Bunga mawar merupakan tanaman dari genus Rosa yang memilik lebih dari 100 spesies dengan berbagai warna. Pada proses pemilihan bunga mawar akan ditemukan bunga mawar yang masih segar dan layu. Dengan itu kita dapat mendeteksi kelayuan bunga mawar dengan menerapkan metode HSI dan HSV pada aplikasi pengolahan citra, proses pengambilan data yaitu dengan melakukan preparasi data pada dataset kaggle yang kemudian dilakukan klasifikasi dan training data dengan menggunakan metode HSI dan HSV. Berdasarkan hasil klasifikasi dari total 820 gambar citra bunga mawar dilakukan pengujian sebanyak 757 gambar dengan menggunakan HSI dan HSV didapat nilai Range pada HSI, H=0-0.5, S=0-1, dan I=0.5372549-1 dengan kategori Segar, sedangkan kategori Layu HSI, H=0-0.5, S=0-1, I=0.5620915-1. Adapun nilai range HSV dengan kategori Segar H=0-0.5, S=0-1, V=0-1, dan kategori Layu H=0-0.5, S=0-1, V=0-1. Selanjutnya tingkat keberhasilan untuk pengujian bunga mawar dengan HSI mencapai 92.3% dimana data yang terbaca benar 757 dan terbaca salah 63 dari 820 data sampel bunga mawar, sedangkan pengujian pada HSV tingkat keberhasilan mencapai 93.2% dimana data yang terbaca benar 765 dan terbaca salah 55 dari 820 data sampel bunga mawar. Berdasarkan hasil diatas deteksi kelayuan bunga mawar dengan metode transformasi ruang warna HSV merupakan yang terbaik pada pengujian data. Kata Kunci : deteksi kelayuan; pengolahn citra; HSI; HSV; klasifikasi.

The rose is a plant of the genus Rosa. The rose consists of more than 100 species with various colors. In selecting and sorting roses, roses are often found that are still fresh and wilted. Based on the problems faced in roses, a system design is carried out that can detect the wilting condition of roses. By applying the HSI and HSV methods to image processing applications, it is hoped that it can help in choosing the condition of roses. With research methods through observation and literature study. To see the conditions, roses can be divided into wilted flowers and fresh flowers. In its implementation and classification, by detecting the color of roses in the HSI and HSV color space, from a total of 230 images of red and white roses that tested 200 images using HSI and HSV, the value of Range was obtained on the HSI, H = 0.240634 - 0.5, S = 0.781818 - 1, and I = 0.477124 - 1 in the Fresh category, while the HSI Wilt Category, H = 0.170495 - 0.5, S = 0.40239 - 1, I = 0.562092 - 1. and also obtained the value of Range with HSV with Fresh category H = 0.240634 - 0.5, S = 0 - 0.988235, V = 0 - 0.988235, and Wilt category H = 0.170495-0.5, S = 0 - 0.996078, V = 0 - 0.996078. With an accuracy value of the HSI and HSV of 86.9%. Therefore, it can be concluded that the detection of wilting in roses using the HSI and HSV methods is the fastest in the process using the HSI method because it reads all the min-max values.

Based on information on the <span>BNPB website on 2 September 2020, the positive rate for coronavirus disease (COVID-19) in Indonesia reached 25.25% on 30 August 2020. This is a big challenge for the Indonesian government to reduce the positivity rate to meet the standards safe accepted by World Health Organization (WHO) is 5%. To ensure the accuracy of government policies, accurate data predictions are needed. Therefore, the prophet's machine learning algorithm can be used to see trends in the spread of COVID-19 in the next one year. This algorithm has a fairly high level of accuracy because the data contains time variables which are adjusted to the dataset. In several previous research, the dataset was vast uncertain and small. Meanwhile in this research, data was taken from 2 March 2020 to 12 February 2021 on the KawalCOVID19 website. This data is used to predict from 13 February 2021 to 12 February 2022. There are 3 data used; namely data confirmed, recovered and died. Based on the analysis, the confirmed patient was 22.60-42.11%, died amounted to 21.67%-39.00%, and recovered by 22.53-41.82%. The prediction percentage that the average cases died was 2.43% every day. The accuracy of data confirmed was 43.97%, died was 72.50% and recovered was 84.24%.</span>

Saat ini, bentuk pengabdian kepada masyarakat dilakukan dengan membuat portal untuk mengarsipkan semua kegiatan yang sudah dilakukan sebelumnya. Target  dalam kegiatan pengabdian adalah anak-anak yang kurang lancar membaca baik itu yang masih bersekolah maupun yang sudah tidak bersekolah lagi. Setiap pelaksanaan kegiatan, Ruang Publik Terpadu Ramah Anak (RPTRA) dan perwakilan dari orang tua selalu ikut serta dalam penyediaan tempat untuk pelasanaan kegiatan. Dokumentasi pada awalnya disimpan secara manual dalam sebuah folder di komputer, akibatnya muncul beberapa masalah seperti diserang virus, human error, ataupun ada bagian sistem yang rusak sehingga beberapa dokumentasi hilang begitu saja. Berdasarkan pengalaman tersebut, maka dibuatlah portal yang berfungsi sebagai alternatif cloud data dan juga monitoring segala aktifitas yang sudah dilakukan. Portal ini dapat dilihat melalui website www.himjar.himtiunas.or.id. Hasil portal saat ini berupa dokumentasi foto kegiatan yang sudah dilaksanakan sebelumnya. Untuk proses monitoring akan dilakukan dengan menu filter, dimana setiap kegiatan yang belum dan sudah terlaksana akan tercatat pada portal ini.

Virtual education is distance teaching in which teachers and students are in different places but can be connected to each other via the internet network. The virtual education system is divided into two, namely Learning Management System (LMS) and virtual classes. One of virtual education is video conferencing (vicon). A fully capable network is required to comply with Quality of Service (QoS) standards for real-time applications. This study analyzes the QoS of vicon features in LMS at the University of Nasional that is BigBlueButtonBN. Based on the QoS parameters obtained 25% throughput, 100% packet loss, 84.75% delay, 37.5% jitter and 58.5% MOS. Based on the bandwidth requirements of each parameter, the results are 61.2% or 27.9411MB. The MOS value for video 100 is 4, which results in very good images on this vicon.Keywords:BigblueButtonBN; Learning Management System; Mean Opinion Score; Quality of Service; Video Conference