Judul: A Study of Sentiment Analysis Using Statistical Machine Learning Approach
Abstrak: Sentiment analysis, which was introduced in the early 2000s, is a method used to analyze opinions and feelings. The goal of sentiment analysis is to determine whether a document contains a positive or negative emotion. Along with the spread of Covid-19 cases, news related to Covid-19 has often become a trending topic in the mass media. The news released by the mass media can influence public opinion, which, in turn, might influence their behavior. This research is essential to understand the public response to Covid-19 so that the governments can make the right decision to handle it. Data acquisition is the initial stage of the analysis. Web scraping is a data acquisition technique that can obtain data from public sources. It is a technique for converting unstructured data into structured data that can be stored and analyzed in a database or spreadsheet. Conducting sentiment analysis using all news becomes more challenging because it might take time and cost. Therefore, the sampling method is needed to obtain representative news for the analysis. Sampling was carried out by using two-stage random sampling. The first stage was used to get sample of news portals using stratified random sampling, and the second stage was to obtain news articles using systematic random sampling. In general, available data tend to come without annotation. Of course, this is a problem because sentiment modelling requires annotated training data, with its annotation as the response variable. As a result, pre-analysis is needed to find representative news for the labelling process. In this research, descriptive analysis and topic modelling are carried out as the basis for selecting news to be labelled. This research used news articles related to Covid-19 in Indonesia. The news data were extracted from Indonesian news portals using web scraping methods. Only articles that were published between March 2020 and March 2021 are extracted. The explanatory variables in this research result from the wordembedding and Bag-of-Word process, while the response variable is the sentiment category (positive, negative, and neutral) The research is conducted in three stages: data acquisition, pre-processing, and modelling. In the data acquisition stage, sampling and web scraping were employed to get news article data. Data pre-processing was done by employing data cleansing, n-gram tokenization, term weighting using TF-IDF, word embedding (word2vec) and data labelling. The models used in this research are multinomial logistic regression by applying L1 and L2 regularization, Support Vector Machine (SVM) with Radial Basic Function (RBF) kernel, and Convolutional Neural Network (CNN). Then, the model is evaluated by using precision, recall, and F1- scores. In order to determine the best model, hypothesis testing is employed by using two-way MANOVA and Tukey testing. Based on the results of topic modeling and positive negative word counting, the news is grouped into nine group. Based on the grouping results, researcher decided to label 1500 news. News selection was made randomly in each group. A linguistic expert will do the labeling process. This process will be repeated three times by three different people, and the sentiment will then be determined based on the voting results of the
Keyword: data acquisition, data preparation, sentiment modeling, web scraping, data acquisition, data preparation, sentiment modeling, web scraping

Judul: Sentiment Analysis Of Twitter Users On COVID-19 Vaccination In Indonesia Using Support Vector Machine Algorithm
Abstrak: Kebijakan vaksinasi COVID-19 di Indonesia menimbulkan pro dan kontra. Pemerintah harus mengevaluasi alasan masyarakat yang kontra terhadap kebijakan tersebut, agar program vaksinasi dapat berjalan dengan lancar. Analisis sentimen sebagai cara untuk melihat polaritas opini, memungkinkan untuk mengklasifikasi tanggapan positif, negatif maupun netral di Twitter terkait kebijakan vaksinasi tersebut. Penelitian ini bertujuan untuk mengetahui tanggapan masyarakat terhadap vaksinasi COVID-19 dengan melihat distribusi kata dan membuat model klasifikasi Support Vector Machine (SVM). Analisis sentimen terdiri dari beberapa tahapan yaitu pengumpulan data, praproses data, pembobotan data, analisis data, pembagian data, pemodelan klasifikasi, hyperparameter tuning dan evaluasi model. Model yang dihasilkan menunjukkan performa yang cukup optimal dalam mengklasifikasi sentimen dengan akurasi, presisi, recall dan f1-score sebesar 90%. Hasil analisis yang diperoleh ialah berupa gagasan, keluhan dan saran terhadap vaksinasi COVID-19., The COVID-19 vaccination policy in Indonesia turns out to be both pros and cons. The government has to evaluate the underlying reason of why some people's are against the policy, so that the vaccination program can run smoothly. Sentiment analysis as a way to see the polarity of opinion, makes it possible to classify positive, negative or neutral responses on Twitter regarding the vaccination policy. This study aims to determine the public's response to COVID-19 vaccination by examining word distribution and creating an Support Vector Machine (SVM) classification model. Sentiment analysis consists of several stages, namely data collection, data preprocessing, data weighting, data analysis, data sharing, classification modeling, hyperparameter tuning and model evaluation. The results of this study are a model with a relatively optimal performance in classifying sentiment with an accuracy, precision, recall and f1-score of 90%. The results of the sentiment analysis obtained are in the form of ideas, complaints and suggestions for the COVID-19 vaccination.
Keyword: COVID-19, Sentiment Analysis, Support Vector Machine, Vaccination

Judul: Closed Knight’s Tour pada Papan Catur Berbentuk Persegi Panjang dengan Ukuran Maksimal 8 × 8
Abstrak: Closed Knight’s Tour merupakan langkah kuda yang mengunjungi setiap kotak pada papan catur tepat satu kali dengan langkah kuda berawal dan berakhir pada kotak yang sama. Graf yang terbentuk dari closed knight’s tour dinotasikan sebagai graf G(m,n), dengan m adalah banyaknya baris dan n adalah banyaknya kolom. Graf tersebut memuat Hamiltonian cycle. Sebuah graf terhubung G adalah graf bipartite jika dan hanya jika graf tersebut tidak memiliki cycle dengan panjang ganjil. Papan catur yang memenuhi syarat cukup untuk memiliki closed knight’s tour adalah papan catur berukuran 5 × 6, 5 × 8, 6 × 6, 6 × 7, 6 × 8, 7 × 8, dan 8 × 8.
Keyword: bipartite, closed knight’s tour, hamiltonian cycle, knight’s tour, teori graf