Judul: Identifikasi Isu Strategis Tangerang Selatan Melalui Analisis Perbincangan di Media Sosial dengan Latent Dirichlet Allocation
Abstrak: Pemerintah Kota Tangerang Selatan telah merumuskan Rencana Pembangunan Jangka Menengah Daerah (RPJMD) untuk periode 2021-2026. Dalam realisasinya, perlu dilakukan monitoring dan evaluasi terhadap RPJMD sehingga diperlukan isu strategis dari setiap sektor pembangunan di wilayah Tangerang Selatan. Salah satu platform yang dapat menjaring segala bentuk aspirasi dan keresahan masyarakat terhadap Tangerang Selatan ialah media sosial X. Penelitian ini bertujuan untuk mengidentifikasi topik yang yang banyak diperbincangkan oleh masyarakat Tangerang Selatan terkait isu strategis pembangunan kota. Pendekatan metode penelitian menggunakan Latent Dirichlet Allocation (LDA) dengan tokenisasi unigram dan bigram yang mempertimbangkan pengaruh stemming maupun tanpa stemming. Pembobotan sebagai input LDA juga turut dipertimbangkan dengan menggunakan Bag-of-Words (BoW) dan Term Frequency-Inverse Document Frequency (TF-IDF) untuk menentukan model terbaik. Data diperoleh dari proses pengumpulan data berupa crawling media sosial X dari periode 1 Agustus 2022 hingga 31 Agustus 2023. Jumlah tweet hasil crawling sebanyak 12.326 tweet yang kemudian setelah praproses menjadi 3.617 tweet. Hasil penelitian menyatakan bahwa pembobotan BoW lebih baik digunakan dibandingkan dengan pembobotan TF-IDF. LDA bigram dengan stemming menggunakan pembobotan BoW menjadi model terbaik dengan 4 topik optimal dan nilai koherensi sebesar 0,744. Nilai koherensi didapat atas hasil tuning parameter untuk K sehingga optimisasi parameter memberikan pengaruh dalam peningkatan nilai koherensi. Topik utama yang banyak dibahas oleh pengguna media sosial X yang berkaitan dengan isu strategis Tangerang Selatan meliputi transportasi umum, polusi udara, dan kemacetan., The South Tangerang City Government has formulated a Regional Medium-Term Development Plan (RPJMD) for the 2021-2026 period. In its realization, it is necessary to monitor and evaluate the RPJMD so that strategic issues from each development sector in the South Tangerang area are needed. One of the platforms that can capture all forms of aspirations and concerns of the community towards South Tangerang is X social media. This study aims to identify topics that are widely discussed by the people of South Tangerang related to strategic issues of city development. The research method approach uses Latent Dirichlet Allocation (LDA) with unigram and bigram tokenization that considers the effect of stemming and without stemming. Weighting as LDA input is also considered using Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) to determine the best model. Data was obtained from the data collection process in the form of crawling social media X from the period August 1, 2022 to August 31, 2023. The number of tweets from crawling was 12.326 tweets which were then preprocessed into 3.617 tweets. The results state that BoW weighting is more suitable for use compared to TF-IDF weighting. LDA bigram with stemming using BoW weighting is the best model with 4 optimal topics and a coherence value of 0,744. The coherence value is obtained from the results of parameter tuning for K so that parameter optimization has an influence in increasing the coherence value. The main topics that are widely discussed by X social media users related to South Tangerang strategic issues include public transportation, air pollution, and congestion.
Keyword: bigram, lda, south tangerang, strategic issues, unigram, x

Judul: Aplikasi Latent Dirichlet Allocation pada Respon Pengguna Twitter Terhadap Penggunaan Kendaraan Listrik di Indonesia.
Abstrak: Indonesia berkomitmen mencapai target emisi nol bersih pada tahun 2060. Sejalan dengan hal tersebut, pemerintah berupaya mendukung percepatan penggunaan kendaraan listrik melalui peraturan perundang-undangan dan penetapan subsidi pembelian. Hal ini memunculkan berbagai tanggapan di masyarakat, salah satunya melalui unggahan di Twitter. Penelitian ini bertujuan mengetahui topik utama yang dibahas terkait penggunaan kendaraan listrik di Indonesia. Metode penelitian yang digunakan adalah Latent Dirichlet Allocation (LDA) dengan tokenisasi unigram dan bigram baik dengan maupun tanpa stemming. Data diambil selama periode 13 September 2022 hingga 28 Februari 2023 menggunakan Python. Jumlah tweet yang diperoleh sebanyak 67124 tweet dan setelah dilakukan proses pembersihan menjadi 14051 tweet. Hasil penelitian menunjukkan bahwa LDA bigram lebih baik daripada LDA unigram karena mampu mengelompokkan kata menjadi topik yang lebih sedikit sehingga lebih mudah untuk diinterpretasikan. LDA bigram menghasilkan topik yang lebih stabil dibandingkan dengan LDA unigram. Banyaknya topik pada LDA bigram baik dengan ataupun tanpa stemming adalah 8 topik, sedangkan LDA unigram menghasilkan 44 topik dengan stemming dan 47 topik tanpa stemming. Nilai koherensi yang dihasilkan oleh LDA bigram juga lebih tinggi dibandingkan dengan LDA unigram. Proses stemming baik diterapkan pada LDA bigram namun tidak pada LDA unigram. Optimisasi parameter K,α, dan β juga berpengaruh dalam peningkatan nilai koherensi. LDA bigram dengan stemming memiliki nilai koherensi tertinggi sebesar 0,639 dan menghasilkan sebanyak 8 topik utama. Topik utama yang dibahas pada masalah penggunaan kendaraan listrik di Indonesia meliputi subsidi pembelian, kendaraan listrik merek Esemka, dan peralihan ke kendaraan listrik., Indonesia is committed to achieving a net zero emission by 2060. In line with this, the government is trying to support the accelerated use of electric vehicles by establishing laws and stipulating subsidies for purchasing. This gave rise to various public responses and conveyed one of which was on Twitter. This study aims to discover the main topics regarding the use of electric vehicles in Indonesia. The research method used is Latent Dirichlet Allocation (LDA) with unigram and bigram tokenization, both with and without stemming. Data were retrieved from 13 September 2022 to 28 February 2023 using Python. The retrieved tweets in this period were 67124 tweets and after the cleaning process, there were 14051 tweets left. The results showed that bigram LDA was better than unigram LDA because it could group words into fewer topics, making it easier to interpret. Bigram LDA produces more stable topics than unigram LDA. Unigram LDA (44 topics with stemming; 47 topics without stemming) has more topics than bigram LDA (8 topics). The coherence score produced by bigram LDA is higher than unigram LDA. Stemming makes bigram LDA better but not for unigram LDA. Optimization of the parameters K, α, and β also increase the coherence score. The bigram LDA with stemming has the highest coherence score of 0,639 and produces 8 main topics. The main topics discussed on the issue of the use of electric vehicles in Indonesia are subsidies of purchasing, Esemka electric vehicles, and switching to electric vehicles.
Keyword: bigram, electric vehicle, latent dirichlet allocation, twitter, unigram

Judul: Pemurnian dan karakterisasi protein inhibitor RNA helikase virus hepatitis C
Abstrak: Hepatitis C virus (HCV) infection is the second most common chronic viral infection in the world with global prevalence. This infection is contribution to chronic hepatitis and liver cirrhosis, often leading to hepatocellular carcinoma. There is no effective drug and vaccine available against HCV. The current standard treatment, a combination of pegylated interferon-α with ribavirin, is limited by its suboptimal response rate in a significant patient population, side effects, and affordability. Therefore, development of effective drug is needed. One approach to find out the candidate of antiviral drug is to discover the inhibitor of enzyme that essential for virus replication, such as RNA helicase. Streptomyces chartreusis 5-095 produce the inhibitor of HCV RNA helicase. The aim of this study was to purify the protein inhibitor which has inhibition activity against ATPase activity of RNA helicase HCV from S. chartreusis 5-095. The supernatant of culture S. chartreusis 5-095 exhibited high inhibiton activity against ATPase activity of RNA helicase HCV (approximately 72%) at 10 days of incubation. The extracellular protein inhibitor was partially purified by procedures of precipitation with ammonium sulfate to 65% saturation and gel filtration (Sephadex G50) using methanol water solution (4:6) as an eluen. The purity of protein inhibitor was monitored by SDS PAGE. The protein inhibitor showed a relative molecular mass were estimated to be 14 kDa and increased the purity 23,08 fold than those of the crude protein. The specific activity and IC50 after purification were 13,1338 U/mg and 0,0767 mg/mL, respectively. The activity of purified protein inhibitor showed its stability over pH range from 5-8 and optimum at pH 7,4. The inhibition activity showed its stability up to 70% at temperature 100 oC. The purified protein inhibitor relatively stable its activity at storage in the range of -20 oC to room temperature for 35 days.
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