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: 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: Synthesis of Apatite - Chitosan Composite Using Duck Eggshell and Chitosan with Temperature and pH Control
Abstrak: Composite of apatite-chitosan was prepared by using chitosan as polymer matrix. Calcium phosphate was prepared from CaO and (NH4)2HPO4, which the ratio Ca : P had to be 1.67. Chitosan is produced by deacetilase from biopolymer chitin. Duck eggshell (95% CaCO3) as starting material was converted to be calcium oxide (CaO) through calcinations with temperature 1000oC for 5 hours. In-situ and ex-situ methods were prepared from apatite and chitosan (dissolved with acetate acid 2%). Calcium phosphate prepared for bone implantation, but it is too fragile and brittle to be implant into the body. So addition of chitosan can decrease crystalinity and increase mechanic characteristic (tougher). Composite apatite-chitosan was resulted by fast precipitation. Fast precipitation was held in a breaker glass with nitrogen atmosphere, under controlled temperature at 37oC and pH 7, in order to increase amount of hydroxyapatite. Precipitation with CaO which was dropped by (NH4)2HPO4. Hydroxyapatite and chitosan were appearing at samples with in-situ and ex-situ methods which indicating by peak matching of XRD (closed lattice parameter, crystal size), overlapping at FTIR spectra and supporting by SEM picture (homogenous and agglomerated particle). However, it still needs further research to examine biocompatibility osteoconductive degree of sample by in-vivo and in-vitro test.
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