The hepatitis C virus (HCV) is considered a problem to the health of societies are the main. There are around 120-130 million or 3% of the world's total population infected with HCV. Without treatment, most major infectious acute evolve into chronic, followed by diseases liver, such as cirrhosis and cancer liver. The data parameters used in this study included albumin (ALB), bilirubin (BIL), choline esterase (CHE), -glutamyl-transferase (GGT), aspartate amino-transferase (AST), alanine amino-transferase (ALT), cholesterol (CHOL), creatinine (CREA), protein (PROT), and Alkaline phosphatase (ALP). This research proposes a methodology based on machine learning classification methods including k-nearest neighbors, naïve Bayes, neural network, and random forest. The aim of this study is to assess and evaluate the level of accuracy using the algorithm classification machine learning to detect the disease HCV. The result show that the accuracy of the method NN has a value of accuracy are high, namely at 95.12% compared to the method KNN, naïve Bayes and RF in a row amounted to 89.43%, 90.24%, and 94.31%.

COVID-19 has become a global pandemic including Indonesia, so the government is taking vaccinations as a preventive measure. The public's response to this continues to appear on social media platforms, one of which is Twitter. Tweets about the COVID-19 vaccine have generated various kinds of positive and negative opinions in the community. Therefore, it is very important to detect and filter it to prevent the spread of incorrect information. Sentiment analysis is a method used to determine the content of a dataset in the form of negative, positive or neutral text. The dataset in this study was obtained from 5000 COVID-19 vaccine tweets with the distribution of 3800 positive sentiment tweets, 800 negative sentiment tweets and 400 neutral sentiment tweets. The dataset obtained is then pre-processed data to optimize data processing. There are 4 stages of pre-processing, including remove punctuation, case folding, stemming and tokenizing. This study examines the performance of RNN and Naïve Bayes by adding the TF-IDF (Term Frequency-Inverse Document Frequency) technique which aims to give weight to the word relationship (term) of a document. The test results show that RNN (TF-IDF) has a greater accuracy of 97.77% compared to Naïve Bayes (TF-IDF) of 80%.

Humans in carrying out communication activities can express their feelings either verbally or non-verbally. Verbal communication can be in the form of oral or written communication. A person's feelings or emotions can usually be seen by their behavior, tone of voice, and expression. Not everyone can see emotion only through writing, whether in the form of words, sentences, or paragraphs. Therefore, a classification system is needed to help someone determine the emotions contained in a piece of writing. The novelty of this study is a development of previous research using a similar method, namely LSTM but improved on the word weighting process using the TF-IDF method as a further process of LSTM classification. The method proposed in this research is called Natural Language Processing (NLP). The purpose of this study was to compare the classification method with the LSTM (Long Short-Term Memory) model by adding the word weighting TF-IDF (Term Frequency–Inverse Document Frequency) and the LinearSVC model, as well to increase accuracy in determining an emotion (sadness, anger, fear, love, joy, and surprise) contained in the text. The dataset used is 18000, which is divided into 16000 training data and 2000 test data with 6 classifications of emotion classes, namely sadness, anger, fear, love, joy, and surprise. The results of the classification accuracy of emotions using the LSTM method yielded a 97.50% accuracy while using the LinearSVC method resulted in an accuracy value of 89%.

Penyebaran pandemi virus COVID-19 telah memberikan tantangan tersendiri bagi lembaga pendidikan di Indonesia. Akibatnya membuat sektor pendidikan seperti sekolah menghentikan proses pembelajaran secara tatap muka. Sebagai gantinya, proses pembelajaran dilaksanakan secara daring (online) yang bisa dilaksanakan dari rumah masing-masing murid. Perpindahan sistem belajar konvensional ke sistem daring amat mendadak dan tanpa persiapan matang. Tetapi semua ini harus tetap dilaksanakan agar proses pembelajaran dapat berjalan lancar dan murid aktif mengikuti walaupun dalam kondisi pandemic COVID-19. Kesiapan dari pihak sekolah maupun murid merupakan tuntutan dari pelaksanaan daring. Pelaksanaan daring ini memerlukan perangkat pendukung seperti komputer dan media pembelajaran online yang terhubung dengan internet. Penggunaan beberapa aplikasi pada pembelajaran daring sangat membantu guru dalam proses pembelajaran saat di masa pandemic. Perlu disadari bahwa ketidaksiapan guru dan siswa terhadap pembelajaran daring juga menjadi masalah. Selain itu permasalahan lain yang dimiliki sekolah yaitu mengenai bahan ajar yang hanya sekedar perintah berupa lisan sehingga membuat murid-siswi TK merasa bosan. Hal tersebut dikarenakan para guru belum memanfaatkan fasilitas Teknologi Informasi dan Komunikasi (TIK) yang ada untuk media pembelajaran online secara optimal. Selain itu, para guru juga belum memiliki kompetensi untuk mengembangkan bahan pembelajaran berbasis TIK selama pembelajaran online. Maka dari itu, kegiatan pengabdian ini mengusulkan workshop dan pendampingan untuk meningkatkan kompetensi guru TK Aisyisyah Busthanul Athfal (ABA) 16 Malang dalam menggunakan media pembelajaran online di masa pandemic ini.

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