JAIS (Journal of Applied Intelligent System)
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.
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<![CDATA[Watermarking using DCT and DWT on Pneumonia images ]]>
<![CDATA[Sudrajat, Ari]]>;
<![CDATA[Rahayu, Ayu Hendrati]]>
<![CDATA[ (JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[LPPM Universitas Dian Nuswantoro ]]>
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DOI: 10.33633/jais.v8i3.8914
<![CDATA[Watermarking 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.]]>
<![CDATA[Encryption of Information on Brain Tumor Images Using Vigenere Cipher Algorithm and Least Significant Bits ]]>
<![CDATA[Burjulius, Renol]]>;
<![CDATA[Rohmayani, Dini]]>;
<![CDATA[Lena, Sonty]]>
<![CDATA[ (JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[LPPM Universitas Dian Nuswantoro ]]>
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DOI: 10.33633/jais.v8i3.8973
<![CDATA[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.]]>
<![CDATA[Prediction of Sleep Disorders Based on Occupation and Lifestyle: Performance Comparison of Decision Tree, Random Forest, and Naïve Bayes Classifier ]]>
<![CDATA[Lestiawan, Heru]]>;
<![CDATA[Jatmoko, Cahaya]]>;
<![CDATA[Agustina, Feri]]>;
<![CDATA[Sinaga, Daurat]]>;
<![CDATA[Erawan, Lalang]]>
<![CDATA[ (JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[LPPM Universitas Dian Nuswantoro ]]>
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DOI: 10.33633/jais.v8i3.8987
<![CDATA[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%.]]>
<![CDATA[Conditional Matting For Post-Segmentation Refinement Segment Anything Model ]]>
<![CDATA[Susanto, Al Birr Karim]]>;
<![CDATA[Soeleman, Moch Arief]]>;
<![CDATA[Budiman, Fikri]]>
<![CDATA[ (JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[LPPM Universitas Dian Nuswantoro ]]>
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DOI: 10.33633/jais.v8i3.9024
<![CDATA[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.]]>
<![CDATA[Identification of Organic and Non-Organic Waste with Computer Image Recognition using Convolutionalneural Network with Efficient-Net-B0 Architecture ]]>
<![CDATA[Sutomo, Heny Indriani]]>
<![CDATA[ (JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[LPPM Universitas Dian Nuswantoro ]]>
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DOI: 10.33633/jais.v8i3.9064
<![CDATA[This 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.]]>
<![CDATA[Implementation Chatbot on Discord for Information Assistance and Conflict Prevention ]]>
<![CDATA[Pratama, Zudha]]>;
<![CDATA[Mintorini, Ery]]>;
<![CDATA[Karmila, Karmila]]>;
<![CDATA[Hermanto, Didik]]>
<![CDATA[ (JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[LPPM Universitas Dian Nuswantoro ]]>
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DOI: 10.33633/jais.v8i3.9089
<![CDATA[Discord, 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.]]>
<![CDATA[Film Review Sentiment Analysis: Comparison of Logistic Regression and Support Vector Classification Performance Based on TF-IDF ]]>
<![CDATA[Ramdan, Dadan Saepul]]>;
<![CDATA[Apnena, Riri Damayanti]]>;
<![CDATA[Sugianto, Castaka Agus]]>
<![CDATA[ (JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[LPPM Universitas Dian Nuswantoro ]]>
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DOI: 10.33633/jais.v8i3.9090
<![CDATA[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%.]]>
<![CDATA[Application of PSO in CNN attribute weighting for banana tree classification based on leaf images ]]>
<![CDATA[Novichasari, Suamanda Ika]]>;
<![CDATA[Nata, Imam Adi]]>
<![CDATA[ (JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[LPPM Universitas Dian Nuswantoro ]]>
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DOI: 10.33633/jais.v8i3.9170
<![CDATA[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]]>
<![CDATA[Enhancing Default Prediction in P2P Lending using Random Forest and Grey Wolf Optimization-based Feature Selection ]]>
<![CDATA[Nugroho, Bagus Winarko]]>;
<![CDATA[Purwanto, Purwanto]]>;
<![CDATA[Himawan, Heribertus]]>
<![CDATA[ (JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[LPPM Universitas Dian Nuswantoro ]]>
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DOI: 10.33633/jais.v8i3.9234
<![CDATA[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.]]>
<![CDATA[Message Hiding Using the Least Significant Bit Method with Shifting Hill Cipher Security ]]>
<![CDATA[Mahendra, Syafrie Naufal]]>;
<![CDATA[Budiman, Fikri]]>
<![CDATA[ (JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[LPPM Universitas Dian Nuswantoro ]]>
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DOI: 10.33633/jais.v8i3.9321
<![CDATA[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.]]>
