User generated content (UGC) provides abundant tourist information regarding destinations. The textual digital traces bring great opportunity along with great challenges. Text mining approaches including sentiment analysis, multiclass text classification, and network analysis are suitable for extracting the buried pattern under piles of unstructured data. We processed 18.721 reviews from worldwide tourists about Bali’s 15 topmost tourist attractions. This study uncovers the tourist perception through textual data using sentiment analysis to extract the positive and negative perceptions, and multiclass classification to extract the tourist cognitive concern for each destination. We discover the tourist visiting patterns deeper by combining perception tone and cognitive concern results using network analysis to map out the destinations’ popularity, interconnectivity, and major cognitive perception. Most of the tourists disclose positive expressions and give their concerns about Bali’s natural attractions. They feel best for the social setting and environment aspect, and worst for the accessibility. Sacred Monkey Forest Sanctuary is the most favorite destination and a potential point of a visit to other destinations. This research provides insight into the global perception of Bali’s topmost destinations for government and other tourism stakeholders to support the development and improvement of Bali’s tourism.
Pretrained language models posses an ability to learn the structural representation of a natural language by processing unstructured textual data. However, the current language model design lacks the ability to learn factual knowledge from knowledge graphs. Several attempts have been made to address this issue, such as the development of KEPLER. KEPLER combines the BERT language model and TransE knowledge embedding method to achieve a language model that can incorporate knowledge graphs as training data. Unfortunately, such knowledge enhanced language model is not yet available for the Indonesian language. In this experiment, we propose IndoKEPLER: a language model trained usingWikipedia Bahasa Indonesia andWikidata. We also create a new knowledge probing benchmark named IndoLAMA to test the ability of a language model to recall factual knowledge. The benchmark is based on LAMA, which is designed to test the suitability of our language model to be used as a knowledge base. IndoLAMA tests a language model by giving cloze style question and compare the prediction of the model to the factually correct answer. This experiment shows that IndoKEPLER increases the ability of a normal DistilBERT model to recall factual knowledge by 0.8%. Moreover, the most significant increase happens when dealing with many-to-one relationships, where IndoKEPLER outperforms it’s original text encoder model by 3%.
Network of vehicles using Internet of Things (IoT) frameworks have efficient characteristics of modern intelligent transportation system with a few challenges in vehicular ad-hoc networks (VANETs). However, its security framework is required to manage trust management by preserving user privacy. Wireless mobile communication (5G) system is regarded as an outstanding technology that provide ultra-reliable with limited latency wireless communication services. By extension, integrating Software Defined Network (SDN) with 5G-VANET enhances global information gathering and network control. Therefore, real-time IoT application for monitoring transport services is efficiently supported. These ensures vehicular security on this framework. This paper provides a technical solution to a self-confidential framework for a smart transport system. This process exploiting IoT for vehicle communication by incorporating SDN and 5G technology. Due to some features of blockchain, this framework has been implemented to provide various alternative support for vehicular smart services. This involves real-time access to cloud to stream video information and protection management to vehicular network. The implemented framework presents a promising technique and reliable vehicular IoT environment while ensuring user privacy. Results of simulation presents that vehicular nodes/messages (malicious) and overhead is detected and the impact on network performance are satisfactory when deployed in large-scale network scenarios.
The use of data visualization in defense is very important. In the application of data visualization can use GIS applications. GIS can help a country in maintaining the integrity of the country. This study aims to show how data visualization using GIS is used in defense. Methodology This study uses a qualitative research methodology. The results of this study are that by knowing visualization data assisted by GIS applications, you can find out the Hazard and Vulnerability of the terrain in the country. With data visualization, it can also provide the supply chain needed in the defense industry as well as in times of war. And everyone who needs this visualization data can better plan the network plan and urban plan that will be used.
The left ventricular of ejection fraction is one of the most important metric of cardiac function. It is used by cardiologist to identify patients who are eligible for life-prolonging therapies. However, the assessment of ejection fraction suffers from inter-observer variability. To overcome this challenge, we propose a deep learning approach, based on hierarchical vision Transformers, to estimate the ejection fraction from echocardiogram videos. The proposed method can estimate ejection fraction without the need for left ventrice segmentation first, make it more efficient than other methods. We evaluated our method on EchoNet-Dynamic dataset resulting 5.59, 7.59 and 0.59 for MAE, RMSE and R2 respectivelly. This results are better compared to the state-of-the-art method, Ultrasound Video Transformer (UVT). The source code is available on https://github.com/lhfazry/UltraSwin.
3D geometric modelling of urban areas has the potential for further development, not only for 3D urban visualization. 3D point cloud, as 3D data commonly used in 3D urban geometry modelling, is needed to extract objects from point clouds to analyze urban landscapes. An automated method to analyze objects from the 3D point cloud can be achieved by using the semantic segmentation method. Unlike other segmentation tasks in 3D point cloud data, 3D urban point cloud segmentation has the challenge of segmenting different object sizes on various types of landscape contours with imbalanced distribution of the object. Therefore, this study modified 3D U-Net Sparse CNN by adding Atrous Spatial Pyramid Pooling (ASPP) as one of the modules in this model, called 3D U-Net ASPP Sparse CNN. The use of ASPP aims to get the contextual multi-scale information of the input feature map from the encoder part of U-Net. Furthermore, 3D U-Net ASPP Sparse CNN is implemented by using weighted dice loss as the loss function. The experiment result shows 3D U-Net ASPP Sparse CNN with weighted dice loss has achieved the best evaluation score in our experiment, with OA = 96.53 and mIoU = 63.59.
This research discusses the development of a new model for task change detection. Siamese Neural networks with U-Net as basic architecture are combined with spatial attention modules to perform task change detection. This model is developed to get a lightweight model with good performance. In the implementation, there is no need to use enormous resources. To benchmark the model, we used the LEVIR-CD dataset, where this dataset has two paired images taken at different times. The information contained in the two paired images is that there are changes such as the presence of buildings such as houses that increase or decrease in a certain area during the time of taking the two images. We compared the proposed model with U-Net and Siamese U-Net without spatial attention modules to see how they differ in performance. Then, We also compared the F1 Score with the baseline model of the LEVIR-CD dataset. After hyperparameter tuning with epochs of 100 is performed, the result is that the F1 Scores tested can balance the baseline model with a faster training time.
The new capital city (IKN) of the Republic of Indonesia has been ratified and inaugurated by President Joko Widodo since January 2022. Unfortunately, there are still many Indonesian citizens who do not understand all the information regarding the determination of the new capital city. Even though the Indonesian Government has created an official website regarding the new capital city (www.ikn.go.id) the information is still not optimal because web page visitors are still unable to interact actively with the required information. Therefore, the development of the Chatting Robot (Chatbot) application is deemed necessary to become an interactive component in obtaining information needed by users related to new capital city. In this study, a chatbot application was developed by applying Natural Language Processing (NLP) using the Term Frequency-Inverse Document Frequency (TF-IDF) method for term weighting and the Cosine-Similarity algorithm to calculate the similarity of the questions asked by the user. The research successfully designed and developed a chatbot application using the Cosine-Similarity algorithm. The testing phase of the chatbot model uses several scenarios related to the points of NLP implementation. The test results show that all scenarios of questions asked can be responded well by the chatbot.
Financial institutions currently use credit history to determine whether to grant creditors credit. However, companies such as P2P Lending has a data shortage, especially credit history data, so innovative credit models emerge to improve the ability to assess creditors. Along with technology development, we have the opportunity to extract data from social media. This study uses social media data to create models for assessing creditworthiness. We collect data from social media and then process it using the credit scoring scorecard, linear correlation formula, credit scoring model weight composition, and threshold according to expert judgments. We find that by using a greater weight of the demographic attributes, we receive more data in the good credit category. This research on establishing model combinations contributes to assisting and making it easier for lenders to assess creditors using available data in a more practical way.
An accurate assessment of heart function is crucial in diagnosing the cardiovascular disease. One way to evaluate or detect the disease can use echocardiography, by detecting systolic and diastolic volumes. However, manual human assessments can be time-consuming and error-prone due to the low resolution of the image. One way to detect heart failure on echocardiogram is by segmenting the left ventricle on the echocardiogram using deep learning. In this study, we modified the MultiResUNet model for left ventricle segmentation in echocardiography images by adding Atrous Spatial Pyramid Pooling block and Attention block. The use of multires blocks from MultiResUnet is able to overcome the problem of multi-resolution segmentation objects, where the segmentation objects have different sizes. This problem has similar characteristics to echocardiographic images, where the systole and diastole segmentation objects have different sizes from each other. Performance measure were evaluated using Echonet-Dynamic dataset. The proposed model achieves dice coefficient of 92%, giving an additional 2% performance result compared to the MultiResUNet.
One of the most common types of social engineering attacks is phishing. This technique uses psychological manipulation of the target to unknowingly hand over the information the attacker wants. Our research tries to find out how a phishing attack can be executed by first pretexting the OmeTV video chat application. The target of our attack is OmeTV players from Indonesia who are over 18 years old. The proposed attack methodology is Social Engineering Session (SES). This study also aims to provide an overview of what kind of information can be extracted from the target during an attack. The results show that the pretexting phishing attack on the OmeTV video chat application was successfully carried out to obtain some of the target’s personal information including: full name, date of birth or age, address, educational status, hobbies, Instagram account, and phone number.
The main objective of this research is to increase security awareness against phishing attacks in the education sector by teaching users about phishing URLs. The educational media was made based on references from several previous studies that were used as basic references. Development of antiphishing game framework educational media using the extended DPE framework. Participants in this study were vocational and college students in the technology field. The respondents included vocational and college students, each with as many as 30 respondents. To assess the level of awareness and understanding of phishing, especially phishing URLs, participants will be given a pre-test before playing the game, and after completing the game, the application will be given a posttest. A paired t-test was used to answer the research hypothesis. The results of data analysis show differences in the results of increasing identification of URL phishing by respondents before and after using educational media of the anti-phishing game framework in increasing security awareness against URL phishing attacks. More serious game development can be carried out in the future to increase user awareness, particularly in phishing or other security issues, and can be implemented for general users who do not have a background in technology.
Emerging cutting-edge technology can change the daily life of human beings, for instance, smart transportation, intelligent healthcare systems, Smart cities, etc. Human can use their device to access other devices remotely to collect and utilize the information for their work. Internet of Things is the backbone of the autonomous environment aforesaid. However, IoT uses the public channel to transmit information, and it opens the opportunity for attackers to intercept, delete, and modify data. Therefore, security is an imperative aspect of IoT technology. One of the critical security features is perfect forward and backward secrecy. This article proposes new protocol authentication using biometrics and Physically Unclonable Function (PUF) with the primary security feature of achieving Perfect Forward and Backward Secrecy (PFBS). Our protocol also achieves other security features such as anonymity, unclonable device, and mutual authentication. In addition, we conduct informal and formal analyses to prove that the protocol achieves security features.
Indonesia is a vast archipelago and a large sea area. Quoting from the Preamble of the 1945 Constitution states that the purpose of the Government of the Republic of Indonesia is to protect the entire Indonesian nation and its homeland. Therefore, defensive aspects need to be taken into special account. The Sea Defense System requires an agile unmanned mini-sea boat maneuvering capability which is able to secure the sea area according to its function and detect foreign ships. Indonesia needs to be more vigilant and detect its underwater defenses so that intruders do not attempt to violate the sovereignty of the Republic of Indonesia. This research is intended as a solution by designing a mini marine prototype to protect and strengthen Indonesian sea border. The system is developed using OpenCV and YOLO (You Only Look Once) method to detect ship. It is developed on an laptop which run on Linux. This research yields the results of the ship detection system by percentage of precision confidence level range 54–96% and several factors of undetectable condition, namely camouflaged ship, half body of ship image, and sunset condition.
The classification technique is one of the popular techniques used in helping humans decide the target class of a data based on machine learning principles. Unfortunately the construction of a classification model has no limits and will always evolve over time. There is no surefire way to make a perfect classification model, but there are ways that at least make the classification model better. This study applies the feature selection method to produce a more optimal classification model accuracy value. Of the many feature selection algorithms, this research chooses Relief which is combined with a classification algorithm, namely Random Forest and Support Vector Machine. This research also applies the Grid Search Optimization method in selecting the most influential features. In addition, it is also used to select the best hyperparameters to build the classification model. For splitting the data set, the K Fold Cross Validation technique is used in order to get the most optimal proportion of data splitting. Compared to the accuracy values before and after feature selection, both classification algorithms after feature selection significantly outperform the classification model before feature selection. It was also found that the model’s capabilities in the real world, through validation with new data, performed quite well.
Banking service transformation from traditional services to bank digitalization has several advantages for both banks and customers. This study aims to examine several theories, including UTAUT, Meta UTAUT and TAM plus the development of variables from security and marketing theory in testing the behavior of digital bank customers in chronological perception by considering 19 hypotheses. The quantitative survey research was carried out by involving 206 respondents using the SEM PLS analysis technique. The results of hypothesis testing summarize the meaning of trust pre-use and post-satisfaction achieved by customers can encourage customer loyalty and intention to recommend digital banks to the public. The negative issue of a technology is a poison that can damage the sustainability of digital banks. So it is necessary to evaluate the improvement and development of digital banks both in terms of systems and services in order to continue to exist in the community.
Misuse of caller ID spoofing combined with social engineering has the potential as a means to commit other crimes, such as fraud, theft, leaking sensitive information, spreading hoaxes, etc. The appropriate forensic technique must be carried out to support the verification and collection of evidence related to these crimes. In this research, a digital forensic analysis was carried out on the BlueStacks emulator, Redmi 5A smartphone, and SIM card which is a device belonging to the victim and attacker to carry out caller ID spoofing attacks. The forensic analysis uses the NIST SP 800-101 R1 guide and forensic tools FTK imager, Oxygen Forensic Detective, and Paraben’s E3. This research aims to determine the artifacts resulting from caller ID spoofing attacks to assist in mapping and finding digital evidence. The result of this research is a list of digital evidence findings in the form of a history of outgoing calls, incoming calls, caller ID from the source of the call, caller ID from the destination of the call, the time the call started, the time the call ended, the duration of the call, IMSI, ICCID, ADN, and TMSI.
Image data is used in various fields, including the health sector. One of the image data in the health sector is an electrocardiogram (ECG). The ECG contains the identifying information of a person which must be guarded and secured. ECG image data security can be done by encrypting image data using encryption algorithms. The encryption algorithms used in this thesis are ECDH and AES-GCM. ECDH is used to generate key pairs which are then used as keys for AES-GCM encryption and decryption. The encryption and decryption process are carried out in the python programming language. The results of the encryption time and decryption time increase because of the dimensions and size of the ECG image file. The nonce value and authentication tag are checked to be able to perform the decryption process. The histogram test results prove the uniformity of pixels in the encrypted ECG image file. PSNR and SSIM test results prove the difference between the initial and encrypted ECG image files. The results of the NIST Statistical Test Suite test prove that the algorithm used produces random output so that it can be used to secure ECG image files.
One of the important milestones of information security, block ciphers, are symmetric-key encryption algorithms that encrypt fixed-length inputs. The main purpose of this research is to analyze software implementations of block ciphers using the research method of the systematic literature review (SLR) proposed in software engineering and analyze them based on their implementation performances. During this process, a total of 39 block ciphers were extracted from 36 papers. The primary studies were reviewed considering the block cipher structures, while the implementation performances were classified according to the encryption throughput and memory utilization. The review results showed that the performance may depend on the clock frequency where low clock frequency might cause a bottleneck in some implementations even though the algorithm was designed using fast mathematical operations. Moreover, it is observed that Feistel structures mostly resulted in average and consistent implementation performances, whereas the block ciphers having Substitution and Permutation Layer (SPN) structure had a wide range of implementation performance results. As another result of this study, memory efficiency is shown as inversely proportional to the throughput in many software implementations. Additionally, some block ciphers having a high-performance software implementation might be able to replace the hardware implementations due to the convenience of the software platforms in many applications.
Network Centric Warfare (NCW) is a design that supports information excellence for the concept of military operations. Network Centric Warfare is currently being developed as the basis for the operating concept, namely multidimensional operations. TNI operations do not rely on conventional warfare. TNI operations must work closely with the TNI Puspen team, territorial intelligence, TNI cyber team, and support task force. Sending digital images sent online requires better techniques to maintain confidentiality. The purpose of this research is to design digital image security with AES cryptography and discrete wavelet transform method on interoperability and to utilize and study discrete wavelet transform method and AES algorithm on interoperability for digital image security. The AES cryptography technique in this study is used to protect and maintain the confidentiality of the message while the Discrete Wavelet Transform in this study is used to reduce noise by applying a discrete wavelet transform, which consists of three main steps, namely: image decomposition, thresholding process and image reconstruction. The result of this research is that Digital Image Security to support TNI interoperability has been produced using the C # programming language framework. NET and Xampp to support application development. Users can send data in the form of images. Discrete Wavelet Transformation in this study is used to find the lowest value against the threshold so that the resulting level of security is high. Testing using the AESS algorithm to encrypt and decrypt image files using key size and block size.
