Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier

Receiving a recommendation for a certain item or a place to visit is now a common experience. However, the issue of trustworthiness regarding the recommended items/places remains one of the main concerns. In this paper, we present an implementation of the Naive Bayes classifier, one of the most powerful classes of Machine Learning and Artificial Intelligence algorithms in existence, to improve the accuracy of the recommendation and raise the trustworthiness confidence of the users and items within a network. Our approach is proven as a feasible one, since it reached the prediction accuracy of 89%, with a confidence of approximately 0.89, when applied to an online dataset of a social network. Naive Bayes algorithms, in general, are widely used on recommender systems because they are fast and easy to implement. However, the requirement for predictors to be independent remains a challenge due to the fact that in real-life scenarios, the predictors are usually dependent. As such, in our approach we used a larger training dataset; hence, the response vector has a higher selection quantity, thus empowering a higher determining accuracy.

Analysis of Social Media Users Sentiments against Omnibus Law Based on Hashtags on Twitter

From the research that has been done, it can be concluded that Sentiment Analysis can be used to know the sentiment of the public, especially Twitter netizens against omnibus law. After the sentiment analysis, it looks neutral artmen with the largest percentage of 55%, then positive sentiment by 35% and negative sentiment by 10%. The results of the analysis showed that the Naïve Bayes Classifier method provides classification test results with accuracy in Hashtag Pro with an average accuracy score of 92.1%, precision values with an average of 94.8% and recall values with an average of 90.7%. While Hashtag Counter For data classification, with an average accuracy value of 98.3%, precision value with an average of 97.6% and recall value with an average of 98.7%. The result of text cloud analysis conducted on a combination of hashtags both Hashtag pros and Hashtags cons, the dominant word appears is Omnibus Law which means that all hashtags in scrap is really discussing the main topic that is about Omnibus Law

Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan Metode Naive Bayes Classifier

Abstrak~Dalam penelitian ini akan dilakukan analisis sentimen masyarakat terhadap kinerja Dewan Perwakilan Rakyat (DPR) yang diungkapkan melalui media sosial twitter. Ada beberapa tahap untuk melakukan analisis sentimen , yaitu pengumpulan data (crawling), preporcessing data yang terdiri dari proses cleaning data, tokenization, stop remova dan case folding, splitting data dan klasifikasi data menggunakan metode Naive Bayes Classifier. Penelitian ini menggunakan sebanyak 1546 data tweet. Hasil dari penelitian ini didapatkan bahwa DPR mendapatkan 95 tweet positif dengan polaritas 0.75 atau 75% sentimen positif, 693 tweet netral dengan polaritas 0.79 atau 79% sentimen netral dan 758 tweet negatif dengan polaritas 0.82 atau 82% sentimen negatif dengan accuracy score 0.8 atau 80% berdasarkan data testing sebanyak 20%.Kata kunci : Sentiment Analysis, DPR, Naive Bayes Classifier

Machine Learning Based Scholarship and Credit Pre-Assessment System

The researcher explained the implementation process of finding the scholarship for the students by using machine learning supervised learning algorithm i.e. Naïve Bayes algorithm. Addition to this it includes a small description of naïve bayes classifier which used to be used through the authors. It explains the significance of training facts set and trying out information set in Machine mastering techniques. Machine learning nowadays becomes plenty used technique in the field of IT industry. It is a very effective instrument and technique for many quite a number fields such as education, IT and even in enterprise industry. In this paper, the researcher attempt to find computerized end result reputation of scholarships of college students by way of using naïve bayes classifier algorithm primarily based on the scholar educational performance, conversation skills, greedy power, IHS, income, time management, regularity etc. A scholarship offers a strength and self assurance to a student. It also boosts the performance of students indirectly. Usually scholarships are furnished by governments or authorities organizations. It is very essential for students to recognize their personal potentiality early in their educational profession so that they faster its growth, receiving attention from an employer or corporation helps college students take this step. Students can apply for scholarships primarily based on the eligibility criteria (such as caste category, annual income, etc). The scholarship will be issued based on merit, student performance and career specific. Different schemes of scholarships are provided for the students based on distinct eligibility criteria. By the use of a naïve bayes classifier, the researcher acquired a end result with accuracy of 96.7% and error of 3.3%. The repute of scholarship students was once displayed in the form of yes or no.

Survei Tentang Analisis Sentimen Pada Media Sosial

Media sosial saat ini telah menjadi bagian penting dalam kehidupan sehari-hari tidak hanya untuk kebutuhan pribadi melainkan bisa di gunakan dalam bisnis, serta banyak hal yang bisa dilakukan. Media sosial yang digunakan seperti Facebook, Twitter, Youtube, Instagram, Likenid, dan Whatsapp. Dengan adanya media sosial tersebut banyaknya data yang ada berupa gambar, comment berupa text atau emoticon, video, dan lainnya, sehingga masyarakat bebas beropini. Dengan adanya analisis sentimen opini yang berkembang dan banyak di media sosial tersebut dapat menghasilkan data dan informasi yang bermanfaat. Dalam analisis sentimen diperlukannya algoritma klasifikasi data diantaranya Naive Bayes Classifier, Support Vector Machine, K-NN, RNN, C4.5, Lexicon Based, LDA Based Topic Modeling, dan beberapa algoritma lainnya. Artikel ini menelaah beberapa literature analisis sentimen pada media sosial. Saat ini media sosial yang sering digunakan dalam analisis adalah Twitter dan pengguna algoritma yang dapat meningkatkan tingkat akurasi adalah algoritma Naive Bayes Classifier dan Support Vector Machine. Hasil perhitungan akurasi klasifikasi data berbeda-beda terlihat pada data uji pada penelitian tersebut.

Analisis Sentimen Opini Publik Terhadap Undang-Undang Cipta Kerja pada Twitter Menggunakan Metode Naive Bayes Classifier

Analisis sentiment atau Opinion Mining merupakan cara memecahkan suatu permasalahan berdasarkan opini masyarakat yang beredar luas di media sosial yang diekspresikan dalam bentuk teks. analisis sentimen sangat membantu pemerintahan/ suatu instansi dalam mengetahui opini publik mengenai suatu kebijakan yang baru saja dikeluarkan tanpa menggunakan metode survey konvensional. Pada analisis sentimen yang dilakukan berfokus pada Trending topik tweet pada Twitter dengan trending topic pada tanggal 5 sampai 10 oktober yaitu #Omnibuslaw, #tolakruuciptakerja, #UUCiptaKerja, dan #tolakomnibuslaw, dan trending topic pada tanggal 21 dan 22 november yaitu "obl makmurkan buruh". Proses Analisis sentimen dilakukan setelah data didapatkan pada tahapan crawling data, dilanjutkan dengan pembersihan kata pada proses preprocessing, dan pembobotan kata dengan algoritma TF-IDF. Analisis sentimen menggunakan metode naive bayes classifier bertujuan agar mendapatkan klasifikasi mengenai opini publik terhadap undang-undang cipta kerja pada twitter. Terdapat dua kelas pada penelitian ini yaitu kelas positif, dan negatif. Dari 2000 dataset yang terdiri dari 1400 tweet yang bersentimen negatif & 600 tweet yang bersifat positif dipakai akan dibagi antara data training dan data testing dengan perbandingan sebesar 60%:40%, 70%:30%, 80%:20%, dan 90%:10%. Dari hasil evaluasi pada Analisis sentimen mengenai opini publik terhadap undang-undang cipta kerja pada twitter didapatkan nilai akurasi tertinggi sebesar 94% dengan data training yang dipakai sebesar 90%, data testing sebesar 10%. Pada implementasinya, hasil dari uji sentimen menunjukkan hasil sentimen negatif yang lebih tinggi dibandingkan sentimen positif.

Sentimen Analisis Customer Review Produk Shopee Indonesia Menggunakan Algortima Naïve Bayes Classifier

Gaining customer satisfaction and trust has become the main challenge in achieving success in the business world. Business people need to identify problems that arise from reviews given by customers. However, reading and classifying each review takes a long time and is considered ineffective. To overcome this, this study aims to analyze the customer sentiment of shopee products using the nave Bayes classifier algorithm. The data used in this study is a customer review of the Xiaomi Redmi Note 9 products which are sold on the Shopee Indonesia website. Customer review data is collected by applying the Web Scraping technique. The algorithm used in this study is the Naïve Bayes Classifier which is known to be popular and effective in classifying data. This study also applies the Knowledge Discovery in Text (KDT) methodology to extract information from text data. The results of the classification using the Naïve Bayes algorithm found an accuracy value of 85%. This study proves that by applying sentiment analysis techniques, business people are able to find out the opinions of customers as an evaluation material that needs to be done to optimize the products and services provided.

Exact Learning Augmented Naive Bayes Classifier

Earlier studies have shown that classification accuracies of Bayesian networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the feature variables, were higher than those obtained by maximizing the marginal likelihood (ML). However, differences between the performances of the two scores in the earlier studies may be attributed to the fact that they used approximate learning algorithms, not exact ones. This paper compares the classification accuracies of BNs with approximate learning using CLL to those with exact learning using ML. The results demonstrate that the classification accuracies of BNs obtained by maximizing the ML are higher than those obtained by maximizing the CLL for large data. However, the results also demonstrate that the classification accuracies of exact learning BNs using the ML are much worse than those of other methods when the sample size is small and the class variable has numerous parents. To resolve the problem, we propose an exact learning augmented naive Bayes classifier (ANB), which ensures a class variable with no parents. The proposed method is guaranteed to asymptotically estimate the identical class posterior to that of the exactly learned BN. Comparison experiments demonstrated the superior performance of the proposed method.

Automatic Face Shape Classification Via Facial Landmark Measurements

This paper tackles the sensitive subject of face shape identification via near neutral-pose 2D images of human subjects. The possibility of extending to 3D facial models is also proposed, and would alleviate the need for the neutral stance. Accurate face shape classification serves as a vital building block of any hairstyle and eye-wear recommender system. Our approach is based on extracting relevant facial landmark measurements and passing them through a naive Bayes classifier unit in order to yield the final decision. The literature on this subject is particularly scarce owing to the very subjective nature of human face shape classification. We wish to contribute a robust and automatic system that performs this task and highlight future development directions on this matter.

Perbandingan Klassifikasi SMS Berbasis Support Vector Machine, Naive Bayes Classifier, Random Forest dan Bagging Classifier

Short message service (SMS) adalah salah satu media komunikasi yang penting untuk mendukung kecepatan pengunaan ponsel oleh pengguna. Sistem hibrid klasifikasi SMS digunakan untuk mendeteksi sms yang dianggap sampah dan benar. Dalam penelitian ini yang diperlukan adalah mengumpulan dataset SMS, pemilihan fitur, prapemrosesan, pembuatan vektor, melakukan penyaringan dan pembaharuan sistem. Dua jenis klasifikasi SMS pada ponsel saat ini ada yang terdaftar sebagai daftar hitam (ditolak) dan daftar putih (diterima). Penelitian ini menggunakan beberapa algoritma seperti support vector machine, Naïve Bayes classifier, Random Forest dan Bagging Classifier. Tujuan dari penelitian ini adalah untuk menyelesaikan semua masalah SMS yang teridentifikasi spam yang banyak terjadi pada saat ini sehingga dapat memberikan masukan dalam perbandingan metode yang mampu menyaring dan memisahkan sms spam dan sms non spam. Pada penelitian ini menghasilkan bahwa Bagging classifier algorithm ini mendapatkan ferformance score tertinggi dari algoritma yang lain yang dapat dipergunakan sebagai sarana untuk memfiltrasi SMS yang masuk ke dalam inbox pengguna dan Bagging classifier algorithm dapat memberikan hasil filtrasi yang akurat untuk menyaring SMS yang masuk.