Puskesmas Cigugur Tengah dalam setiap harinya melayani pasien sekitar 150 orang dari berbagai wilayah didaerah Cigugur Tengah. Dengan bertambahnya jumlah pasien tersebut, maka bertambah pula data pasien setiap harinya, sehingga sejumlah data tidak dapat dipelajari lebih lanjut dan data tersebut hanya digunakan sebagai arsip saja. Berdasarkan latar belakang tersebut, maka penulis ingin mengolah data tersebut untuk mengelompokan penyakit pasien berdasarkan penyakit akut dan penyakit tidak akut menggunakan teknik data mining dengan metode clustering dengan algoritma k-means dan algoritma k-medoids sebagai pembanding. Sehingga nantinya dapat membantu pihak Puskesmas Cigugur Tengah untuk mengetahui penyakit apa yang paling banyak diderita pasien, kemudian dapat membantu pihak pemerintah khususnya Dinas Kesehatan dalam pemberian penyuluhan kesehatan kepada masyarakat sekitar. Berdasarkan hasil pengujian dari algoritma k-means dan algoritma k-medoids, didapat cluster model untuk algoritma k-means sebanyak 241 items pada cluster_0 atau penyakit akut sebesar 96% dan 9 items pada cluster­_1 atau penyakit tidak akut sebesar 4% dari 250 data, sedangkan untuk algoritma k-medoids sebanyak 224 items pada cluster_0 atau penyakit akut sebesar 90% dan 26 items pada cluster­_1 atau penyakit tidak akut sebesar 10% dari 250 data, maka penyakit yang paling banyak diderita pasien pada Puskesmas Cigugur Tengah adalah penyakit akut sebesar 93%, dengan nilai Davies Bouldin untuk algoritma k-means sebesar -0.453 dan algoritma k-medoids sebesar -1.276. Dari hasil tersebut dapat dikatakan bahwa algoritma yang menghasilkan nilai Davies Bouldin terkecil dianggap sebagai algoritma yang lebih baik, maka dapat disimpulkan bahwa algoritma k-means lebih baik dari algoritma k-medoids yang menghasilkan nilai rata – rata Davies Bouldin sebesar -1.276.

The huge impact caused by the COVID-19 pandemic has made many people express their opinions on Twitter social media. There are various responses given by the community that are negative and positive. The dataset comes from kaggle with more than 750 tweets of data. Classification designed by the Naive Bayes method. Implementation through preprocessing, case folding, tokenizing, stopword removal, TF-IDF, and cross validation has been able to produce quite high accuracy. After classification, validation will be carried out with Cross Fold Validation. The best value is on cv5 where accuracy = 0.847, precision = 0.855, recall = 0.83, and f1 score = 0.842.

Humans express technology starting from their intellect and mind. With this, humans have a desire to get out of trouble, wanting to live better, more comfortable and safer. Productive counselling that we do to residents of Taman Bukit Cibogo RT. 08 RW. 17 Leuwigajah Cimahi Selatan regarding technology counselling in household life. The purpose of this service is to convey to residents about applications that can be used in everyday life to help activities carried out by each of them. The application used is on average a digital application that we can download on the playstore or Appstore platform. Based on the results of the service carried out on residents of Taman Bukit Cibogo RT. 08 RW. 17 Leuwigajah Cimahi Selatan, they were quite enthusiastic about the application that the service team conveyed, some even asked for help to install the application they wanted.

Lung disease has a very serious impact on the respiratory system and can be dangerous if not treated immediately. At this time, lung diseases that are often encountered by the public include pneumonia and 2019 coronavirus. Many people mistake the disorder that occurs to him because the symptoms of Covid-19 and pneumonia are very similar. Thus, it is very important to know the difference between the two diseases so that early treatment can be carried out. Based on the problems that have been described, the author will propose a study entitled "Classification of X-ray Images of Normal Lungs, Pneumonia, and Covid-19 Using the Fuzzy C-Means (FCM) Algorithm". The aim of this study is to assist in classifying normal, pneumonia, and Covid-19 lungs. The reason for choosing this algorithm is that this algorithm has advantages in grouping cluster centers which are more optimal than other methods.