Judul: Sentiment Analysis of Oppenheimer Movie Trailer Comments on YouTube using GRU and FastText Embedding
Abstrak: Pada era internet saat ini, trailer umumnya diunggah melalui platform media sosial seperti YouTube. Melalui platform ini, penonton dapat mengakses serta memberikan sentimen mereka terhadap trailer film dengan mudah. Analisis sentimen terhadap komentar trailer film perlu dilakukan agar diperoleh trailer yang disukai oleh penonton sehingga minat penonton untuk menonton film tersebut dapat meningkat. Penelitian ini bertujuan mengembangkan model Natural Language Processing (NLP) dengan metode word embedding FastText dan arsitektur Gated Recurrent Unit (GRU) untuk analisis kecenderungan sentimen komentar di dalam trailer film Oppenheimer. Berdasarkan hasil pelatihan dan evaluasi diperoleh model TextBlob VADER sebagai model terbaik dengan accuracy bernilai 0,93. Hasil analisis kecenderungan sentimen pada masing-masing trailer film menggunakan model ini menghasilkan pengamatan bahwa trailer 2 lebih diminati penonton dibandingkan trailer 1. Selain itu, penelitian ini juga telah berhasil mengembangkan aplikasi web app sederhana untuk membantu proses analisis sentimen., In today's internet era, trailers are commonly uploaded on social media platforms like YouTube where viewers can easily access them and express their opinions. Sentiment analysis for a movie trailer is needed in order to create a trailer that is liked by the viewer so that it can increase the viewer’s interest in watching the movie. This research aims to develop a Natural Language Processing (NLP) model using the FastText word embedding method and Gated Recurrent Unit (GRU) architecture for analyzing sentiment tendencies in comments on the Oppenheimer’s movie trailers. Based on the training and evaluation results, the TextBlob VADER model was found to be the best model with an accuracy of 0,93. The sentiment trend analysis on each movie trailer using this model showed that trailer 2 was more favored by viewers compared to trailer 1. Additionally, this research successfully developed a simple web app to assist in the sentiment analysis process.
Keyword: analisis sentimen, fasttext word embedding, gated recurrent unit, trailer film, youtube

Judul: Perbandingan Analisis Sentimen Menggunakan Naive Bayes,  Lexicon-Based, dan Hybrid pada Ulasan Pengguna Aplikasi Livin’ by Mandiri
Abstrak: Perkembangan teknologi mengalami peningkatan yang sangat pesat hampir di seluruh sektor kehidupan manusia, salah satunya peningkatan inovasi perbankan dalam menghadirkan akses layanan transaksi digital. Analisis sentimen digunakan untuk menganalisis serta mengklasifikasikan ulasan pengguna aplikasi yang bertujuan menentukan kebijakan atau evaluasi pada pengembangan aplikasi. Penelitian ini membandingkan performa analisis sentimen berbasis Naive Bayes, Lexicon-based, dan Hybrid dalam mengklasifikasikan sentimen ulasan pengguna aplikasi Livin’ by Mandiri pada Google Play Store. Hasil pada penelitian ini menunjukkan bahwa metode Naive Bayes menghasilkan model dengan performa terbaik dengan nilai akurasi dan macro avg F1-score sebesar 0,96. Model Naive Bayes menggunakan information gain sebagai seleksi kata dan stratified k-fold cross validation sebagai ukuran validasi. Mayoritas ulasan pada rentang waktu yang diamati pada penelitian ini memiliki sentimen positif. Keluhan utama dari pengguna aplikasi Livin’ by mandiri adalah frekuensi pembaruan yang terlalu sering serta memiliki rentang waktu yang berdekatan. Meskipun pada penelitian ini model Hybrid bukan merupakan model terbaik, namun model ini dapat menjadi model alternatif yang mudah dan cepat untuk menganalisis sentimen., Development of technology has advanced very rapidly in almost all sectors of human life, and one of them is the advancement of banking innovation, which provides access to digital transaction services. Sentiment analysis can be used to analyze and classify user reviews of applications, aiming to determine policies or evaluate application development. This study compared the performance of Naive Bayes-based, Lexicon-based, and Hybrid-based sentiment analysis to classify the sentiment of user reviews of the Livin' by Mandiri application on the Google Play Store. The results of this study showed that the Naive Bayes method produced the model with the best performance, achieving an accuracy and macro avg F1-score of 0.96. The Naive Bayes model used information gain as the basis for word selection and stratified k-fold cross validation as a validation measure. The majority of reviews during the study period showed positive sentiments. The main complaint from users of the Livin' by Mandiri application was the frequency of updates, which were considered too frequent and had a short time frame. Although the Hybrid model was not the best in this study, it could serve as an easy and fast alternative for analyzing sentiment.
Keyword: hybrid, lexicon-based, livin' by mandiri, naive bayes, sentiment analysis, analisis sentimen

Judul: Acute Oral Toxicity Lethal
Dose 50 (LD50) of Cayenne Pepper (Capsicum annuum) Infusion in DDY Strain
Mice (Mus musculus)
Abstrak: In recent years, there has been a growing interest in herbal remedies such as cayenne pepper which is known for its versatility beyond its culinary use. Cayenne pepper’s phytochemicals, including capsaicinoids, carotenoids, phenolic compounds, vitamins, and minerals, offer antioxidant, antimicrobial, and antiinflammatory properties, making it a valuable ingredient in various fields such as veterinary medicine, agro-food, cosmetics, and pharmaceuticals. However, concerns regarding the potential risks of cayenne pepper consumption as herbal medicine arise. This study aims to determine the acute oral toxicity lethal dose 50 (LD50) of cayenne pepper infusion in female DDY strain mice and its effects on the mice's organs. The research involved 20 DDY strain female mice divided into 4 groups which are control group (administered with distilled water) and treatment groups that are given cayenne pepper infusion at doses of 5, 10, and 15 g/kg BW orally. The observed variables included mortality, clinical symptoms, physiological responses, and organ weight. Acute toxicity testing indicated that the cayenne pepper infusion falls into the category of practically non toxic formulations. Administration of the cayenne pepper infusion up to a dose of 15 g/kg BW did not induce toxicity symptoms, such as clinically pathological symptoms, macro anatomical organ changes, or mortality nor affect both weight gain and loss.
Keyword: cayenne pepper, infusion, acute oral toxicity, DDY strain mice, herbal medicine