Using k-Means and Self Organizing Maps in Clustering Air Pollution Distribution in Makassar City, Indonesia

Air pollution is an important environmental problem for specific areas, including Makassar City, Indonesia. The increase should be monitored and evaluated, especially in urban areas that are dense with vehicles and factories. This is a challenge for local governments in urban planning and policy-making to fulfill the information about the impact of air pollution. The clustering of starting points for the distribution areas can ease the government to determine policies and prevent the impact. The k-Means initial clustering method was used while the Self-Organizing Maps (SOM) visualized the clustering results. Furthermore, the Geographic Information System (GIS) visualized the results of regional clustering on a map of Makassar City. The air quality parameters used are Suspended Particles (TSP), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), Carbon Monoxide (CO), Surface Ozone (O3), and Lead (Pb) which are measured during the day and at night. The results showed that the air contains more CO, and at night, the levels are reduced in some areas. Therefore, the density of traffic, industry and construction work contributes significantly to the spread of CO. Air conditions vary, such as high CO levels during the day and TSP at night. Also, there is a phenomenon at night that a group does not have SO2 and O3 simultaneously. The results also show that the integration of k-Means and SOM for regional clustering can be appropriately mapped through GIS visualization.

Stability of Traveling Waves to a Burgers Equation with 2nd-Order Nonlinear Diffusion

We are interested in the study of asymptotic stability for Burgers equation with second-order nonlinear diffusion. We first transform the original equation by the ansatz transformation to establish the existence of traveling wave. We further employ the energy estimate under small perturbation and arbitrary wave amplitude. This energy estimate is then used to establish the stability.

Application of Recursive Algorithm on Shamir's Scheme Reconstruction for Cheating Detection and Identification

Information data protection is necessary to ward off and overcome various fraud attacks that may be encountered. A secret sharing scheme that implements cryptographic methods intends to maintain the security of confidential data by a group of trusted parties is the answer. In this paper, we choose the application of recursive algorithm on Shamir-based linear scheme as the primary method. In the secret reconstruction stage and since the beginning of the share distribution stage, these algorithms have been integrated by relying on a detection parameter to ensure that the secret value sought is valid. Although the obtained scheme will be much simpler because it utilizes the Vandermonde matrix structure, the security aspect of this scheme is not reduced. Indeed, it is supported by two detection parameters formulated from a recursive algorithm to detect cheating and identify the cheater(s). Therefore, this scheme is guaranteed to be unconditionally secure and has a high time efficiency (polynomial running time).

Model Petri Net Sistem Pembayaran Pajak Kendaraan Bermotor Jenis 5 Tahun

Paying taxes is an example of public service. In the process of serving, the service is often synonymous with the queuing process. Queuing is a condition in which several people or objects from a waiting line to be served are generally caused by the need for services to exceed the service capacity or service facilities so that users of arriving facilities cannot immediately receive service. Therefore, overcoming many complaints due to queues can be done by improving services and maximizing time efficiency using the Petri net model. In this study, a Petri net model of the 5-year tax payment service system for a motor vehicle at SAMSAT Oku Timur 1 was made as many as 17 places, 15 transitions, two operators, and 30 arcs using WOPED 3.2.0 software.

Assessing Forecasting Performance of Daily Mean Temperature at 1st and 2nd Perak Station, Surabaya Using ARIMA and VARIMA Model with Outlier Detection

Air temperature is an important data for several sectors. The demand of fast, exact and accurate forecast on temperature data is getting extremely important since it is useful for planning of several important sectors. In order to forecast mean daily temperature data at 1st and 2nd Perak BMKG Station in Surabaya, this study used the univariate method, ARIMA model and multivariate method, VARIMA model with outlier detection. The best ARIMA model was selected using in-sample criteria, i.e. AIC and BIC. While for VAR model, the minimum information criterion namely AICc value was considered. The RMSE values of several forecasting horizons of out-sample data showed that the overall best model for mean daily temperature at 1st and 2nd Perak Station was the multivariate model, i.e. VARX (10,1) with four outliers incorporated in the model, indicated that it was necessary to consider the temperature from the nearest stations to improve the forecasting performance. This study recommends performing the overall best model only for short term forecasting, i.e. two weeks at maximum. By using the one week-step ahead and one day-step ahead forecasting scheme, the forecasting performance is significantly improved compared to default the k-step ahead forecasting scheme.

Pendekatan Univariate Time Series Modelling untuk Prediksi Kuartalan Pertumbuhan Ekonomi Indonesia Pasca Vaksinasi COVID-19

The development of COVID-19 has had a significant negative impact on Indonesia’s economic growth based on the indicator of the value of the quarterly year of year data in 2020 and 2021. Economic growth is still experiencing a recession per first quarter with a percentage of - 2.19 percent at the beginning of 2021. The government has to take vaccination measures for the community gradually with the aim of reducing the number of sufferers of these cases. The purpose of this study is to predict economic growth quarterly after vaccination using 3 (three) univariate time series models, namely ARIMA, Holt-Winters and Dynamic Linear models for policymaking. Holt-Winters and Dynamic Linear models make it possible to handle time-series data containing trends and seasonality. The data is divided into training data and test data obtained from the ministry of finance and the Indonesian Central Statistics Agency (BPS). The goodness of the model uses MSE, MAE and U-Theil criteria. Based on the results of the analysis using the R library, the results show that the best modelling for economic growth data is the ARIMA model with the lowest MSE, MAE and U-Theil values with the difference between the models being 0.000242. The ARIMA model looks better than other models because the economic growth data only contains trends and assumes a seasonal element in the data. In addition, the Holt-Winters and Dynamic Linear models produce a forecast for Indonesia’s economic growth to still experience a recession (negative growth) in the next four quarterly data, while the ARIMA model produces a positive growth forecast in the fourth quarter.

Bilangan Terhubung Titik Pelangi pada Graf Hasil Operasi Korona Graf Prisma (P_(m,2)) dan Graf Lintasan (P_3)

Rainbow vertex-connection number is the minimum k-coloring on the vertex graph G and is denoted by rvc(G). Besides, the rainbow-vertex connection number can be applied to some special graphs, such as prism graph and path graph. Graph operation is a method used to create a new graph by combining two graphs. Therefore, this research uses corona product operation to form rainbow-vertex connection number at the graph resulting from corona product operation of prism graph and path graph (Pm,2 P3) (P3 Pm,2). The results of this study obtain that the theorem of rainbow vertex-connection number at the graph resulting from corona product operation of prism graph and path graph (Pm,2 P3) (P3 Pm,2) for 3 = m = 7 are rvc (G) = 2m rvc (G) = 2.

Penerapan Peta Kendali Neutrosophic Exponentially Weighted Moving Average (NEWMA) X dalam Monitoring Rata-Rata Proses Ketebalan Kaca

In the industrial sector, the measurement results of a quality characteristic often involve an uncertainty interval (interval indeterminacy). This causes the classical control chart to be less suitable for monitoring quality. Currently, a control chart with a neutrosophic approach has been developed. The neutrosophic control chart was developed based on the concept of neutrosophic numbers with control charts. One of the control charts that have been developed to monitor the mean process is the Neutrosophic Exponentially Weighted Moving Average (NEWMA) X control chart. This control chart is a combination of neutrosophic with classical EWMA control chart. The neutrosophic control chart consists of two control charts, namely lower and upper, each of which consists of upper and lower control limits. Therefore, NEWMA X is more sensitive to detect out-of-control observations. In this research, the NEWMA X control chart will be used to monitor the average process of the thickness of the panasap dark grey 5mm glass produced by a glass industry. Through the analysis in this research, it was found that by using weighting λN [0, 10; 0, 10] and constant value kN [2, 565; 2, 675], the average process of the thickness of panasap dark grey 5mm glass has not beet controlled statistically because 21 observations were identified that were outside the control limits (out of control). When compared with the classical EWMA control chart with the same weighting λ, 17 observations were detected out of control. This proves that the NEWMA X control chart is more sensitive in detecting observations that are out of control because the determination of the in-control state is based on two values, lower and upper, both at the lower and upper control limits.

Sensitivity, Optimal Control, and Cost-Effectiveness Analysis of Intervention Strategies of Filariasis

In this work, sensitivity, optimal control, and cost-effectiveness of several intervention strategies of filariasis are discussed. We study the intervention strategies that are related to bednet use, insecticide, and the combination of bed-net use and insecticide. We use Pontryagin’s maximum principle to characterize the optimal controls. The Average Cost-Effectiveness Ratio (ACER) and Infection Averted Ratio (IAR) are used to identify the most cost-effective strategy. We also determine the basic reproduction number and investigate the sensitivity of the basic reproduction number on the parameters that are related to bed-net use and insecticide. Based on the ACER values, the most cost-effective strategy to control filariasis is insecticide intervention. On the other hand, the IAR values indicates that bed-net use intervention is the most cost-effective strategy. Furthermore, it is also the most effective strategy to eliminate filariasis. The sensitivity analysis results show that the control parameter related to bed net use and treatment have a central role in reducing the basic reproduction number and filariasis spread.

Studi Longitudinal Pada Analisis Data Gula Darah Pasien Diabetes melalui Principal Component Analysis

Multicollinearity is a relationship or correlation between predictor variables. Multicollinearity can also occur in longitudinal data, which is a combination of cross-section data and time-series data. The impact of multicollinearity causes the influence of the predictor variable on the response variable to be insignificant, the least-squares estimator, and the error to be sensitive to changes in the data. Therefore, the procedure to overcome multicollinearity uses the principal component analysis method. This study aims to model PCA longitudinal data regression with a fixed-effect model that is applied to blood sugar data of diabetic patients with a time span of January 2019 to July 2019 at Ibnu Sina Hospital Makassar City. The results of this study indicate that there are two main components formed from PCA longitudinal data regression modelling with a fixed-effect model. Obtained variable values are systolic blood pressure of -0.007, diastolic blood pressure of -0,016, the body temperature of -0.098, and platelets of 0.005 which affect blood sugar in patients with diabetes.