Judul: Application of Support Vector Machine with SMOTE for Sentiment Classification of Omnibus Law News on CNNIndonesia.com Site
Abstrak: Pencanangan omnibus law menuai pro kontra di masyarakat. Media sudah seharusnya bersikap netral dalam pemberitaannya pada situasi seperti ini. Salah satu media yang masih menjaga netralitas dalam pemberitaannya adalah Detik (Rumata 2017). Detik membawahi beberapa kanal seperti detikNews, detikFinance, dan CNN Indonesia. Pada penelitian ini, akan dikaji netralitas dari media CNN Indonesia sebagai bagian dari Detik berdasarkan kecenderungan sentimen dalam pemberitaan mengenai omnibus law. Analisis sentimen digunakan untuk memeriksa kecenderungan opini pada data judul berita. Dalam melakukan analisis sentimen, diperlukan metode yang menunjang klasifikasi. Metode klasifikasi yang akan digunakan pada penelitian ini adalah Support Vector Machine (SVM). Terdapat ketidakseimbangan data pada tiga kategori sentimen sehingga digunakan metode Synthetic Minority Oversampling Technique (SMOTE) untuk mengatasi ketidakseimbangan tersebut. Omnibus law cenderung diberitakan secara netral oleh situs CNNIndonesia.com. Metode SVM one vs all memiliki hasil klasifikasi yang lebih baik dibandingkan dengan metode one vs one. Penerapan SMOTE hanya memberikan hasil yang sedikit lebih baik daripada klasifikasi data tanpa penerapan SMOTE karena ketidakseimbangan yang terdapat pada data tidak terlalu ekstrim. Pemodelan menggunakan metode one vs all dengan penanganan SMOTE dan pembagian data 90% data latih 10% data uji memberikan hasil klasifikasi terbaik dengan nilai macro average f1-score sebesar 60,33%., The declaration of the omnibus law reaped the pros and cons in the community. In a situation like this, the media should be neutral. One of the media that still maintains neutrality is Detik (Rumata 2017). Detik owns several channels such as detikNews, detikFinance, and CNN Indonesia. In this study, the neutrality of the CNN Indonesia media as part of Detik will be studied based on the tendency of sentiment on the omnibus law-related news. Sentiment analysis is used to examine the trend of opinion on news headlines. In conducting sentiment analysis, a method that supports classification is needed. The classification method that will be used in this research is the Support Vector Machine (SVM). There is an imbalance of data in the three categories of sentiment so that the Synthetic Minority Oversampling Technique (SMOTE) method is used to overcome this imbalance. The omnibus law tends to be reported neutrally by CNNIndonesia.com site. The one vs all method has a better classification result than the one vs one method. The application of SMOTE only gives slightly better results than data classification without the application of SMOTE because the imbalance in the data is not too extreme. Modeling using the one vs all method with SMOTE and distribution of data 90% train data 10% test data gives the best classification results with a macro average f1-score of 60,33%.
Keyword: media neutrality, omnibus law, sentiment analysis, SMOTE, support vector machine

Judul: Sentiment Analysis Of Twitter Users On COVID-19 Vaccination In Indonesia Using Support Vector Machine Algorithm
Abstrak: Kebijakan vaksinasi COVID-19 di Indonesia menimbulkan pro dan kontra. Pemerintah harus mengevaluasi alasan masyarakat yang kontra terhadap kebijakan tersebut, agar program vaksinasi dapat berjalan dengan lancar. Analisis sentimen sebagai cara untuk melihat polaritas opini, memungkinkan untuk mengklasifikasi tanggapan positif, negatif maupun netral di Twitter terkait kebijakan vaksinasi tersebut. Penelitian ini bertujuan untuk mengetahui tanggapan masyarakat terhadap vaksinasi COVID-19 dengan melihat distribusi kata dan membuat model klasifikasi Support Vector Machine (SVM). Analisis sentimen terdiri dari beberapa tahapan yaitu pengumpulan data, praproses data, pembobotan data, analisis data, pembagian data, pemodelan klasifikasi, hyperparameter tuning dan evaluasi model. Model yang dihasilkan menunjukkan performa yang cukup optimal dalam mengklasifikasi sentimen dengan akurasi, presisi, recall dan f1-score sebesar 90%. Hasil analisis yang diperoleh ialah berupa gagasan, keluhan dan saran terhadap vaksinasi COVID-19., The COVID-19 vaccination policy in Indonesia turns out to be both pros and cons. The government has to evaluate the underlying reason of why some people's are against the policy, so that the vaccination program can run smoothly. Sentiment analysis as a way to see the polarity of opinion, makes it possible to classify positive, negative or neutral responses on Twitter regarding the vaccination policy. This study aims to determine the public's response to COVID-19 vaccination by examining word distribution and creating an Support Vector Machine (SVM) classification model. Sentiment analysis consists of several stages, namely data collection, data preprocessing, data weighting, data analysis, data sharing, classification modeling, hyperparameter tuning and model evaluation. The results of this study are a model with a relatively optimal performance in classifying sentiment with an accuracy, precision, recall and f1-score of 90%. The results of the sentiment analysis obtained are in the form of ideas, complaints and suggestions for the COVID-19 vaccination.
Keyword: COVID-19, Sentiment Analysis, Support Vector Machine, Vaccination

Judul: Hydrothermal Synthesis of Biphasic Calcium Posphate
Abstrak: Biphasio calsium phosphate (BCP), which was a mixture of hydroxypatite (HA) and tricalcium phosphate (TCP), was synthesized by hydrothermal method followed by sintering process. Hydroxyapatite was produced by using natural calcium source CaO from hen's eggshell and (NH4)2HPO4 as phosphate souce, other hydroxyapatite sample was produce by using synthetic calcium source Ca(OH) and H3PO4 as phosphate source.
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