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: Analisis Sentimen Pengguna Twitter Terhadap Pandemi COVID-19 di Indonesia Menggunakan Algoritme Klasifikasi Multinomial Naive Bayes
Abstrak: Twitter sebagai situs jejaring sosial memiliki keunikan di mana fokusnya adalah berbagi pendapat dan informasi daripada interaksi sosial timbal-balik. Pada Maret 2020 WHO mengumumkan COVID-19 sebagai pandemi. Media sosial membuat pertukaran informasi dan opini pada masa pandemi berjalan dengan cepat. Dalam opini publik ini terdapat sentimen masyarakat. Analisis sentimen pada masa pandemi perlu dilakukan untuk memperlambat penyebaran virus COVID-19 dan mengurangi beban pada sistem kesehatan. Maka, perlu sebuah model yang dapat memprediksi bagaimana sentimen masyarakat terhadap COVID-19. Pengumpulan opini publik dilakukan melalui media sosial Twitter menggunakan bahasa Indonesia. Tweet kemudian diberikan label secara otomatis menggunakan library polyglot pada Python 3. Model dibangun menggunakan algoritme klasifikasi Multinomial Naïve Bayes dengan 10-fold cross validation. Data yang digunakan dalam penelitian ini adalah data tweet berbahasa Indonesia dari tanggal 2 Maret hingga 9 November 2020. Model berhasil memprediksi sentimen tweet dengan akurasi 67,66% dan nilai f1-score untuk kelas negatif 0,7184, kelas netral 0,5538, dan kelas positif 0,7308., Twitter as a social network site has a uniqueness where its focus is to share opinions and information instead of reciprocal social interaction. In March 2020 WHO announced the COVID-19 pandemic. Social media makes information and opinion trading during the pandemic fast. In these public opinions, there is public sentiment. Sentiment analysis during the pandemic is necessary to slow down the spread of the COVID-19 virus and to reduce the strain on the healthcare system. Therefore, there needs to be a model that can predict the public sentiment to COVID-19. Public opinion gathering is done through Twitter social media using Indonesian. A tweet is then given a label automatically using the library polyglot on Python 3. The model is using a Multinomial Naïve Bayes classification algorithm with 10-fold cross-validation. Data used in this research is tweet data in Indonesian from March 2nd to November 9th 2020. Model successfully predicted tweet sentiment with accuracy of 67,66% and f1-score for negative class 0,7184, neutral class 0,5538, and positive class 0,7308.
Keyword: COVID-19, Multinomial Naive Bayes, sentiment analysis

Judul: Characterizations of low alloy steel sae 4422  plated with chromic acid (Cro3)
Abstrak: Characterizations of low alloy steel SAE 4422 plated with chromic acid (CrOrO3) was use Optical Microscopy (OM), Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), RD),Energy Dispersive Analysis of X-Ray (EDAX), Hardness Test and Roughness Test. Microstructures analysis exhibits cracks and grain boundaries microcracks as a characteristics of chrome metal found at plating metal. Perlite and Ferrite as grains of steel found at base metal with grain boundaries around it. Phase analysis of plating metal appropriate to chrome metal with lattice constant (a) is 2.559 Å and phase analysis of base metal appropriate to iron ferrite with lattice constant (a) is 3.318 Å. Both plating and base metal have cubic system. Chemical analysis of plating metal at surface section exhibits that cracks area have highest value of carbon element compare with grains and grains boundaries area. While chemical analysis of base metal at cross section exhibits that perlite have highest value of carbon element compare with ferrite and grain boundaries. The hardness value of plating metal at surface section and cross section are 753 and 637 VHN. While, the hardness value of base metal and interface area at cross section are 205 and 373 VHN. The roughness value at measurement points of chrome surface for as received sample are 0.445 μm. Observation used Optical Microscopy and Scanning Electron Microscopy exhibits surface irregularities combine to form surface texture of plating metal at surface section.
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