The Novel Coronavirus that just appeared at the end of 2019 was named SARS-COV-2 which caused a pandemic of a respiratory disease known as COVID-19. In Indonesia itself, there was a case of COVID-19 first announced on March 2, 2020. The spread of COVID-19 in Indonesia is so fast because of one factor namely the lack of knowledge about COVID-19 prevention and early detection. This study will discuss the system to provide the latest information about the development of the COVID-19 case in Indonesia and help the community to conduct an independent detection of COVID-19 using an expert system. Information that will later be displayed on this application is obtained by web scraping techniques from the official website of the task force for the acceleration of handling COVID-19 in Indonesia. This system also has an early detection feature using the rule base method expert system. The results of this system are obtained from the responses of respondents and the results of respondents interested in this application with a percentage of 95.12%, and testing about the validation of the results of the expert system is the same as expected.

Abstract— Rhizome is part of the plant that has many benefits. Some types of rhizomes that are often found are ginger, turmeric and galangal. But in reality, for the three types of rhizomes, there are still many that cannot be recognized. This is because some types of rhizomes do have properties and textures. This research proposes a rhizome recognition system with the classification of SVM (Support Vector Machine) and KNN (K-Neirest Neighbor). SVM searches for the best hyperplane by maximizing the distance between classes. KNN classifies objects based on the learning data that is the most distant from the object. The types of rhizomes used in this research data collection are the three types of rhizomes mentioned above. Meanwhile, the number of images in this study consisted of 150 training images and 30 testing images. The test is carried out by calculating the accuracy value of the classification of testing data in 3 classes, namely Ginger, Kuyit, and Galangal classes using both methods. The rhizome recognition system using the second method of classification is expected to help get good accuracy and can be more easily recognized by the name of the rhizome. Keywords— Rhizome; SVM;  KNN   Abstrak— Rimpang merupakan bagian dari tanaman yang memiliki banyak manfaat. Beberapa jenis rimpang yang sering dijumpai adalah jahe, kunyit dan lengkuas. Namun pada kenyataannya untuk ketiga jenis rimpang tersebut masih banyak yang tidak bisa dalam mengenalinya. Hal tersebut dikarenakan pada beberapa jenis rimpang memang memiliki kemiripan dalam bentuk dan teksturnya. Dalam penelitian ini diajukan sebuah sistem pengenalan rimpang dengan metode klasifikasi SVM (Support Vector Machine) dan KNN (K-Neirest Neighbor). SVM mencari hyperplane terbaik dengan memaksimalkan jarak antar kelas. KNN melakukan klasifikasi terhadap objek yang berdasarkan dari data pembelajaran yang jaraknya paling dekat dengan objek tersebut Jenis rimpang yang digunakan dalam dataset penelitian ini adalah ketiga jenis rimpang yang disebutkan di atas. Sedangkan untuk jumlah citra dalam penelitian ini terdiri dari 150 citra training dan 30 citra testing. Pengujiannya dilakukan dengan menghitung nilai akurasi dari klasifikasi data testing pada 3 kelas, yaitu kelas Jahe, Kuyit, dan Lengkuas dengan menggunakan kedua metode tersebut. Sistem pengenalan rimpang menggunakan kedua metode klasifikasi ini diharapkan mendapatkan akurasi yang baik dan dapat membantu masyarakat untuk lebih mudah mengenali nama rimpang. Keywords— Rimpang; SVM; KNN

Rhizomes, also called rootstalks, are stems that help plants to reproduce asexually, survive in winter, store food, and make stem tubers. They possess many functions and merits. Some of the commonly found rhizomes are ginger, turmeric, and galangal, yet still a lot of people still find it difficult to distinguish those three rhizomes. That's because those mentioned rhizomes do share several similarities in their shape and texture. This research submits a rhizome identification system with SVM (Support Vector Machine) classification method. Based on the experiments done, this particular method is chosen because it showed great results, quite high-valued accuracy level for data classification, and has minimum error rate. The types of rhizomes used in this research's dataset are those three varieties mentiones above, while the amount of images in this experiment consists of 150 training images and 30 testing images. The experiment is done by calculating the accuracy value from data testing classification of three classes, which are ginger class, turmeric class, and galangal class utilizing the mentioned method. This rhizome identification system that uses the SVM classificafion method gets 78% accuracy value.

The dry season and the rainy season in the Lamongan area always cause several problems, including water scarcity, flooding, damaged roads, etc. The public report site, which is used to accommodate the aspirations of Lamongan residents who are in trouble, requires image evidence to ensure that there are no false reports. The site has a limit on the size of the file to be uploaded, so if the file size is too large, the upload process cannot be carried out. In this study, an analysis will be carried out to compare what compression method produces photos with the smallest size for further upload on the Lamongan community report site. Huffman compression and Run Length Encoding (RLE)were chosen because the algorithm includes lossless compression where the compressed image will not be damaged even though the size is compressed. From the two methods or algorithms, testing is carried out to find out which algorithm is the best that can produce compressed images with the smallest size. From the experiments conducted, it is known that the RLE method is a better method than Huffman coding. With the RLE method we can compress images up to 93.17%.

Abstrak— Kasus terkonfirmasi positif COVID-19 pada klaster perkantoran semakin meningkat. Hal ini terjadi seiring dengan diizinkannya kembali pembukaan beberapa perusahaan oleh pemerintah untuk membantu menggerakkan ekonomi. Beberapa protokol kesehatan telah dibuat oleh Kementerian Kesehatan. Akan tetapi pada penerapan yang dilakukan di perkantoran, penyebaran COVID-19 belum bisa dicegah sepenuhnya. Salah satu cara agar penyebaran COVID-19 pada klaster perkantoran bisa dicegah adalah memastikan karyawan yang datang dalam kondisi yang sehat. Untuk memastikan karyawan yang datang berkemungkinan terinfeksi virus COVID-19 atau tidak, diperlukan sebuah sistem deteksi dini COVID-19 yang cepat dan handal. Dalam penelitian ini, diajukan sebuah sistem deteksi dini COVID-19 dengan memadukan sistem monitoring suhu dan sistem pakar yang diimplementasikan pada sistem absensi karyawan. Setiap karyawan yang hendak masuk, diwajibkan melakukan presensi pada sistem. Sebelum sistem merekam kehadiran karyawan tersebut, sistem akan memproses informasi suhu karyawan dari sistem monitoring suhu dan sistem pakar untuk mendeteksi apakah karyawan tersebut berkemungkinan terkonfirmasi COVID-19 atau tidak. Hasil dari deteksi dini ini dapat menjadi rekomendasi kepada perusahaan untuk menentukan seorang karyawan boleh tetap bekerja di kantor atau harus bekerja di rumah.