Klasifikasi Tingkat Rumah Tangga Miskin Saat Pandemi Dengan Naïve Bayes Classifier

Secara definisi kemiskinan merupakan suatu kondisi individu ditingkat rumah tangga yang dinilai berdasarkan karaktersitik kemiskinan. Sebagai dampak dari pandemi covid-19 prosentase rumah tangga miskin di Indonesia meningkat sekitar 9,78%. Berdasarkan hal tersebut, maka penelitian ini akan melakukan klasifikasi dengan algoritma Naïve Bayes Classification untuk menentukan rumah tangga miskin melalui parameter survey ekonomi Nasional Tahun 2020 Modul Ketahanan Sosial yang berfokus pada pengeluaran dan konsumsi perkapita responden selama pandemic. Sedangkan tujuan dari penelitian ini adalah mendapatkan akurasi tertinggi yang dihasilkan oleh Naïve Bayes Classification dalam penentuan rumah tangga miskin. Menurut hasil pengujian dengan confusion matrix dan 10-fold cross validation didapatkan bahwa rata-rata akurasi tertinggi terjadi pada fold ke-10 dengan nilai accuracy 93,21%; precision 86,3%; dan recall 80,11%. Hal ini berarti bahwa akurasi yang dihasilkan oleh naïve bayes classifier dalam melakukan clasifikasi rumah tangga miskin cukup tinggi

PHÂN LỚP KHÁCH HÀNG DỰA TRÊN HÀNH VI, SỬ DỤNG KỸ THUẬT KHAI PHÁ DỮ LIỆU

Khai phá dữ liệu là một kỹ thuật phổ biến, được sử dụng để trích xuất thông tin hữu ích từ dữ liệu đã có, từ đó hỗ trợ ra các quyết định có lợi cho tương lai. Trong bài báo này, nhóm tác giả tập trung vào vấn đề phân lớp khách hàng, từ đó hỗ trợ tìm ra nhóm khách hàng tiềm năng bằng phương pháp cây quyết định Decision Tree J48, Naïve Bayes Classification và rừng ngẫu nhiên Random Forest. Kết quả cho thấy, mô hình dựa trên thuật toán cây quyết định cho độ chính xác cao nhất, có tính khả thi cao trong việc phân lớp dự đoán hành vi khách hàng. Kết quả này được kỳ vọng sẽ là gợi ý hiệu quả về một hướng tiếp cận cho các nhà phân tích khách hàng trong việc tìm ra nhóm khách hàng tiềm năng thuộc lĩnh vực ngân hàng.

A COMPARATIVE STUDY OF THE DIFFERENT CLASSIFICATION ALGORITHMS ON FOOTBALL ANALYTICS

Sports analytics is on the rise, with many teams looking to use data science and machine learning algorithms to augment their teams research and boost team performance. This is especially true in the case of Football Clubs. In this work, we have taken the statistics of matches for each team from five major football leagues. These include the English Premier League, La Liga, Serie A, Bundesliga, and Ligue 1. We use this data for two kinds of classification to predict a teams win, loss, or draw. First, we implement Multiclass Classification using Naive Bayes classification, Decision Tree classification, and K-Nearest Neighbours classification. We use f1-score, recall, and precision to evaluate the model. Next, we use Binary Classification to predict if a team wins or does not win, i.e., a loss or a draw. We achieve this by using Support Vector Machines, Logistics Regression, K-Nearest Neighbours classification, Decision Tree classification, and Naive Bayes classification. We evaluate the results using the evaluation metrics mentioned above. Now, we compare the accuracy and efficacy of these algorithms based on the evaluation metrics. This will help standardize the means of classification in sports and football analytics in the future.

Community Analysis Sentiment Against Palestinian People with Naive Bayes Classification

Pada zaman teknologi 4.0 media social sangat pesat perkembangannya, ada yang menggunakan media social untuk berjualan, aksi pengumpulan dana, meberitakan kejadian secara live. Beberapa hari yang lalu, palestina mendapatkan agresi dari militer Israel sehingga jagad dunia mengetahui aksi yang sungguh sangat tidak terpuji yang dilakukan oleh militer Israel. Dari banyaknya informasi media online maka perlu dilakukan analisis sentiment terhadap agresi militer yang dilakukan kepada palestina. Data yang digunakan adalah salah satu platform media social yaitu Twitter. Penelitian ini dibuat untuk menganalisa tanggapan dari masyarakat dengan menggunakan data berupa tweet yang kemudian diklasifikasikan dengan metode naïve bayes. Berdasarkan tools yang digunakan adalah orange, maka didapatkan hasil sentiment positif 56%, sentiment negative 11% dan sentiment netral 33% dengan tingkatan akurasi 75%. Dari hasil tersebut telah membuktikan tingkat sentiment positif dari tweet masyarakat lebih besar dari pada tingkat sentiment negative bahkan netral.

How Are Curiosity and Interest Different? Naïve Bayes Classification of People’s Beliefs

Researchers studying curiosity and interest note a lack of consensus in whether and how these important motivations for learning are distinct. Empirical attempts to distinguish them are impeded by this lack of conceptual clarity. Following a recent proposal that curiosity and interest are folk concepts, we sought to determine a non-expert consensus view on their distinction using machine learning methods. In Study 1, we demonstrate that there is a consensus in how they are distinguished, by training a Naïve Bayes classification algorithm to distinguish between free-text definitions of curiosity and interest (n = 396 definitions) and using cross-validation to test the classifier on two sets of data (main n = 196; additional n = 218). In Study 2, we demonstrate that the non-expert consensus is shared by experts and can plausibly underscore future empirical work, as the classifier accurately distinguished definitions provided by experts who study curiosity and interest (n = 92). Our results suggest a shared consensus on the distinction between curiosity and interest, providing a basis for much-needed conceptual clarity facilitating future empirical work. This consensus distinguishes curiosity as more active information seeking directed towards specific and previously unknown information. In contrast, interest is more pleasurable, in-depth, less momentary information seeking towards information in domains where people already have knowledge. However, we note that there are similarities between the concepts, as they are both motivating, involve feelings of wanting, and relate to knowledge acquisition.