Parameter: Journal of Statistics

WORLD GREENHOUSE GAS EMISSION CLASSIFICATION USING SUPPORT VECTOR MACHINE (SVM) METHOD

Kurnia Ramadani — 2024-06-11

The phenomenon of Heatwaves has struck several countries across the globe due to climate change. This climate change has led to an increase in greenhouse gas emissions surpassing the limits set by the IPCC Fourth Assessment Report GWPs. This study utilizes the Support Vector Machine (SVM) classification method to identify and categorize greenhouse gas emission data from 1990 to 2020 using four kernels function such as linear, polynomial, radial basis function (RBF), and sigmoid. The SVM method demonstrates excellent performance in constructing classification models with a polynomial kernel function. This is evidenced by high values of training accuracy, testing accuracy, and F1-score, accompanied by short training and testing analysis times. Successively, these values are 97.39%, 97.69%, 96.82%, 0.59 seconds, and 0.22 seconds.

APPLICATION OF THE LIGHTGBM ALGORITHM IN THE CLASSIFICATION OF GREENHOUSE GAS EMISSIONS

Rini Latifah — 2024-06-11

Ada banyak dampak negatif yang dapat ditimbulkan oleh peningkatan emisi gas rumah kaca. Oleh karena itu, penting untuk mengetahui tingkat emisi gas rumah kaca di masa depan dengan membuat prediksi sehingga kita dapat merencanakan kebijakan untuk memitigasi dampaknya. Pada penelitian ini, klasifikasi tingkat emisi gas rumah kaca dilakukan dengan menggunakan metode lightGBM. Tujuannya untuk melihat kinerja metode light GBM dalam melakukan klasifikasi emisi rumah kaca. Hasil yang diperoleh dari penelitian ini adalah akurasi sebesar 96,26%, sensitivitas sebesar 97,62%, spesifisitas sebesar 93,97%, dan MAE sebesar 0,0374.

EFFECTIVENESS OF INTERACTIVE MULTIMEDIA ARTICULATE STORYLINE 3 USING PAIRED SAMPLE T-TESTS

Rizka Pitri — 2024-06-11

In facing the era of globalization, especially in the field of education, the government must improve and prepare a better government system. Therefore, efforts are needed to be made, one of which is by increasing basic abilities in the mathematics. The lack of innovation in the process of learning mathematics makes students feel bored with the material provided. Therefore, students often get mathematics score below the graduate competency standards and even fail. Based on these facts, it is necessary to develop interactive multimedia learning innovations in mathematics by creating interactive multimedia using articulate storyline 3 in mathematics learning. So the research was conducted on the effectiveness of interactive multimedia using storyline articulation 3 using paired sample t-tests. This research was using 166 students of class X SMAN 4 Kotabumi. This study aims to see the efficiency of interactive multimedia articulating storyline 3 on understanding concepts and increasing students' academic scores in mathematics. Based on the results of the paired sample t-test, the p-value is 0.000, which is less than the 0.05 significance level. So it can be concluded that the application of multimedia articulate storyline 3 is efficient in increasing students' understanding of mathematical concepts and academic values.

Predicting Drought in East Nusa Tenggara: A Novel Approach Using Wavelet Fuzzy Logic and Support Vector Machines

Hartayuni Sain — 2024-06-11

The water crisis, or what is hereinafter referred to as drought, has become a systemic and crucial problem in several regions in Indonesia. Indonesia is an agricultural country, where the presence of water is very influential so that drought can become a natural disaster if it starts to cause an area to lose its source of income due to disturbances in agriculture and the ecosystem it causes. Drought forecasting can provide support solutions in preventing the impact of drought. In this paper, we compare the performance of wavelet fuzzy logic and the support vector machine (SVM) as a supervised learning method for drought forecasting in East Nusa Tenggara. This study examines the monthly rainfall data for 1999-2015 which is the basis for calculating the drought index based on the Standardized Precipitation Index (SPI). The SPI value used is SPI-3 at a station in East Nusa Tenggara. The performance of models is compareded on R². The results showed that R²of wavelet fuzzy logic is smaller than one of SVMVM is better than the wavelet fuzzy logic for forecasting SPI value of drought in East Nusa Tenggara.

APPLICATION OF THE ARIMA METHOD IN FORECASTING THE PRICE RED CAYENNE PEPPER IN MAKASSAR CITY

Arwini Arisandi — 2024-06-26

Red chili is one of the commodities with very tall cost changes. The cost variance of red chili can be caused by a huge amount of supply and request. The higher the amount of supply, the lower the cost, and the lower the amount of supply, the higher the cost. This study aims to implement the ARIMA method in forecasting red cayenne pepper prices in Makassar City. Data analysis to forecast red cayenne pepper prices used the ARIMA method with the results show that the price range of chili is from IDR 13,000 to IDR 80,000, with a mean value of IDR 38,218. The model with the minimum SSE and MSE value is ARIMA(1,1,1), so this model be used in time series data modeling for forecasting. The results of forecasting using the best model obtained a MAPE value of 15.90%, which is in the range of 10-20%, so it can be concluded that the ability of the ARIMA(1,1,1) model in forecasting the price of red cayenne pepper includes the good category.

FORECASTING INFLATION IN INDONESIA USING THE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE METHOD

Nur Azizah Komara Rifai — 2024-06-26

Indonesia faces significant economic challenges, particularly inflation, which affects the economic, social, and cultural sectors. High inflation can exacerbate poverty, alter consumption patterns, and contribute to social injustice, whereas low inflation can enhance national income and stimulate economic activities. Given its fluctuating nature, inflation in Indonesia requires accurate forecasting to inform policy-making and economic decisions. This study aims to forecast inflation in Indonesia for the next eight months using the Autoregressive Integrated Moving Average (ARIMA) method. Monthly inflation data from January 2020 to April 2024 obtained from Bank Indonesia were analyzed. The ARIMA model, suitable for short-term forecasting, was selected due to its ability to handle data trends, non-stationarity, and noise filtering. The Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests to ensure stationarity. Initial ADF tests showed the presence of a unit root in the original data and the first differencing data, but data became stationary after the second differencing. The KPSS test confirmed a unit root in the original data and trend stationarity after the second and third differencing. Ordinary Least Squares (OLS) regression on the original data revealed a significant time trend, indicating deterministic trends. The optimal model identified was ARIMA(0,2,1) with AIC=51.81, as it met the criteria for normality, independence, and zero mean of residuals. This model effectively forecasts inflation from May to December 2024, which showed an increase with inflation values of 3.02, 3.05, 3.07, 3.10, 3.12, 3.14, 3.17, and 3.19.

PANEL DATA REGRESSION ANALYSIS FOR MODELING THE HUMAN DEVELOPMENT INDEX IN NORTH SULAWESI PROVINCE

Siti Nurmardia Abdussamad — 2024-06-26

The regression analysis is a technique used in hypothesis testing to determine the impact of one variable on another. This study uses Panel Data Regression Analysis, which combines cross-sectional and time series data. This study aims to analyze the impact of Life Expectancy, Income Per Capita, Expected School Years, and Average School Years on the Human Development Index. According to the result of the analysis, the Common Effect Model (CEM), which used Ordinary Least Squares (OLS) estimation, was the most suitable model. The equation obtained is . Moreover, according to the significance test, all independent variables were significantly related to the dependent variable