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cARica: enhancing travelling experiences in wonosobo through location-based mobile augmented reality
Get PDFWonosobo, as a Regency in Central Java Province, Indonesia, has attractions including the Dieng Plateau Theater Kalianget, and Menjer Lake. The research is intended to provide more experience for tourists who visit the tour through Location-Based Mobile Augmented Reality (MAR), an application we developed, cARica. This application includes experience travelling in Wonosobo and is aware of other information displayed through AR content. It was an alternative medium for tourism promotion to be easy, attractive, and inexpensive. It is a practical guide to attract tourists to visit tourist sites. In its development, we use the prototyping method so that each stage is carried out under the procedures that have been prepared. To get the point of Interest (PoI) points of tourist sites, use Global Positioning System (GPS) data taken through Google Maps to get the Latitude and Longitude of each object. The results of this study present that cARica is a Location-Based Mobile Augmented Reality service platform that can be accessed using an Android smartphone and has three-dimensional animated character content with the Wonosobo regency icon. cARica is a form of innovation in providing exceptional services and experiences for tourists and has the potential to be continuously developed
Tiny encryption algorithm (TEA) for analysis and implementation of cryptool2-based text message encryption and decryption processes
No full textData security is one of the main factors in the world of information technology. The human need for information is increasing along with the changing times today. To maintain the authenticity of the information presented, it is necessary to have a high level of security. In this study, a data security simulation will be designed using the TEA (Tiny Encryption Algorithm) algorithm which includes a block cipher encryption algorithm and includes a symmetric key cryptography algorithm with the advantage of being on the Feistel network. The practicum was analyzed based on the process of encrypting and decrypting messages in text format. The software used to simulate the TEA (Tiny Encryption Algorithm) algorithm using Cryptool2 software which is open source software is used to explain the concept of cryptography. This research is used to decipher, schema, and implement the TEA (Tiny Encryption Algorithm) algorithm in the form of simulating encryption and decryption of text messages (both text and text files) so that text messages are sent in the safest way from the sender to the recipient
Ensemble learning technique to improve breast cancer classification model
Get PDFCancer is a disease characterized by abnormal cell growth and is not contagious, such as breast cancer which can affect both men and women. breast cancer is one of the cancer diseases that is classified as dangerous and takes many victims. However, the biggest problem in this study is that the classification method is low and the resulting accuracy is less than optimal. the purpose of this study is to improve the accuracy of breast cancer classification. Therefore, a new method is proposed, namely ensemble learning which combines logistic regression, decision tree, and random forest methods, with a voting system. This system is useful for finding the best results on each parameter that will produce the best prediction accuracy. The prediction results from this method reached an accuracy of 98.24%. The resulting accuracy rate is more optimal by using the proposed method
Mask Detection System with Computer Vision-Based on CNN and YOLO Method Using Nvidia Jetson Nano
Get PDFHealth is an essential aspect of life. The World Health Organization (WHO) has officially declared the Corona Virus (Covid-19) a global pandemic that has spread to Indonesia. For preventive measures against Covid-19, the Indonesian government is trying to deal with the Covid-19 pandemic with 3M health protocol aimed at community activities, such as Memakai Masker (wearing masks), Mencuci Tangan (washing hands), and Menjaga Jarak (maintaining distance). In this study, software and hardware design was carried out to detect mask users and immediately warn violators who do not use masks automatically and can function automatically offline by utilizing digital image processing using NVIDIA Jetson Nano using the YOLO (You Only Look Once) method. The CNN YOLOv4-tiny model is chosen to obtain measurement results for mask user detection accuracy because it has a relatively minor computational value and is faster. The best camera detection angle is obtained at a vulnerable angle of 45O-90O or in the range of 90O-135O with value confidence that the average is 99.94% and the best accuracy is at a lux value greater than 70, and a minimum camera height of 1 meter and a maximum of 3 meters. Under conditions of lux 96 (bright), the maximum distance for detecting a face object is 12 meters, and the ability of the system to output a warning sound has been successfully integrated with a relay to run the mp3 module separately from the system, so as not to interfere with the Jetson Nano computation process and the model is successfully run on the Jetson Nano with an average computation of 13 frames per second
News text classification using Long-Term Short Memory (LSTM) algorithm
Get PDFOver the past few years, the classification of texts has become increasingly important. Because knowledge is now available to users through various sources namely electronic media, digital media, print media, and many more. One of them is the development of so much news every day. LSTM is one of the algorithms of deep learning methods that can classify a text. This research proves for the LSTM algorithm on the classification of news text sentences. The data used is the news text from the Kaggle data center set i.e. aggregator news data. The results of the LSTM experiment from 10 epochs obtained with an accuracy value of 93,15% on the classification of texts into four categories, namely entertainment, bussines, science, and health
Developed an expert system for analysis of covid-19 affected
Get PDFThe expert system solves problems within a specific area of the knowledge base. Prolog is a logical programming language which works on its knowledge base and effectively can be used to develop an expert system. Covid 19 is a pandemic deices and an expert system can be developed to diagnose this disease with the help of its symptoms that can be used as a knowledge base in Prolog. This expert system can make a fast diagnosis process for the covid 19 which is important to prevent the spread of the virus. Here we developed an expert system using prolog for diagnosis purposes. Like humans, these systems can get better with time as they gain more experience. Expert systems combine their experiences and expertise into a knowledge base that is then used by an inference or rules engine, a set of rules that the software employs, to apply to certain scenarios. Prolog is ideal for use with intelligent systems for a few reasons. Prolog can be viewed as a straightforward theorem prover or inference engine that derives from predefined rules. With the help of Prolog's built-in search and backtracking techniques, simple expert systems can be created
Comparison of KNN, naive bayes, and decision tree methods in predicting the accuracy of classification of immunotherapy dataset
No full textHealth is crucial for humans to carry out daily activities, and cancer is the second leading cause of death worldwide. Maintaining health is essential in minimizing factors associated with cancer. Immunotherapy is a new cancer treatment technique that has s shown a bigger success rate compared with conventional techniques. However, the effectiveness of this method depends on accurate diagnosis, which requires deeper analysis and research on classification methods. This study compares the accuracy of KNN, Naive Bayes, and Decision Tree classification methods in predicting the accuracy of immunotherapy treatment. The goal is to find the most effective classification techniques that can provide more accurate predictive results in treating diseases using immunotherapy. Based on the test results of Naive Bayes, Decision Tree, and K-Nearest Neighbor, the result obtained of accuracy rates are 81.11%, 80.00%, and 74.44%. From the accuracy comparison, it is known that the Naive Bayes algorithm is the most effective algorithm with the highest accuracy value of 81.11%
Implementation of Grid Mapping Method for Firefighting Legged Robot
Get PDFThe firefighting robot contest is an event to encourage high education students to explore electronics and robotic skill. The difficulty level of the contest increases from year to year though the arena layout is relatively similar. The field of arena consists of maze, detachable doors and distractorswhich requires smart idea to enable the robot to be well-performed. The combination of home position and random fire spot at each round of the game make each team have to find the most effective method to win the match. This paper presents an implementation the grid map method on the Azmi four legged robot using ultrasonic PING sensor, GPYA021 infrared sensor and GY-955 gyroscope. An ultrasonic sensor is attached on each of four sides of the robot. The data from each sensor will be interpreted as the distance of the front, right, left and back of the robot relative to the wall. Interpretation depends on the robot's current position. Virtual mapping technique was used by assuming that the arena consists of grids which have uniform size. Each grid was given a consecutive index numbering i.e a symbolic number which in practice be manifested in terms of distance unit. The results showed that the grid map method in this study worked well, tested on four randomized configurations, the robot successfully carried out the search task and returned to the home position with an acceptable execution time
Purchasing decision behavior of kudus residents on amanda brownies
No full textThis study aims to analyze the effect of brand image, price perception, and product quality on purchasing decisions for Amanda Brownies Kudus products. The problem in this study is that there was a decrease in the percentage of the top brand index value for the branded brownies category carried out by the frontier consulting agency, price perception by consumers who judge Amanda's brownies to be more expensive than another competitor. Several consumer reviews state that the quality of brownie products is still lacking. The sample in this study amounted to 110 respondents, using a purposive sampling technique. This study uses multiple linear regression analysis. The results of this study indicated that brand image has a positive and significant effect on purchasing decisions, price perceptions have a negative and insignificant effect on purchasing decisions, and product quality has a positive and significant effect on purchasing decisions. Brand image, price perception, and product quality positively and significantly affect purchasing decisions
Comparison of the suitability of the otsu method thresholding and multilevel thresholding for flower image segmentation
Get PDFThe digital representation of flowers, characterized by their vivid chromatic attributes, establishes them as viable candidates for deployment as input imagery within the object recognition paradigm. Within the context of object recognition, the imperative of a proficient image segmentation process is underscored, serving to effectively discern the object from its background and, consequently, optimizing the efficacy of the object recognition process. This research unfolds through a methodologically structured tripartite framework, encompassing the initial stage involving input imagery, the subsequent intermediate phase dedicated to image segmentation, and a conclusive stage centered on the quantitative evaluation of methodological outcomes. The second stage, focusing on image segmentation, employs the Otsu thresholding and multilevel thresholding methods. The subsequent third stage involves a thorough assessment of segmentation outcomes through the application of quantitative metrics, including Peak signal-to-oise ratio (PSNR) and Root Mean Square Error (RMSE). Empirical investigations, incorporating a diverse array of floral input images, reveal a conspicuous inclination towards a specific segmentation methodology. Specifically, the Otsu Thresholding method emerges as the more judicious choice relative to multilevel Thresholding, demonstrating superior performance with a diminished RMSE value and an augmented PSNR value, substantiated by an average RMSE value. This research is propelled by the overarching objective of discerning the most optimal method for the segmentation of flower images, particularly in the face of diverse input images. Its significant contribution lies in providing nuanced insights into the discerning selection of segmentation methodologies, attuned to the variability inherent in diverse forms of input imagery, thereby culminating in optimized outcomes within the domain of flower image recognition. Where did these results come from? please show it in the sub-discussion