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: Analisis Sentimen Pengguna Twitter terhadap Pemindahan Ibu Kota Negara Indonesia Menggunakan Metode Ensemble
Abstrak: Pada Maret 2019, pemerintah Indonesia memutuskan untuk memindahkan ibu kota negara Indonesia ke Pulau Kalimantan. Adanya keputusan ini ramai diperbincangkan di Twitter sehingga menimbulkan opini yang mengandung sentimen dari publik. Penelitian ini bertujuan menentukan sentimen dari opini masyarakat kedalam tiga kelas yaitu positif, netral dan negatif menggunakan model klasifikasi Ensemble. Model Ensemble ini dibangun dari empat base classifier yaitu Multinomial Naïve Bayes, Random Forest, SVM, dan Logistic Regression dengan menggunakan metode weighted soft voting. Analisis sentimen dilakukan dengan beberapa tahap yaitu pengambilan data, pelabelan data secara manual, praproses data, ekstraksi fitur, pembagian data menggunakan 10-Fold Cross Validation, hyperparameter tuning, pemodelan klasifikasi, dan evaluasi model dengan confusion matrix. Model klasifikasi Ensemble mendapatkan hasil terbaik dibandingkan dengan base classifier lainnya dengan akurasi sebesar 87.93% dan F-Score sebesar 85.61% untuk kelas negatif, 91.10% untuk kelas netral, dan 86.97% untuk kelas positif. Hasil analisis sentimen menunjukkan bahwa kelas negatif, netral, dan positif berturut-turut memiliki data sebanyak 831, 368, dan 642 tweet dengan persentase berturut-turut sebesar 45%, 19%, dan 35%., In March 2019, the Indonesian Government decided to relocate the capital city of Indonesia to the island of Borneo. Topic about this relocation is widely discussed on Twitter, giving rise to pros and cons from the public. This study aims to determine the sentiment of the public's response on twitter into three categories which are positive, negative, and neutral using Ensemble classifier. This Ensemble model will be built from Multinomial Naïve Bayes, Random Forest, SVM, and Logistic Regression as a base classifier using a weighted soft voting method. Sentiment analysis will be performed with several phases, including data acquisition, data preprocessing, manual data labelling, feature extraction, data splitting using 10-Fold Cross Validation, hyperparameter tuning, classification modeling, and model evaluation. Ensemble classification model gets the best result compared to other base classifier with 87.93% accuracy with 85.61% F-score for negative class, 91.10% F-score for neutral class, and 86.97% F-Score for the positive class. The results of the sentiment analysis show that the negative, neutral, and positive classes consisted of 831, 368, and 642 tweets with a percentage of 45%, 19%, and 35% respectively.
Keyword: Sentiment Analysis, Classification, Ensemble, Relocation of Indonesian Capital

Judul: Synthesis of Apatite - Chitosan Composite Using Duck Eggshell and Chitosan with Temperature and pH Control
Abstrak: Composite of apatite-chitosan was prepared by using chitosan as polymer matrix. Calcium phosphate was prepared from CaO and (NH4)2HPO4, which the ratio Ca : P had to be 1.67. Chitosan is produced by deacetilase from biopolymer chitin. Duck eggshell (95% CaCO3) as starting material was converted to be calcium oxide (CaO) through calcinations with temperature 1000oC for 5 hours. In-situ and ex-situ methods were prepared from apatite and chitosan (dissolved with acetate acid 2%). Calcium phosphate prepared for bone implantation, but it is too fragile and brittle to be implant into the body. So addition of chitosan can decrease crystalinity and increase mechanic characteristic (tougher). Composite apatite-chitosan was resulted by fast precipitation. Fast precipitation was held in a breaker glass with nitrogen atmosphere, under controlled temperature at 37oC and pH 7, in order to increase amount of hydroxyapatite. Precipitation with CaO which was dropped by (NH4)2HPO4. Hydroxyapatite and chitosan were appearing at samples with in-situ and ex-situ methods which indicating by peak matching of XRD (closed lattice parameter, crystal size), overlapping at FTIR spectra and supporting by SEM picture (homogenous and agglomerated particle). However, it still needs further research to examine biocompatibility osteoconductive degree of sample by in-vivo and in-vitro test.
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