The analysis used in this study uses the help of Google Analytics to understand how the user's behavior on the Calculus learning material educational website page. Are users interested in recommendation articles? The answer to this question provides insight into the user's decision process and suggests how far a click is the result of an informed decision. Based on these results, it is hoped that a strategy to generate feedback from clicks should emerge. To evaluate the extent to which feedback shows relevance, versus implicit feedback to explicit feedback collected manually. The study presented in this study differs in at least two ways from previous work assessing the reliability of implicit feedback. First, this study aims to provide detailed insight into the user decision-making process through the use of a recommendation system with an implicit feedback feature. Second, evaluate the relative preferences that come from user behavior (user behavior). This differs from previous studies which primarily assessed absolute feedback.

Every organism has DNA (deoxyribonucleic acid) which carries genetic information. One of the methods for analyzing strings of DNA sequences is n-mers frequency. It is a data mining method on strings of DNA sequences that is converted into numerical data. We studied 13 deadly viruses, consisting of Rabies, HIV, Ebola, Smallpox, Marburg, Herpes B, Lujo, Avian Influenza, Spanish Flu H1N1, Dengue, HPV, SARS-CoV, and SARS-CoV-2. This study aims to establish the phylogenetic tree and find out the genetic relationship of the deadly viruses. The first method we used are collecting viral DNA sequences from the NCBI database. Afterward, the strings of DNA sequences were converted into numerical data using the n-mers frequency. After that, the dissimilarity matrix was calculated and the phylogenetic tree was established using the AGNES algorithm. Based on the phylogenetic tree, the aforementioned 13 viruses were classified into three clusters, namely cluster 1 from the realm Riboviria, cluster 2 from the realm Duplodnaviria and cluster 3 from the realm Varidnaviria. The clustering results of 13 viruses are valid because each virus is clustered based on its taxon. In addition, viruses that have the closest genetic relationship are grouped first, while viruses that have the distant genetic relationship are grouped later.

The analysis used in this study uses the help of Google Analytics to understand how the user's behavior on the Calculus learning material educational website page. Are users interested in recommendation articles? The answer to this question provides insight into the user's decision process and suggests how far a click is the result of an informed decision. Based on these results, it is hoped that a strategy to generate feedback from clicks should emerge. To evaluate the extent to which feedback shows relevance, versus implicit feedback to explicit feedback collected manually. The study presented in this study differs in at least two ways from previous work assessing the reliability of implicit feedback. First, this study aims to provide detailed insight into the user decision-making process through the use of a recommendation system with an implicit feedback feature. Second, evaluate the relative preferences that come from user behavior (user behavior). This differs from previous studies which primarily assessed absolute feedback.

Indonesia merupakan negara endemik hepatitis peringkat ketiga sedunia. Hepatitis merupakan penyakit menular yang disebabkan oleh virus. Penyakit hepatitis terbagi menjadi beberapa tipe, salah satunya virus hepatitis A (HAV). Model matematika yang memodelkan penyebaran penyakit ini adalah model yang dibuat oleh Marco Ajelli. Marco Ajelli membuat model metapopulasi pada transmisi virus hepatitis A (HAV) yang diterapkan di negara Italia. Hasil yang diperoleh adalah vaksinasi yang dilakukan di salah satu negara bagian (Puglia) dapat mengurangi secara signifikan jumlah penderita di negara tersebut secara keseluruhan. Skripsi ini mengajukan sebuah model yang dapat diterapkan di Indonesia khususnya di Jawa Barat, Jawa Tengah dan Jawa Timur. Simulasi dilakukan  untuk mengetahui pengaruh program vaksinasi yang dilakukan pada satu wilayah terhadap wilayah yang lain dan mengetahui wilayah yang paling optimal untuk diberikan program vaksinasi secara massal jika program vaksinasi massal hanya dapat dilakukan pada satu wilayah saja. Oleh karena itu, faktor mobilitas spatial merupakan faktor yang sangat diperhatikan. Dari hasil simulasi yang dilakukan di daerah Jawa Barat, Jawa Tengah dan Jawa Timur diperoleh kesimpulan bahwa program vaksinasi yang dilakukan di Jawa Timur, akan secara optimal mengurangi jumlah penderita hepatitis A di Jawa Barat, Jawa Tengah dan Jawa Timur.