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This research analyzes public sentiment towards the topic of the House of Representatives' Right of Inquiry on Twitter using Naïve Bayes Classifier and Decision Tree algorithms. The goal is to compare the effectiveness of the two algorithms in political sentiment analysis. . The research methodology includes data collection from Twitter, data pre-processing, sentiment classification, and result analysis. Sentiment analysis reveals the dominance of positive sentiment related to the DPR's Right of Inquiry. However, this study has limitations in terms of dataset size and depth of text-based sentiment analysis. This research contributes to a better understanding of public sentiment towards political issues in Indonesia and highlights the importance of proper algorithm selection in social media sentiment analysis. Development suggestions include exploration of deep learning techniques, integration of multimodal analysis, data balancing (oversampling or undersampling) and improvement of pre-processing so that the model is better able to capture negative contexts. The results of the study showed excellent performance of both Naive Bayes Classifier and Decision Tree algorithms with accuracy above 95%. Decision Tree excels with an accuracy of 99%, while Naïve Bayes Classifier performs better with an accuracy of 96%. The results with the Confusion Matrix test are precision 0.98, recall 1.00, and F1-Score 0.99.

This research addresses the pressing issue of malnutrition among toddlers in Indonesia, aiming to classify their nutritional status using the Naive Bayes Classifier (NBC). The study utilizes a dataset comprising 958 records from Puskesmas Cilandak and categorizes nutritional status into six class labels: good nutrition, at risk of excess nutrition, excess nutrition, obesity, undernutrition, and severe malnutrition. The methodology includes data preprocessing techniques such as class weighting to tackle class imbalance and Principal Component Analysis (PCA) for effective feature extraction. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1 score, achieving an impressive accuracy of 85.76% when class weighting is applied, which significantly enhances the recall and F1 scores for minority classes. The findings highlight the critical importance of robust preprocessing and evaluation metrics in improving machine learning models for public health applications. Furthermore, they suggest that further exploration of alternative algorithms and dataset expansion could yield more comprehensive insights into the classification of toddler nutritional status.

Diabetes is a global health problem that is increasing in prevalence worldwide. This study compares the performance of two data analysis methods, namely binary logistic regression and naïve bayes classifier in predicting diabetes risk. This study aims to identify factors that significantly affect diabetes risk and classify diabetes risk using binary logistic regression, then compare the classification with the naive bayes classifier algorithm. Binary logistic regression models the relationship between independent predictor variables and binary dependent variables, while naïve bayes classifier uses the assumption of independence between variables. In this study, both methods were evaluated based on accuracy, sensitivity, specificity and positive predictive value. The results show that the factors that influence the risk of diabetes are Age, Gender, Polyuria, Polydipsia, Genital thrush, Itching, Irritability, and Partial paresis. Furthermore, the binary logistic regression results have a higher classification accuracy (92.31%) compared to the naïve bayes classifier (84.61%). Therefore, binary logistic regression was identified as the best method to predict diabetes risk in the context of this study

Kepribadian Myers-Briggs Type Indicator ( MBTI ) telah menjadi topik populer dalam memahami karakteristik individu dan dampaknya pada interaksi sosial, karir, dan pengambilan keputusan. Model Machine Learning dengan algoritma Naive Bayes Classifier sering digunakan untuk memprediksi kepribadian MBTI berdasarkan data Twitter. Namun, seringkali terjadi ketidakseimbangan kelas, dengan beberapa jenis kepribadian yang memiliki sampel lebih sedikit. Untuk mengatasi hal ini, penelitian ini menggunakan teknik Synthetic Minority Over-sampling Technique (SMOTE) untuk meningkatkan jumlah sampel pada kelas minoritas. Selain itu, metode Term Frequency-Inverse Document Frequency (TF-IDF) digunakan untuk mengekstraksi fitur penting dari teks. Penelitian ini bertujuan menerapkan teknik SMOTE untuk mengatasi ketidakseimbangan kelas dalam klasifikasi kepribadian MBTI menggunakan beberapa algoritma Naive Bayes Classifier, termasuk Gaussian, Multinomial, Bernoulli, Complement, dan Logistic Regression berdasarkan model Keirsey: Artisan, Guardian, Rational, dan Idealist. Evaluasi menggunakan metode Hold-Out-Validation dengan membagi data menjadi 90% data latih dan 10% data uji. Hasil evaluasi menunjukkan performa rendah algoritma Naive Bayes Classifier untuk kelas Artisan dan Guardian, tetapi baik untuk kelas Rational dan Idealist. Algoritma Logistic Regression memiliki akurasi tertinggi 80% dan performa yang lebih baik secara keseluruhan, meskipun masih rendah untuk kelas Artisan dan Guardian. Dengan demikian, penelitian ini memberikan pemahaman tentang penggunaan algoritma Naive Bayes Classifier dan teknik SMOTE dalam prediksi kepribadian MBTI, dengan potensi peningkatan kinerja melalui penggunaan algoritma Logistic Regression. Abstract Myers-Briggs Type Indicator (MBTI) personality is becoming a popular topic in understanding individual characteristics and their impact on social interaction, career, and decision-making. Machine Learning models with Naive Bayes Classifier algorithms are often used to predict MBTI personalities from Twitter data. However, there is often a class imbalance, with some personality types having a smaller sample. To overcome this, this study used the Synthetic Minority Over-sampling Technique (SMOTE) technique to increase the number of samples in minority classes. Additionally, the Term Frequency-Inverse Document Frequency (TF-IDF) method is used to extract important features from text. This study aims to apply SMOTE techniques to address class imbalances in MBTI personality classification using several Naïve Bayes Classifier algorithms, including Gaussian, Multinomial, Bernoulli, Complement, and Logistic Regression based on Keirsey's model: Artisan, Guardian, Rational, and Idealist. Evaluation using the Hold-Out-Validation method by dividing the data into 90% training data and 10% test data. The evaluation results showed low performance of the Naive Bayes Classifier algorithm for the Artisan and Guardian classes, but both for the Rational and Idealist classes. The Logistic Regression algorithm has the highest accuracy of 79% and better performance overall, although it is still low for the Artisan and Guardian classes. Thus, this study provides insight into the use of Naive Bayes Classifier algorithm and SMOTE technique in MBTI personality prediction, with potential performance improvement through the use of Logistic Regression algorithm.

Game is a form of entertainment that is often used to refresh the mind from the fatigue of daily activities and routines. Honkai: Star Rail is a popular turn-based game from Hoyoverse available on Google Play Store. Several studies have proposed Sentiment Analysis with Naïve Bayes classification method. However, not many have identified the reviews of a game to the extent of identifying on its aspects. In aspect-based sentiment analysis, text is analyzed to identify various attributes or components, then the relevant sentiment (positive, negative, or neutral) for each of these attributes is determined. This research aims to analyze aspect-based sentiment using the Naïve Bayes Classifier method, as well as categorize sentiment into positive and negative, and classify reviews into certain aspects. The results obtained after 5-fold iteration obtained the best average accuracy of 79%, The evaluation results show that it is necessary to tune the model using Grid Search Hyperparameter Tuning. Optimization of smoothing parameters with alpha = 0.1 proved effective in improving model performance with the highest weighted average accuracy of 93%. The evaluation results show that Grid Search Hyperparameter Tuning optimization gives better performance to the Naive Bayes algorithm model in multi-label classification.

Peran rumah sakit dalam kehidupan masyarakat sangatlah penting terkait tingkat kepuasan Masyarakat terhadap pelayanan, fasilitas, dan aspek lainnya. Opini dan penilaian masyarakat turut menjadi penilaian terhadap kinerja pelayanan rumah sakit. Pada Google Maps Reviews banyak ulasan dari berbagai rumah sakit.Penilaian yang sangat besar dapat kita lihat pada Google Maps Reviews akan memakan waktu bagi masyarakat. Keluhan-keluhan Masyarakat disekitar penulis terhadap pelayanan rumah sakit di Malang menjadikan penilaian pelayanan rumah sakit di Malang menjadi objek dari penelitian dasar ini. Penulis memanfaatkan algoritma Naïve Bayes Classifier dan Cross Validation untuk mengkategorikan penilaian berdasarkan sentimen positif dan negatif serta aspek agar mempermudah pengkategorian. Aspek yang dipergunakan tersebut adalah aspek penanganan, fasilitas, administrasi, dan biaya. Penulis juga menggunakan analisis Root Cause untuk mempermudah masyarakat dan pihak terkait dalam menemukan masalah dan rekomendasi pemecahan masalah. Awalnya data di proses dengan text preprocessing lalu pembobotan kata TF-IDF, pelabelan data, penerapan algoritma Naïve Bayes Classifier dan mengambil sentimen negatif untuk menentukan Root Cause. Hasil pengujian dengan menggunakan Cross Validation dengan fold k-9 memiliki nilai accuracy 82,97% , precision sebesar 83,13%, recall 82,93%, dan f-measure sebesar 82,92%. Hasil uji dengan menggunakan 20% data tes diperoleh akurasi 90%. Abstract The role of hospitals in society is crucial in terms of the level of satisfaction that the community derives from their services, facilities, and other aspects. Public opinions and assessments also contribute to evaluating hospital service performance. On Google Maps Reviews, there are numerous reviews from various hospitals. A significant evaluation can be observed on Google Maps Reviews, which might take time for the community. The complaints of the community around the writer regarding the hospital services in Malang make the assessment of hospital services in Malang the subject of this basic research. The author utilizes the Naive Bayes Classifier algorithm and Cross Validation to categorize assessments based on positive and negative sentiments, as well as 4 aspects to facilitate categorization. The author also employs Root Cause analysis to aid the public and relevant parties in identifying issues and providing problem-solving recommendations. After processing the data through text preprocessing and TF-IDF word weighting, data labeling, applying the Naive Bayes Classifier algorithm, and extracting negative sentiments to determine the Root Cause in negative hospital sentiments. Based on this process, applying Cross Validation with k-9 folds yields the highest values: an accuracy of 82.97%, precision of 83.13%, recall of 82.93%, and an f-measure of 82.92%. Through the sentiment classification and Cross Validation process, the accuracy results in 90% for hospital reviews with the highest number of assessments divided into 2 sentiments and 4 aspects: positive and negative sentiments, as well as treatment, facilities, administration, and costs.

Since the onset of the COVID-19 pandemic in Indonesia, many people have turned to telemedicine programs as an alternative to minimize social interactions, opting for consultations from the safety of their homes using smartphones and internet connectivity. Given the necessity for physical distancing and avoiding crowded places, these applications have become indispensable substitutes for in-person medical consultations. Numerous apps facilitating access to healthcare services have been introduced in Indonesia, ranging from business startups to initiatives by the Ministry of Health. Telemedicine can potentially revolutionize healthcare in Indonesia, addressing critical health challenges. A significant issue within Indonesia's healthcare system is the scarcity of doctors and their uneven distribution. With only four doctors per 10,000 people, this figure falls far below the WHO guideline of 10 doctors per 1,000. Sentiment analysis of these applications was conducted to evaluate how telemedicine applications meet public needs and offer an alternative solution. Lexicon-based and naive Bayes methods were employed to classify tweet data into positive, neutral, and negative sentiments. The results revealed 908 positive tweets, 172 negative tweets, and 168 neutral tweets, indicating predominantly positive public perceptions of telemedicine applications. The naive Bayes classifier exhibited a 74% accuracy rate, with a precision of 98% and a recall of 86%. These findings underscore the positive impact and acceptance of telemedicine applications among the Indonesian populace, emphasizing their significance in augmenting the nation's healthcare landscape.

The Covid-19 pandemic has caused significant changes in people's lifestyles which are further strengthened by the rapid development of technology. This has resulted in increased use of the internet and accelerated dissemination of information through social media platforms. Not only for self-expression, social media can also be a means of communication, information, education, and even used as a marketing tool. Several social media platforms have recently been popular and widely used, the number of users is increasing from year to year, and each user can provide a rating review of the application. To find out public opinion on social media platforms, sentiment analysis will be carried out on several social media platform applications on the Google Play Store, namely Twitter, Instagram and Tiktok which will later be used as material for evaluating these applications. In this study, the dataset was taken based on ratings from user reviews on the Google Play Store using the NBC (Naïve Bayes Classifier) method with the Python programming language. Based on testing of 1000 comment review data from each application, it was found that the majority gave positive sentiment (Twitter 57.2%, Instagram 74.1%, Tiktok 83.9%), and negative sentiment (Twitter 42.8%, Instagram 25.9%, Tiktok 16.1%) with an accuracy rate of 85.6% for the Twitter application, 83.6% for the Instagram application, and 84.8% for the Tiktok application.

Taiwan’s auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target risker samples. We first classify data using a Naive Bayes classifier into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring its representativeness. The user-based approach samples data symmetrically around the median of a class as audit evidence. It may be equivalent to a combination of monetary and variable samplings. The item-based approach represents asymmetric sampling based on posterior probabilities for obtaining risky samples as audit evidence. It may be identical to a combination of non-statistical and monetary samplings. Auditors can hybridize those user-based and item-based approaches to balance representativeness and riskiness in selecting audit evidence. Three experiments show that sampling using machine learning integration has the benefits of drawing unbiased samples; handling complex patterns, correlations, and unstructured data; and improving efficiency in sampling big data. However, the limitations are the classification accuracy output by machine learning algorithms and the range of prior probabilities.

Cerebral microbleeds (CMBs) in the brain are the essential indicators of critical brain disorders such as dementia and ischemic stroke. Generally, CMBs are detected manually by experts, which is an exhaustive task with limited productivity. Since CMBs have complex morphological nature, manual detection is prone to errors. This paper presents a machine learning-based automated CMB detection technique in the brain susceptibility-weighted imaging (SWI) scans based on statistical feature extraction and classification. The proposed method consists of three steps: (1) removal of the skull and extraction of the brain; (2) thresholding for the extraction of initial candidates; and (3) extracting features and applying classification models such as random forest and naive Bayes classifiers for the detection of true positive CMBs. The proposed technique is validated on a dataset consisting of 20 subjects. The dataset is divided into training data that consist of 14 subjects with 104 microbleeds and testing data that consist of 6 subjects with 63 microbleeds. We were able to achieve 85.7% sensitivity using the random forest classifier with 4.2 false positives per CMB, and the naive Bayes classifier achieved 90.5% sensitivity with 5.5 false positives per CMB. The proposed technique outperformed many state-of-the-art methods proposed in previous studies. Key words: cerebral microbleeds, random forest classifier, naive Bayes classifier, brain bleeds, hemosiderin deposits, 1. Introduction Cerebral microbleeds (CMBs) are small bleedings inside the brain that are believed to be caused by leakage of blood from blood vessels due to some medical conditions (Greenberg [...]

Heart failure (ARF) is a health problem that has relatively high mortality and morbidity rates in developed or developing countries, including Indonesia. In 2016, WHO stated that 17.5 million people died from cardiovascular disease, while in 2008, HF disease represented 31% of patient deaths worldwide. One of the new breakthroughs for early diagnosis is utilizing data mining techniques. In this study, the Correlated Naive Bayes Classifier (C-NBC) and Naive Bayes Classifier (NBC) algorithms are used to obtaining the best accuracy results so that they can be used for the Heart Failure dataset. Based on the results of the tests that have been carried out, it shows that the Correlated Naive Bayes Classifier (C-NBC) algorithm accuracy of 80.6% obtains higher accuracy than the Naive Bayes Classifier (NBC) algorithm of 67.5%. With the results of this study, the use of the Correlated Naive Bayes Classifier (C-NBC) algorithm can be used to diagnose patients with heart failure (heart failure) because it has a high level of accuracy and is categorized as Good Classification.

Emotion analysis from Bengali text data is challenging due to the intricate structure of the language itself and lack of resource availability tailored to Sentiment Classification. In this paper, the authors have used machine learning algorithms, particularly Gaussian Naive Bayes and Support Vector Machine, for the classification of six emotions in Bengali text. The data is comprehensively pre-processed through segmentation, emoticon handling, removal of stop words, and stemming. It uses feature selection techniques like unigram, bi-gram, and term frequency-inverse document frequency to improve classification accuracy. The main aim of the paper is to present an in-depth analysis of emotion detection in Bengali text, which would be very helpful to scholars working on NLP problems in non-English languages. This research, hence, fills up the gap in emotion analysis research for Bengali text, which has comparatively remained underexplored compared to other languages. The methodology involves dataset preparation, extensive preprocessing, feature extraction with selection, and classification. After rigorous experimentation, the accuracy attained with the GNB classifier is 93.83%, proving the effectiveness of the proposed model in capturing subtle emotional nuances in Bengali text.

Unsatisfactory public services encourage the public to submit complaints/ reports to public service providers to improve their services. However, each complaint/ report submitted varies. Therefore, the first step of the community complaint resolution process is to classify every incoming community complaint. The Ombudsman of The Republic of Indonesia annually receives a minimum of 10,000 complaints with an average of 300-500 reports per province per year, classifies complaints/ community reports to divide them into three classes, namely simple reports, medium reports, and heavy reports. The classification process is carried out using a weight assessment of each complaint/ report using 5 (five) attributes. It becomes a big job if done manually. This impacts the inefficiency of the performance time of complaint management officers. As an alternative solution, in this study, a machine learning method with the Naïve Bayes Classifier algorithm was applied to facilitate the process of automatically classifying complaints/ community reports to be more effective and efficient. The results showed that the classification of complaints/ community reports by applying the Naïve Bayes Classifier algorithm gives a high accuracy value of 92%. In addition, the average precision, recall, and f1-score values, respectively, are 91%, 93%, and 92%.

Decision Support System (DSS) is a computer-based interactive application that combines data and mathematical models to assist the decision-making process in dealing with a problem.[1] At the Faculty of Engineering, Informatics Study Program, Nurul Jadid University, there are several stages in the process of preparing a thesis that students need to do, namely submission of thesis titles, submission of thesis proposals, proposal seminars, research and thesis guidance. After writing is considered ready and finished, students present the results of their thesis at the lecturer examines the thesis exam, but students whose thesis exam results pass with revisions, carry out the revision process in accordance with the examiner's input. The problem that is often experienced by students at the Faculty of Engineering Informatics Study Program is the process of submitting thesis titles, where students have difficulty determining the topic of thesis title. Then a Decision Support System for Thesis Title Topic Recommendations was created using the Naive Bayes Classifier Method at the Faculty of Engineering, Informatics Study Program, Nurul Jadid University, which aims to help facilitate lecturers and students in the management process of determining the recommendation criteria for thesis title topics, the process of managing thesis title recommendations and thesis title submissions, the method used is prototyping with the PHP programming language, MySQL database and the Naive Bayes Classifier method, for system design using Flowcharts, DFD, and ERD. Based on the results of the Naive Bayes Classifier method, it produces test results with very good Likert scale calculations with a high accuracy value of 96.6%.

An increasing number of methods are being used to extract rock discontinuities from 3D point cloud data of rock surfaces. In this paper, a new method for automatic extraction of rock discontinuity based on an improved Naive Bayes classifier is proposed. The method first uses principal component analysis to find the normal vectors of the points, and then generates a certain number of random point sets around the selected training points for training the classifier. The trained, improved Naive Bayes classifier is based on point normal vectors and is able to automatically remove noise points due to various reasons in conjunction with the knee point algorithm, realizing high-precision extraction of the discontinuity sets. Subsequently, the individual discontinuities are segmented using a hierarchical density-based spatial clustering method with noise application. Finally, the PCA algorithm is used to complete the orientation by plane fitting the individual discontinuities. The method was applied in two cases, Kingston and Colorado, and the reliability and advantages of the new method were verified by comparing the results with those of previous research, and the discussion and analysis determined the optimal values of the relevant parameters in the algorithm.

Motivation State-of-the-art tools for classifying metagenomic sequencing reads provide both rapid and accurate options, although the combination of both in a single tool is a constantly improving area of research. The machine learning-based Naïve Bayes Classifier (NBC) approach provides a theoretical basis for accurate classification of all reads in a sample. Results We developed the multithreaded Minimizer-based Naïve Bayes Classifier (MNBC) tool to improve the NBC approach by applying minimizers, as well as plurality voting for closely related classification scores. A standard reference- and test-sequence framework using simulated variable-length reads benchmarked MNBC with six other state-of-the-art tools: MetaMaps, Ganon, Kraken2, KrakenUniq, CLARK, and Centrifuge. We also applied MNBC to the "marine" and "strain-madness" short-read metagenomic datasets in the Critical Assessment of Metagenome Interpretation (CAMI) II challenge using a corresponding database from the time. MNBC efficiently identified reads from unknown microorganisms, and exhibited the highest species- and genus-level precision and recall on short reads, as well as the highest species-level precision on long reads. It also achieved the highest accuracy on the "strain-madness" dataset. Availability and implementation MNBC is freely available at: https://github.com/ComputationalPathogens/MNBC. [ABSTRACT FROM AUTHOR]