Computer Engineering and Applications Journal
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101 research outputs found
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Development of a Littering Behavior Detection Using 3D Convolutional Neural Networks (3D CNN)
No full textLittering has become a significant problem that negatively impacts public health and environmental cleanliness. This research introduces an innovative solution using 3D Convolutional Neural Networks (3D CNN) technology to automatically detect littering behavior through real-time CCTV recordings. Two models were developed and tested. Model 1, which employs Conv3D, Batch Normalization, and Dropout, showed high training accuracy but exhibited fluctuations in validation accuracy, indicating potential overfitting. In contrast, Model 2, designed with a simpler structure without Batch Normalization and Dropout, achieved higher classification accuracy and efficiency. Both models significantly contribute to addressing littering in public areas, increasing awareness, and supporting environmental law enforcement. The integration of 3D CNN technology in detecting littering behavior demonstrates its potential to reduce pollution and promote environmentally responsible behavior
A Low-cost Antenna Tracking System Integrated with GPS for UAVs
No full textIn today’s world, unmanned aerial vehicles (UAVs) are increasingly incorporated into different sectors to perform different functions for both military and commercial applications. Depending on the pace of use, the environment affects the signal quality, transmission and reception ability between the UAV and its ground control station (GCS). To mitigate the poor communication and avoid the disruption of communication between the GCS and UAV, an antenna tracking system (ATS) can be used. This work aims to design a good performance ATS using a helical antenna, integrated with GPS. The helical antenna is controlled by proportional-integral-derivative (PID) controller. To improve accuracy and provide redundancy in case of system failure, GPS is integrated to the ATS. The PID controller provides stability of the system in varying system operational stages against internal and external disturbances.
 
Exploration U-Net Architecture for Cervical Precancerous Lesions Segmentation
No full textThe automatic analysis of images for the early detection of cervical cancer relies on the segmentation of cervical precancerous lesions. This paper investigates the incorporation of various CNN-based backbones into a U-Net model for improved segmentation accuracy. A set of twelve backbones was tested, including VGG16, VGG19, ResNet50, ResNext50, EfficientNetB7, InceptionResNetv2, DenseNet201, InceptionV3, MobileNet V2, SE-ResNet50, SE-ResNext50, and SE-Net154. Evaluation metrics were computed using Intersection over Union, pixel accuracy, and Dice coefficient. The findings demonstrate that U-Net with EfficientNetB7 backbone outperforms all other models with an IoU of 73.13%, pixel accuracy of 89.92%, and a Dice coefficient of 77.64%. These results were visually confirmed; segmentation outputs were examined, showing accurate delineation of lesion borders. The dominating performance of EfficientNetB7 was observed to be due to high feature extraction efficiency coupled with powerful spatial information representation. The study is, however, limited by a lack of clinical validation and expert evaluation from trained medical personnel. The results demonstrate the effectiveness of combining the U-Net architecture with advanced CNN backbones towards designing automated systems to analyze medical images
Development of Finite State Machine Computational Model for Dynamic Difficulty in an Educational Platformer Video Game
No full textThis research aims to address the issue of static and unengaging educational history games for players with diverse skill levels. To this end, a 2D platformer game titled "Parahyangan" was developed, implementing a Dynamic Difficulty Adjustment (DDA) system based on a Finite State Machine (FSM), which allows the difficulty level to adapt to the player\u27s performance. Using the Game Development Life Cycle (GDLC) methodology, the game was designed and quantitatively tested through a User Acceptance Test (UAT) with 85 respondents. The analysis shows that the game was well-received, falling into the "Good" category with a total satisfaction score of 77.55%. The core DDA feature was proven to be functional and well-accepted by the players. The user interface was identified as a major strength, while level progression was noted as an area for improvement. It is concluded that the implementation of DDA using an FSM is an effective solution for creating a more personalized, engaging, and sustainable learning medium for history that maintains player involvement
Augmented Reality in STEM Using Personalized Learning to Promote Students’ Understanding
No full textThe current curriculum highlights the premise of self-directed learning performed by students. Additionally, technological uses in educational settings prove to be a challenging task in a sense of implementing them in learning media and materials used in the classroom. This study aims at investigating the utilization of augmented reality (AR) in STEM (Science, Mathematics, Engineering, and Technology) using personalized learning. This study employed pre-experimental research design, specifically adopting One-Group Pretest-Posttest Design. The findings highlight that students’ pretest scores on average reached 51,6 and significantly improved to 82,67 in their posttest, whereas students’ gain score reached 0,64 which is considered as moderate. Their perspectives towards the use of augmented reality with personalized learning were significantly positive with the percentage of 82,1%. It is evident that the use of augmented reality with personalized learning is a viable option when it comes to affecting the learning outcomes
Application of Machine Learning in Clustering Maize Producing Regions in Indonesia
No full textMaize is considered an important commodity with promising market prospects. Given the importance of maize, there is a need to increase maize production to meet people\u27s needs and maintain price stability. This study aims to group maize production in Indonesia by region, with the hope of finding areas that have the potential to become maize production centers to reduce dependence on imports. The data used in this research was obtained from the Central Statistics Agency, covering information from 34 provinces during the 2017-2021 period. This analysis uses the K-Means method with the Python programming language. The number of groups is determined using the Elbow Method. The results of this research show that there are three categories of maize production regions: regions with low maize production (below average), regions with medium maize production, and regions with high maize production. A total of 25 provinces are in the low production category, eight provinces are in the medium category, and only East Java is in the high production category. 
Image Classification of Traditional Indonesian Cakes Using Convolutional Neural Network (CNN)
No full textIndonesia is one of the countries famous for its traditional culinary. Traditional cakes in Indonesia are traditional snacks typical of the archipelago\u27s culture which have a variety of textures, shapes, colors that vary and some are similar so that there are still many people who do not know the name of the cake from the many types of traditional Indonesian cakes. The problem can be solved by creating a traditional cake image recognition system that can be programmed and trained to classify various types of traditional Indonesian cakes. The Convolutional Neural Network method with the AlexNet architecture model is used in this research to predict various kinds of traditional Indonesian cakes. The dataset used in this research is 1846 datasets with 8 classes of cake images. This study trained the AlexNet model with several optimizers, namely, Adam optimizer, SGD, and RMSprop. The best parameters from the model testing results are at batchsize 16, epoch 50, learning rate 0.01 for SGD optimizer and learning rate 0.001 for Adam and RMSprop optimizers. Each optimizer tested produces different accuracy, precision, recall, and f1_score values. The highest test results that have been carried out on the image dataset of typical Indonesian traditional cakes are obtained by the Adam optimizer with an accuracy value of 79%
Optimization of Distributed RSA Encryption and Decription Processing Using Process Scheduling Method In Single Board Computer Cluster Architecture (SBC)
No full textData security is still a major issue regarding the need for data confidentiality. The encryption process using the RSA algorithm is still the most popular method used in securing data because the complexity of the mathematical equations used in this algorithm makes it difficult to hack. However, the complexity of the RSA algorithm is still a major problem that hinders its application in a more complex application. Optimization is needed in the processing of this RSA algorithm, one of which is by running it on a distributed system. In this paper, we propose an approach with a FIFO process scheduling algorithm that runs on a single board computer cluster. The test results show that the allocation of resources in a system that uses a FIFO process scheduling algorithm is more efficient and shows a decrease in the overall processing time of RSA encryption
The Eye and Nose Identification Chip Controller-Based on Robot Vision Using Weightless Neural Network Method
No full textIncreasingly advanced image analysis in computer vision, allowing computers to interpret, identify, and analyze pictures with accuracy comparable to humans. The availability of data sources in decimal, hexadecimal, or binary forms enables researchers to take the initiative in applying their study findings. Decimal formats are typically used on traditional computers like desktops and minicomputers, whereas hexadecimal and binary formats were utilized on single-chip controllers. Weightless Neural Network is a method that can be implemented in a single chip controller. The aim of this research is to develop a facial recognition system, for eye and mouth identification, that works in a single chip controller or also called a microcontroller. The suggested method is a Weightless Neural Network with Immediate Scan approach for processing and identifying eye and nose patterns. The data will be handled in many memory locations that are specifically designed to handle massive volumes of data. The data is made up of primary face data sheets and face input data. The data sets utilized are (x,y) pixels, and frame sizes range from 90x90 pixels to 110x110 pixels. Each face shot will be processed by selecting the region of the eyes and nose and saving it as an image file. The eye and nose will identify the face frame. Next, the photos will be converted to binary format. A magazine matrix will be used to transmit binary data from a minicomputer to a microcontroller via serial connection. Based on a known pattern, the resultant similarity accuracy is 83,08% for the eye and 84,09% for the sternum. In contrast, the similarity percentage for an eye ranges from 70% to 85% for an undefined pattern
Analysis and Implementation of Blowfish and LSB Algorithm on RGB Images using SHA-512
No full textThe growth of the internet globally keeps increasing as time goes. There\u27s a big amount of data type saved there too. Those data need to be secured so anyone who doesn\u27t have the right to access them can access it. The purpose of this article is to secure text information into image media using the Blowfish method for encrypting text information and securing it using the Hash function SHA-512 and then embedded it in image media using the Least Significant Bit (LSB) method. The result of implementing those methods using image media sized 138Kb and 39.85Kb with plaintext measuring 27 and 85 characters shows that integrity data is secured with SHA-512 method. The test result using PSNR method to get the score of image quality after embedding information to the image shows that the average number of PSNR’s score is 70,74 dB which means the quality is good and has less difference from the original image