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: Analisis Sentimen Terhadap Vaksinasi Covid-19 di Indonesia Menggunakan Data Twitter
Abstrak: Jenis baru virus corona ditemukan di Kota Wuhan, Tiongkok pada Desember 2019 dan terus menyebar ke seluruh dunia. Virus ini menyebabkan penyakit yang dinamakan Coronavirus Disease-19 (Covid-19). World Health Organization (WHO) telah menetapkan wabah ini sebagai pandemi global pada 11 Maret 2020. Salahsatu cara untuk menghentikan pandemi adalah dengan membentuk kekebalan komunitas yang bisa didapat melalui program vaksinasi. Program ini masih menimbulkan pro dan kontra pada awal pelaksanaannya di Indonesia. Masih banyak yang meragukan keamanan dan efek samping yang ditimbulkan dari vaksin. Penelitian ini membagi sentimen menjadi tiga kelas yaitu positif, negatif, dan netral. Proses pelabelan dilakukan secara manual dan kemudian dilakukan pemodelan menggunakan metode Bernoulli naive Bayes dan regresi logistik. Hasil penelitian menunjukkan Bernoulli naive Bayes menghasilkan model dengan performa lebih baik dan stabil dengan rataan nilai F1-score sebesar 0,63 dan simpangan baku 0,02. Mayoritas tweet pada tanggal 15 Januari 2021- 28 Januari 2021 memiliki sentimen netral. Banyaknya tweet dengan sentimen negatif mengalami tren penurunan setelah pekan pertama program vaksinasi. Topik yang sering dibicarakan pada tweet dengan sentimen negatif adalah mengenai hasil uji klinis dan efek samping dari vaksin., A new variant of coronavirus was discovered in Wuhan, China on December 2019 and has spread to worldwide. This virus causes a disease called Coronavirus Disease-19 (Covid-19). World Health Organization (WHO) have stated Covid-19 as a global pandemic on March 11, 2020. One way to end this pandemic is to make herd immunity that can be obtained through the vaccination program. This program still raises pros and cons at the beginning of its implementation in Indonesia. Many people doubt the safety and side effects of the vaccine. This research divides sentiment into three classes: positive, negative, and neutral. The labelling process is done manually and modeled using Bernoulli naive Bayes and logistic regression. The result shows Bernoulli naive Bayes produces more good and stable model with 0.63 average F1-score and 0.02 standard deviation. The majority of tweet from January,15 2021 until January,28 2021 have neutral sentiment. There is a decreasing trend on negative sentiment after the first week of vaccination program. Frequently discussed topic for negative sentiment is about clinical trial results and side effect of vaccine.
Keyword: Bernoulli naive Bayes, Covid-19 vaccination, Twitter

Judul: Macroalgae development as feeding stimulants for the adult sea urchin (Tripneustes gratilla Linnaeus 1758)
Abstrak: Development of artificial feed for somatic growth and gonad quality is one of the focus of research on sea urchin aquaculture activities. Palatability is important factor when developing artificial feed for aquaculture diets. This study investigated the effects of macroalgae (Sargassum polycystum, Gracilaria lichenoides, and Ulva reticulata) as feeding stimulants to artificial diets consumption and gonad profile for the adult sea urchin Tripneustes gratilla, by three feeding trials. Previously starved T. gratilla were held in replicated (3X) 50 L aquaria with recirculation water system (in the first feeding trial) and flow through water system (in the second and third feeding trial). In the first feeding trial, preference of T. gratilla for dried macroalgae was assessed in choice feeding experiment. T. gratilla displayed significant preference for the agar containing dried U. reticulata at 20% agar weight over nine other diets treatment when they were offered simultaneously. Total protein, energy and the ratio of protein to energy in the dried macroalgae had no correlation on agar consumption by T. gratilla. Three artificial diets were made by incorporating one of each of these dried macroalgae at 5% feed weight used in the second feeding trial. Although no significant difference in artificial diets containing dried macroalgae, urchins fed diets containing dried macroalgae consumed more than basal diet when they were offered as single diet. These results indicate that dried macroalgae added to the artificial diets act as feeding stimulants and increasing the acceptability of artificial sea urchin diets. The third feeding trial used dried U. reticulata as feeding stimulant to artificial diet. Data suggest that artificial diet containing 10% dried U. reticulata boosting feed consumption and significantly increasing nutrient accumulation, gonad index, egg diameter and fix gonad color.
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