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JAIS (Journal of Applied Intelligent System)
Published by Universitas Dian Nuswantoro
ISSN : 25020493     EISSN : 25029401     DOI : -
Core Subject :
Journal of Applied Intelligent System (JAIS) is published by LPPM Universitas Dian Nuswantoro Semarang in collaboration with CORIS and IndoCEISS, that focuses on research in Intelligent System. Topics of interest include, but are not limited to: Biometric, image processing, computer vision, knowledge discovery in database, information retrieval, computational intelligence, fuzzy logic, signal processing, speech recognition, speech synthesis, natural language processing, data mining, adaptive game AI.
Arjuna Subject : -
Articles 191 Documents
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Application of PSO in CNN attribute weighting for banana tree classification based on leaf images Novichasari, Suamanda Ika; Nata, Imam Adi
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9170

Abstract

Banana (Musa paradisiaca) is a very popular fruit in Indonesia. Banana production in Indonesia, with more than 200 types of bananas, accounts for more than 50% of banana production in Asia. Differences in how to consume Ambon bananas and Kepok bananas and their various benefits encourage cultivators to be careful in choosing seeds to avoid mistakes. Distinguishing the seeds of Ambon bananas and kepok bananas is more difficult than distinguishing between Ambon bananas and kepok bananas. This is because the leaves and stems of the seeds look the same. The purpose of this study is to use an optimization algorithm to improve the performance of the image classification algorithm on the image of kepok banana leaves and Ambon bananas to assist in the selection of banana plant seeds that can be used by banana cultivators to get the maximum benefit according to the desired type of banana. The results of this study are used as the basis for making a decision support system to assist in the selection of banana plant seeds that can be used by banana cultivators in order to get the maximum benefit according to the desired type of banana
Enhancing Default Prediction in P2P Lending using Random Forest and Grey Wolf Optimization-based Feature Selection Nugroho, Bagus Winarko; Purwanto, Purwanto; Himawan, Heribertus
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9234

Abstract

Online lending services such as Peer to Peer (P2P) loans provide convenience for lenders to transact directly without involving banks as intermediaries. Identifying potential loan recipients who are at risk of default is a crucial step in preventing financial losses, as lenders are responsible for default risk. However, predicting default risk becomes a challenge when P2P lending datasets have various complex features. Some features in P2P lending are redundant, while others do not significantly contribute to an effective solution. Therefore, feature selection is an important process to choose a relevant subset of features from input or target data. Traditional feature selection methods often fail to provide optimal results. A better approach is to use heuristic search algorithms capable of finding suboptimal feature subsets. We employ the Grey Wolf Optimization (GWO) technique, inspired by the hierarchy of leadership and grey wolf hunting mechanisms. Combined with Random Forest (RF), which has limitations in classifying data with very high dimensions, our GWO+RF combination has proven to enhance classification performance better than previous research. It achieves an accuracy score of 97.31%, compared to previous research with scores of only 67.72% for RBM+RF, 64% for Binary PSO+ERT, and 92% for GA+RF.
Optimization of Yolov5 Hyperparameter Using Adam Optimizer in Vehicle Object Detection Irawan, Bambang; Andono, Pulung Nurtantio; Basuki, Ruri Suko
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.9244

Abstract

Utilization of computer vision can be applied in various aspects of daily life, reducing dependence on human labor. One of its implementations is in industry, such as in the production process of motorized vehicles, to sort or classify parts or goods. The computer vision process involves many stages, such as image capture, image processing, image analysis, image recognition, and decision-making. In the automotive industry, computer vision has been used in autonomous or driverless electric vehicles, as well as in creating intelligent transportation systems. To detect objects in real-time, one of the options that can be used is to use the YOLO algorithm, which can detect objects in one stage with predictions of bounding boxes and class probabilities simultaneously. However, although YOLO has good performance, the architecture has some drawbacks, such as complexity and complicated hyperparameter congurations. To remedy this, the Adam optimization algorithm was introduced, which combines the momentum and RMSprop algorithms to adjust the learning rate adaptively and provide faster convergence in model training. This is evidenced by an increase in the value of mAP on Yolov5. These results prove that the Yolov5 method with Adam`s optimization is better than the Yolov5 method without optimization.
Message Hiding Using the Least Significant Bit Method with Shifting Hill Cipher Security Mahendra, Syafrie Naufal; Budiman, Fikri
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9321

Abstract

Technological developments go hand in hand with advances in digital messaging. In protecting the confidentiality of the message, it is necessary to double secure the data. This security can be done with a combination of steganography and cryptographic techniques. Steganography algorithm which is a technique for hiding messages well, one of which is Least Significant Bit (LSB). The LSB algorithm is a simple method because it only converts the value of the last bit in a message with the inserted message bit, which is a convenience of the LSB algorithm, but it becomes vulnerable to message theft attacks if not combined with other algorithms for security. So it is necessary to increase security. This research developed a combination method of LSB algorithm for steganography technique with Hill Cipher algorithm for cryptographic technique, Hill Cipher was developed with shifting (shifting) 2 (two) characters. With the development of this method, hackers will find it difficult to crack messages, and is expected to improve the performance of the algorithm in affecting image quality and travel time in running the algorithm. The results of this study will be tested using several evaluation tools MSE, PSNR, BER, CER, AE, and Entropy. With the development of this method, hackers will find it difficult to decipher messages, and from the results of this experiment has been able to improve the performance of the algorithm in maintaining image quality and can shorten travel time in running the algorithm.
A Comparative study of Transfer Learning CNN for Flower Type Classification Sumpena, Jaya
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9380

Abstract

Flowers are plants that had many types and often found around. But because the many types of flowers, sometimes difficult to distinguish the type from one flower to another. Therefore, in this study, will discuse about the process of identification and classification of flower types, namely daisy, dandelion, rose, sunflower and tulip. The data that would used in this research is image data that consisting of 764 daisy images, 1052 dandelion images, 784 rose images, 733 sunflower images and 984 tulip images. From the total images used, would be divided again into 60% training data, 30% testing data and 10% validation data that would been used to train and evaluate the CNN model. In this study, the classification process would using transfer learning CNN method using the DenseNet and NasNetLarge architectures, which later from these two architectures would compare to find which architecture is best for classifying flower types. The results that obtained after testing in this study are in the flower classification process using the DenseNet architecture to get a test accuracy of 89% and using the NasLargeNet architecture to get a test accuracy of 86%.
Enhancing Augmentation-Based Resnet50 for Car Brand Classification Sugiarto, Triga Agus; Soeleman, Moch Arief; Pujiono, Pujiono
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9385

Abstract

This research focuses on car classification and the use of the ResNet-50 neural network architecture to improve the accuracy and reliability of car detection systems. Indonesia, as one of the countries with high daily mobility, has a majority of the population using cars as the main mode of transportation. Along with the increasing use of cars in Indonesia, many automotive industries have built factories in this country, so the cars used are either local or imported. The importance of car classification in traffic management is a major concern, and vehicle make and model recognition plays an important role in traffic monitoring. This study uses the Vehicle images dataset which contains high-resolution images of cars taken from the highway with varying viewing angles and frame rates. This data is used to analyze the best- selling car brands and build car classifications based on output or categories that consumers are interested in. Digital image processing methods, machine learning, and artificial neural networks are used in the development of automatic and real-time car detection systems.The ResNet-50 architecture was chosen because of its ability to overcome performance degradation problems and study complex and abstract features from car images. Residual blocks in the ResNet architecture allow a direct flow of information from the input layer to the output layer, overcoming the performance degradation problem common in neural networks. In this paper, we explain the basic concepts of ResNet-50 in car detection and popular techniques such as optimization, augmentation, and learning rate to improve performance and accuracy. in this study, it is proved that ResNet has a fairly high accuracy of 95%, 92% precision, 93% recall, and 92% F1-Score.
Optimization Of The Simple Additive Weighting Method Using The Entropy Method In Tourist Recommendation Decision Support Sophia, Aya
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9407

Abstract

Travel recommendations are ideas or suggestions of cool places to see while traveling. Depending on the interests and preferences of each visitor, these tourist attractions can be nature tourism, beach tourism, cultural tourism or other interesting places to visit. Tourism recommendations can be offered based on criteria including scenic beauty, street access, distance traveled, children's entertainment venues, ticket prices, menu variations, parking, places to relax, toilets, prayer rooms. Therefore, tourism recommendations are needed for tourists to determine the tourist destinations they want to visit. The SAW method is applied to decision making using many criteria, and to avoid subjectivity in determining the criteria weights, the Entropy method is used. The results of this study indicate that the ranking results from the optimization of the SAW method with the entropy method in supporting tourism recommendation decisions.
Road Crack Detection using Yolo-V5 and Adaptive Thresholding Suhendar, Heri
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9437

Abstract

Road crack detection is a critical aspect of infrastructure maintenance, ensuring the safety and durability of roadways. This study presents an innovative approach leveraging image processing techniques, YOLO-V5 model, and adaptive thresholding for efficient and accurate road crack detection. The utilization of adaptive thresholding enables the system to handle complex lighting variations and diverse road textures, enhancing the precision of crack identification. Integrating the YOLO-V5 model further facilitates real-time detection and precise localization of road crack regions, contributing to effective and timely maintenance strategies. The research findings underscore the robustness and efficacy of the proposed methodology, emphasizing its potential for enhancing road safety and durability. 
Optimization Water Conservation Through IoT Sensor Implementation At Smartneasy Nusantara Abdurrafi, Alfin; Maulana, Donny; Kurniadi, Nanang Tedi
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9475

Abstract

The use of IoT sensors in technology is a fascinating research subject due to the possibility of real-time monitoring of water usage and temperature effects. Developing tools and systems that make observations of water usage through Max6675 sensors can optimize data collection and processing through remote monitoring. Utilizing the IoT technique, the ESP8266 WeMOS D1 R2 microcontroller and the Max6675 temperature sensor are utilized to regulate the water pump via a relay. This approach enhances water sustainability for plant assumptions based on the predetermined temperature. The device activates the water pump when the temperature surpasses the standard level at the observation site. The outcome indicated that this device operates accordingly with a 100% success rate. The device was determined to function effectively by activating the water pump based on temperature. Additionally, it can provide real-time monitoring data and process sensor data for analysis.
Covid-19 Classification using Convolutional Neural Networks Based on Adam, RMSP, and SGD Optimalization Hidajat, Moch Sjamsul; Wibowo, Dibyo Adi
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9492

Abstract

In this comprehensive study, a meticulous analysis of the application of Convolutional Neural Network (CNN) methodologies in the classification of Covid-19 and non-Covid-19 cases was conducted. Leveraging diverse optimization techniques such as RMS, SGD, and Adam, the research systematically evaluated the performance of the CNN model in accurately discerning intricate patterns and distinct features associated with Covid-19 pathology. the implementation of the RMS and Adam optimization methods resulted in the highest accuracy levels, with both models achieving an impressive 98% accuracy in the classification of Covid-19 and non-Covid-19 cases. Leveraging the robust capabilities of these optimization techniques, the study successfully demonstrated the effectiveness of the RMS and Adam models in enhancing the precision and reliability of the Convolutional Neural Network (CNN) for the accurate identification and differentiation of Covid-19 patterns within the medical imaging datasets. The notable achievement of 98% accuracy further emphasizes the potential of these optimization methods in advancing the capabilities of CNN-based diagnostic tools, thus contributing significantly to the ongoing efforts in Covid-19 diagnosis and management.  

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