Journal Of Data Insights

Comparison of Holt-Winters Exponential Smoothing (HWES) and Singular Spectrum Analysis (SSA) Methods in Forecasting the Number of Passengers at PT KAI in Indonesia

2024-12-11T08:39:59+00:00

Penelitian ini mengkaji penerapan dua metode peramalan, yaitu Holt Winters Exponential Smoothing (HWES) dan Singular Spectrum Analysis (SSA), dalam meramalkan jumlah penumpang di PT Kereta Api Indonesia. Hasil penelitian menunjukkan bahwa penerapan metode HWES dengan model additive menghasilkan nilai parameter pemulusan optimal dengan alpha , beta dan gamma model ini memiliki nilai MAPE sebesar 10.75%. Sementara itu, pada HWES model multiplicative menghasilkan nilai parameter pemulusan alpha , beta dan gamma , menghasilkan nilai MAPE 14.50%. Metode SSA dengan window length menghasilkan nilai MAPE 13.33%. Perbandingan nilai MAPE anatara metode HWES additive, HWES multiplicative dan SSA menunjukkan bahwa HWES additive lebih unggul dengan MAPE sebesar 10.75%. Peramalan jumlah penumpang Kereta Api Indonesia menggunakan metode terbaik Holt Winters Exponential Smoothing Additive untuk periode Januari hingga Desember 2024 memperlihatkan variasi jumlah penumpang terendah pada bulan Agustus dan tertinggi pada bulan Januari.

Forecasting Red Onion Prices in Riau Islands Using the Seasonal Autoregressive Integrated Moving Average (SARIMA) Method

2024-12-17T08:25:07+00:00

The price of shallots is one of the crucial commodities that affects economic stability and community welfare in the Riau Islands. The main factors influencing shallot production are seed variety, land, and weather. This study aims to forecast the price of shallots in the Riau Islands using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. The data used in this study is sourced from official data and covers a specific period to ensure the accuracy of the forecasting model. The SARIMA (0 1 1) (0 1 1)⁵ model with the smallest AIC of 2211.59 was selected as the best model based on data analysis and model performance evaluation, with a Mean Absolute Percentage Error (MAPE) of 2.690835 percent, indicating that the model's ability to predict shallot prices in the Riau Islands is very accurate. The prediction results indicate that the price of shallots will decrease in the coming days according to the developed model. Based on these results, this forecast is expected to serve as a reference for the government and market participants in decision-making related to the production, distribution, and control of shallot prices in the Riau Islands.

Fuzzy Gustafson Kessel for Infrastructure Development Strategy in South Sumatra Province

2024-12-31T01:32:48+00:00

Infrastructure development is a strategic element in improving public services and economic growth. South Sumatra Province, with its large economic potential, faces challenges in managing efficient and sustainable infrastructure development. This research aims to apply the Fuzzy Gustafson Kessel (FGK) method in decision making related to infrastructure development in South Sumatra Province. FGK combines fuzzy logic with Gustafson Kessel clustering algorithm to handle uncertainty and data variation from various stakeholders. The data used in this study includes population and geographic census data from the Central Bureau of Statistics of South Sumatra Province in 2023, with five indicators: population, area, population growth rate, population density, and poverty rate. The results show that South Sumatra is divided into three main clusters based on its infrastructure and demographic characteristics. This clustering is expected to improve the effectiveness and efficiency of infrastructure development decision-making, provide more appropriate policy recommendations, and potentially be applied in other regions with similar challenges.

Application of Random Forest Method to Analyze the Effect of Smoking History on The Type and Outcomes of TB Examinations

2024-12-31T01:31:15+00:00

Tuberculosis (TB) continues to pose a major global health challenge, especially in developing countries. One of the key risk factors that exacerbates the condition of TB patients is smoking, which increases susceptibility to infections and worsens disease prognosis. This study aims to evaluate the influence of smoking history on the type and outcomes of TB diagnoses using a Random Forest machine learning model. The dataset comprises information from TB-diagnosed patients, including demographic details such as age, gender, smoking status, patient type, and diagnostic results. The Random Forest model achieved an accuracy of 87.36%, performing best in classifying non-TB-infected patients. However, the model struggled to accurately identify healthy individuals without TB, likely due to data imbalance. This research offers fresh insights into the potential of machine learning to enhance TB diagnosis and prevention, while deepening the understanding of smoking as a risk factor in TB management.

Decision Tree Classification Prediction of Covid-19 Cases in Indonesia

2024-12-06T02:42:59+00:00

Forecasting is the prediction of an event in the present and future using past event data. The purpose of forecasting is to minimize errors in predictions (forecast errors) to provide a higher level of confidence. In the context of the COVID-19 pandemic, forecasting the number of cases can help anticipate surges, allowing for better-preparedness to minimize its impact. Forecasting methods can be categorized into three common classifications: qualitative methods, time series, and causal methods. Time series methods are further divided into statistical methods and machine learning. Machine learning methods are more effective in forecasting as they can accommodate non-linear and complex relationships between inputs and outputs. One of the machine learning methods used is the Decision Tree, which is a predictive model structured in a tree or hierarchical format. The Decision tree is a data processing method for predicting the future by constructing classification and regression models in a tree structure. The decision tree is also the most popular and easily understood classification method. In this study, a classification decision tree is used to forecast positive COVID-19 cases in Indonesia using the Python programming language.