Judul: Application of Support Vector Machine with SMOTE for Sentiment Classification of Omnibus Law News on CNNIndonesia.com Site
Abstrak: Pencanangan omnibus law menuai pro kontra di masyarakat. Media sudah seharusnya bersikap netral dalam pemberitaannya pada situasi seperti ini. Salah satu media yang masih menjaga netralitas dalam pemberitaannya adalah Detik (Rumata 2017). Detik membawahi beberapa kanal seperti detikNews, detikFinance, dan CNN Indonesia. Pada penelitian ini, akan dikaji netralitas dari media CNN Indonesia sebagai bagian dari Detik berdasarkan kecenderungan sentimen dalam pemberitaan mengenai omnibus law. Analisis sentimen digunakan untuk memeriksa kecenderungan opini pada data judul berita. Dalam melakukan analisis sentimen, diperlukan metode yang menunjang klasifikasi. Metode klasifikasi yang akan digunakan pada penelitian ini adalah Support Vector Machine (SVM). Terdapat ketidakseimbangan data pada tiga kategori sentimen sehingga digunakan metode Synthetic Minority Oversampling Technique (SMOTE) untuk mengatasi ketidakseimbangan tersebut. Omnibus law cenderung diberitakan secara netral oleh situs CNNIndonesia.com. Metode SVM one vs all memiliki hasil klasifikasi yang lebih baik dibandingkan dengan metode one vs one. Penerapan SMOTE hanya memberikan hasil yang sedikit lebih baik daripada klasifikasi data tanpa penerapan SMOTE karena ketidakseimbangan yang terdapat pada data tidak terlalu ekstrim. Pemodelan menggunakan metode one vs all dengan penanganan SMOTE dan pembagian data 90% data latih 10% data uji memberikan hasil klasifikasi terbaik dengan nilai macro average f1-score sebesar 60,33%., The declaration of the omnibus law reaped the pros and cons in the community. In a situation like this, the media should be neutral. One of the media that still maintains neutrality is Detik (Rumata 2017). Detik owns several channels such as detikNews, detikFinance, and CNN Indonesia. In this study, the neutrality of the CNN Indonesia media as part of Detik will be studied based on the tendency of sentiment on the omnibus law-related news. Sentiment analysis is used to examine the trend of opinion on news headlines. In conducting sentiment analysis, a method that supports classification is needed. The classification method that will be used in this research is the Support Vector Machine (SVM). There is an imbalance of data in the three categories of sentiment so that the Synthetic Minority Oversampling Technique (SMOTE) method is used to overcome this imbalance. The omnibus law tends to be reported neutrally by CNNIndonesia.com site. The one vs all method has a better classification result than the one vs one method. The application of SMOTE only gives slightly better results than data classification without the application of SMOTE because the imbalance in the data is not too extreme. Modeling using the one vs all method with SMOTE and distribution of data 90% train data 10% test data gives the best classification results with a macro average f1-score of 60,33%.
Keyword: media neutrality, omnibus law, sentiment analysis, SMOTE, support vector machine

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: Electrophoretic Coating of Apatite Compouds on Stainless Steel 316L.
Abstrak: Stainless steel (SS) 316L is a metal which is used to bone plate. However, this metal still has low biocompatibility in a human body. The metal is coated by HAp in order to increase its biocompatibility. HAp on SS 316L is coated by electrophoretic deposition (EPD) method. We used two variables in EPD process, such as voltage and coating time to obtain the best coating. The XRD difractogram showed that SS 316L has been coated by HAp. Physical observation used by microscope optics showed that the best coating is achieved on 120V during 2 minutes.
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