Judul: Latent Dirichlet Allocation to Identify Indonesia’s  Twitter Response on Covid-19 in 2020-2021
Abstrak: Covid-19 merupakan penyakit yang sangat meresahkan masyarakat Indonesia. Memahami pendapat masyarakat Indonesia sangat diperlukan untuk mencari solusi serta mengevaluasi kinerja pemerintah terhadap penanganan Covid-19. Data tweet pada Twitter dapat digunakan untuk mengidentifikasi pendapat masyarakat terhadap suatu fenomena. Data tweet pada Twitter merupakan big data berbentuk teks sehingga diperlukan penarikan contoh dan text mining agar data tersebut dapat diproses dengan efisien. Stratified random sampling diaplikasikan dengan pengulangan sebanyak 20 kali dan asumsi hari sebagai strata kemudian pemodelan topik dengan latent Dirichlet allocation (LDA). Tujuan dari penelitian ini adalah mengetahui pendapat masyarakat Indonesia terkait Covid-19 serta perkembangannya dari waktu ke waktu. Selain itu, ingin diketahui pengaruh penarikan contoh dalam data tweet menggunakan teknik stratified random sampling. Oleh karena itu, hasil pemodelan topik perlu ditransformasikan menjadi bentuk data deret waktu dan memerhatikan keragaman pola yang terbentuk. Kemudian, hasil transformasi tersebut akan dieksplorasi dan diinterpretasikan. Penelitian ini mengemukakan perbincangan terkait Covid-19 dibagi menjadi empat topik dari pemodelan pertama, yaitu: “Vaksin”, “Orang positif atau terkena covid”, “Protokol kesehatan”, serta “Indonesia” dan sembilan topik dari pemodelan kedua, yaitu: “Vaksin”, “Doa”, “Protokol Kesehatan”, “Bantuan sosial dan korupsi”, “Orang terkena Covid”, “Ekonomi Indonesia”, “Kerja”, “Ajakan memakai masker”, dan “Keinginan untuk menonton”. Selain itu, kuantitas perbincangan topik terkait Covid-19 di Indonesia memuncak apabila terdapat kejadian besar di Indonesia. Kemudian, penelitian ini juga membuktikan bahwa pengulangan stratified random sampling sebanyak 20 kali dapat mendapatkan hasil yang cukup baik., Covid-19 is a very troubling disease in Indonesia. Therefore, understanding public opinion is required to find solutions and evaluate the government performance in handling the pandemic. Twitter can be helpful to identify the public opinion of significant events. Twitter’s tweet is a large dimension text-based big data. It requires text sampling and text mining to be processed efficiently and effectively. Stratified random sampling with 20 repetitions applied to assume days as strata followed by topic modeling with latent Dirichlet allocation (LDA). This research aims to find out public opinion regarding Covid-19 and its growth over time. Other than that, this research also aims to find out sampling effects on tweet data using stratified random sampling. Therefore, the extracted topics will be transformed into time-series data and considering the variety of the pattern made. Afterward, the transformation results will be explored and interpreted. This research suggests that discussions related to Covid-19 are divided into four topics by the first model, namely: “Vaccine”, “Positive or affected people”, “Health protocol”, and “Indonesia” then nine topics by the second model, namely: “Vaccine”, “Prayer”, “Health protocol”, “Social aid and corruption”, “Affected people”, “Indonesian economy”, “Work”, “Persuading to wear mask”, and “Willing to watch”. Furthermore, some topics peak whenever a significant event occurs in Indonesia. Afterward, this research suggests that 20 repetitions of stratified random sampling could provide good results.
Keyword: Covid, Latent Dirichlet allocation, Text mining, Text sampling, Twitter

Judul: Topic Analysis of Twitter User About Covid-19 Vaccination in Indonesia Using Latent Dirichlet Allocation
Abstrak: Pandemi Covid-19 mendorong banyak pihak agar mampu beradaptasi dengan kondisi terkini. Salah satu program yang diluncurkan pemerintah agar dapat mengatasi penyebaran Covid-19 adalah dengan menjalankan progam vaksinasi. Agar dapat mengetahui animo masyarakat terkait program vaksinasi Covid-19 yang diluncurkan, perlu dilakukan analisis topik. Tujuan penelitian ini adalah mengetahui topik-topik terkait vaksin Covid-19 yang dibicarakan masyarakat di Twitter dan melakukan analisis sentimen pengguna Twitter terhadap vaksinasi corona. Untuk mendapatkan topik-topik pembicaraan terkait vaksin Covid-19 digunakan metode Latent Dirichlet Allocation (LDA). Metode penelitian yang dilakukan meliputi praproses data, pelabelan sentimen, penentuan jumlah, pemodelan topik, dan analisis topik. Hasil dari penelitian yang yang dilakukan adalah berupa topik-topik terkait vaksinasi Covid-19 yang sedang diperbincangkan di media Twitter di Indonesia. Tiga topik besar yang dibicarakan yaitu mengenai vaksinasi gratis oleh pemerintah Indonesia, sebab akibat mengikuti vaksinasi, dan varian vaksinasi., The Covid-19 pandemic has pushed many stakeholders to be able to adapt to the current conditions. One of the programs launched by the government to overcome the spread of Covid-19 is to run a vaccination program. To know the public's interest regarding the Covid-19 vaccination program that was launched, it is necessary to conduct a topic analysis. The purpose of this study was to find out the topics related to the Covid-19 vaccine that were discussed by the public on Twitter and to analyze the sentiments of Twitter users towards corona vaccination. To get topics of discussion related to the Covid-19 vaccine, Latent Dirichlet Allocation (LDA) method is used, while the Random Forest algorithm is used to carry out sentiment analysis. The research methods include data preprocessing, sentiment labeling, number determination, topic modeling, and topic analysis. The results of the research carried out are in the form of topics related to Covid-19 vaccination which are being discussed on Twitter media in Indonesia. The three major topics discussed were about free vaccination by the Indonesian government, the causes and effects of following vaccination, and vaccination variants.
Keyword: topic analysis, Covid-19, vaccination, LDA, Twitter

Judul: Sifat Mekanis Polipaduan Polistirena-Pati Menggunakan Zat Pemlastis Epoksida Minyak Jarak Pagar
Abstrak: Polyblend is a physical mix between two or more polymers resulting in different properties to get degradable polymer and better mechanical properties. Starch is degradable polymer, but polystyrene is synthetic polymer which is difficult to degrade. Polyblend polystyrene-starch will make the polymer easily to degrade but low mechanical properties. To increase the mechanical properties of polyblend, the plasticizers is needed. The plasticizers in this experiment was epoxide of jathropha oil. The polyblend of polystyrene-starch was prepared with various compositions, i.e. 0; 2.5; 5.0; 7.5; and 10.0%. The density of the polyblend decreased with the addition of the plasticizers. The reduction of density shown that jathropha oil epoxides can be used as plasticizers. The mechanical properties showed that the additions of plasticizer could increase the elongation and reduce the tensile strength. Thermal analysis using differential scanning calorymetry showed that glass transition temperature decreased from 95.37 ºC to 91.79 ºC.
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