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: 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: 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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