The product delivery strategy using n-vehicle is the application of optimization for transportation problems. The product delivery strategy using n-vehicle is useful for minimizing the shipping costs of a company’s production. This article presents a peer-reviewed bibliometric analysis based on the topic of production delivery strategies using n-vehicle. Overall, there are 91 articles from the Dimension, Science Direct, and Google Scholar databases in 2013-2021 that use the topic of production delivery strategies using n-vehicle based on the keywords ”Capacitated transportation problem” and ”cost” and ”vehicle” and” optimal solutions”. The researcher presents the relationship of each cited article so that it can show the collaboration of all the cited articles. This article aims to generate and review analysis results through Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) and State of The Art. Bibliometric analysis, PRISMA, and State of The Art show how the development of research on production delivery strategies using n-vehicle. So, it can produce suggestions in conducting the latest research related to studies on the topic of production delivery strategies using n-vehicle. Based on PRISMA’s analysis, 91 articles were obtained, of those 91 articles, 11 articles discussed the strategy of delivering production products using n-vehicle in depth. The State of The Art also shows how the development of research on production delivery strategies using n-vehicle is developing. It can be seen that apart from the classical method, other methods are also emerging to solve transportation problems. One of them is Vogel Total Difference Approach Method (VTDM).

This study discusses the application of two linear algebraic materials, namely vector and matrix spaces. The application of the two materials is related to an article, the writing can be in the form of an article, book, and so on. The writings examined in this study use example sentences made by the author. Two materials of linear algebra, namely the vector space and the matrix are used to analyze whether there is a similarity between the writing made with other writing. As a result, vector space and matrix can be used to detect similarities in a text.

This study discusses the application of two linear algebraic materials, namely vector and matrix spaces. The application of the two materials is related to an article, the writing can be in the form of an article, book, and so on. The writings examined in this study use example sentences made by the author. Two materials of linear algebra, namely the vector space and the matrix are used to analyze whether there is a similarity between the writing made with other writing. As a result, vector space and matrix can be used to detect similarities in a text.

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