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: 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: 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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