At present, the franchise business in Indonesia has a relatively high attractiveness. However, many business actors also failed. For someone who wants to start a business needs to consider the public sentiment towards the franchise business. Although, it is not easy to do with sentiment analysis because of the large number of conversations on Twitter about franchising and unstructured data.
In recent years, the diabetes mellitus in Indonesia has become a health problem in the community because its population has increased 2-3 times faster than other countries. Diabetes prevalence in Indonesia ranks 4th highest in the world after China, India and the United States. People can prevent complications and premature death if they detect early symptoms of diabetes. However, people do not know that they are at risk of diabetes, not had knowledge about the symptoms of diabetes, complexity of the process diagnosis and the high cost of examinations. Therefore, we need an application that can provide the results of the type of diabetes and its management solutions as practiced by experts. The aim of this research is to develop an expert system for detection types of diabetes such as: type one diabetes, type two diabetes, neuropathy diabetes, diabetes retinopathy, and diabetes nephropathy. The object of this research is diabetes carried out in March to April 2019 in the Klinik Pratama Desa Putera. This study uses primary data from patients who had a history of diabetes at Klinik Pratama Desa Putra and secondary data in the form of literature, research journals, and data documents needed to compile this study. In addition, we generated a knowledge base using forward chaining. The test results show that the expert system meets the functional requirements and the system performance reaches an accuracy of 100%. This expert system helps people in Indonesia to detect diabetes early so that it can prevent complications.
The Formula E racing series has become one of the world's most prestigious competitions. In 2022, Indonesia hosted the famous Formula E race. The event possesses the potential for economic benefits for Indonesia worth 78 million euros through the arrival of 35,000 spectators. Indonesians are enthusiastic about Formula E since it allows their nation to encourage tourists and gain international prominence. However, some people do not support this event. Since they regard that amid the COVID-19 pandemic, it is preferable for the government to focus on people affected by the pandemic rather than support a Formula E event. This study compares the Support Vector Machine and Naive Bayes algorithms in classifying public opinion in the Formula E race. This study gets its information from user comments on social media platforms, especially Twitter. The stages start with text preprocessing and include cleaning, case folding, tokenization, filtering, and stemming. Proceed with weighting using the TF-IDF approach. Data testing uses a confusion matrix to evaluate the classification results by testing accuracy, precision, and recall. Categorizing public opinion using the SVM algorithm has an accuracy of 82 percent, a precision of 97.86 percent, and a recall of 77.90 percent. On the other hand, the accuracy of the Naive Bayes technique is more limited, at 87.54 percent. Society's opinion on Twitter shows positive sentiment towards implementing Formula E.
Digital wallet services provide many conveniences and benefits to its users. However, not all digital wallet service users have a positive opinion of the service. Sentiment analysis in this study aims to determine the opinions given by Dana and Isaku digital wallet service users whether they contain positive or negative opinions and apply the Naïve Bayes Classifier and Particle Swarm Optimization (PSO) method to the sentiment analysis of digital wallet service users. The Naïve Bayes Classifier method is used because it is simple, fast, high accuracy, and has good enough performance to classify data, but the Naïve Bayes Classifier has the disadvantage that each independent variable is assumed to cause a decrease in the accuracy value. Therefore, this research added an attribute weighting method, namely Particle Swarm Optimization (PSO) to increase the accuracy of the classification of the Naïve Bayes Classifier. This study uses data taken from Twitter as many as 490 tweet data. The test results using the confusion matrix and ROC curve show an increase in accuracy of the Naïve Bayes Classifier Dana digital wallet from 60.00% to 91.67% and I.Saku digital wallet from 53.23% to 85.00%. T-Test and Anova test results show that the two classification methods tested have significant (significant) differences in Accuracy values.
In business, cooperatives have an important role in improving the national economy. The Inability of member to pay installments of credit is a major problem that occurs in cooperatives. Consequently, it was occurred non-performing loan. Cooperatives can avoid non-performing loan by making predictions of cooperative members who are potentially late in paying credit. In several studies have used Naive Bayes for classification problems because of the efficient calculation and the high accuracy. But Naive Bayes assumes all the attributes of the class are independent of other attributes. Making it especially suitable for the classification problems with large attributes. However, this assumption is often untenable in the real classification problem. So in several documents, Naive Bayes performance is not perfect. The aim of this study is to optimize the Naive Bayes method using Particle Swarm Optimization (PSO) and Sample Stratified to improve accuracy in predicting non-performing loan in cooperatives. This study uses data from Pusat Data Koperasi (PUSKOPDIT) DKI Jakarta. The credit set data obtained were 565 records with 15 predictor attributes and 1 class attribute. The test results with confusion matrix and ROC curve obtained an accuracy value of 86% and an AUC value of 0.867 with a diagnosis of good classification. This study shows that the use PSO on NBC to predict non-performing loan in cooperatives increases the accuracy of 21.03% and AUC by 0.069. The results of the T-Test and ANOVA test showed that the two classification methods tested had significant (significant) differences in the AUC values. AbstrakDalam bisnis, koperasi memiliki peranan penting dalam meningkatkan perekonomian nasional. Ketidakmampuan anggota untuk membayar angsuran kredit merupakan masalah utama yang terjadi pada koperasi. Akibatnya, terjadi kredit macet. Koperasi dapat menghindari kredit macet dengan membuat prediksi dari anggota koperasi yang berpotensi terlambat membayar kredit. Dalam beberapa penelitian telah menggunakan Naive Bayes untuk masalah klasifikasi karena perhitungan yang efisien, dan akurasi tinggi. Tetapi Naive Bayes mengasumsikan bahwa semua atribut kelas tidak tergantung pada atribut lainnya. Naive Bayes sesuai untuk masalah klasifikasi dengan atribut besar. Namun, asumsi ini sering tidak dapat dipertahankan dalam masalah klasifikasi nyata. Dalam beberapa dokumen, kinerja Naive Bayes tidak sempurna. Tujuan dari penelitian ini adalah untuk mengoptimalkan metode Naive Bayes menggunakan Particle Swarm Optimization (PSO) dan untuk meningkatkan akurasi dalam memprediksi kredit macet di koperasi. Penelitian ini menggunakan data dari Pusat Data Koperasi (PUSKOPDIT) DKI Jakarta. Data set kredit yang diperoleh sebanyak 565 record dengan 15 prediktor atribut dan 1 atribut kelas. Hasil pengujian dengan confusion matrix dan kurva ROC diperoleh dari nilai akurasi sebesar 86% dan nilai sebesar 0,867 dengan diagnosis klasifikasi baik. Penelitian ini menunjukkan bahwa penggunaan PSO pada NBC untuk memprediksi kredit macet meningkatkan ...
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