Judul: Analisis Topik Data Media Sosial Twitter Menggunakan Model Topik Latent Dirichlet Allocation
Abstrak: Media sosial merupakan sebuah platform yang penggunanya dapat berbagi pandangan, ide, perasaan, dan emosi mengenai suatu konteks peristiwa atau hal yang sedang terjadi saat itu. Dengan adanya media sosial Twitter, para pengguna dapat menyampaikan pendapatnya dengan mengikuti isu terkini yang sedang berkembang. Penelitian ini bertujuan untuk melakukan analisis topik menggunakan metode pemodelan topik Latent Dirichlet Allocation (LDA) sehingga diperoleh topik utama yang sedang ramai diperbincangkan. Pemodelan topik menghasilkan distribusi kata yang membentuk topik dan kumpulan dokumen dengan topik tertentu. Jumlah topik yang digunakan berbeda untuk masing-masing data yang diolah, berdasarkan pada konteks dari kumpulan tweet yang diakuisisi. Pemodelan topik menggunakan parameter yang sama untuk semua data kecuali parameter jumlah topik. Analisis topik dilakukan berdasarkan data keluaran hasil pemodelan topik dengan LDA. Analisis topik menghasilkan deskripsi topik yang dapat mewakili isi dari topik tersebut. Deskripsi topik dapat dimanfaatkan oleh pembaca atau pengguna Twitter dalam memahami segala perkembangan isu terkini.
Keyword: latent dirichlet allocation, pemodelan topik, Twitter

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: Development of DNA Vaccine Encoding Glycoprotein of Koi Herpes Virus Using Local Isolate.
Abstrak: The aim of this research was to develop DNA vaccine of Koi Herpesvirus in carp. Glycoprotein gene originated from ORF 25 KHV virus that is 1.8 kbp in size, is a potential gene in DNA vaccine construction. The sequencing and similarity analysis of the gene using BLAST software showed that this gene is a GP25 gene and was found to be very similar (99%) to GP25 of KHV virus from the USA and Israel. When this GP25 gene was ligated with pGEMT Easy (pT, 3 kbp in size) vector, plasmid size was increasing up to 5 kb. Following this, a digestion of pT-GP25 plasmid using Sal I enzyme was applied resulted in a GP25 fragment (1.8 kbp). Subsequently, pAct-D6 with a size of 8.5 kbp was also cut using the same enzyme to eleminate a D6 gene with a size of 1.6 kbp. GP25 gene was then ligated with pAct resulted a pAct-GP25 plasmid with a size of 8.8 kbp. As there was only one restriction site applied, ligation can be occurred in two different directions. Thus, in order to determine the plasmid with the desired direction PCR analysis was carried out, and plasmid with the right direction was the one that sized approximately 2.1 kbp (no. 6, 17 and 20). High level of GFP gene expression was detected in all tissues analyzed, i.e. kidney, gil, spleen, and muscle 24 hours after injection which indicated that β-actin promoter of Japanese medaka could be active in common carp. Although not as high as on 24 hours, expression was still detected until day 7 and disappeared after day 28 post injection indicating that pAct-GFP can be remained and β-actin promoter of Japanese medaka can be active in muscle, gills and kidney at least a week after injection. The result of RT-PCR analysis on pAct-GP25 injected common carp showed that GP25 gene expression can be detected 14 days after injection in all tissues observed. The expression level differences between GFP and GP25 genes possibly related to the primer sensitivity on annealing process to cDNA template and the size of PCR targeted DNA fragment, as smaller PCR targeted product will be easier to be amplified than a bigger one. The length of PCR targeted DNA with GP25 primer was 1.8 kbp whereas GFP gene was 0.6 kbp.The challenge test on common carp results showed that all unvaccinated and negative control (not infected with KHV) was alive until the end of the experiment. Whereas, the survival of fish vaccinated with a dose of 12.5 was ranged from 60.9 and 96.7%. In relation to the activity of β-actin promoter that was used in this DNA vaccine, gene expression was occured 24 hours after vaccination and remained until 2 weeks. The control fish that was not injected by GP25 showed no GP25 expression either in the observed tissues or organs and resulted in a lower survival, i.e. 23.3%. The challenge test results has indicated that the fish vaccinated with GP25 at doses of 7.5 and 12.5 μg/100μL were able to produce KHV glycoprotein in their body which would be
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