Pandemi Coronavirus disease 2019 (COVID-19) telah melanda dunia, termasuk Indonesia. Di Indonesia, banyaknya kasus terkonfirmasi positif terus bertambah, kadang turun kadang naik secara drastis, demikian juga dengan banyaknya kasus sembuh yang mengalami fluktuasi setiap harinya. Hubungan variabel banyaknya kasus terkonfimasi positif COVID-19 dengan banyaknya kasus sembuh setiap harinya tersebut menunjukkan trend yang berkesinambungan. Model Vector Autoregressive Integrated (VARI) dapat digunakan untuk memodelkan hubungan banyaknya kasus terkonfirmasi dan sembuh COVID 19 secara simultan dan meramalkan amatan di waktu mendatang. Penelitian ini bertujuan untuk membentuk model prediksi hubungan variabel banyaknya kasus terkonfirmasi positif dan kasus sembuh COVID-19 harian di Indonesia dengan model VARI. Data kasus COVID-19 yang digunakan mulai dari tanggal 1 November 2020 sampai dengan 17 Mei 2021. Pengolahan data dilakukan dengan program R. Hasil penelitian menunjukkan korelasi antara kedua variabel pengamatan bernilai 0,77, yangberarti adanya hubungan positif yang kuat antara kedua variabel. Hasil uji kestasioneran memperlihatkan kedua variabel tidak stasioner, sehingga dilakukan differencing sebanyak satu kali. Hasil analisis data menunjukkan model terbaik yang diperoleh adalah model VARI (7,1). Pencocokan model dilakukan menggunakan Mean Absolute Percentage Error (MAPE), diperoleh nilai MAPE untuk untuk kasus terkonfirmasi adalah sebesar 20% dan untuk kasus sembuh sebesar 11%, yang berarti bahwa model VARI (7,1) memberikan hasil yang baik untuk peramalan di waktu mendatang terhadap kedua variabel. Banyaknya kasus terkonfirmasi COVID-19 dipengaruhi oleh kasus terkonfirmasi pada hari –hari sebelumnya tapi tidak dipengaruhi oleh kasus sembuh pada hari-hari sebelumnya. Sedangkan banyaknya kasus sembuh COVID-19 dipengaruhi oleh banyaknya kasus terkonfirmasi dan kasus sembuh pada hari – hari sebelumnya

Internet Shopping Optimization Problem (ISOP) is the application of optimization to online shopping activities of all complexity. The ISOP is useful for consumers in minimizing the cost of purchasing goods. This paper presents a bibliometric analysis of peer-reviewed papers based on ISOP topics by utilizing the R application program in the mapping. Overall, 101 papers (233 authors) in the Scopus database have used ISOP topics with research growth of 11.61% annually. The researcher presents a network of citations from productive authors, the impact of research, trends in terms that have been used, and shows a collaborative network of citations. Finally, the researcher presents the thematic analysis of the papers that apply the ISOP as a research topic and shows how the research forms clusters based on analytical solutions and numerical simulations that generate suggestions in finding the latest topics in the ISOP study. Another target for this paper is to produce review analysis results through Preferred Reporting Items for Systematic reviews and Meta Analyses (PRISMA). Through bibliometric and PRISMA analysis, it was found that the latest method in completing ISOP optimization is the ARC method. The ARC method in the ISOP is still little published among researchers in the world.

Coronavirus disease (COVID-19) is a new disease found in the late 2019. The first case was reported on December 31, 2019 in Wuhan, China and spreading all over the countries. The disease was quickly spread to all over the countries. There are 206.900 cases confirmed by March 18, 2020 causing 8.272 death. It was predicted that the number of confirmed cases will continue to increase. On January 30, 2020, WHO declared this as pandemic for the 6th time ever since the swine influenza. There are a lot of researchers which discuss pandemic spreading caused by virus with mathematical modelling. In this paper, we discuss a long-term prediction over the COVID-19 spreading using stationary distribution markov chain. The goal is to analyze the prediction of infected people in long-term by analyzing the COVID-19 daily cases in an observation interval. By analyzing the daily cases of COVID-19 in Indonesia from March 2nd, 2020 until November 1st, 2020, result shown that 53.91% of probability that the COVID-19 daily case will incline in long-term, 44.86% of chance will decline, and 1.23% of chance will stagnant.