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: Implementation of Support Vector Machine Algorithm for Sentiment Analysis of Flip Application Reviews on Google Play Store Site
Abstrak: Flip merupakan start-up kreasi pemuda Indonesia yang memiliki popularitas dan rating tinggi pada situs Google Play Store. Hal tersebut membuat tuntutan terhadap mutu pelayanan aplikasi juga semakin meningkat, sehingga muncul beragam komentar di kolom ulasan. Analisis sentimen merupakan salah satu cara untuk mengetahui polaritas opini yang memungkinkan klasifikasi ulasan ke dalam kelas bersentimen positif, netral, atau negatif. Penelitian ini bertujuan untuk mengidentifikasi perbedaan performa algoritma Support Vector Machine (SVM) menggunakan kernel linear, kernel polynomial, dan kernel Radial Basis Function (RBF) pada tiga fitur n-gram (unigram, bigram, dan trigram) serta melakukan analisis sentimen terhadap ulasan para pengguna aplikasi Flip. Algoritma Multi-Class SVM One Against All (OAA) diterapkan karena terdapat tiga kelas sentimen. Selain itu, algoritma Synthetic Minority Oversampling Technique (SMOTE) diterapkan untuk mengatasi ketidakseimbangan pada data. Pemodelan SVM menggunakan kernel RBF pada fitur unigram menghasilkan performa terbaik dalam mengklasifikasikan sentimen dengan nilai akurasi dan macro average F1-score yang didapat sebesar 89% dan 70%. Secara keseluruhan, kernel linear dan RBF pada ketiga fitur n-gram memberikan hasil klasifikasi terbaik. Sebaliknya, kernel polynomial memberikan hasil klasifikasi terendah. Fitur unigram memberikan hasil klasifikasi terbaik dan fitur trigram memberikan hasil klasifikasi terendah untuk ketiga kernel., Flip is a start-up created by Indonesian youth that has high popularity and ratings on the Google Play Store site. This makes the demands on the quality of application services also increase, thus various comments appear in the review column. Sentiment analysis is one way to find out the polarity of opinions that allows the classification of reviews into classes that have positive, neutral or negative sentiments. This study aims to identify differences in the performance of the Support Vector Machine (SVM) algorithm using a linear kernel, a polynomial kernel, and a Radial Basis Function (RBF) kernel on three n-gram features (unigram, bigram, and trigram) and to conduct sentiment analysis on the reviews of Flip application users. The Multi-Class SVM One Against All (OAA) algorithm is applied because there are three sentiment classes. In addition, the Synthetic Minority Oversampling Technique (SMOTE) algorithm was applied to overcome the data imbalance. SVM modeling using the RBF kernel on the unigram feature produces the best performance in classifying sentiment with an accuracy value and a macro average F1-score obtained at 89% and 70%. Overall, the linear kernel and RBF kernel on the three n-gram features give the best classification results. On the other hand, the polynomial kernel gives the lowest classification result. The unigram feature gives the best classification result and the trigram feature gives the lowest classification result for the three kernels.
Keyword: flip, kernel, n-gram, sentiment analysis, support vector machine

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