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ANALISIS LOYALITAS KONSUMEN DALAM PEMBELIAN PRODUK AIR MINERAL KEMASAN BOTOL MENGGUNAKAN METODE RANTAI MARKOV
Get PDFSENTIMENT ANALYSIS WITH A CASE STUDY OF PRACTICE CARD ON TWITTER SOCIAL MEDIA USING NAIVE BAYES METHOD
Get PDF-In early March 2022, Indonesia experienced a coronavirus pandemic which caused COVID-19 to enter for the first time. Since then, all sectors have been affected by the COVID-19 pandemic, not only health, the economic sector has also been seriously affected by this pandemic. In overcoming employment problems, the government makes a policy of the Pre-Employment Card program. The Pre-Employment Card Program is one of the government's efforts to expand job opportunities and to increase competitiveness which later became one of the social assistance for the community to overcome the Covid-19 pandemic. The implementation of the Pre-Employment Card program received pros and cons from the community, one of which was on Twitter social media. The results of the sentiment analysis of the pre-employment card program are mostly positive. The test results show that the Naïve Bayes Classifier method is successful in classifying sentiment with the highest accuracy value of 96%, the highest precision value of 98%, and the highest recall value of 96%, and AUC of 96%
DIAGNOSE OF MENTAL ILLNESS USING FORWARD CHAINING AND CERTAINTY FACTOR
Get PDFThe prevalence of mental disorders in Indonesia is increasingly significant, as seen from the 2018 Riskesdas data. Riskesdas records mental, emotional health problems (depression and anxiety) as much as 9.8%. This shows an increase when compared to the 2013 Riskesdas data of 6%. Based on these data, it can be said that many people still suffer from mental disorders. Meanwhile, the number of medical personnel, medicines and public treatment facilities for people with mental disorders is still limited. In addition, the lack of public awareness, concern and knowledge about mental health causes a lack of public interest in consulting a psychologist, so people tend to self-diagnose. One solution for self-diagnosis is to use an expert system. This study developed an expert system using the forward chaining method and certainty factor. Based on the research conducted, the results are as follows. First, the expert-based system that has been developed can help provide the results of a diagnosis that is carried out before there are complaints and will be detected early by efforts to increase awareness of the prevention of mental illness and reduce the tendency to self-diagnose. Second, applying the forward chaining method and certainty factor to this expert system can produce an accuracy rate of 95.918%. An expert has also validated these results; in this study, the expert was a psychologist at a hospital in Yogyakarta
IMPROVING LEARNING MOTIVATION BY APPLYING USER-CENTRED DESIGN AND AUGMENTED REALITY ON 3D INTERACTIVE APPLICATION
Get PDFConventional methods in teaching are still prevalent in the educational field to present knowledge to students. This method is still relevant today; however, it needs technology to enhance the experience and support an intelligent education system. Witama Primary School is one of the schools that applies modern technologies in teaching processes to improve learning motivation, especially in natural science. Students need an interactive instrument or tool that attracts attention and boosts their learning motivation while studying solar systems using a smartphone. Therefore, to present a virtual solar system experience, this study aims to create an interactive 3D solar system using Augmented Reality (AR) on iOS mobile-based by implementing a user-centered design (UCD) method to understand requirements and expectations. UCD consists of four main stages, specifying the context of use, user and organizational requirements, product design solution, and evaluation design before developing the system to ensure the finery development process as the goal is to improve the student's focus and learning motivation. According to the User Experience Questionnaire results to thirty respondents, the obtained aspect of attractiveness is 2.222, the perspicuity aspect is 2.129, the efficiency aspect is 1.902, the dependability aspect is 1.689, the stimulation aspect is 2.222, and the novelty aspect is 1.727. Overall results showed that the interactive mobile app is in the good to excellent range. Moreover, the students and teachers find the product exciting and feel motivated to use the mobile app furthe
IMPLEMENTATION OF STRING SIMILARITY ALGORITHM IN PUBLIC COMPLAINT APPLICATIONS TO MINIMIZE SIMILAR COMPLAINTS
Get PDFPresently, the complaining service in Kartasura local government relies on manual recording where people must come to the government office, write their complaints and submit them to the office staff. This situation causes inefficiency since people have to travel from their places to the local government office. Moreover, the manual recording makes the complaints cannot be managed properly since the same complaints can be submitted more than one time. Additionally, it also causes some confusion to the government staff since they need to carefully check whether it has been submitted previously and approve the complaints accordingly. To solve the issue, a web-based application complaint system is developed to reduce the number of the same complaints from the citizen as well as to help staff manage the complaints data. The Jaro-Winkler String Similarity algorithm is adopted to check the similarity of newly submitted complaints with existing complaints data. The algorithm detects similarities of newly submitted complaints by determining the level of string equality to the existing complaints. Experimental results using one month period of complaints data in December 2022 show that the application is able to detect the similarity between the newly submitted complaints to the existing complaints. As the value of similarity threshold is higher, the number of rejected complaints also increases. Meanwhile, the test results of the system using the System Usability Scale Score obtained an average value of 76.5, which means the system is included in the Acceptable category and can be used for daily activity
RAINFALL PREDICTION USING MULTIPLE LINEAR REGRESSION ALGORITHM
Get PDFIndonesia is a tropical region with ever-changing weather changes. It is necessary to conduct a research on weather prediction as a decision making regarding weather information that will occur in the future. Rainfall is one of the factors that cause changes in weather in an area. This research was conducted on the climate in the Yogyakarta region in the form of mountains and lowlands causing differences in rainfall. The variables that are used to make predictions are several parameters that affect rainfall, namely temperature, humidity, wind speed and duration of solar radiation. These 5 variables are processed through the data obtained then carried out research and comparisons with the previous data. Multiple linear regression is the algorithm used. This algorithm is one of the machine learning techniques by making rainfall data as the dependent variable and other parameters as independent variables. This study uses Yogyakarta City, Central Java climate data for 2010-2020. The results obtained are an R2 score of 12.99%. Prediction of rainfall is obtained at 14.41778516. Then the RMSE evaluation resulted in a deviation between predicted rainfall and actual rainfall of 14.78316110508722. Based on these results, it shows that there is light rain because it is in the intensity category of 5 mm – 20 mm/day
ENSEMBLE STACKING DALAM ANALISA SENTIMEN REAKSI VETERAN MILITER AS TERHADAP PENGAMBILALIHAN AFGHANISTAN OLEH TALIBAN
Get PDFAbstrak— Sentiment analysis can be used to glean information about user opinions and identify social or political trends. There have been many studies on sentiment analysis using machine learning or lexicon-based methods that have been quite impressive. However, machine learning models often have difficulty generalizing to new data due to various reasons, such as overfitting and limited training data. These models are also prone to bias and variance, which negatively affect the accuracy of their predictions. This study discusses the application of the ensemble stacking method in sentiment analysis with the topic of the takeover of Afghanistan by the Taliban. By monitoring social media, the author uses a dataset in the form of comments on YouTube news channels related to the topic raised. Several studies have shown how the ensemble stacking method predicts better than the single model. The research was carried out by creating a sentiment classification model with logistic regression machine learning algorithms, SVM, KNN, and CART then the ensemble stacking classifier formed by the base learner of the four algorithms. As a result, for a single classifier, the highest average accuracy is the logistic regression algorithm of 74.6 percent. The four algorithms are compiled and predicted by logistic regression, and the stacking ensemble classifier that is applied produces better accuracy than the stand-alone classifier, which is 75.3 percen
COMPARISON OF SIFT AND ORB METHODS IN IDENTIFYING THE FACE OF BUDDHA STATUE
Get PDFThe statue is part of the heritage facial recognition process which is immobile and artistically stylized. Identifying the similarities between the statues can help provide an important reference for tourism in recognizing the faces of the statues which are different and have almost the same characteristics in every country, especially in Indonesia, among the facial recognition of the statues based on the condition, color, and shape of the face. The purpose of this study is to apply the original images that have characteristics, partially done manually to various types of transformations and calculate matching evaluation parameters such as the number of key points in the image, the level of matching, and the required execution time for each algorithm. To confirm the efficiency of the proposed method, experiments were carried out on private data sets obtained from statues under low light conditions and in different poses. The data was taken based on the image of the Buddha's face and matched with the facial image of the Buddha statue available in the database using comparisons resulting from data processing using the Sift and ORB methods with various types of transformations. The result will be seen in the image that is matched with the best algorithm for each type of distortion. The faces tested are images of the faces of the Buddha statues that are recognized, and photos of some of the original statues that were not saved due to unclear lighting and camera distance factors. The results show that the number of key points generated is the number of key points, the ORB method gives fewer results compared to the SIFT method and the average SIFT recognition and processing time shows better performance for an average of 100% at a SIFT matching rate of 2% with time 0.400285 and the ORB method is 1% for the time 0.40096
MUSIC RECOMMENDATION SYSTEM BASED ON COSINE SIMILARITY AND SUPERVISED GENRE CLASSIFICATION
Get PDFCategorizing musical styles can be useful in solving various practical problems, such as establishing musical relationships between songs, similar songs, and finding communities that share an interest in a particular genre. Our goal in this research is to determine the most effective machine learning technique to accurately predict song genres using the K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) algorithms. In addition, this article offers a contrastive examination of the K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) when dimensioning is considered and without using Principal Component Analysis (PCA) for dimension reduction. MFCC is used to collect data from datasets. In addition, each track uses the MFCC feature. The results reveal that the K-Nearest Neighbors and Support Vector Machine offer more precise results without reducing dimensions than PCA results. The accuracy of using the PCA method is 58% and has the potential to decrease. In this music genre classification, K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) are proven to be more efficient classifiers. K-Nearest Neighbors accuracy is 64,9%, and Support Vector Machine (SVM) accuracy is 77%. Not only that, but we also created a recommender system using cosine similarity to provide recommendations for songs that have relatively the same genre. From one sample of the songs tested, five songs were obtained that had the same genre with an average accuracy of 80%
KOMPARASI FUNGSI AKTIVASI NEURAL NETWORK PADA DATA TIME SERIES
Get PDFAbstract— The sophistication and success of machine learning in solving problems in various fields of artificial intelligence cannot be separated from the neural networks that form the basis of its algorithms. Meanwhile, the essence of a neural network lies in its activation function. However because so many activation function which are merged lately, it’s needed to search for proper activation function according to the model and it’s dataset used. In this study, the activation functions commonly used in machine learning models will be tested, namely; ReLU, GELU and SELU, for time series data in the form of stock prices. These activation functions are implemented in python and use the TensorFlow library, as well as a model developed based on the Convolutional Neural Network (CNN). From the results of this implementation, the results obtained with the CNN model, that the GELU activation function for time series data has the smallest loss valu