The purpose of this study was to find out student errors in solving problems about mathematical literacy. This study uses a qualitative research approach with sampling method is purposive sampling. The study subjects were three students who were taken based on KAM (Student Initial Ability), namely high, medium, and low ability categories. Each subject is one person per category from the Department of Accounting, Bandung State Polytechnic. This study analyzes student errors in solving mathematical literacy problems in the statistics course for the sub-discussion of hypothesis testing. The error analysis method used is Newman analysis. This study uses two instruments, namely a test instrument with four questions in the form of descriptions and interviews. The data will then be classified into five error categories according to Newman's analysis. The conclusions in this study indicate that there are errors in answering questions, especially for mathematical literacy questions; students are expected to be able to analyze questions in the form of story questions; Understanding the problem instructions and information contained in the problem is the first step in working on mathematical literacy problems so that the level of errors and errors in working on the questions is more resolved. Of the five errors that became the standard for Newman Error Analysis assessment, three errors were made by respondents, namely errors in understanding story questions correctly, errors in transforming story questions into mathematical sentences, and errors in writing the final answer.

Dijkstra algorithm is one of the algorithms that is used to determine the path with the minimum total weight in the computer network, communication network, and transportation network problems. Some problems that have been studied using Dijkstra algorithm, namely multi-hop calibration of networked embedded system, achieving superior timing-driven routing trees and adaptive protection in microgrids. This article will determine the fastest path in the distribution of logistics by Bulog in West Java region by using Dijkstra algorithm manually and also by using Matlab. Data and information including the path connecting a warehouse to another Bulog warehouse will be used to build a connected weighted graph model. This data was obtained directly from Bulog office in West Java and through Google Maps application during the Covid-19 pandemic. Furthermore, path optimization is carried out by using Dijkstra algorithm, so that the fastest path tree is obtained. The fastest path tree is a path which edge set is a subset of the connected edge set of the connected weighted graph and has minimum total weight. Based on this optimization, the fastest path from Cibitung warehouse Bekasi to Bojong warehouse Cianjur is 154 minutes.

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Penggunaan Analisis Spektral dengan periodogram  pada data deret waktu curah hujan dapat memberikan informasi perioditas data. Dengan data curah hujan Kota Bandung periode Januari   2006 sampai dengan Desember 2015, diperoleh nilai periodogram terbesar I(ω) = 503086,2562 pada frekwensi(ω) kesepuluh dan menghasilkan nilai periode T  = 12 bulan. Dengan pola data deret waktu periodik, model peramalan untuk mempekirakan curah hujan Kota Bandung adalah SARIMA(0,1,1)12 (0,1,1) dengan persamaan xt   = xt -1  + xt -12  - xt -13  + et  - 0,6875et -1  - 0,8386et -12  + 0,5765et -13 Model ini memprediksi rata-rata curah hujan Kota Bandung kedepan yaitu maksimum terjadi pada bulan Desember dan minimum pada bulan Agustus.   Kata Kunci : Analisis Spektral, SARIMA, Peramalan, Curah Hujan Kota Bandung

Regression method that pays attention to neighborhood or region is Spatial Regression with Spatial Autoregressive (SAR) and Spatial Error Model (SEM) approaches. This method is used as an approach for cases of the spread of Covid-19  , namely the number of active Covid-19   patients in the city of Bandung with the independent variable in the form of population per hectare. , population age 60 and over, poverty and population not working. The results of the Moran index test show that there is a spatial effect on the spread of positive active Covid-19   patients, and the SAR model is better than the classical linear regression model (OLS; Ordinary Least Square) and SEM with variables/factors having a significant effect, namely population 60 and over, poverty and residents do not work. The AIC value of the SEM model is 262.845 with a coefficient of determination (R-squared) of 0.457302. Keywords: Spatial Regression, OLS, SAR, AIC, Coefficient of Determination.