Journal of Statistical Methods and Data Science <p><strong>Journal of Statistical Methods and Data Science (JSMDS) </strong> publishes articles consisting of research findings, case studies, or literature reviews within the field of statistical methods and data science, along with their applications.</p> en-US (Arisman Adnan, Ph.D) (Anne Mudya Yolanda) Fri, 05 Apr 2024 03:47:36 +0000 OJS 60 K-MEANS CLUSTERING USING ELBOW METHOD IN CASE OF DIABETES MILLETUS TYPE II IN INDONESIA <p>Indonesia is ranked 7th out of 10 countries with the<br>highest number of sufferers. BPJS Health include First Level<br>Health Facilities (FKTP) and Advanced Referral Health<br>Facilities (FKRTL) which Type 2 diabetes mellitus is one of<br>the ten most common diagnoses at FKRTL visits and ranks<br>third after follow-up examinations after treatment for<br>conditions other than malignant neoplasms and kidney failure<br>with a percentage of 3.54% and a total of 62,455 for 2019 to<br>2020. In deciding policies related to the funding of BPJS<br>participants who suffer from diabetes mellitus, it is necessary<br>to have the characteristics of each region so that policy making<br>is more appropriate. The method used in this study uses<br>clustering analysis using the K-means algorithm for type<br>II diabetes mellitus in Indonesia from 2015-2020 by<br>province. Based on the outcome of the clustering<br>provinces in indonesia using K-Means algorithm with<br>optimization of the determination of the number of<br>cluster using elbow method formed 3 cluster. 1 st cluster has<br>21 province, the 2 nd cluster has 9 province and the 3 rd cluster<br>has 4 province.</p> Ayu Sofia Copyright (c) 2024 Journal of Statistical Methods and Data Science Fri, 05 Apr 2024 00:00:00 +0000 APPLICATION OF THE FUZZY TIME SERIES CHEN MODEL IN FORECASTING THE RUPIAH EXCHANGE RATE AGAINST THE US DOLLAR (USD) <p>The importance of the rupiah exchange rate for many individuals in Indonesia in everyday life can be explained by the fact that the country's economy is very dependent on the international economy. Exchange rate used to do future payments using a certain currency and so on the link between two currencies of different countries. In context In international trade, the US Dollar (USD) currency has a very important role very significantly for developing countries because it is used as an eye transaction money. Therefore, the movement of the rupiah exchange rate is an important factor for a country. So forecasting techniques are needed to anticipate exchange rate changes. In this study, the method used is Chen's Fuzzy Time Series (FTS) model to predict the rupiah exchange rate against the United States Dollar (USD) in the future. The results of this study show that forecasting the rupiah exchange rate against the US Dollar (USD) from May 2023 to July 2023 is stable at a value of 1412.40 Rupiah, with a rate average absolute error (MAPE) of 1.6717%.</p> Saskia Amalia Putri Lahuma, Junaidi Junaidi, Iman Setiawan Copyright (c) 2024 Journal of Statistical Methods and Data Science Fri, 05 Apr 2024 00:00:00 +0000 AUTOMATIC WEIGHT CRITERIA FOR SAW-BASED DECISION SUPPORT SYSTEM USING GRADIENT DESCENT <p>Modern organizations always utilize a Decision Support Systems (DSS) to have informed decision-making. The Simple Additive Weighting (SAW) method is a prevalent approach used in DSS for evaluating alternatives based on specific criteria. However, the subjectivity inherent in determining criteria weights in SAW poses a significant challenge. This research introduces a new approach, combining the SAW method with Gradient Descent to automatically determine criteria weights. By doing so, it takes advantage of the similarities between SAW and linear equations. Our research shows that this method produces more accurate and unbiased criteria weights, as confirmed by the Mean Square Error (MSE) analysis. In conclusion, incorporating Gradient Descent into the Decision Support System has the potential to greatly improve its effectiveness by automating the criteria weight determination process in various decision-making scenarios, leading to more accurate and less subjective decision support in organizations.</p> Ibnu Daqiqil ID, Aditia Anhar Copyright (c) 2024 Journal of Statistical Methods and Data Science Fri, 05 Apr 2024 00:00:00 +0000 IMPLEMENTATION OF THE RANDOM FOREST METHOD FOR PREDICTING STUDENTS’ LENGTH OF STUDY <p>Predicting a student's duration of study is essential for universities to ensure students complete their studies on time. This research aims to develop an effective prediction model for determining the length of study based on related factors. To overcome the complexity and diversity of student data, the Random Forest method was chosen. The results indicate that the Random Forest method is an effective tool for predicting the duration of study for university students. A study was conducted on 1,535 graduates from the five departments at the Faculty of Mathematics and Natural Sciences, Riau University. The study employed cross-validation techniques to measure model performance. The model's accuracy was evaluated using a confusion matrix, which revealed that the Random Forest model had an average accuracy of 95.12%. Additionally, feature importance analyses identified grade point average in the eighth semester as a major contributor to the prediction outcome.</p> Ali Akbar, Zul Indra, Yanti Andriyani, Tisha Melia Copyright (c) 2024 Journal of Statistical Methods and Data Science Fri, 05 Apr 2024 00:00:00 +0000 IMPLEMENTATION OF GEOGRAPHICALLY WEIGHTED LASSO (GWL) IN ANALYZING RICE PRODUCTION FACTORS IN INDONESIA <p><em>Geographically Weighted Lasso (GWL) is a combination of two regression methods, namely Geographically Weighted Regression (GWR) and Least Absolute Shringkage Selection Operator (LASSO). Both methods have their own uses. GWR is a regression that takes into account the geographical location aspect because the spatial heterogeneity test is not met. LASSO is a regression method to overcome multicollinearity in the data. The two problems are simultaneously contained in one regression model, namely the GWL method. This study will analyze the factors that affect rice production in 34 provinces in Indonesia by applying and interpreting the results of the Geographically Weighted Lasso method. The results of the analysis show that the coefficient of determination of the GWL model is 0.9703 so it can be concluded that the explanatory variables in this study can that the global level of rice production in each province in Indonesia is 97.03%.</em></p> Reka Agustia Astari, Megawati, Setyo Wahyudi Copyright (c) 2024 Journal of Statistical Methods and Data Science Fri, 05 Apr 2024 00:00:00 +0000