Judul: Sentiment Analysis of the Public Towards the
Jakarta-Bandung High-Speed Train Service Using the Multiclass Support Vector
Machine Method
Abstrak: Penambahan volume kendaraan bermotor terutama kendaraan pribadi dapat menimbulkan berbagai dampak negatif. Saat ini pemerintah menghadirkan fasilitas transportasi publik terbaru yaitu kereta cepat Jakarta – Bandung sebagai salah satu solusi untuk mengatasi hal tersebut. Penelitian ini bertujuan untuk mengklasifikasikan sentimen masyarakat terhadap layanan kereta cepat Jakarta – Bandung dan mengidentifikasi performa penggunaan metode Multiclass Support Vector Machine, serta mengidentifikasi topik dalam sentimen masyarakat terhadap layanan kereta cepat Jakarta – Bandung. Hanya sebanyak 40% data yang diberikan label sentimen secara manual yang digunakan dalam proses pelatihan model dan sisanya akan diprediksi menggunakan model terbaik. Hasil dari penelitian ini diperoleh model terbaik adalah algoritma One-Against-All kernel sigmoid dengan hyperparameter C = 1000, dan gamma = 0,0001. Performa yang dihasilkan yaitu akurasi sebesar 74,8% dan macro average f1-score sebesar 74,6%. Pelabelan menggunakan model terbaik terhadap 14.255 data diperoleh sentimen positif sebanyak 3.715, netral sebanyak 5.288, dan negatif sebanyak 5.252. Hasil clustering sentimen positif menunjukkan berbagai pujian dari masyarakat, serta rasa bangga karena merupakan kereta cepat pertama di Asia Tenggara. Masyarakat juga merasa kagum dengan kecepatan dan efisiensi waktu perjalanan kereta cepat. Sedangkan sentimen negatif menunjukkan kekecewaan biaya kereta cepat yang menggunakan APBN dan kritik bahwa kereta cepat tidak terlalu penting karena tidak ada urgensinya apalagi ibu kota Indonesia akan dipindahkan. Masyarakat juga mengkritik biaya kereta cepat yang terus membengkak hingga ratusan triliun dan stasiunnya yang hanya sampai di Padalarang., The increase in the volume of motorized vehicles, especially private vehicles, can lead to various negative impacts. Currently, the government has introduced a new public transportation facility, the Jakarta-Bandung high-speed train, as a solution to address these issues. This research aims to classify public sentiment towards the Jakarta - Bandung high-speed train service and identify the performance of the Multiclass Support Vector Machine method, as well as identifying topics in public sentiment towards the Jakarta - Bandung high-speed train service. Only 40% of the data manually labeled with sentiment was used in the model training process, with the remainder being predicted using the best model. The results of this research show that the best model is the One-Against-All algorithm with a sigmoid kernel, using hyperparameters C = 1000 and gamma = 0.0001. The performance achieved was an accuracy of 74.8% and a macro average F1-score of 74.6%. Labeling the remaining 14,255 data points using the best model resulted in 3,715 positive sentiments, 5,288 neutral sentiments, and 5,252 negative sentiments. Clustering of the positive sentiments revealed various praises from the public, including pride in having the first high-speed train in Southeast Asia and admiration for its speed and travel time efficiency. On the other hand, negative sentiments highlighted disappointment over the high-speed train's use of state budget funds, criticisms about its perceived lack of necessity given the upcoming relocation of Indonesia's capital, and concerns over the escalating costs exceeding hundreds of trillions, with the train only reaching Padalarang station.
Keyword: high speed rail, k-means clustering, multiclass support vector machine, sentiment analysis, twitter

Judul: Sentiment Analysis of Twitter Users on Biskita  Transpakuan Bogor Transportation Services Using the Ensemble Method
Abstrak: Bogor merupakan kota terpadat kelima di Indonesia pada tahun 2021. Tingginya jumlah angkutan umum dan kendaraan pribadi membuat pemerintah Kota Bogor berupaya meluncurkan angkutan umum yang lebih ramah pengguna dengan nama Biskita Transpakuan. Umpan balik dari masyarakat diperlukan untuk terus meningkatkan kualitas pelayanan dalam memenuhi kebutuhan pengguna. Umpan balik tersebut dapat berupa dukungan, kritik, dan saran. Analisis sentimen digunakan untuk mengekstrak informasi berharga salah satunya dari media sosial. Metode Ensemble dengan model dasar regresi logistik multinomial, random forest dan multinomial naïve bayes digunakan untuk melakukan klasifikasi. Tujuan penelitian ini untuk mengetahui performa dari model Ensemble dalam melakukan klasifikasi sentimen. Hasil penelitian menunjukkan bahwa model Ensemble dapat memanfaatkan kelebihan masing – masing model dasar dan mendapatkan Akurasi serta skor F1 tertinggi yaitu sebesar 78%., Bogor is the fifth most populous city in Indonesia in 2021. The high number of public transportation and private vehicles has led the Bogor City government to launch a more user-friendly public transportation named Biskita Transpakuan. Feedback from the community is certainly needed to continue to improve service quality in meeting user needs. The feedback can be in the form of support, criticism, and suggestions. Sentiment analysis is used to extract valuable information from social media. Ensemble method with basic models of multinomial logistic regression, random forest and multinomial naïve bayes are used to perform classification. The purpose of this study is to determine the performance of the Ensemble model in performing sentiment classification. The results showed that the Ensemble model can utilize the advantages of each basic model and get the highest Akurasi and F1 score of 78%
Keyword: Biskita, Congestion, Ensemble Method, Sentiment Analysis, Twitter

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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