022

作者： Al-Azam, Moh Noor Achlaq, Mochamad Mizanul Darujati, Cahyo Program Studi Teknik Informatika Fakultas Teknik dan Ilmu Komputer Universitas Narotama Surabaya Indonesia Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer Universitas Narotama Surabaya Indonesia Program Studi Sistem Komputer Fakultas Teknik dan Ilmu Komputer Universitas Narotama Surabaya Indonesia

ISBN: (数字)9781665460811

ISBN: (纸本)9781665460811

Many city/district governments have installed Closed-Circuit Television (CCTV) on the streets as a form of their smart city. This CCTV installation is usually only useful as a recording of events at a location that needs to be seen at a time. Actually, with the same capital, moving image recordings produced by CCTV can also be used for various things, such as vehicle counters that can show the level of road density. In this paper, the researcher tries to use 'You Only Look Once' or YOLO with several configurations of the moving image feature to recognize objects as vehicles. Because one factor that need to be considered in object detection for moving images is the need for large computational resources and long processing time. This study examines the effect of reducing the resolution size feature and the number of frames per second (fps) of moving images in the YOLO process, to obtain optimal use of computing resources and processing time. Reducing the resolution and the number of frames per second does not have much effect on the use of Central Processing Unit (CPU) computing resources used by researchers, which is around 9S.5l% on average (with Xeon 8 cores). However, it can reduce processing time by up to 70%. © 2022 IEEE.

关键词： Smart city

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Enhancing Larval Classification Accuracy Through Hyperparameter Optimization in ResNet50 with Three Different Optimizers

Enhancing Larval Classification Accuracy Through Hyperparame...

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Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), International Conference on

作者： Rujito Muhathir Nurul Khairina Veri Ilhadi Mutammimul Ula Ilham Sahputra Fakultas Teknik Program Studi Teknik Informatika Universitas Medan Area Medan Indonesia Fakultas Teknik Prodi Teknik Informatika Universitas Medan Area Medan Indonesia Fakultas Teknik Program Studi Sistem Informasi Universitas Malikussaleh Aceh Indonesia

Recent research has explored the application of deep learning models in the classification of Zophobas Morio and Tenebrio Molitor larvae, focusing on optimizing the choice of optimizer. This study aimed to investigate and compare the performance of the ResNet50 model using three different optimizers (Adam, Nadam, and Adagrad) for accurate classification of these larvae. We conducted an experimental study, training and evaluating the R model on a dataset of Zophobas Morio and Tenebrio Molitor larvae images using the mentioned optimizers. This study followed a quantitative approach to assess model performance. Our findings indicated that the Adam optimizer yielded the highest accuracy (approximately 97.1%), outperforming Nadam and Adagrad. However, recognizing Tenebrio Molitor still presented challenges, highlighting room for improvement. The results suggest that the choice of optimizer significantly influences the model's performance in larva classification. While Adam showed promising results, further enhancements are needed for accurate Tenebrio Molitor recognition. Future research should explore different deep learning architectures and larger datasets for improved classification outcomes.

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Robust Classification of Red Chili Plant Leaves Using Smartphone Camera Data and ResNet Model in Noisy Environments

Robust Classification of Red Chili Plant Leaves Using Smartp...

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Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), International Conference on

作者： Muhammad Azwan Muhathir Dian Noviandri Asmah Indrawati Rizal Aziz Fakultas Teknik Program Studi Teknik Informatika Universitas Medan Area Medan Indonesia Fakultas Pertanian Program Studi Agroteknologi Universitas Medan Area Medan Indonesia

Classification of yellow leaves, curly leaves and leaf spots on red chili plants is an important task in monitoring plant health. However, real-world scenarios often involve challenges such as environmental variations and noise from different leaf types, which can hinder accurate classification. This study aims to develop a robust classification model for identifying the three mentioned leaf types under dynamic and noisy conditions using smartphone camera data. Data collection involved capturing leaf images with a smartphone camera at a fixed distance. The images were intentionally taken with the inclusion of noise from surrounding leaves. The collected data were then preprocessed, including resizing and adjusting image format and resolution. Three scenarios were evaluated: default ResNet, ResNet with Grid Search-tuned model, and ResNet with Random Search-tuned model. The models were evaluated using metrics such as Accuracy, Precision, Recall, and F1-Score. The default ResNet scenario demonstrated moderate performance, while the ResNet model with Grid Search-tuned parameters showed the best overall performance with improved Precision and F1-Score. The model's ability to classify the three classes was highlighted, considering the trade-off between Precision and Recall. Collecting data with intentional noise from different leaf types simulates real-world conditions, aiding in the development of a robust classification model. The ResNet model tuned through Grid Search yielded the best performance. Further validation on larger datasets and improving adaptability to environmental variations is advised. The research contributes to effective plant disease detection in agricultural practices, leveraging smartphone cameras for efficient field observations and early problem identification.

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T-Shirt Vending Machine Using ESP32

T-Shirt Vending Machine Using ESP32

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Informatics Engineering, Science & Technology (INCITEST), International Conference on

作者： Mochamad Fajar Wicaksono Sri Nurhayati Myrna Dwi Rahmatya Syahrul Syahrul Program Studi Teknik Komputer Universitas Komputer Indonesia Bandung Indonesia Program Studi Sistem Komputer Universitas Komputer Indonesia Bandung Indonesia Program Studi Manajemen Informatika Universitas Komputer Indonesia Bandung Indonesia

At tourist locations, souvenirs are usually found at specific spots, but when utilizing a vending machine, souvenir sales can be made anywhere. The purpose of this research is to create a t-shirt sales system using a vending machine. This vending machine can be placed at tourist attractions, airports, or terminals. The method used in this research is experimental. From the client-side, this system is controlled using ESP32. Meanwhile, from the server-side, the system is supervised by the admin using a web application. The admin is tasked with verifying incoming orders via the web application and sending the barcode to the buyer's email. ESP32 will wait for the buyer to scan the barcode on the vending machine. If it’s is valid, the ESP32 will inform the buyer via OLED. Furthermore, the ESP32 will activate the DC motor to run the conveyor. The completion of the t-shirt release process is detected using an infrared sensor. ESP32 then informs the server that the process has been completed. Modular testing is carried out to test each tool component's function. Meanwhile, overall testing is carried out to test system functionality. Based on the results of these two tests, the Vending Machine Using ESP32 works 100% as expected. This tool can read the QR-Code input, validate data to the server, issue t-shirts according to validated orders, and request order deletion to the server when the transaction has been completed. With this tool, it is hoped that it can help sellers and make it easy for tourists.

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Analyzing the Effectiveness of VGG Deep Learning Architecture for Mushroom Type Classification

Analyzing the Effectiveness of VGG Deep Learning Architectur...

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International Conference of Computer Science and Information Technology

作者： Rahmad Syuhada Muhathir Nurul Khairina Rizki Muliono Susilawati Zulfikar Sembiring Fakultas Teknik Program Studi Teknik Informatika Universitas Medan Area Medan Indonesia Fakultas Teknik Prodi Teknik Informatika Universitas Medan Area Medan Indonesia

Indonesia is a center of biodiversity, one of the diversity of plant species is mushrooms. Fungi are very simple plants, have nuclei, spores, without chlorophyll, in the form of cells or branching filaments with walls of cellulose or chitin or both. Identification of mushrooms is still difficult due to the large number of types of mushrooms, lack of knowledge about mushrooms, and lack of experts in the mushroom field. Additionally, most mushrooms have a high degree of similarity in certain characteristics, making it difficult for unskilled humans to visually identify the type of mushroom. Therefore, it is important to be able to classify the types of mushrooms so that the public will better understand the types of each mushroom. This research uses a transfer learning approach with the VGG-19 architecture to provide an accurate method for classifying fungal species. There are 4 model scenarios that are trained and the best model performance is obtained using hyperparameter number of epochs 50, batch size 64, and the SGD optimizer gets an accuracy of 77.3% in the training process. After testing using data testing and evaluating using a confusion matrix and classification report, an accuracy score of 70% was obtained

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Classification of Mango Leaf Diseases Using XGBoost Method and HoG Feature Extraction

Classification of Mango Leaf Diseases Using XGBoost Method a...

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Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), International Conference on

作者： Rizky Suprayoga Suparsianto Zega Muhathir Siti Mardiana Fakultas Teknik Program Studi Teknik Informatika Universitas Medan Area Medan Indonesia Fakultas Sains dan Teknologi Program Studi Biologi Universitas Medan Area Medan Indonesia

Mango (Mangifera indica L.; Anacardiaceae) is a globally significant tropical fruit, renowned for its unique flavor and rich nutritional value. With over 90 countries engaged in mango cultivation, Asia stands out as the primary producer, contributing approximately 75% to the world's mango production. However, mango cultivation faces substantial threats from fungal diseases, affecting fruit quality and yield, particularly in countries heavily reliant on agriculture, like Indonesia. This study aims to classify mango leaf diseases through the analysis of leaf images using the XGBoost method. Additionally, we evaluate the XGBoost-based classification approach with the assistance of HoG feature extraction. The study contributes to the identification of mango leaf diseases by analyzing relevant leaf image features. We conducted a quantitative study, utilizing image processing and computer vision techniques for disease identification. The XGBoost algorithm was employed for classification, with feature extraction carried out using the Histogram of Oriented Gradients (HoG) technique. The research resulted in a successful classification of mango leaf diseases, demonstrating the effectiveness of the XGBoost algorithm when combined with HoG feature extraction. The study achieved a significant level of accuracy in disease classification. The findings indicate the potential of the XGBoost algorithm combined with HoG feature extraction for mango leaf disease classification. While the study achieved its primary objectives, further research can explore the optimization of this method and its application in practical agricultural settings to enhance disease management and mango production.

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