https://publikasi.dinus.ac.id/index.php/jais/issue/feedJournal of Applied Intelligent System2023-12-14T17:30:10+07:00Heru Agus Santosojournal.jais@gmail.comOpen Journal Systems<p>Journal of Applied Intelligent System (JAIS) is published by the Department of Informatics Universitas Dian Nuswantoro Semarang and IndoCEISS, which focuses on research in Intelligent System.</p><p>Topics of interest include, but are not limited to:</p><p>Biometric, image processing, computer vision, knowledge discovery in the database, information retrieval, computational intelligence, fuzzy logic, signal processing, speech recognition, speech synthesis, natural language processing, data mining, adaptive game AI, smart city technology and framework.</p>https://publikasi.dinus.ac.id/index.php/jais/article/view/8914Watermarking using DCT and DWT on Pneumonia images2023-11-30T22:48:10+07:00Ari Sudrajatarisud@poltektedc.ac.idAyu Hendrati Rahayuayuhendrati@poltektedc.ac.idWatermarking is a branch of the data hiding technique. Watermarking is a technique used to insert a copyright label on an image, so that the copyright of the image can be protected. Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are techniques that can be used to watermark. In this study, the Discrete Cosine Transform and Discrete Wavelet Transform methods will be used to watermark images to 5 different host images. In the tests carried out, watermarking techniques will be compared using DCT, DWT, DCT-DWT combination and DWT-DCT combination. The results obtained in this study were the highest PSNR value obtained at 41.931, the highest SSIM obtained 0.99515, the highest entropy was also obtained at 7.4186, The best UACI value is 0.0071158 and the best NCPR value is obtained at 93.9068% then, for the best CC value is obtained at 0.99953. As well as the NCC value, the value obtained is the same all in each test, namely with a value of 1.2023-11-30T22:48:10+07:00Copyright (c) 2023 Ari Sudrajat, Ayu Hendrati Rahayuhttps://publikasi.dinus.ac.id/index.php/jais/article/view/8973Encryption of Information on Brain Tumor Images Using Vigenere Cipher Algorithm and Least Significant Bits2023-11-30T22:48:11+07:00Renol Burjuliusburjuliusrenol@gmail.comDini Rohmayanidinirohmayani@poltektedc.ac.idSonty Lenasontylena18@gmail.com<span lang="EN-US">Cryptography is a branch of existing methods in mathematics which has the goal of being able to maintain the confidentiality of the information contained in the data so that the information is not known by parties who have no interest. Confidentiality of this information is important so that the information sent is not misused irresponsibly. Vigenere Cipher is a method used for cryptography. Vigenere Cipher works by using a tabula recta table where the table contains an alphabet arranged based on the Caesar Cipher shift. In this study, the Vigenere Chiper algorithm will be used to encrypt information into 25 brain tumor images. In the tests carried out on 25 images, the best MSE obtained was 1.541e-05, while the best PSNR was 48.1219, for the best SSIM it was 0.99995, then for the BER value, all images obtained a BER value of 0 and also for the entropy of the best steganography image, which was 6.8204.</span>2023-11-30T22:48:11+07:00Copyright (c) 2023 Renol Burjulius, Dini Rohmayani, Sonty Lenahttps://publikasi.dinus.ac.id/index.php/jais/article/view/8987Prediction of Sleep Disorders Based on Occupation and Lifestyle: Performance Comparison of Decision Tree, Random Forest, and Naïve Bayes Classifier2023-11-30T22:48:11+07:00Heru Lestiawanheru.lestiawan@dsn.dinus.ac.idCahaya Jatmokojatmoko14@dsn.dinus.ac.idFeri Agustinaferi.agustina@dsn.dinus.ac.idDaurat Sinagadauratsinaga@dsn.dinus.ac.idLalang Erawanlalang@dosen.dinus.ac.id<p>Health is a very important thing in life. Therefore, to maintain health, we need adequate rest. Without adequate rest, the body will not be healthy and fit. In this study, a person's sleep disorder prediction will be made based on their lifestyle and work. The predictions made will classify sleep disorders that are absent, sleep apnea and insomnia from certain lifestyles and work. The methods used to make predictions are decision tree classifier, random forest classifier and naïve Bayes classifier. The test was carried out using a total of 375 data which was broken down into 70% training data and 30% testing data. The results obtained after testing with test data are by using the decision tree classifier algorithm to get an accuracy of 89.431%, using the random forest classifier algorithm to get an accuracy of 90.244% and by using the naïve Bayes classifier algorithm to get an accuracy of 86.992%.</p>2023-11-30T22:48:11+07:00Copyright (c) 2023 Heru Lestiawan, Cahaya Jatmoko, Feri Agustina, Daurat Sinaga, Lalang Erawanhttps://publikasi.dinus.ac.id/index.php/jais/article/view/9024Conditional Matting For Post-Segmentation Refinement Segment Anything Model2023-12-05T09:15:42+07:00Al Birr Karim Susantoalbirkarim1@gmail.comMoch Arief Soelemanarief22208@gmail.comFikri Budimanfikri.budiman@dsn.dinus.ac.id<span lang="IN">Segment Anything Model (SAM) is a model capable of performing object segmentation in images without requiring any additional training. Although the segmentation produced by SAM lacks high precision, this model holds interesting potential for more accurate segmentation tasks. In this study, we propose a Post-Processing method called Conditional Matting 4 (CM4) to enhance high-precision object segmentation, including prominent, occluded, and complex boundary objects in the segmentation results from SAM. The proposed CM4 Post-Processing method incorporates the use of morphological operations, DistilBERT, InSPyReNet, Grounding DINO, and ViTMatte. We combine these methods to improve the object segmentation produced by SAM. Evaluation is conducted using metrics such as IoU, SAD, MAD, Grad, and Conn. The results of this study show that the proposed CM4 Post-Processing method successfully improves object segmentation with a SAD evaluation score of 20.42 (a 27% improvement from the previous study) and an MSE evaluation score of 21.64 (a 45% improvement from the previous study) compared to the previous research on the AIM-500 dataset. The significant improvement in evaluation scores demonstrates the enhanced capability of CM4 in achieving high precision and overcoming the limitations of the initial segmentation produced by SAM. The contribution of this research lies in the development of an effective CM4 Post-Processing method for enhancing object segmentation in images with high precision. This method holds potential for various computer vision applications that require accurate and detailed object segmentation.</span>2023-11-30T22:48:12+07:00Copyright (c) 2023 Al Birr Karim Susanto, Moch Arief Soeleman, Fikri Budimanhttps://publikasi.dinus.ac.id/index.php/jais/article/view/9064Identification of Organic and Non-Organic Waste with Computer Image Recognition using Convolutionalneural Network with Efficient-Net-B0 Architecture2023-12-05T10:26:51+07:00Heny Indriani Sutomohenyindriani@gmail.comThis study aims to develop a method for identifying organic and non-organic waste using a computer image recognition technique based on Convolutional Neural Network (CNN) with Efficient-Net-B0 architecture. Efficient and accurate waste identification is important in sustainable waste management. The primary goal of this research is to distinguish between organic and non-organic waste in images. Manually labeling waste images as organic or non-organic can be a time-consuming and error-prone task. Configuring and fine-tuning the EfficientNet-B0 architecture and CNN parameters for optimal performance can be a complex and iterative process. Hyperparameter tuning may be needed. Ensuring accurate labels is essential for training a reliable model. The choice of using the Convolutional Neural Network (CNN) with the EfficientNet-B0 architecture is a crucial part of the solution. EfficientNet-B0 is known for its balance between accuracy and computational efficiency. The use of CNNs and EfficientNet-B0 for this task indicates the system's ability to discern visual differences between the two waste types. The method proposed in this study utilizes CNN's ability to study important features of waste images to recognize various types of waste. This research includes the waste data collection stage which includes organic and non-organic waste in the form of 2D images. To evaluate the performance of the proposed method, a test was carried out using a waste dataset taken from a predetermined environment. The test results show that the proposed method is able to identify organic and non-organic waste with a high degree of accuracy. In test scenarios, this method achieves an accuracy of 98%, which demonstrates its ability to effectively identify the type of waste. Through the use of CNN-based computer image recognition techniques with the Efficient-Net-B0 architecture, this research succeeded in solving the problem of identifying organic and non-organic waste automatically and accurately. The proposed method has the potential to be applied in more efficient waste management systems, helps minimize human identification errors, and makes a positive contribution to environmental protection efforts. This research is expected to be the basis for further development in the introduction and management of waste in a sustainable manner.2023-11-30T22:48:12+07:00Copyright (c) 2023 Heny Indriani Sutomohttps://publikasi.dinus.ac.id/index.php/jais/article/view/9089Implementation Chatbot on Discord for Information Assistance and Conflict Prevention2023-11-30T22:48:12+07:00Zudha Pratamazudhapratama@dsn.dinus.ac.idEry Mintoriniery.mintorini@dsn.dinus.ac.idKarmila Karmilakarmila@dsn.dinus.ac.idDidik Hermantodidik.hermanto@dsn.dinus.ac.idDiscord, which was originally created for the gamer community, can now be found used by hobby groups and communities that are used for shared learning purposes. But the downside is the gamer culture that comes with it. Rude and toxic words that are synonymous with the gamer community should be avoided in study group communities. Meanwhile, the facilities for minimizing harsh and toxic words are still limited to word filters that can be tricked so that they can still be sent to the chat room. This can trigger conflict and interfere with learning activities together. This paper proposed an information assistance chatbot that is able to answer question, and conflict prevention with detection toxic sentences using pre-processing from NLP (Natural Language Processing) and text classification so that the chatbot is able to limit toxic sentences a little more accurately than the word filter feature alone. Also, Chatbots are given the ability to determine the value / level of toxic conversations so that they are had been able to determine the punishment action to be carried out by warning, suspending, or even being issued for the most severe cases. In addition, by looking at the frequency of sending messages from several senders, which indicates toxic, it was able to determine when the conflict occurs. The result shows that chatbot can work fine to answer question and detecting toxic include do punishment to toxic sender. With 10% error on detecting conflict and 30% error on answer question. That 30% error false positive on make an answer that should not be answered.2023-11-30T22:48:12+07:00Copyright (c) 2023 Zudha Pratama, Ery Mintorini, Karmila, Didik Hermantohttps://publikasi.dinus.ac.id/index.php/jais/article/view/9090Film Review Sentiment Analysis: Comparison of Logistic Regression and Support Vector Classification Performance Based on TF-IDF2023-11-30T22:48:13+07:00Dadan Saepul Ramdandsramdan@poltektedc.ac.idRiri Damayanti Apnenariri.damayanti.apnena@poltektedc.ac.idCastaka Agus Sugiantocastaka@poltektedc.ac.id<p><span lang="EN-US">Film sentiment analysis is a process for evaluating a sentiment value that exists in film reviews, so that positive or negative responses from films can be identified. In this study, a sentiment analysis will be carried out on film reviews on IMBD. The analysis was carried out to find out which reviews were positive and negative from film critics. The method used to carry out sentiment analysis in this study is review analysis and processing with TF-IDF and a positive or negative prediction process based on reviews that have been processed using a logistic regression algorithm and support vector classification. The data to be used is film reviews on IMBD, which consists of 2000 data, which is divided into 1000 positive data and 1000 negative data. Which is where the data will be preprocessed first and split with a percentage of 70% training data and 30% testing data. In the prediction process using the logistic regression algorithm, obtaining a test accuracy of 80.61%. While the prediction process using the support vector classification algorithm obtains a test accuracy of 82.42%.</span></p>2023-11-30T22:48:13+07:00Copyright (c) 2023 Dadan Saepul Ramdan, Riri Damayanti Apnena, Castaka Agus Sugiantohttps://publikasi.dinus.ac.id/index.php/jais/article/view/9170Application of PSO in CNN attribute weighting for banana tree classification based on leaf images2023-11-30T22:48:13+07:00Suamanda Ika Novichasarimanda@untidar.ac.idImam Adi Nataimamadinata@untidar.ac.id<span>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</span>2023-11-30T22:48:13+07:00Copyright (c) 2023 Suamanda Ika Novichasari, Imam Adi Natahttps://publikasi.dinus.ac.id/index.php/jais/article/view/9234Enhancing Default Prediction in P2P Lending using Random Forest and Grey Wolf Optimization-based Feature Selection2023-11-30T22:48:14+07:00Bagus Winarko Nugrohobagoescomputer@gmail.comPurwanto Purwantopurwanto@dsn.dinus.ac.idHeribertus Himawanhimawan26@dsn.dinus.ac.idOnline 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.<br /><strong></strong>2023-11-30T22:48:14+07:00Copyright (c) 2023 BAGUS WINARKO NUGROHO, Purwanto Ph.D, Heribertus Himawanhttps://publikasi.dinus.ac.id/index.php/jais/article/view/9321Message Hiding Using the Least Significant Bit Method with Shifting Hill Cipher Security2023-12-05T09:44:24+07:00Syafrie Naufal Mahendrasyafrienaufal@gmail.comFikri Budimanfikri.budiman@dsn.dinus.ac.id<span>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.</span>2023-11-30T22:48:14+07:00Copyright (c) 2023 Syafrie Naufal Mahendra, Fikri Budimanhttps://publikasi.dinus.ac.id/index.php/jais/article/view/9380A Comparative study of Transfer Learning CNN for Flower Type Classification2023-11-30T22:48:15+07:00Jaya Sumpenajaya.sumpena@itg.ac.id<span lang="EN-US">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%.</span>2023-11-30T22:48:15+07:00Copyright (c) 2023 Jaya Sumpenahttps://publikasi.dinus.ac.id/index.php/jais/article/view/9385Enhancing Augmentation-Based Resnet50 for Car Brand Classification2023-12-05T10:28:53+07:00Triga Agus Sugiartotriga3390@gmail.comMoch Arief Soelemanarief22208@gmail.comPujiono Pujionopujiono@dsn.dinus.ac.id<p>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.</p>2023-11-30T22:48:16+07:00Copyright (c) 2023 Triga Agus Sugiarto, Moch Arief Soeleman, Pujionohttps://publikasi.dinus.ac.id/index.php/jais/article/view/9407Optimization Of The Simple Additive Weighting Method Using The Entropy Method In Tourist Recommendation Decision Support2023-11-30T22:48:17+07:00Aya Sophiaayasophia57@gmail.com<span lang="EN">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.</span>2023-11-30T22:48:17+07:00Copyright (c) 2023 aya sophiahttps://publikasi.dinus.ac.id/index.php/jais/article/view/9437Road Crack Detection using Yolo-V5 and Adaptive Thresholding2023-11-30T22:48:17+07:00Heri Suhendarherysuhendar@itg.ac.id<span lang="EN-US">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. </span>2023-11-30T22:48:17+07:00Copyright (c) 2023 Heri Suhendarhttps://publikasi.dinus.ac.id/index.php/jais/article/view/9475Optimization Water Conservation Through IoT Sensor Implementation At Smartneasy Nusantara2023-12-14T17:30:10+07:00Alfin Abdurrafialfinrafi2@gmail.comDonny Maulanadonny.maulana@pelitabangsa.ac.idNanang Tedi Kurniadinanang@pelitabangsa.ac.id<span lang="EN-US">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.</span>2023-11-30T22:48:17+07:00Copyright (c) 2023 Alfin Abdurrafi, Donny Maulana, Nanang Tedi Kurniadihttps://publikasi.dinus.ac.id/index.php/jais/article/view/9492Covid-19 Classification using Convolutional Neural Networks Based on Adam, RMSP, and SGD Optimalization2023-11-30T22:48:18+07:00Moch Sjamsul Hidajatmoch.sjamsul.hidajat@dsn.dinus.ac.idDibyo Adi Wibowodibyoadiwiboow@dsn.dinus.ac.idIn 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. <strong> </strong>2023-11-30T22:48:18+07:00Copyright (c) 2023 Moch Sjamsul Hidajat, Dibyo Adi Wibowo