Bayes Algorithm Research Articles

Comparison of Naive Bayes and PSO-Based Naive Bayes Algorithms for Prediction of Covid-19 Patient Recovery Data in Indonesia

A brand new disease known as COVID 19 was identified in 2019 but has yet to infect humans (World Health Organization, 2019). This group of viruses can infect mammals, including humans and birds, and cause sickness. People commonly contract coronaviruses from the flu and other minor respiratory diseases, but they can also spread serious diseases such as SARS, MERS, and the deadly COVID-19. Therefore, to avoid further casualties, this number must be decreased. It is crucial to understand the variables that can truly reduce the danger of death and gauge the propensity for recovery in Covid-19 patients. Several techniques in data mining can be used to forecast patient recovery rates depending on various characteristics. The criteria of this study included gender, age, province, and status. The Naive Bayes (NB) and Pso-based Naive Bayes algorithms are compared in this study using patient data sets to determine whether the strategy is more accurate. The findings of this study reveal that the NB method has a 94.07% accuracy rate, a precision value of 14%, a recall value of 1% and an AUC value of 0.613, according to the study data. The accuracy rate of the Naive Bayes based on PSO is 95.56%, the precision is 25%, the recall is 1%, and the AUC is 0.540.

OPTIMASI NAIVE BAYES BERBASIS PSO UNTUK ANALISA SENTIMEN PERKEMBANGAN ARTIFICIAL INTELLIGENCE DI TWITTER

At present the development of Artificial Intelligence technology is progressing rapidly. There are many new artificial intelligence technologies available in various fields. Artificial Intelligence is an artificial intelligence program that can study data, perform processes of thinking and acting like humans. The presence of Artificial Intelligence technology has many positive impacts, especially in increasing work effectiveness and efficiency. However, AI is also a threat to human resources because slowly human work is being replaced by Artificial Intelligence. Various opinions about the development of Artificial Intelligence are widely discussed on social media such as Twitter. Sentiment analysis is a computational study to automatically categorize opinions into positive or negative categories. In this study, the Naive Bayes algorithm was used to analyze sentiment or public opinion regarding the development of Artificial Intelligence for Twitter users. The data collection method used is crawling data on Twitter. The results of the sentiment classification test for the development of Artificial Intelligence using Naive Bayes yield an accuracy value of 86.42%. Meanwhile, the results of the sentiment classification test using Naive Bayes based on Particle Swarm Optimization (PSO) increased with an accuracy value of 87.55%. Based on the results of this study, the use of PSO as an optimization technique for the Naive Bayes algorithm is proven to be the best algorithm model in sentiment analysis for the development of Artificial Intelligence for English text.

SENTIMEN ANALISIS CHATGPT DENGAN ALGORITMA NAÏVE BAYES DAN OPTIMASI PSO

Abstract— ChatGPT which is an OpenAI technology that responds to conversations between humans and machines. enabling users of all ages and backgrounds to communicate naturally in multiple languages ​​without having prior knowledge or experience in programming or the computer world. However, a technology will always be at odds and has flaws on the human side, various assumptions about chatGPT are formed from many sides, such as in the world of education, chatGPT creates parallels for teachers and lecturers. When giving assignments, students/students can use chatGPT as material in answering assignments from teachers/lecturers. And that results in students/students not carefully reading the answers to these assignments, if that continues to happen, students/students will find it too easy to get something and then will lose interest in solving problems with their own efforts. This article aims to analyze sentiment analysis whose data is taken from Twitter using the keyword "CahtGPT OpenAI". With 2,000 data calculated using the naive Bayes algorithm and optimized using PSO, it is found that sentiment analysis for chatGPT itself has an accuracy of 69.23% with a positive class of 0.503 and a negative of 0.497 and obtains an AUC curve value of 0.68 +/- 0.55..

Multilabel sentiment analysis for classification of the spread of COVID-19 in Indonesia using machine learning

This study aims to use datasets on Twitter to find out public opinion on the spread of coronavirus in Indonesia by conducting sentiment analysis. The resulting sentiment analysis will benefit the community by helping the Indonesian government take various strategic measures to prevent and counter the spread of the COVID-19. This research was conducted through the data collection stage, namely crawling data tweet words in Bahasa Indonesia containing the meaning of the spread of COVID-19, the next stage of the process of creating labels manually. Next, the pre-process stage by removing the character, symbols and special features from Twitter. The last stage, classification using learning machine with 3(three) methods namely K-nearest neighbor (K-NN), Naïve Bayes and decision tree. The study analyzed sentiment of 1,119 valid Tweets data and found that K-NN algorithm had the highest accuracy value compared to Naïve Bayes and decision tree algorithms, which was 95.10%. However, the Twitter data analyzed obtained 78.19% of Tweets that fall into the negative category and only 13.85% of public opinion that is positive. This indicates that most of the Tweets of Indonesians in twitter do not mean the spread of COVID-19 disease somewhere.

Sistem Pakar Untuk Mendeteksi Kerusakan Sepeda Motor Menggunakan Algorima Naïve Bayes


 Damage to the motor occurs due to negligence in carrying out maintenance. Currently, the process of checking motorcycle damage in a workshop generally still uses manual methods in analyzing motorcycle damage. This results in inefficient and time-consuming motor checking. The purpose of this study was to design and build an expert system to detect such damage using the Naïve Bayes algorithm method. It is hoped that with the expert system, the checking time will be more efficient. An expert system is a computer program created to solve a problem in a particular field based on knowledge, facts and reasoning techniques from an expert in solving problems. The method of data collection carried out includes interviews with mechanics, so that reliable data can be obtained. The symptoms used in motor damage detection are eroded pulleg nut (G13), eroded V-belt (G15), eroded gear (G16). So that two damages were identified, namely damage to the CVT engine (K3) and the basic clutch (K5). Based on the percentage results of each damage, namely K3 of 83.33% and K5 of 16.66%. So it can be concluded the probability of damage that occurs, namely damage to the CVT engine (K3).

Detecting Spammers on Social Networks

Social media is an online platform, one person can make social relations with the other persons easily via social platform (Facebook, twitter, LinkedIn).Nowadays in social media platform peoples are sharing their personal information’s, habits, carrier interest, activities to their friends. Various information is propagating through online social networks including both the positive and negative. Online information sharing is becoming ubiquitous every day. In this paper, naïve Bayes algorithm is used to detect the wrong information for example the online rumors, and it is also used to work out the distinguish and predictive problems. And Naïve Bayes algorithm is also used for text classification, spam filtering, hybrid recommender system and collaborative filtering.

Sentiment Analysis of Nanovest Investment Application Using Naive Bayes Algorithm

Various applications provide simple ways for individuals interested in investing in crypto assets or stocks - both domestic and international - to do so. One of the companies in this industry, Nanovest, has launched the Nanovest investment application. Since its release in 2022, numerous positive and negative responses have been on Google Play, the App Store, and Twitter. However, Nanovest faces two main problems regarding the use of its application. First, they often receive complaints submitted to the operational team, indicating dissatisfaction or problems faced by users. Second, Nanovest has never conducted formal research regarding user experience in using their application. This indicates a lack of understanding of the perspectives, needs and challenges faced by users. This study tries to find out how the public responds to the Nanovest application through a sentiment analysis. This study used tweet and review data from January 1, 2022, to February 17, 2023. The data underwent sentiment analysis, employing the Naïve Bayes algorithm, and were classified into positive and negative sentiments. The findings revealed that 96.07% of the sentiments expressed towards Nanovest were positive, while 22.11% were negative, with these percentages calculated based on the total number of sentiments detected in the data. To evaluate the model's performance, a 10-fold cross-validation approach was utilized alongside the Naïve Bayes algorithm, resulting in an impressive accuracy rate of 94.8391%. This positive sentiment suggests that users are highly favorable towards the crypto assets and global stock investment services offered by the Nanovest application. Nevertheless, 3.93% of users still expressed dissatisfaction with the app due to some flaws that existed when Nanovest was initially launched. Based on the results that have been obtained and analyzed for the development team, it is recommended to make three improvements, namely reducing application size to minimize memory usage, increasing overall application performance, and increasing access speed across all features to allow application users to access more efficiently. It is recommended for the product team and stakeholders to consider developing the Candlestick chart feature into the application. This also increases the competitiveness of the Nanovest application against other applications.

Algoritma Naïve Bayes untuk Rekomendasi Seleksi Peserta Paskibraka Berbasis Website

In the series of intensive Paskibraka activies, each individual strives to build strong character. Paskibraka serves as a means to cultivate a sense of love for the homeland. Every year, on the commemoration of the raising ceremony is conducted on August 17th. One important part of the ceremony is the hosting of the red and white flag by Paskibraka member. PASKIBRAKA (Flag Raising Troop) represent the selected new generation of Indonesia through a selection process participated by student from various high school. The PASKIBRAKA selection involves several stages, and to facilitate the selection process, a guideline for activities has been formulated in the Minister of Youth and Sports Regulation (Permenpora) No. 0065 of 2015. The classification appiled utilizes the Naïve Bayes algorithm with the Knowladge Discovery in Databases (KDD) method. The naïve bayes algorithm is a data mining and statistical classification algorithm that applies Bayes’theorem under the assumption of independence between variables. The advantages of the naïve bayes algorithm lie in its scability in handing the number of predictors and data points, its ability to make probability prediction, and its capability to handle both continuous and discrate data. The result of this research have achieved the from of automated classification of the eligibility of Paskibraka participant selection, data mining whether the are aligible or not to become members of Paskibraka in Sukabumi Regency.

Performance Evaluation of Computer Science Students in Mathematical Courses Over Prgramming Language Courses

Abstract: The low level of performance observed among many computer science students in mathematical and computer programming courses served as the motivation for this work. The prediction of computer science students’ overall performance in mathematical courses over programming courses was done using WEKA and machine learning algorithms. Dataset of students performance from Yaba College of Technology, Lagos, Nigeria and Lagos State Polytechnic, Lagos, Nigeria, for both National Diploma and Higher National Diploma were used. These data set were studied and analyzed using WEKA and Random Forest, J48, Naïve Bayes and Logistic Regression algorithms. The algorithms were applied for the HND and ND dataset and there was comparison based on their accurancy, learning time and percentage of correctly classified instances. This comparison showed that there is direct relation between the execution time in building models and volume of data records. This shows that the predictor did not only predict the number of students that are likely to be in distinction, upper credit, lower credit, pass and fail but also show the relationship between having the knowledge of mathematics and programming language for an overall performance in computer science. The knowledge pattern represented further satisfies the exertion that it is imperative for students to have a standard knowledge of mathematics as this will help in being the best in their chosen profession.

Development of Web-Based Plant Sensor Tool Data Processing Application

The rapid development of technology in the 4.0 era brings changes in the use of technology. The industrial 4.0 era makes IoT (Internet of Things) media a technology that can support all sectors of activity including the agriculture or farming sector. IoT media takes place in the form of plant sensor device that can be used to measure changes in environmental parameters. These changes include light intensity, temperature, humidity, soil fertility, are the supporting factors for plant growth. This study aims to design a website-based application that can read plant sensor data. Sensor device data is collected and downloaded in CSV (Comma Separated Values) format. Data processing requires the Naïve Bayes algorithm as a method for grouping parameter data. The results of the study are in the form of a website design that can help analyze the ideal environmental parameters for farming activities.
 

Analisis Sentimen Komentar Netizen Terhadap Pembubaran Konser NCT 127 Menggunakan Metode Naive Bayes

The present rate of technological advancement has resulted in the rapid spread of information, which is easily available through social media platforms such as Twitter. Users of Twitter can send and read content in the form of text or videos using the facilities that Twitter itself offers. Numerous Twitter users have commented on the NCT 127 concert's recent dissolution, which has drawn both supportive and critical remarks. A dataset of 2451 tweets was created by gathering information from Twitter using the keyword "nct" between November 4 and November 6, 2022. The data was subsequently cleaned, yielding a total of 2451 useable data points. Labeling and the Naive Bayes algorithm were then applied to the data. The goal of this study was to count the number of favorable and unfavorable tweets and evaluate how well the Naive Bayes algorithm was applied. According to the trials done, there were 559 favorable remarks and 1,892 negative ones. The accuracy of the evaluation tests was 82.01%. Additionally, the analysis of negative sentiment produced a f1-score of 79.21%, a recall of 68.52%, and precision of 93.84%. Contrarily, the evaluation of positive attitude produced a f1-score of 84.15%, a recall of 95.50%, and a precision of 75.21%. The Naive Bayes method, it may be inferred, can categorize and process with a very consistent accuracy that approaches near-perfect outcomes.

Comparison of KNN, naive bayes, and decision tree methods in predicting the accuracy of classification of immunotherapy dataset

Health 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%.

Insecticide Monitoring in Cattle Dip with an E-Nose System and Room Temperature Screen-Printed ZnO Gas Sensors

Taktic, an Amitraz-based insecticide, is commonly used in sub-Saharan Africa to treat cattle for ticks. Due to misuse in rural dipping pools, some ticks are showing resistance to Taktic. This work presents a low-cost e-nose with commercial sensors to monitor Taktic levels in dipping pool water. The device shows distinctly different measurements for the odours of air, distilled water, farm water, and four levels of Taktic insecticide in farm water. A naive Bayes algorithm with a Gaussian distribution is trained on the data and a validation set achieves a 96.5% accuracy. This work also compares two sol-gel ZnO nanoparticle solutions with an off-the-shelf ZnO nanoparticle ink for use as active material in chemiresistive gas sensors to be employed in an e-nose array. The ZnO solutions are screen-printed onto gold electrodes, auto-sintered with a built in heater, and used with UV illumination to operate as low-power, room temperature gas sensors. All of the screen-printed ZnO sensors show distinct changes in resistance when exposed to Taktic vapours under room temperature and humidity conditions. The custom room temperature ZnO gas sensors fabricated via facile and low-cost processes are suitable for future integration in a point-of-need microsystem for the detection of Taktic in water.

Personality Prediction Based on Facebook Media Social Status Using the Method Naïve Bayes and KNN

Social media is now almost a part of life for people of almost all ages with diverse personalities. This study aims to predict a person's personality based on Facebook social media status using the Naïve Bayes algorithm and K-NN personalities based on the big five. The data taken from MyPersonality which is divided into 40% training data, and 60% test data produces an accuracy rate of 100%, 100% precision, 100% recall can be concluded that the prediction variables are (significant) with the Naive Bayes method. While predictions using the KNN method showed an average personality prediction value with an accuracy of 58.96% (Insignificant), precision 99.12% (Significant), and recall 2.34% (Insignificant). Based on research can be concluded that both Naïve Bayes and KNN methods can be used to predict personality based on Facebook status.

IMPLEMENTASI METODE TF-IDF DAN ALGORITMA NAIVE BAYES DALAM APLIKASI DIABETIC BERBASIS ANDROID

Diabetes is a serious disease that occurs when the pancreas does not produce enough insulin as a hormone that regulates blood sugar in the body. This disease also has an impact on health. This research builds an Android-based application called Diabetic to help classify and provide information related to diabetes and analyze the performance of the Term Frequency Inverse Document Frequency method and the Naive Bayes algorithm. The Term Frequency Inverse Document Frequency method is a technique for calculating the presence of words in a collection of documents by creating document vectors. The Naive Bayes algorithm is an algorithm that uses probability to solve a classification case. This algorithm has an efficient and fast calculation. Based on this research, it is known that the Naive Bayes Algorithm produces an accuracy of 66% by taking a computation time of 39 seconds with a memory consumption of 80 to 351 mb.

Komparasi Algoritma Data Mining Dalam Ketuntasan Belajar Daring Siswa Pada Masa Pandemi Covid 19

This research was conducted at SMA Negri 3 Selong and became the focus of students in class XI IPA and Social Studies. The sampling technique used purposive sampling method. This study aims to describe the extent to which the level of completeness of students during post-covid-19 pandemic learning with online media. This study uses a classification algorithm that functions to find a model that distinguishes data classes or data concepts, with the specific objective of determining the class of unknown object labels. The method used is the PSO-based Naïve Bayes and Naïve Bayes Comparison Algorithms. The results of this study indicate that the use of online media during online learning using the naïve Bayes algorithm is 83.91%, and the PSO-based naïve Bayes algorithm is 91.98%, from the experimental results and testing of the two algorithms, the results of the confusion matrix and AUC testing can be obtained which can be determined the best accuracy value is the PSO-based Naïve Bayes algorithm. As for the comparison of the results in the form of an accuracy value obtained by the Naïve Bayes Algorithm of 83.91% and the PSO-Based Naïve Bayes Algorithm of 91.98% and the difference in the level of accuracy of 8.07%, so it can be concluded that the algorithm that is suitable for classifying student learning completeness during the covid 19 pandemic is Naive Bayes based on particle swarm optimization.

Prediksi Tingkat Kesehatan Lingkungan Masyarakat Dalam Program Sustainable Development Goals Menggunakan Algoritma Naive Bayes

Indications of a decrease in the level of public awareness about protecting the environment have a direct effect on the urgent need for integrated environmental planning and management, so that the impact affects other aspects, such as the physical and socio-economic environment. The fact is that environmental damage is closely related to poverty and economic growth. In maintaining community environmental health and implementing the Sustainable Development Goals Program in East Lombok Regency, West Nusa Tenggara. Hamzanwadi University is working with the East Lombok Regency government to conduct sampling of the community with a total data of 4624 residents in ten sub-districts in the East Lombok Regency area. The purpose of this study is to help predict the level of public environmental health in the East Lombok region, the data will be classified and then processed using the Naïve Bayes algorithm with the multinomial naïve Bayes method. The results of testing the naïve Bayes algorithm after splitting the data five times, the best results were obtained, with the dataset divided by 20% testing data and 80% training data, an prediction value of 93.28% was obtained. The population environment in ten sub-districts in East Lombok Regency was classified as healthy

Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Untuk Klasifikasi Kelayakan Pemberian Pinjaman

Cooperatives are social organizations or economic bodies that have a very important role in the growth, development of economic potential and community success. One of the cooperative activities is the provision of credit or loans to community members. Cooperative credit is one of the most important banking activities and serves to provide credit to the community. In practice, errors often arise due to inaccurate credit analysis, or the behavior of the customers themselves. The purpose of this research is to compare the accuracy results between the Naive Bayes algorithm and Support Vector Machine (SVM), where the best accuracy results can later be used as a reference to determine the profitability of lending. The attributes used in this study consist of 11 attributes, namely: Gender, marital status, occupation, relatives, nominal income, income criteria, loan amount, loan term, interest rate, installments and class as income characteristics. The dataset used in this study includes 166 members of the Daru Nahdla Capita Shari'ah cooperative. The results of testing the naive bayes algorithm after dividing the data five times, dividing the data set 70% as test data and 30% as training data, obtained a precision value of 97.00%, recall 100.00%, F1 score 99.00%. and accuracy 98.00%. Thus, the Naive Bayesian algorithm is an algorithm that shows accurate classification and prediction

Application of Naïve Bayes and Support Vector Machine Algorithms to Classify Exclusive Breastfeeding in Indonesia

Application of Naïve Bayes and Support Vector Machine Algorithms to Classify Exclusive Breastfeeding in Indonesia

Sentiment Analysis of Twitter Discussions on Rafael Alun: Multinomial Naïve Bayes and Decision Tree Approach

Political events in Indonesia often draw significant attention on the social media platform Twitter, with netizens using the medium to express their opinions and feelings. One such viral topic on Twitter revolves around Rafael Alun, a former Director General of Taxation at the Indonesian Ministry of Finance who has been implicated in a possible gratification scandal and subsequently investigated by the Corruption Eradication Commission (KPK). As a result, Rafael Alun's name has been trending on Twitter. By using sentiment analysis, it is possible to identify the prevailing sentiment elements within tweets related to Rafael Alun. The application of Multinomial Naïve Bayes and Decision Tree algorithms serves to determine the accuracy of sentiment analysis results derived from Twitter tweets data. The research process involves steps such as data pre-processing, data processing, classification and evaluation. The sentiment analysis of tweets containing the mention of "Rafael Alun" shows that the majority of the tweets express a negative sentiment. The accuracy rates obtained by implementing the Multinomial Naïve Bayes and Decision Tree algorithms are 77% and 72% respectively. It is worth noting that these percentages are relatively moderate due to the unbalanced distribution of positive, negative and neutral sentiments on the topic.