JOIV : International Journal on Informatics Visualization
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Facial Expression Recognition Using Convolutional Neural Network with Attention Module
Get PDFHuman Activity Recognition (HAR) is an introduction to human activities that refer to the movements performed by an individual on specific body parts. One branch of HAR is human emotion. Facial emotion is vital in human communication to help convey emotional states and intentions. Facial Expression Recognition (FER) is crucial to understanding how humans communicate. Misinterpreting Facial Expressions can lead to misunderstanding and difficulty reaching a common ground. Deep Learning can help in recognizing these facial expressions. To improve the probation of Facial Expressions Recognition, we propose ResNet attached with an Attention module to push the performance forward. This approach performs better than the standalone ResNet because the localization and sampling grid allows the model to learn how to perform spatial transformations on the input image. Consequently, it improves the model's geometric invariance and picks up the features of the expressions from the human face, resulting in better classification results. This study proves the proposed method with attention is better than without, with a test accuracy of 0.7789 on the FER dataset and 0.8327 on the FER+ dataset. It concludes that the Attention module is essential in recognizing Facial Expressions using a Convolutional Neural Network (CNN). Advice for further research first, add more datasets besides FER and FER+, and second, add a Scheduler to decrease the learning rate during the training data
Enterprise Architecture: A Strategy to Achieve e-Government Dimension of Smart Village Using TOGAF ADM 9.2
Get PDFTransformation in village government with the enterprise architecture smart village design model is significant for developing digital technology in the village government environment to realize a government with aspects of integrity values that are in synergy with the SDGs sustainable development goals. The method used in this study uses the adoption of the TOGAF ADM 9.2 framework, which consists of five phases: Preliminary, Architecture vision, Business architecture, Data architecture, Application architecture, while determining the clustering of villages by taking into account several aspects of the assessment released directly by the Ministry of Village, The Development of Disadvantaged Regions and Transmigration of the Republic of Indonesia in the form of a village index building the IDM which aims to identify and facilitate analysis of village capabilities and the characteristics of village government at each level. The output produced in this study is in the form of an enterprise architecture smart village design that will assist the village government in describing a target in the form of a digital development design on the e-Government dimension in the form of several application platforms covering various management of public administration services, public development aspirations, and management. Village development, as well as the management of village government documents. From a village sample that was used as the object of research with the value of the advanced village clustering index according to the Village Index data, the IDM was built so that the smart village design concept and the synergy of the SDGs development goal
Karonese Sentiment Analysis: A New Dataset and Preliminary Result
Get PDFAmount social media active users are always increasing and come from various backgrounds. An active user habit in social media is to use their local or national language to express their thoughts, social conditions, socialize, ideas, perspectives, and publish their opinions. Karonese is a non-English language prevalent mostly in North Sumatra, Indonesia, with unique morphology and phonology. Sentiment analysis has been frequently used in the study of local or national languages to obtain an overview of the broader public opinion behind a particular topic. Good quality Karonese resources are needed to provide good Karonese sentiment analysis (KSA). Limitation resources become an obstacle in KSA research. This work provides Karonese Dataset from multi-domain social media. To complete the dataset for sentiment analysis, sentiment label annotated by Karonese transcribers, three kinds of experiments were applied: KSA using machine learning, KSA using machine learning with two variants of feature extraction methods. Machine learning algorithms include Logistic Regression, Naïve Bayes, Support Vector Machine and K-Nearest Neighbor. Feature extraction improves model performance in the range of 0.1 – 7.4 percent. Overall, TF-IDF as feature extraction on machine learning has a better contribution than BoW. The combination of the SVM algorithm with TF-IDF is the combination with the highest performance. The value of accuracy is 58.1 percent, precision is 58.5 percent, recall is 57.2, and F1 score is 57.84 percen
Blockchain-based Smart Contract for Decentralized Marketplace
Get PDFThe advance of information technology has a growing influence on one of the most popular social trends: online shopping. The rising popularity of online shopping among the general public, as indicated by the growth in the number of online purchasers each year, has prompted business owners to pursue online ventures. The marketplace is intrinsically tied to online buying activity that connects merchants and customers, allowing customers to search for various goods and services from various providers. However, service failures are vulnerable to centralized market systems that emerge frequently. When the company's services to customers fail to satisfy consumer expectations. A breakdown in purchasing and selling essential services, including product delivery and customer support, is referred to as service failure. As a result, not only does this harm confidence, but it may also cause clients to migrate to an alternative marketplace. The marketplace's competitiveness is based on consumer confidence. The decentralized marketplace can address this security concern. A decentralized marketplace is meant to build a system that does not require the confidence of a third party using blockchain technology and smart contracts that can record all transactions clearly and consistently, allowing them to serve as a single point of truth between distrusting entities. The findings largely support the feasibility of Ethereum Smart Contracts to construct a decentralized marketplace. However, there are some places where further study and development are needed
Smart City Architecture Development Framework (SCADEF)
Get PDFSmart City is a city that implements the latest technologies, such as big data, IoT, Artificial Intelligence, and other new technologies. Smart City has different system characteristics than other systems. Smart City involves several independent stakeholders, so the development of a smart city needs to be designed with a system analysis system and service-based planning. Smart City Architecture Development Methodology (SCADM) has been defined from the previous research. However, the existing Enterprise Architecture approach has yet to specify the artefact to complete the framework. This study recommends the Smart City Architecture Framework (SCADEF) as a comprehensive Enterprise Architecture Framework to develop Smart City Architecture. The architecture framework produced by SCADEF becomes the proposed architecture framework for realizing Smart City. SCADEF consists of SCADM, Meta-model Smart City Architecture Development Methodology Artefact, and guidelines by the implementation SCADEF. The research uses observation, classification, and construction methodologies in Information System Design Methodology. In addition, this study also tested the framework by implementing it on city objects. This implementation is a practical test tool for the resulting enterprise architecture framework. This study implemented SCADEF in the education and health field at Bandung Smart City. Implementing testing on the implementation of SCADEF is to explain the implementation in Bandung Smart City and ask for an assessment from enterprise architecture experts. The results of the expert assessments were calculated statistically to assess the methodology, artefacts, and uses. The measurement results show that SCADEF can be accepted and used to develop enterprise smart city architecture
An Intrusion Detection System Using SDAE to Enhance Dimensional Reduction in Machine Learning
Get PDFIn the last decade, the number of attacks on the internet has grown significantly, and the types of attacks vary widely. This causes huge financial losses in various institutions such as the private and government sectors. One of the efforts to deal with this problem is by early detection of attacks, often called IDS (instruction detection system). The intrusion detection system was deactivated. An Intrusion Detection System (IDS) is a hardware or software mechanism that monitors the Internet for malicious attacks. It can scan the internetwork for potentially dangerous behavior or security threats. IDS is responsible for maintaining network activity under the Network-Based Intrusion Detection System (NIDS) or Host-Based Intrusion Detection System (HIDS). IDS works by comparing known normal network activity signatures with attack activity signatures. In this research, a dimensional reduction and feature selection mechanism called Stack Denoising Auto Encoder (SDAE) succeeded in increasing the effectiveness of Naive Bayes, KNN, Decision Tree, and SVM. The researchers evaluated the performance using evaluation metrics with a confusion matrix, accuracy, recall, and F1-score. Compared with the results of previous works in the IDS field, our model increased the effectiveness to more than 2% in NSL-KDD Dataset, including in binary class and multi-class evaluation methods. Moreover, using SDAE also improved traditional machine learning with modern deep learning such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). In the future, it is possible to integrate SDAE with a deep learning model to enhance the effectiveness of IDS detectio
Intervention Strategies through Interactive Gamification E-Learning Web-Based Application to Increase Computing Course Achievement
Get PDFThis study aims to help students improve their knowledge capability based on their active participation through gamification. Gamification is one of the newer methods of education that has the potential to improve student learning. This research looked into gamification's efficacy in student engagement and learning retention during teaching and learning sessions for computer science or information technology courses. The assessment involved in this study is through Pre-Test and Post-Test through instructional intervention by adapting interactive Quizizz gamification e-learning web-based application. The flow of research works begins with a survey of the problem, pre-intervention analysis, and action was taken during the intervention, ending with the implementation and observation phase. The pre and post-analysis of test results and questionnaires were accomplished and discussed. Fifty-six respondents participate in this study. Results show that 87% of the respondents have increased their percentage of marks. In the pre-test result, 56% of the respondents achieved below the 55 marks, while in the post-test, it reduced to 14%. Adoption of other gamification applications, a larger target demographic, and the addition of computer science or information technology courses will help improve the study in the future
A Multi-Agent Simulation Evacuation Model Using The Social Force Model: A Large Room Simulation Study
Get PDFResearch on evacuation simulation has received significant attention over the past few decades. Disasters, whether they were caused by nature or by humans, which claimed lives were also the impetus for the establishment of various evacuation studies. Numerous research points to the possibility of simulating an evacuation utilizing the Social Force Model (SFM) and a leading person or leader, but without using the multi-agent architecture. Within the scope of this article, the multi-agent architecture for crowd steering that we suggest will be investigated. The architecture will utilize a model known as the Social Force Model to figure out how evacuees will move around the area. After this step, the model is simulated in NetLogo to determine whether the architecture can model the evacuation scenario. A simulation test is carried out for us to investigate the degree to which the behavior of the original SFM and the message-passing model is comparable to one another. The result demonstrates that the proposed architecture can simulate the evacuation of pedestrians. In addition, the simulation model can simulate utilizing the grouping strategy as well as the no grouping technique. The findings also showed that the model can capture many evacuation patterns, such as an arch-shaped pattern at the opening of the exit
Smart Automation Aquaponics Monitoring System
Get PDFModern agriculture, such as aquaponics, has become a well-known solution nowadays for farming, especially in Asia countries. It provides an alternative to support food demands and maintain environmental sustainability. However, it requires manpower and time to maintain and monitor the system. This research proposes a smart automation aquaponic monitoring system that helps users maintain and monitor the system through smartphone applications. The system uses DHT11 to record temperature and humidity, HC-SR04 for water level, and FC-28 to maintain soil moisture. The sensors are integrated with WeMos D1 Wi-Fi Uno based ESP8266 microcontroller to process the data. The data collected is stored in the cloud and retrieved via the Blynk application, which also performs as an actuator and allows users to control the parameters involved. The application helps to monitor the humidity, temperature, and water level in the fish tank and control the actuator for feeding fish. The system also sends a notification to the user for any activities performed, such as watering plants, feeding fish, and abnormality of temperature in the surroundings. The performance of the system was evaluated using regression modeling. The result indicates positive growth for both plants and fish during the monitoring duration, suggesting the proposed system's effectiveness. Overall, this solution helps to reduce manpower and operation costs as well as alternatives for food demand and stabilize environmental sustainability, especially in the urban residency
Neural Machine Translation of Spanish-English Food Recipes Using LSTM
Get PDFNowadays, food is one of the things that has been globalized, and everyone from different parts of the world has been able to cook food from other countries through existing online recipes. Based on that, this study developed a translation formula using a neural machine translation (NMT). NMT is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder–decoders. Our experiment led to novel insights and practical advice for building and extending NMT with the applied long short-term memory (LSTM) method to 47 bilingual food recipes between Spanish-English and English-Spanish. LSTM is one of the best machine learning methods for translating languages because it can retain memories for an extended period concurrently, grasp complicated connections between data, and provides highly useful information in deciding translation outcomes. The evaluation for this neural machine translation is to use BLEU. The comparing results show that the translation of recipes from Spanish-English has a better BLEU value of 0.998426 than English-Spanish with a data-sharing of 70%:30% during epoch 1000. Researchers can convert the country's popular cuisine recipes into another language for further research, allowing it to become more widely recognized abroad