Judul: Perbandingan antara Multinomial Naïve Bayes dan Regresi Logistik pada Analisis Sentimen Ulasan Aplikasi Sayurbox
Abstrak: Sayurbox needs to know the satisfaction of users regarding the applications they make to keep users' loyalty. One way to find out users satisfaction is to look at the users' reviews through the Google Play Store. Sentiment analysis is useful for managing these reviews and allows reviews to have certain categories such as positive or negative. The classification process can be done manually, but it will take a long time if the number of reviews that need to be classified is large. Therefore, a model is required to classify reviews automatically for saving time. The algorithms to be used in this study are Multinomial Naïve Bayes and logistic regression, and these algorithms will be combined with different kinds of n-gram (unigram, bigram, trigram, unibigram, unitrigram, bitrigram). The study was conducted to compare both algorithms and the six types of n- gram, select the best model combinations, and predict review sentiment from June 2022 to July 2022. The results have shown that the use of two different algorithms and several types of n-gram had a significant effect on the accuracy value, AUC, and f1-score. Interaction between algorithms and n-gram also had significant influence. The result shows that Multinomial Naïve Bayes with unibigram has been the best algorithm because it has shown the highest average of accuracy (91,02%), AUC (83,66%), and f1-score (94,38%). Multinomial Naïve Bayes with unibigram was used to predict reviews from June 2022 – July 2022. It has been predicted that there were 153 positive reviews and 37 negative reviews.
Keyword: logistic regression, Multinomial Naïve Bayes, n-gram, Sayurbox, sentiment analysis

Judul: Application of Bernoulli Naïve Bayes for Twitter User’s Sentiment Analysis Towards Online Food Delivery Service in Indonesia
Abstrak: Online food delivery merupakan salah satu pendorong pertumbuhan ekonomi digital yang semakin digemari masyarakat saat ini. Tren penggunaan layanan ini semakin meningkat seiring terjadinya perubahan perilaku dan gaya hidup masyarakat sebagai adaptasi pandemi Covid-19. Mayoritas platform digital layanan pesan-antar makanan di Indonesia yang sering digunakan, seperti GoFood, ShopeeFood, dan GrabFood yang memberikan kemudahan dalam bertransaksi dengan berbagai pilihan bagi konsumen. Peningkatan penggunaan layanan ini memunculkan beragam ulasan dan opini publik, salah satunya melalui cuitan di twitter. Penelitian ini bertujuan melakukan analisis sentimen terhadap ulasan pengguna layanan online food delivery menggunakan algoritma Bernoulli Naïve Bayes dengan membagi sentimen menjadi kelas positif dan negatif. Mayoritas ulasan pada periode 15 Maret 2022 hingga 30 Maret 2022 ini termasuk dalam sentimen positif yang mengindikasikan bahwa masyarakat memberikan kesan positif selama menggunakan layanan online food delivery. Hasil penelitian ini menunjukkan pemodelan Bernoulli Naïve Bayes dengan seleksi fitur information gain menghasilkan performa yang cukup baik dalam mengklasifikasikan label sentimen dengan nilai akurasi yang didapatkan sebesar 89%, 87%, 86%, dan 85% untuk seluruh data maupun setiap platform GoFood, ShopeeFood, dan GrabFood., Online food delivery is one of the drivers of the digital economy that all societies today are interested in. The trend of these services has intensified as changes in people's behavior and lifestyle in the Covid-19 pandemic. The digital platforms of food delivery services in Indonesia are GoFood, ShopeeFood, and GrabFood, present ease in both competitive transactions and multiple options by consumers. Its widespread use of these platforms certainly generates a variety of reviews and public opinion; one is through tweets on Twitter. This study aims to classify the sentiments on the various reviews into the label of positive and negative sentiments using the Bernoulli Naïve Bayes algorithm. The majority of reviews from March 15, 2022 to March 30, 2022 were positive sentiments, which indicated that people gave a positive impression during these online food delivery service. The results of this study show that Bernoulli Naïve Bayes with the feature selection of information gain generates a good performance in classifying sentiment labels based on accuracy scores obtained at 89%, 87%, 86%, and 85% in all data and each online food delivery platform (GoFood, ShopeeFood, and GrabFood).
Keyword: Bernoulli Naïve Bayes, information gain, online food delivery, sentiment analysis, Twitter

Judul: Perubahan Fase Padat pada Pembentukan Hidroksiapatit Akibat Suhu
Abstrak: Hidroksipatit [HAp, Ca10(PO4)6(OH)2] adalah salah satu bahan biomaterial yang dapat digunakan untuk implan tulang buatan karena memiliki komposisi yang sama dengan tulang manusia, bersifat bioaktif dan biokompatibilitas yang baik. HAp sintesis dengan menggunakan metode kering dan metode kering modifikasi sonikasi dari reaksi antara serbuk CaO hasil pemanasan cangkang kerang darah 1200 ºC selama 2.5 jam dan diamonium hidrogen fosfat (NH4)2HPO4 sebagai sumber fosfat. Difraktogram sampel menggunakan metode kering selama 2.5 jam pada suhu 400 ºC menghasilkan fase HAp, β-TCP, AKA, OKF, Ca(OH)2, dan CaO, pada suhu 600 dan 800 ºC terbentuk fase HAp, β-TCP, AKA, OKF, dan CaO, pada suhu 1000, 1200, dan 1250 ºC (serbuk dan pelet) terbentuk fase HAp dan β-TCP. Difraktogram sampel bersumber CaO cangkang kerang darah dan CaO komersial terbentuk fase HAp, β-TCP dan AKA. Metode kering-sonikasi (pelet) pada suhu 400 1250 serta 1250 ºC (serbuk) selama 3 jam menghasilkan fase HAp, β-TCP. Pada sampel 1250 ºC (serbuk) metode kering-sonikasi menunjukkan puncak tertinggi 2θ adalah hidroksiapatit dengan nilai 2θ 31,835º dan 32,918º, dan memperlihatkan morfologi permukaan yang lebih halus dan homogen berdasarkan mikroskopi payaran elektron.
Keyword: Cangkang kerang darah, Hidroksiapatit, Suhu