Judul: Application of Sentiment Analysis on Evaluation of Teaching and Learning Process Suggestion at IPB University Using IndoBERT and LDA
Abstrak: Evaluasi Proses Belajar Mengajar menjadi kunci dalam meningkatkan kualitas pembelajaran dan mendukung perkembangan mahasiswa. Namun, permasalahan dalam evaluasi sering terkait dengan ketidakseimbangan antara hasil belajar dan kualitas proses pembelajaran. Analisis sentimen diharapkan dapat memberikan wawasan mendalam terhadap saran mahasiswa pada proses belajar mengajar dengan menggunakan IndoBERT dan Latent Dirichlet Allocation. Penelitian ini bertujuan mengklasifikasikan analisis sentimen ke dalam kategori positif, negatif, dan netral serta mengidentifikasi topik utama yang sering dibicarakan dan menjadi permasalahan serta relevan pada saran evaluasi proses belajar mengajar. Tahapan dari penelitian ini meliputi pengumpulan data, pra proses data, pelabelan data, pembagian data, pemodelan, evaluasi serta pemodelan topik. Dari penelitian ini didapatkan hasil bahwa model IndoBERT mampu mengklasifikasikan sentimen dengan tingkat akurasi 94% sementara pemodelan topik menggunakan Latent Dirichlet Allocation mampu mengidentifikasi topik utama dari saran-saran mahasiswa pada setiap kategori., Evaluation of the Teaching and Learning Process is key in improving the quality of learning and supporting student development. However, problems in evaluation are often related to the imbalance between learning outcomes and the quality of the learning process. Sentiment analysis is expected to provide in-depth insight into student suggestions on the teaching and learning process using IndoBERT and Latent Dirichlet Allocation. This research aims to classify sentiment analysis into positive, negative, and neutral categories and identify the main topics that are often discussed and become problems and relevant to the evaluation of the teaching and learning process. The stages of this research include data collection, data pre-processing, data labeling, data sharing, modeling, evaluation and topic modeling. From this research, it is found that the IndoBERT model is able to classify sentiment with 94% accuracy while topic modeling using Latent Dirichlet Allocation is able to identify the main topics of student suggestions in each category.
Keyword: indobert, lda, sentiment anaylsis, teaching and learning process evaluation

Judul: Opinion Analytics of Project-Based Learning Evaluation Using Text Mining
Abstrak: Model pembelajaran Project-Based Learning (PjBL) seringkali diterapkan pada perguruan tinggi, dimana pembelajaran berpusat kepada peserta didik. Peserta didik melakukan beberapa hal untuk mendapatkan hasil belajar yang aktif dan kreatif. Analitika data dapat dilakukan untuk analisis hasil belajar peserta didik, salah satunya dengan analitika opini dalam mengukur pemerolehan hasil belajar terhadap suatu model pembelajaran dengan memanfaatkan opini peserta didik. Analitik merupakan teknik pengumpulan data untuk mengeksplorasi, menganalisis, dan memvisualisasikan data. Analitik dapat mendukung pendidik dalam menganalisis data untuk meningkatkan proses pembelajaran bagi peserta didik dan memberikan pengalaman belajar. Analitika opini menggunakan metode atau teknik analisis data teks yang tidak terstruktur untuk mendapatkan suatu fakta atau informasi yang berguna. Data teks banyak digunakan dalam suatu penelitian karena banyak mengandung informasi penting yang tidak diketahui. Sentiment analysis atau opinion mining merupakan bagian dari text mining yang diterapkan pada data opini sebagai dataset. Sentiment analysis bertujuan untuk melakukan analisis, ekstraksi, dan mengolah informasi agar mendapatkan informasi kecenderungan berupa opini positif, negatif, atau netral terhadap suatu masalah. Penelitian ini bertujuan untuk mengetahui pendapat mahasiswa terhadap penerapan metode Project-Based Learning (PjBL) pada mahasiswa S1 dan S2 Teknologi Industri Pertanian (TIP) dan mahasiswa S2 Ilmu Komputer. Data yang digunakan merupakan opini mahasiswa dengan 16 responden sarjana dan 28 pasca sarjana. Tahapan yang dilakukan dalam penelitian ini meliputi pengumpulan data, pelabelan kelas, praproses data, TF-IDF, klasifikasi menggunakan algoritme Naïve Bayes, dan Evaluasi Model. Data dibagi menjadi 2 model yaitu dataset-1 (60% training, 40% testing) dan dataset-2 (70% training, 30% testing). Keakuratan tertinggi model klasifikasi sentimen mencapai 100% pada indikator PjBL "Independence" dan "Curiosity" mahasiswa S1. Berdasarkan indikator pencapaian PjBL, hasil penelitian ini membuktikan adanya peningkatan kemampuan pemecahan masalah pada mahasiswa TIP. Berdasarkan klasifikasi kelas, sentimen positif lebih banyak ditemukan pada data. Hasil ini menunjukkan bahwa model pembelajaran PjBL dapat diterima dan berdampak positif bagi siswa tersebut, mampu mengidentifikasi dan mendefinisikan masalah, mengelola informasi, mengalokasikan sumber informasi, dan menyelesaikan proses pemecahan masalah., The Project-Based Learning (PjBL) learning model is often applied to universities, where learning is centered on students. Students do several things to get active and creative learning outcomes. Data analysis can be done to analyze student learning outcomes, one of which is opinion analysis in measuring the acquisition of learning outcomes against a learning model by utilizing student opinions. Analytics is a data collection technique to explore, analyze, and visualize data. Analytics can support educators in analyzing data to improve the learning process for students and provide learning experiences. Opinion analysis uses unstructured text data analysis methods or techniques to obtain valuable facts or information. Text data is widely used in a study because it contains much important information that is not known. Sentiment analysis or opinion mining is part of text mining applied to opinion data as a dataset. Sentiment analysis aims to analyze, extract, and process information to obtain information on trends in the form of positive, negative, or neutral opinions on a problem. This study aims to determine student opinions on applying the Project-Based Learning (PjBL) method to undergraduate and postgraduate students of Agricultural Industrial Technology (TIP) and Master's Degree students in Computer Science. The data
Keyword: Opinion Analytics, Project-Based Learning, Text Mining

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
Keyword: