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Social media is one of the technological developments that has contributed greatly in making it easy for us to communicate and socialize, one of which is using Twitter social media. Twitter in this study is used as a data source to analyze tweets discussing KRI Nanggala 402. Analysis of KRI Nanggala 402 twitter sentiment is used to see the tendency of public responses to the sinking of the KRI Nanggala 402 submarine whether to give positive or negative opinions. This Sentiment analysis uses the Naïve Bayes Classifier method, which is a classification method. The first research stage is crawling, processing, classification, and evaluation. The classification stage is carried out after the processing phase, where the classification results tend to be positive or negative, using the Naïve Bayes Classifier method. The accuracy of the system in the Sentiment analysis of the KRI Nanggala 402 tweet is 73.00%.

Halodoc is a telemedicine-based healthcare application that connects patients with health practitioners such as doctors, pharmacies, and laboratories. There are some comments from halodoc users, both positive and negative comments. This indicates the public's concern for the Halodoc application so it is necessary to analyze the sentiment or comments that appear on the Halodoc application service, especially during the COVID-19 pandemic in order for Halodoc application services to be better. The Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms are used to analyze the public sentiment of Halodoc's telemedicine service application users. The negative category sentiment classification result was 12.33%, while the positive category sentiment was 87.67% from 5,687 reviews which means that the positive review sentiment is more than the negative review sentiment. The accuracy performance of the Naive Bayes Classifier Algorithm resulted in an accuracy rate of 87.77% with an AUC value of 57.11% and a G-Mean of 40.08%, while svm algorithm with KERNEL RBF had an accuracy value of 86.1% with an AUC value of 60.149% and a G-Mean value of 49.311%. Based on the accuracy value of the model can be known SVM Kernel RBF model better than NBC on classifying the review of user sentiment of halodoc telemedicine service

Background: A resolve engaged by energy regulators worldwide to curtail the liability of Non-Technical Losses (NTLs) in power is the installation of Smart Meters to measure consumed power. However, power regulators in developing countries are confronted with huge metering gap in an era of momentous energy theft which has resulted in deficits in revenue, debts and ultimately power cuts. Objective: The objective of this research is to predict whether the customers’ who are unmetered are eligible to be metered by identifying worthy and unworthy customers for metering given their bill payment history. Methods: The approach analyses the performance accuracy of some machine learning algorithms on small datasets by exploring the classification abilities of Deep learning, Naïve Bayes, Support Vector Machine and Extreme Learning Machine using data obtained from an electricity distribution company in Nigeria. Results: The performance analysis shows that Naïve Bayes classifier outperformed the Deep Learning, Support Vector Machine and Extreme Learning Machine algorithms. Experiments in deep learning have shown that the alteration of batch sizes has an effect on the outputs. Conclusion: This paper presents a data-driven methodology for the prediction of consumers’ eligibility to be metered. The research has shown the performance of deep learning, Naive Bayes, SVM and ELM on small dataset. The research will help utility companies in developing countries with large populations and huge metering gaps to prioritise the installation of smart meters based on consumer’s payment history.

With the increasing collection of users' data, protecting individual privacy has gained more interest. Differential Privacy is a strong concept of protecting individuals. Naive Bayes is one of the popular machine learning algorithm, used as a baseline for many tasks. In this work, we have provided a differentially private Naive Bayes classifier that adds noise proportional to the Smooth Sensitivity of its parameters. We have compared our result to Vaidya, Shafiq, Basu, and Hong in which they have scaled the noise to the global sensitivity of the parameters. Our experiment results on the real-world datasets show that the accuracy of our method has improved significantly while still preserving $\varepsilon$-differential privacy., 14 Pages, PETS'21

Sentiment analysis is an important research topic and is currently being developed. Sentiment analysis is carried out to see the opinion or tendency of a person's opinion on a problem or object, whether it tends to have a negative or positive view. The main purpose of this research is to find out public sentiment towards the Full Day school policy comments from the Facebook Page of the Ministry of Education and Culture of the Republic of Indonesia and to determine the performance of the Na-ïve Bayes Classifier Algorithm. The results of this study indicate that the public's negative sentiment towards the Full Day School policy is higher than positive or neutral sentiment. The highest accuracy value is the Naïve Bayes Classifier algorithm with the trigram feature selection of the 300 data training model with a value of 80%. This simulation has proven that the larger the training data and the selection of features used in the NBC Algorithm affect the accuracy of the results. Meanwhile, the simulation results from 10 test data with 5 different NBC and Lexicon algorithms also show that the Full Day School Policy proposed by the Indonesian Minister of Education and Culture has a higher negative sentiment than positive or neutral by most Facebook users who express opinions through comments. The highest accuracy value is the Naïve Bayes Classifier algorithm with the trigram feature selection of the 300 data training model with a value of 80%. This simulation has proven that the larger the training data and the selection of features used in the NBC Algorithm affect the accuracy of the results. Meanwhile, the simulation results from 10 test data with 5 different NBC and Lexicon algorithms also show that the Full Day School Policy proposed by the Indonesian Minister of Education and Culture has a higher negative sentiment than positive or neutral by most users. Facebook that expresses opinions through comments. The highest accuracy value is the Naïve Bayes Classifier algorithm with the tri-gram feature selection of the 300 data training model with a value of 80%. This simulation has proven that the larger the training data and the selection of features used in the NBC Algorithm affect the accuracy results.

Virus Corona atau Covid-19 menjadi perhatian khusus diseluruh dunia. Banyak masyarakat membicarakan virus ini melalui unggahan komentar dan opini di Media Sosial.Twitter merupakan salah satu media sosial yang saat ini masih banyak digunakan masyarakat untuk menyampaikan opini berupa kumpulan kata yang disebut tweets. Tweets yang berkaitan dengan topik covid-19 ini dapat di klasifikasikan menggunakan metode Topic Modeling untuk menghasilkan sebuah data topic yang sering dibicarakan pengguna twitter. Salah satu algoritma yang digunakan untuk melakukan Topic Modeling adalah menggunakan Latent Dirichlet Alocation (LDA). Pada penelitian ini LDA digunakan untuk mengetahui kata-kata apa saja yang banyak muncul pada data tweets tentang Covid-19 yang telah di unggah masyarakat melalui twitter. Sebelum data tweet dimodelkan dengan LDA, dilakukan terlebih dahulu analisis sentiment dengan Naïve Bayes Classifier untuk menghasilkan sentiment Positif, Negati dan Netral. Terdapat 5000 tweets dijadikan dataset untuk diklasifikasikan menggunakan Topic Modeling. Semua tweets yang yang di tambang masih perlu dilakukan preprocessing text yang bertujuan untuk menghapus kata-kata yang tidak baku, menghapus tanda baca, dan menghapus kata penyambung. Tweets yang sudah dilakukan preprocessing text lalu diberikan nilai bobot sehingga diketahui kata apa saja yang banyak muncul dalam tweets yang berkaitan dengan Covid-19. Kata-kata yang banyak muncul dan sudah diberikan bobot akan divisualisasikan menggunakan World Cloud sehingga dapat dilihat pemetaan kata apa saja yang banyak muncul dalam bentuk gambar.

The low interest of prospective students in study programs at universities could be influenced by internal factors within the study program. These factors become the main variables in assessing the condition of the study program. For this reason, it is necessary to classify the internal conditions of the study program. A good method is needed in terms of accuracy and minimal misclassification to obtain the final classification results of the assessment. The purpose of this research is to classify the internal conditions of the study program. Classification of the internal conditions of the study program was carried out using the Naive Bayes Classifier (NBC) method which is a simple form of Bayesian Network with the assumption that all features are independent of each other. The NBC method shows an overall superior performance in terms of accuracy and misclassification rate. The NBC method can be used to determine the internal conditions of the study program, which could help identify factors that need to be addressed to increase the interest of prospective students enrolling in the study program.

Indonesia Lawyers Club (ILC) is a talk show on TVOne that discusses topics around public phenomena, legal issues, crime, and other similar topics. In 2018, ILC won the Panasonic Gobel Awards as the best news talk show program. But in 2019, ILC failed to win the award which was won by Mata Najwa which featured a talk show event that appeared on Trans7. As one of the television shows that has won awards, ILC has pros and cons for its shows from the public. This study applies a sentiment analysis approach to examine public opinion on Twitter about Mata Najwa and ILC in 2018 and 2019. This study applies K-Nearest Neighbor, Naïve Bayes Classifier, and Decision Tree classification algorithm to validate the result. The contribution of this study is to show that public opinion on Twitter can be examined to figure out community sentiment on a tv talk show as well as to confirm the Award winner of tv Talkshow. Index Terms—datamining; Decision Tree; K-NN; Naïve Bayes Classifier; sentiment analysis

Stunting describes a state of chronic malnutrition during growth and development since early life. This situation is represented by the height z-score for age (TB/U), which is less than minus 2 standard deviations (SD), based on WHO growth standards.Data from the Semarang City Health Office stated that the results of monitoring nutritional status based on indicators of body length for age (PB/U) or height for age (TB/U) the incidence of stunting in the city of Semarang was 20.37%. This research will make it easier to determine information regarding the classification of stunting nutritional status in toddlers. Stunting data will be processed and used as information regarding normal or not stunting nutritional status in toddlers. With this information, it can make it easier to collect data on toddlers who experience stunting nutritional status, besides that it can also be used to hold counseling to increase stunting nutritional levels and prevent stunting in toddlers by using the Naive Bayes Classifier. The accuracy result of the Naive Bayes Classifier method in classifying stunting nutritional status is 88%

World Health Organization (WHO) states that Diabetes Mellitus is the world's top deadly disease. several studies in the health sector including diabetes mellitus have been carried out to detect diseases early. In this study optimization of naive bayes classifier using particle swarm optimization was applied to the data of patients with 2 classes namely positive diabetes mellitus and negative diabetes mellitus and data on patients with 3 classes, those who tested positive for diabetes mellitus type 1, diabetes mellitus type 2 and negative diabetes mellitus. After testing, the algorithm of Naive Bayes Classifier and Naive Bayes Classifier based on Particle Swarm Optimization, the results obtained are the Naive Bayes Classifier method for 2 classes and 3 classes each producing an accuracy value of 78.88% and 68.50%. but after adding Particle Swarm Optimization the value of accuracy increased respectively to 82.58% and 71, 29%. The classification results for 2 classes have an accuracy value higher than 3 classes with a difference of 11.29%

The 2020 regional elections in the midst of the COVID-19 pandemic are starting to get crowded starting from the real world and in cyberspace, especially on Twitter social media. Twitter's existence has been widely used by various communities in recent years. Twitter is one of the media that represents the public response regarding public issu. Ahead of the general election (PEMILU), there are usually some parties who want to know the results of public sentiment or response to the issue, namely academics, intellectuals or even political opponents. Nevertheless, the implementation of local elections is very polemic in the community, therefore this study tries to analyze tweets that talk about issue public, namely the 2020 elections in the wake of the COVID-19 Pandemic. The analysis usually uses the classification of tweets containing public sentiment about the issue. The classification method used in this research is Naive Bayes Classifier (NBC) And Support Vector Machine (SVM). Naive Bayes Classifier is combined with features that can detect weighting using probability. The classification of tweets in this study was obtained based on a combination of two classes namely sentiment class and category class. The classification of sentiment consists of positive and negative. Test results on built-in applications show that accuracy with Naive Bayes delivers better results than Support Vector Machine. However, overall the use of the Naive Bayes method has a good performance to classify tweets with an accuracy rate of 92.2%

Companies need several types of communication technology that can predict customer purchase interest, the goal is that the company can properly consider product sales and determine the company's paint product supply. So far, the decision of the Home Smart sales manager has been made by looking at the closeness of the supplier relationship and how many sponsors are funding the company. So that sometimes the product cannot compete with other companies. The Naive Bayes classifier algorithm is one of the algorithms included in the classification technology. The application of the Naive Bayes method is expected to predict paint purchases from suppliers. From 60 paint purchase data tested with the Naive Bayes method, the results reached 80% of the accuracy of the predictions. Of the 60 tested paint purchase data, 48 paint purchase data were successfully classified correctly.

Nowadays, the method of simple-feature extraction has been extensively studied and is used in PPG biometric recognition; some promising results have been reported. However, some useful information is often lost in the process of PPG signal denoising; the time-domain, frequency-domain, or wavelet feature extracted is often partial, which cannot fully express the raw PPG signal; and it is also difficult to choose the appropriate matching method. Therefore, to make up for these shortcomings, a method of PPG biometric recognition based on multifeature extraction and naive Bayes classifier is proposed. First, in the preprocessing of the raw PPG data, the sliding window method is used to rerepresent the raw data. Second, the feature-extraction methods based on time-domain, frequency-domain, and wavelet are analysed in detail, then these methods are used to extract the time-domain, frequency-domain, and wavelet features, and the features are concatenated into a multifeature. Finally, the multifeature is normalized and combined with classifiers and Euclidean distance for matching and decision-making. Extensive experiments are conducted on three PPG datasets, it is found that the proposed method can achieve a recognition rate of 98.65%, 97.76%, and 99.69% on the respective sets, and the results demonstrate that the proposed method is not inferior to several state-of-the-art methods.

Congestion often hinders human mobility. This problem occurs due to the constant increase in vehicles every year. Reliable predictions of traffic conditions would allow drivers to choose their routes to avoid traffic jams while providing the police with traffic management strategies. Therefore, this paper tests the ability of various machine learning methods to predict traffic conditions. The study assesses Neural Networks, Bayes Classifier, Decision Trees, SVM, Deep Neural Network, and Deep Learning. Of these methods, the Decision Tree, Deep Neural Network, and Bayes Classifier show the highest performance in predicting traffic conditions using static data testing. However, in dynamic testing to assess the growth of knowledge, the performance of the Knowledge-Growing Decision Tree tends to decrease as the training data grows. Its performance decreased 3.89 points (88.24% to 84.35%) in accuracy, and 7.55 points (76.25% to 68.70%) for each precision, recall, and F1 Score. Conversely, the Knowledge-Growing Deep Neural Network and Bayes Classifier had a better performance than Decision Tree. The performances of Knowledge-Growing Deep Neural Network increased slightly by 0.35 points (93.38% to 93.73%) for accuracy and 0.69 points (86.77% to 87.64%) in other measurements. Although its performance increased, the processing time takes very long, namely 139452.76 seconds and 318832.80 seconds for sub-scheme (a) and (b), respectively. Meanwhile, the Knowledge-Growing Bayes Classifier offers a greater performance increase of 2.3 points (80.52% to 82.82%) for the accuracy and 4.6 points (65.63% to 61.03%) for the other performance measurements. In addition, it also scored better for processing time, as predictions only take 3 seconds using sub-scheme (a), and 7 seconds when using sub-scheme (b). Therefore, the paper proposes the Knowledge-Growing Bayes Classifier to predict rapidly changing traffic conditions. This method outperform the others. These can be attributed to its ability to 1) adjust to ever-changing the traffic conditions; 2) predict the result as soon as the data are acquired; and 3) make decentralized predictions.

In this paper I propose a wrapped feature selection method using Recursive Feature Elimination and Cross-validated selection. In my work I use Bernoulli Naive Bayes classifier on the NSL-KDD dataset.

The surface electromyography (sEMG) signals are the human muscle signals which are employed in various biomedical and engineering applications. Classification of sEMG signals acts as a vital roleinthe developmentof an assistive device for older age people and amputees.The present study compared the classification of sEMG signals using SVM and Naive Bayesclassifier, for six different hand movements’ identificationby using discrete wavelet transform (DWT) based features. DWT is employed mainly for de-noising and extracting time–frequency domain(TFD) features of sEMG signals. For the classification purpose, a feature vector was formed which includes TFD and time-domain (TD) features. SVM exhibit 95.8% accuracy and is found better as compared to the Naive Bayes classifier. The result shows that SVM has fast response, less error and it is suitable for the classification for sEMG signals for designing the assistive robotic technology.

Dampak dari pandemi covid-19 begitu besar sehingga pemerintah harus memiliki kebijakan agar dapat mengurangi dampaknya. Salah satu kebijakan pemerintah yaitu new normal yang mewajibkan seluruh masyarakat untuk pakai masker, jaga jarak dan cuci tangan. Dalam penerapannya tentu ada sentimen-sentimen baik positif maupun negatif yang diunggah ke dalam Twitter. Penelitian ini bertujuan untuk membuat pemodelan analisis sentimen masyarakat mengenai kebijakan new normal pemerintah pada masa pandemi covid-19 di Indonesia. Tahapan penelitian ini yakni crawling data, labeling, penghapusan data netral, preprocessing, pembagian training data dan testing data, pembuatan sistem klasifikasi naïve bayes, uji coba sistem dan visualisasi hasil penelitian dengan menggunakan wordcloud. Performa sistem klasifikasinya antara lain, tingkat akurasi 80,37%, presisi 87,38%, recall 82,57% dan f-measure 84,91%. Hasil dari penelitian ini yaitu 5194 tweets terklasifikasi sentimen positif dan 2908 tweets terklasifikasi sentiment negative, hal ini menunjukkan bahwa sentimen positif lebih banyak daripada sentimen negatif. Tetapi dari jumlahnya bisa dilihat bahwa perbandingannya tidak terlalu jauh antara sentimen positif dan sentimen negatif, artinya ada respon masyarakat yang masih kurang terhadap kebijakan pemerintah new normal pada masa pandemi.

Perkembangan teknologi dan penyebaran informasi di internet terus mengalami peningkatan. Salah satu bentuk informasi yang jumlahnya terus bertambah adalah berita. Media cetak dan elektronik yang kini telah dikemas dalam bentuk digital atau sering dikenal dengan portal berita online atau media online. PT Merah Putih Media merupakan media berita online. Berita yang disampaikan terdiri dari tiga kategori mulai dari berita tentang Indonesia, Hiburan dan Gaya Hidup, serta Olahraga. Namun, pembagian artikel berita ke dalam kategori dilakukan secara manual oleh kepala redaksi jurnalis. Text Mining adalah salah satu teknik yang dapat digunakan untuk melakukan klasifikasi sebuah dokumen. Pada penelitian ini dilakukan klasifikasi kategori otomatis dengan algoritma Multinomial Naïve Bayes, Complement Naïve Bayes, dan gabungan kedua model. Model yang memiliki performa terbaik dinilai dari metrik F1-Score dengan jumlah pembagian data latih dan data uji sebanyak 80:20, diperoleh keberhasilan performa sebesar 90,13% F1-Score.

Peanuts are easily contaminated by a variety of mycotoxins during growth, transportation, and storage, of which aflatoxin B1 is the most common. Aflatoxin B1 is one of the most toxic carcinogens kn...

The prediction of protein-protein interaction sites (PPIs) is a vital importance in biology for understanding the physical and functional interactions between molecules in living systems. There are several classification approaches for the prediction of PPI sites; the naïve Bayes classifier is one of the most popular candidates. But the ordinary naïve Bayes classifier is sensitive to unusual protein sequence profiling feature dataset and sometimes it gives ambiguous prediction results. To overcome this problem we have been modified the naïve Bayes classifier by radial basis function (RBF) kernel for the prediction of PPI sites. We investigate the performance of our proposed method compared with the popular classifiers like linear discriminant analysis (LDA), naïve Bayes classifier (NBC), support vector machine (SVM), AdaBoost and k-nearest neighbor (KNN) by the protein sequence profiling data analysis. The mNBC method showed sensitivity (86%), specificity (81%), accuracy (83%) and MCC (65%) for prediction of PPI sites.

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.

The innovation of services offered by cellular networks gained the attention of researchers in the communication field. Thus, mobile industries deal with remarkable technological competition regarding service quality. The quality is determined by how superior, consistent, and quick a service is delivered to the user. Thus, mobility management is a basic factor as it deals with imperative information for managing user’s mobility. However, due to the expansion of connected devices, the users are set up densely which inspires the researcher for devising a novel mode switching model. This paper devises a novel mode switching model using the Naive Bayes classifier. Here, the switching of modes is based on certain quality parameters, like link utilization, bandwidth, delay, energy consumption, and signal strength. Whenever the network switches the communication link from cellular-mode to user-mode, it must maintain the quality parameters. For enhancing the performance of network mobility management, a mobility management model is devised in which user mobility is computed. Thus, the proposed method is essential for supporting improved user mobility during communication The proposed mode switching using Naive Bayes classifier provides superior performance with a minimal delay of 0.164 s, maximal power of 58.786 bpm, maximal link utilization ratio of 0.727 and maximal throughput of 1,641,723 respectively.

Setiap mahasiswa memiliki kebiasaan tersendiri dalam menyerap dan memproses materi kuliah yang diberikan. Kebiasaan ini disebut dengan gaya belajar. Mengetahui gaya belajar mahasiswa merupakan hal yang sangat penting bagi seorang dosen karena dengan mengetahui gaya belajar mahasiswa dalam satu kelas, dosen dapat menerapkan metode pembelajaran yang dapat mengakomodir seluruh gaya belajar mahasiswa. Pada mata kuliah Komputer di Program Studi Pendidikan Bahasa Indonesia dan Program Studi Pendidikan Bahasa Inggris, masih terdapat beberapa mahasiswa yang kesulitan memahami materi kuliah karena metode pembelajaran yang diberikan dosen hanya terpaku pada gaya belajar tertentu. Untuk itu, penelitian ini akan membantu dosen untuk mengetahui gaya belajar mahasiswa berdasarkan data-data terdahulu dengan menggunakan metode Naïve Bayes Classifier pada Data Mining. Beberapa penelitian menyebutkan bahwa metode Naïve Bayes Classifier lebih baik dibandingkan dengan metode klasifikasi yang lain. Dalam penelitian ini, peneliti menggunakan Rapid Miner sebagai alat bantu dalam melakukan klasifikasi. Setelah melakukan pengujian terhadap data uji, diperoleh nilai akurasi sebesar 90%. Hal ini membuktikan bahwa model klasifikasi yang dibentuk dari data latih dapat memberikan hasil klasifikasi gaya belajar yang baik serta model ini dapat diterapkan oleh dosen untuk mengetahui gaya belajar mahasiswa.

Berita adalah salah satu sarana informasi bagi masyarakat umum, dengan media online sebagai salah satu sarana untuk mengaksesnya. Di Indonesia, media online memiliki presentase paling besar dalam penyebarluasan berita. Dibutuhkan mekanisme yang dapat mengklasifikasikan setiap topik berita secara akurat. Klasifikasi adalah proses yang krusial, karena memerlukan tahap preprocessing untuk mengubah data tidak terstruktur menjadi informasi yang bermakna. Preprocessing diawali dengan case folding , tokenizing , stemming , dan filtering , diahiri dengan klasifikasi. Penelitian ini menggunakan Enhanced Confix Stripping Stemmer untuk memisahkan kata dasar dari partikel awalan dan imbuhan, yang sebelumnya sulit dilakukan. Algoritma Naive Bayes Classifier kemudian diterapkan untuk proses klasifikasi. Dataset dari portal www.jawapos.com yang digunakan berjumlah 600 dokumen berita. Data tersebut digunakan sebagai data training , terbagi merata ke dalam kategori Olahraga, Teknologi, Ekonomi, dan Lain-lain. Dari 40 data testing yang diuji, akurasi tiap kategori diperoleh 90%, 90%, 100%, dan 100%, yang menghasilkan rata-rata akurasi keseluruhan kategori sebesar 95%.

The Bayes classifier is often used because it is simple, and the maximum posterior probability (MPP) criterion it uses is equivalent to the least error rate criterion. However, it has issues in the following circumstances: (i) if information instead of correctness is more important, we should use the maximum likelihood criterion or maximum information criterion, which can reduce the rate of failure to report small probability events. (ii) For unseen instance classifications, the previously optimised classifier cannot be properly used when the probability distribution of true classes is changed. (iii) When classes’ feature distributions instead of transition probability functions (TPFs) are stable, it is improper to train the TPF, such as the logistic function, with parameters. (iv) For multi-label classifications, it is difficult to optimise a group of TPFs with parameters that the Bayes classifier needs. This study addresses these issues by comparing the MPP criterion with the maximum likelihood criterion and maximum mutual information (MMI) criterion. It suggests using the MMI criterion for most unseen instance classifications. It presents a new iterative algorithm, the channel matching (CM) algorithm, for the MMI classification. It uses two examples to show the advantages of the CM algorithm: fast and reliable.

Under demand response enabled demand-side management, the home energy management (HEM) schemes schedule appliances for balancing both energy and demand within a residence. This scheme enables the user to achieve either a minimum electricity bill (EB) or maximum comfort. There is always the added burden on a HEM scheme to obtain the least possible EB with comfort. However, if a time window that contains comfortable time slots of the day for an appliance operation, is identified, and if the cost-effective schedule-pattern gets generated from these windows autonomously, then the burden can be reduced. Therefore, this paper proposes a two-level method that can assist the HEM scheme by generating a cost-effective schedule-pattern for scheduling home appliances. The first level uses a classifier to identify the comfortable time window from past ON and OFF events. The second level uses pattern generation algorithms to generate a cost-effective schedule-pattern from the identified window. The generated cost-effective schedule-pattern is applied to a HEM scheme as input to demonstrate the proposed two-level approach. The simulation results exhibit that the proposed approach helps the HEM scheme to schedule home appliances cost-effectively with a satisfactory user-comfort between 90% and 100%.

Keywords Ego Network, Link Prediction, Machine Learning, Performance Analysis, Similarity Score, Social Network

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

Tahapan awal skripsi adalah pengajuan proposal skripsi. Proposal skripsi akan diproses untuk menentukan dosen pembimbing, kemudian skripsi dapat dilanjutkan ke tahap penyusunan. Saat ini pengolahan skripsi menggunakan cara manual, dari penentuan dosen pembimbing hingga pengumpulan laporan akhir. Koordinator Skripsi juga harus mencocokkan data proposal dengan data dosen pembimbing secara manual. Maka, penggunaan Sistem Informasi dapat membantu menentukan dosen pembimbing dan sebagai layanan skripsi. Langkah awal dalam menentukan dosen pembimbing adalah mengetahui tema dan konsentrasi proposal skripsi. Untuk mengetahui tema dan konsentrasi proposal dilakukan analisis isi proposal menggunakan metode Text Mining. Text Mining bekerja dengan cara preprocessing menggunakan tokenizing, filtering, dan stemming untuk mendapatkan kata dasar dari setiap kata dalam setiap kalimat. Kemudian melakukan klasifikasi dokumen proposal sesuai dengan tema dan konsentrasi menggunakan algoritma Naïve Bayes Classifier berdasarkan hasil preprocessing. Tema dan konsentrasi merupakan salah satu kriteria penentukan dosen pembimbing menggunakan algoritma Simple Additive Weighting untuk dilakukan perangkingan pembobotan setiap dosen. Berdasarkan hasil penelitian yang telah dilakukan, proses penentuan tema dan konsentrasi dari proposal skripsi mahasiswa dapat membantu dalam melakukan klasifikasi dokumen dengan tingkat akurasi mencapai 78%. Pembobotan dosen pembimbing proposal skripsi sesuai dengan kriteria menunjukkan hasil dengan nilai perangkingan yang beragam sesuai dengan bobot kriteria setiap dosen pembimbing.

In the context of digital social media, where users have multiple ways to obtain information, it is important to have tools to detect the authorship within a corpus supposedly created by a single author. With the tremendous amount of information coming from social networks there is a lot of research concerning author profiling, but there is a lack of research about the authorship identification. In order to detect the author of a group of tweets, a Naïve Bayes classifier is proposed which is an automatic algorithm based on Bayes’ theorem. The main objective is to determine if a particular tweet was made by a specific user or not, based on its content. The data used correspond to a simple data set, obtained with the Twitter API, composed of four political accounts accompanied by their username and tweet identifier as it is mixed with multiple user tweets. To describe the performance of the classification model and interpret the obtained results, a confusion matrix is used as it contains values like accuracy, sensitivity, specificity, Kappa measure, the positive predictive and negative predictive value. These results show that the prediction model, after several cases of use, have acceptable values against the observed probabilities.

Usually, scientific research begins with the collection of data in which online social media tools can be some of the most rewarding and informative resources. The extensive measure of accessible information pulls in users from undergraduate students to postdoc. The search for scientific themes has popularized due to the availability of abundant publications that resides in scientific social networks such as Mendeley, ResearchGate etc. Articles are published on these media inform of text for knowledge dissemination, scientific support, research, updates etc, and are frequently uploaded after its publication in a proceedings or journal. In this sense, data collected from database often contains high noise and its analysis can be treated as a characterization undertaking as it groups the introduction of a content into either good or bad. In this text, we present quantitative and qualitative analysis of papers popularity in Mendeley repository by using naive Bayes Classifier.

Congestive heart failure (CHF) is gradually becoming more prevalent due to the stressed lifestyles in modern life. Accurate detection with lower computational complexity and lower cost of diagnosis is a challenge to the researchers in this domain. In this work, I have proposed an approach using naive Bayes algorithm with a lesser number of significantly discriminating features for differentiating the CHF subjects from the normal subjects. The small size of feature sets enhances the computational efficiency and the choice of strong features improves the accuracy. The features are chosen on the basis of p-value of the 2-sample t-test performed between the two types of subjects. Using the p-value, 6 features are selected to train, validate and test the classifier. Publicly available benchmark PhysioNet datasets for congestive heart failure patients and normal subjects are used to carry out the experimentation. This approach is able to provide 100% classification accuracy as well as sensitivity and specificity of 100% in identifying CHF patients employing Gaussian naive Bayes algorithm.

The quality of students can be seen from the academic achievements, which are evidence of the efforts made by students. Student academic achievement is evaluated at the end of each semester to determine the learning outcomes that have been achieved. If a student cannot meet certain academic criteria that are stated by fulfilling the requirements to continue his studies, the student may have the potential to not graduate on time or even Drop Out (DO). The high number of students who do not graduate on time or DO in higher education institutions can be minimized by detecting students who are at risk in the early stages of education and is supported by making policies that can direct students to complete their education. Also, if the time for completion of student studies can be predicted then the handling of students will be more effective. One technique for making predictions that can be used is data mining techniques. Therefore, in this study, the Naive Bayes Classifier (NBC) algorithm will be used to predict student graduation at Telkom University. The dataset was obtained from the Information Systems Directorate (SISFO), Telkom University which contained 4000 instance data. The results of this study prove that NBC was successfully implemented to predict student graduation. Prediction of the graduation of these students is able to produce an accuracy of 73,725%, precision 0.742, recall 0.736 and F-measure of 0.735.

One of the public e-government services is a web-based online complaints portal. Text of complaint needs to be classified so that it can be forwarded to the responsible office quickly and accurately. The standard classification approach commonly used is the Naive Bayes Classifier (NBC) and k-Nearest Neighbor (k-NN), which still classifies one label and needs to be optimized. This research aims to classify the complaint text of more than one label at the same time with NBC, which is optimized using Particle Swarm Optimization (PSO). The data source comes from the Sambat Online portal and is divided into 70 % as training data and 30 % as testing data to be classified into seven labels. NBC and k-NN algorithms are used as a comparison method to find out the performance of PSO optimization. The 10-fold cross-validation shows that NBC optimization using PSO achieves an accuracy of 87.44 % better than k-NN of 75 % and NBC of 64.38 %. The optimization model can be used to increase the effectiveness of services to e-government in society.

System and network security from interference from parties who do not have access to the system is the most important in a system. To realize a system, data or network that is safe at unauthorized users or other interference, a system is needed to detect it. Intrusion-Detection System (IDS) is a method that can be used to detect suspicious activity in a system or network. The classification algorithm in artificial intelligence can be applied to this problem. There are many classification algorithms that can be used, one of which is Naïve Bayes. This study aims to optimize Naïve Bayes using Univariate Selection on the UNSW-NB 15 data set. The features used only take 40 features that have the best relevance. Then the data set is divided into two test data and training data, namely 10%: 90%, 20%: 70%, 30%: 70%, 40%: 60% and 50%: 50%. From the experiments carried out, it was found that feature selection had quite an effect on the accuracy value obtained. The highest accuracy value is obtained when the data set is divided into 40%: 60% for both feature selection and non-feature selection. Naïve Bayes with unselected features obtained the highest accuracy value of 91.43%, while with feature selection 91.62%, using feature selection could increase the accuracy value by 0.19%.

In recent years, face detection has been widely applied to a variety of fields, such as face recognition, image focusing, and surveillance systems. This study proposes a real-time multi-faces detection system based on naive Bayesian classifier using Field Programmable Gate Array (FPGA). The system includes three main parts, feature extraction, candidate face detection, and false elimination. First, downscale the image to the image pyramid and extract local binary image features from each downscaling image. With the bit-plane slicing for Local Binary Pattern (LBP) can save the memory consumption and speed up the computation. Then, adopt the naive Bayesian classifier to identify candidate faces. Finally use skin color filter and face overlapping elimination to remove false positives. The experimental results show that the accuracy rate is up to 96.14% in face detection, which demonstrates the proposed real-time multi-faces detection system is indeed effective and efficient.

Sebagian besar penyebab kematian diakibatkan oleh kecelakaan lalu lintas. Makalah ini bertujuan mendapatkan parameter identifikasi aktivitas berkendara yang dapat dikembangkan untuk mendeteksi kecelakaan pada penelitian selanjutnya. Data dikumpulkan dengan memanfaatkan sensor pada smartphone, menggunakan sensor akselerometer dan giroskop. Metode statistik yang diusulkan menggunakan algoritme Naive Bayes Classifiers (NBC) untuk menentukan aktivitas berkendara, dengan membagi dataset ke dalam data latih dan data uji menggunakan beberapa parameter k-fold. Algoritme NBC dapat bekerja dengan sedikit data latih, dengan cara menghitung nilai probabilitas setiap kelas dari perhitungan jumlah rata-rata dan varians setiap fitur untuk mengklasifikasikan kelas secara linier dengan sangat efisien. Hasil menunjukkan bahwa akurasi klasifikasi aktivitas berkendara lebih tinggi apabila terdapat proses smoothing menggunakan metode single exponential smoothing sebelum proses klasifikasi algoritme NBC. Hasil pengujian menggunakan 8 k-fold CV tanpa proses smoothing, dengan smoothing alpha (α) = 0,1, dan dengan α = 0,9 mendapatkan akurasi, berturut-turut, sebesar 98,43%, 99,27%, dan 98,43%. Hasil komparasi metode klasifikasi aktivitas berkendara pada makalah ini membuktikan bahwa metode NBC yang dikombinasikan dengan metode smoothing α = 0,1 menghasilkan akurasi yang lebih besar.

Within the field of supervised classification, the naïve Bayes (NB) classifier is a very simple and fast classification method that obtains good results, being even comparable with much more complex models. It has been proved that the NB model is strongly dependent on the estimation of conditional probabilities. In the literature, it had been shown that the classical and Laplace estimations of probabilities have some drawbacks and it was proposed a NB model that takes into account the a priori probabilities in order to estimate the conditional probabilities, which was called m-probability-estimation. With a very scarce experimentation, this approximation based on m-probability-estimation demonstrated to provide better results than NB with classical and Laplace estimations of probabilities. In this research, a new naïve Bayes variation is proposed, which is based on the m-probability-estimation version and takes into account imprecise probabilities in order to calculate the a priori probabilities. An exhaustive experimental research is carried out, with a large number of data sets and different levels of class noise. From this experimentation, we can conclude that the proposed NB model and the m-probability-estimation approach provide better results than NB with classical and Laplace estimation of probabilities. It will be also shown that the proposed NB implies an improvement over the m-probability-estimation model, especially when there is some class noise.