Judul: Analisis Sentimen Program Kampus Merdeka pada Twitter Menggunakan Long Short-Term Memory
Abstrak: Pendidikan merupakan suatu hal terpenting yang mendorong perkembangan ilmu pengetahuan dan teknologi melalui peningkatan kualitas Sumber Daya Manusia. Untuk itu pemerintah membuat sebuah program Merdeka Belajar Kampus Merdeka (MBKM) untuk mendorong kualitas mahasiswa yang unggul dan siap untuk menjadi generasi terbaik. Masyarakat dapat menyuarakan pendapat tersebut melalui platform Twitter karena merupakan salah satu media sosial yang paling sering digunakan dalam menyampaikan pendapat mengenai suatu topik dan sudah diakui validitasnya dalam melakukan analisis sentimen. Penelitian ini bertujuan membangun model Long Short-Term Memory untuk mengklasifikasi sentimen masyarakat terkait keberhasilan program Kampus Merdeka. Sentimen terbagi menjadi kategori positif, negatif, dan netral. Perfoma model yang dihasilkan sebesar 85.58% pada tahun 2020, 86.31% pada tahun 2021, dan pada tahun 2022 sebesar 87.19%. Dari hasil penelitian diketahui bahwa masyarakat memiliki sentimen positif terhadap program Kampus Merdeka di setiap tahun dan tidak terjadi perubahan sentimen yang signifikan., Education is the most important thing that encourages the development of science and technology through improving the quality of Human Resources. For this reason, the government created an Independent Learning Campus Merdeka (MBKM) program to encourage quality students who excel and are ready to become the best generation. The public can voice their opinions through the Twitter platform because it is one of the most frequently used social media in conveying opinions on a topic and its validity has been recognized in conducting sentiment analysis. This study aims to build the LSTM model to classify public sentiment regarding the success of the Merdeka Campus Program. Sentiments are divided into positive, negative, and neutral categories. The resulting model performance is 85.58% in 2020, 86.31% in 2021, and in 2022 it is 87.19%. From the results of this study, it is known that the community has positive sentiments towards the Merdeka Campus program every year and there has been no significant change in sentiment.
Keyword: Campus Merdeka, Deep Learning, Long Short-Term Memory, sentiment, Twitter

Judul: Sentiment Analysis of Twitter Users’  Opinion Towards Face-to-Face Learning
Abstrak: Awal tahun 2022, pemerintah mengizinkan kembali pembelajaran tatap muka (PTM) setelah menjalani pembelajaran jarak jauh (PJJ) kurang lebih satu tahun. Saat akan diberlangsungkan PTM di beberapa wilayah, angka Covid-19 kembali meningkat dan pemerintah kembali memberlakukan PPKM. Hal ini berpengaruh pada kegiatan PTM yang akan dan sedang berlangsung. Pro dan kontra terhadap PTM juga terjadi di media sosial, salah satunya di twitter. Penelitian ini menggunakan data twitter periode 30 Januari 2022 hingga 7 Februari 2022. Opini di twitter mengenai PTM dikaji dengan analisis sentimen menggunakan metode regresi logistik biner dengan membagi menjadi kelas positif dan negatif. Pelabelan berdasarkan skor selisih antara banyaknya kata positif dan negatif. Tujuan dari penelitian ini untuk mengetahui persepsi masyarakat terhadap kebijakan penerapan PTM di era pandemi Covid-19 di media sosial khususnya twitter. Dari penelitian ini didapatkan persepsi masyarakat cenderung ke arah negatif yang menandakan belum cukup setuju dengan adanya PTM di periode bulan Februari 2022 dengan nilai akurasi 85%, sensitivitas 77%, spesifisitas 88%, dan AUC 91%.
Keyword: Binary Logistic Regression, Face-to-Face Learning, Sentiment Analysis, Twitter

Judul: Studi sistematika  Oreophryne Boettger, 1895Sulawesi (Anura: Microhylidae)
Abstrak: The cross frogs, genus Oreophryne Boettger, 1895 are most diverse in the mainland of New Guinea and adjacent islands, to the Wallacea region in the Moluccas, Sulawesi, and the Lesser Sunda Islands, even marginally to the Oriental region in Bali and to the southern Philippine Islands. In Sulawesi, this taxon consists of three species but has rarely been reported since their original descriptions and remained unstudied in both taxonomical work and molecular study. This research aims to evaluate the taxonomic status of the cross frogs in Sulawesi based on morphology and estimate the phylogenetic using the 16S rRNA gene. The phylogenetic tree was reconstructed under Maximum Likelihood (ML) and Bayesian Inference (BI) analyses. We used the specimens of Sulawesian Oreophryne which were stored at the Museum Zoologicum Bogoriense (MZB). A total of about 19 morphological characters were measured by a digital caliper and ocular micrometer to the nearest 0.01 mm. The phylogenetic tree shows that the three nominal species of Oreophryne from Sulawesi are clearly clustered. Correspondingly, the phylogenetic analysis supports the recognition of a new taxon, Oreophryne sp. However, the phylogenetic relationships between Oreophryne within Wallacea remain unresolved. Consequently, further systematic analysis along with additional sequences from the type locality is necessary to clarify the interspecific relationship among this region. We examined the morphology of 50 voucher specimens of Sulawesian Oreophryne. The validity of O. celebensis, O. variabilis, and O. zimmeri was confirmed in this study, but still the latter remains to be established. The three nominal species examined here were not collected from the type locality, so additional specimens, either in the type locality, new locations, or in other areas on this island are needed to evaluate the taxonomic status of these species deeply. Moreover, the unnamed species from the highland of the Mekongga mountains differ morphologically and genetically from all previously described Sulawesian Oreophryne. This distinct species will increase the number of cross frogs in Sulawesi to four species, all of which are endemic.
Keyword: frog, morphology, Oreophryne, phylogenetic, Sulawesi, systematics