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: Analisis Sentimen Pengguna Twitter Terhadap Pandemi COVID-19 di Indonesia Menggunakan Algoritme Klasifikasi Multinomial Naive Bayes
Abstrak: Twitter sebagai situs jejaring sosial memiliki keunikan di mana fokusnya adalah berbagi pendapat dan informasi daripada interaksi sosial timbal-balik. Pada Maret 2020 WHO mengumumkan COVID-19 sebagai pandemi. Media sosial membuat pertukaran informasi dan opini pada masa pandemi berjalan dengan cepat. Dalam opini publik ini terdapat sentimen masyarakat. Analisis sentimen pada masa pandemi perlu dilakukan untuk memperlambat penyebaran virus COVID-19 dan mengurangi beban pada sistem kesehatan. Maka, perlu sebuah model yang dapat memprediksi bagaimana sentimen masyarakat terhadap COVID-19. Pengumpulan opini publik dilakukan melalui media sosial Twitter menggunakan bahasa Indonesia. Tweet kemudian diberikan label secara otomatis menggunakan library polyglot pada Python 3. Model dibangun menggunakan algoritme klasifikasi Multinomial Naïve Bayes dengan 10-fold cross validation. Data yang digunakan dalam penelitian ini adalah data tweet berbahasa Indonesia dari tanggal 2 Maret hingga 9 November 2020. Model berhasil memprediksi sentimen tweet dengan akurasi 67,66% dan nilai f1-score untuk kelas negatif 0,7184, kelas netral 0,5538, dan kelas positif 0,7308., Twitter as a social network site has a uniqueness where its focus is to share opinions and information instead of reciprocal social interaction. In March 2020 WHO announced the COVID-19 pandemic. Social media makes information and opinion trading during the pandemic fast. In these public opinions, there is public sentiment. Sentiment analysis during the pandemic is necessary to slow down the spread of the COVID-19 virus and to reduce the strain on the healthcare system. Therefore, there needs to be a model that can predict the public sentiment to COVID-19. Public opinion gathering is done through Twitter social media using Indonesian. A tweet is then given a label automatically using the library polyglot on Python 3. The model is using a Multinomial Naïve Bayes classification algorithm with 10-fold cross-validation. Data used in this research is tweet data in Indonesian from March 2nd to November 9th 2020. Model successfully predicted tweet sentiment with accuracy of 67,66% and f1-score for negative class 0,7184, neutral class 0,5538, and positive class 0,7308.
Keyword: COVID-19, Multinomial Naive Bayes, sentiment analysis

Judul: Solusi Geometri Schwarzschild untuk Dimensi 4+1 dengan Invarian Rotasi Isotropik.
Abstrak: Metrik Schwarzschild merupakan solusi eksak pertama dari persamaan medan Einstein dengan mangasumsikan metrik statik dan simetris. Metrik Schwarzschild yang ditemukan oleh Karl Schwarzschild masih dalam dimensi 3+1, namun seiring dengan teori-teori terbaru yang menunjukkan bahwa alam semesta tidak hanya di dimensi 3+1. Maka melalui penambahan dimensi, tujuan penelitian ini memperoleh solusi pada geometri Schwarzschild untuk dimensi 4+1 dengan memodifikasi koordinat bola yang invarian rotasi isotropik dan melalui penurunan rumus seperti koordinat bola, simbol Christoffel, dan tensor Ricci. Selain mendapatkan solusi ini, metrik Schwarzschild dimensi 4+1 dapat digunakan sebagai metrik dasar untuk memperoleh solusi blackhole dan wormhole pada dimensi 4+1. Hasil akhirnya, dengan memanfaatkan modifikasi koordinat bola, metrik Schwarzschild dapat dibentuk sehingga diperoleh solusi geometri Schwarzschild untuk dimensi 4+1.
Keyword: metrik Schwarzschild, persamaan medan Einstein, modifikasi koordinat bola, penambahan dimensi, invarian rotasi isotropik