OJS UNPATTI Publication Center (Universitas Pattimura)
Not a member yet
8549 research outputs found
Sort by
VOLATILITY ANALYSIS AND INFLATION PREDICTION IN PANGKALPINANG USING ARCH GARCH MODEL
Get PDFOne of the concerns of both developed and developing countries, as well as in a region, is the amount of inflation that occurs. Inflation is a serious problem. Inflation is a macroeconomic variable that affects people's welfare and is defined as a complex phenomenon resulting from general and continuous price increases. This research aims to analyze the volatility and projected value of the inflation rate, especially in Pangkalpinang City, using the Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. This research uses time series data on inflation rate of Pangkalpinang, Bangka Belitung Island Province from January 2014 to May 2024. This data was obtained through publications from the Central Statistics Agency of Bangka Beliltung Islands Province. The ARCH model is used to handle heteroscedasticity in data, while the GARCH model is a development of the ARCH model and serves as a generalization of the volatility model. This research shows that the predicted inflation rate in Pangkalpinang City from June 2024 to November 2024 tends to decrease with a MAPE prediction accuracy level of 200.04%. The high MAPE value is caused by actual data moving toward 0
THE EXPLOITATION STATUS OF WORKING SCHOOL-AGE CHILDREN IN INDONESIA: A MULTILEVEL BINARY LOGISTIC REGRESSION ANALYSIS
Get PDFMany children in Indonesia are exploited in the workforce. In 2022, 12.22 percent of school-age children worked more than 40 hours per week. Children are considered exploited if they work more than 20 hours a week. Children who work for a long time have serious impacts. This study aims to determine a general picture of the exploitation of working school-age children in Indonesia and its influence factors. This study uses the March 2023 Socioeconomic Survey (SUSENAS) data by utilizing multilevel analysis specifically the two-level binary logistic regression method. The study results showed that 54.22 percent of school-age children are working and exploited in Indonesia. The individual and regional contextual factors that are significantly associated with the exploitation status of working school-age children are age, sex, education level, education of household head, sex of household head, employment status of household head, Smart Indonesia Programme (PIP) ownership status, family size, expected years of schooling (HLS), and poverty level. This study finds that increasing age, male sex, lack of access to the PIP, low household head education, female-headed households, unemployed household heads, and larger household sizes increased the likelihood of child exploitation. Moreover, children residing in districts with lower HLS scores had a higher chance of being exploited. These findings highlight the importance of considering both individual and regional contextual factors when addressing child exploitation. A two-level binary logistic regression model with random effects provides a better fit than the intercept-only model. Therefore, it is recommended to prioritize interventions for children without access to the PIP and those from household heads with low education levels. Furthermore, programs emphasizing the importance of education for children should be strengthened
COMPARISON OF SARIMA AND SARIMAX METHODS FOR FORECASTING HARVESTED DRY GRAIN PRICES IN INDONESIA
Get PDFHarvested dry grain (HDG) is a vital commodity for rice availability and plays a strategic role in Indonesia’s agricultural economy. Farmers typically sell HDG to rice millers post-harvest, yet disparities between farm-level selling prices and consumer-level purchase prices. This price gap can lead to financial losses for farmers, highlighting the need for accurate forecasting can lead to potential losses for farmers. SARIMA models are effective in capturing seasonality and trends but often fail to incorporate external factors influencing the dependent variable, resulting in less accurate forecasts when such factors have significant impacts. SARIMAX models, however, can include exogenous variables like the government purchase price (GPP), which supports farmer income by establishing a price floor for HDG and directly influencing farm-level price dynamics. This study aims to compare the SARIMA and SARIMAX models in forecasting HDG prices at the farm level in Indonesia, using GPP as an exogenous variable. The dataset, obtained from Statistics Indonesia, covers January 2008 to March 2024, and the forecasting accuracy is measured using Mean Absolute Percentage Error (MAPE). The findings indicate that the best model is the SARIMAX model (1,1,1)(0,1,2)12, achieving a MAPE of 10.919%. The forecasted results show that HDG prices in 2024 are expected to remain stable, with only a gradual increase throughout the year
OPTIMAL CONTROL ON MATHEMATICAL MODEL OF MPOX DISEASE SPREAD
Get PDFThe Global emergency related to mpox infection outside endemic areas occurred in 2022. The United States is one of the areas that has been significantly impacted by the mpox virus. To reduce the number of infection cases, it is essential to control the spread of the disease. This can be achieved through optimal control. The intervention provided to combat the dynamic spread of mpox can be represented in the form of a mathematical model. This model comprises the animal population (SEI) and the human population (SEIR). Furthermore, the model that has been formed also divides humans into high-risk and low-risk populations. The classification is based on the risk of complications and death caused by infection. The model will be analyzed in order to ascertain its disease-free and endemic stability. The spread of mpox is then controlled by healthy living behaviors and antiviral administration to reduce the number of infection cases. To this end, numerical simulations were conducted to visualize the spread of mpox with and without the function of control variables so that optimal results were obtained. The results of the numerical simulation demonstrate that a reduction in infection cases by 64.62% can be achieved by implementing an average rate of healthy living behaviors of 93.15% and distributing an average rate of antivirus at 75.11%
Optimization of the Esterification Process of Crude Palm Oil (CPO) with Natural Zeolite Catalyst Using Response Surface Methodology (RSM)
Get PDFEsterification is one of the important processes in the production of biodiesel. This is done to ensure that the FFA content in CPO is less than 3%. The esterification reaction can be accelerated by using natural zeolite as a catalyst. Optimization needs to be carried out to select the appropriate conditions to reach the optimal region quickly. The purposes of this research are to analyze the impact of esterification time and the natural zeolite catalyst size on the reduction of FFA levels and find the optimal parameters in the CPO esterification through RSM. Esterification is operated by maintaining the reaction temperature at 60 °C, agitation speed at 150 rpm, and using a molar ratio of methanol:CPO of 6:1. The independent variables used in the research are esterification time (90, 110, 130, 150, and 170 minutes) and natural zeolite size (20, 40, 60, 80, and 100 mesh). The optimization results using RSM indicate that the optimum points in the study are at an esterification time of 170 minutes and a natural zeolite size of 97.3909 mesh
OPTIMASI MASSA ADSORBEN DAN pH PADA ADSORPSI ION Fe MENGGUNAKAN ABU CANGKANG KELAPA SAWIT
Get PDF
ABSTRAK
Limbah abu cangkang kelapa sawit dapat dimanfaatkan sebagai adsorben dalam mengadsorpsi ion Fe, karena mengandung 75,8% SiO2. Tujuan penelitian ini adalah untuk mengetahui karakteristik pori abu cangkang kelapa sawit serta kapasitas dan efisiensi adsorpsi abu cangkang kelapa sawit terhadap ion Fe berdasarkan variasi massa adsorben dan pH adsorbat. Karakteristik pori dianalisis menggunakan Surface Area Analyzer (SAA). Adsorpsi abu cangkang kelapa sawit tehadap ion Fe dilakukan secara batch dengan variasi massa adsorben 0,0001, 0,0005, 0,001 dan 0,005 g serta variasi pH 3, 6, 7 dan 8. Konsentrasi ion Fe teradsorpsi dianalisis menggunakan Spektrofotometer Serapan Atom (AAS). Berdasarkan hasil analisis dengan SAA menunjukkan bahwa abu cangkang kelapa sawit memiliki karakteristik pori berukuran mesopori dengan diameter pori sebesar 2,9273 nm, volume pori sebesar 0,010 cc/g dan luas permukaan sebesar 1,120 m2/g. Kapasitas adsorpsi optimum 
SMALL AREA ESTIMATION OF CHILD UNDERNOURISHMENT PREVALENCE IN BALI AND NUSA TENGGARA
Get PDFChildren under the age of 17 are particularly prone to undernutrition. Undernutrition can impair children’s growth and development. In the process of policy formulation, it is necessary to calculate a reliable estimate of the prevalence of child undernourishment at the smallest level possible. Using the data of SUSENAS 2023 from BPS, direct estimates at the regency/city level in Bali, West Nusa Tenggara (NTB), and East Nusa Tenggara (NTT) have relative standard error values of over 25% (RSE > 25%), making them less reliable for usage. To solve this, an indirect estimating method known as small area estimation (SAE) can be applied. This study employs SAE HB Lognormal to estimate the prevalence of undernutrition in children. The results of this study show that small area estimation using the HB Lognormal approach improved the reliability of estimates (RSE) of the prevalence of undernutrition in children at the regency/city level in Bali, NTB, and NTT
THE PERFOMANCE OF THE ARIMAX MODEL ON COOKING OIL PRICE DATA IN INDONESIA
Get PDFForecasting is crucial for planning, particularly in addressing potential issues. While ARIMA models are commonly used for time series forecasting, they may need more accuracy by overlooking external factors. The ARIMAX model, which incorporates exogenous variables, is employed to enhance accuracy. This study applies the ARIMAX model to forecast cooking oil prices in Indonesia, known for its complex patterns. Using data from the Directorate General of Domestic Trade and Price Stability (2024), the research highlights fluctuating cooking oil prices from 2010 to 2023 every month. Both ARIMA and ARIMAX models are utilized, with domestic fresh fruit bunch (FFB) prices and the COVID-19 pandemic indicator as exogenous variables. Evaluation based on Mean Absolute Percentage Error (MAPE) shows that the ARIMAX model has a MAPE of 17.31%, compared to 17.69% for the ARIMA model. The lower MAPE value for ARIMAX indicates improved forecasting accuracy by incorporating external factors. Thus, the ARIMAX model is recommended for predicting cooking oil prices, offering better accuracy and valuable insights for policymakers and stakeholders.
 
ESTIMATION OF VALUE AT RISK FOR GENERAL INSURANCE COMPANY STOCKS USING THE GARCH MODEL
Get PDFInvestment plays a crucial role in supporting economic development by allocating funds to generate future profits. Among various investment options, stock investment is widely popular. However, investors face the challenge of developing strategies to maximize returns while minimizing risks. Effective investment requires understanding the potential maximum risk of loss, known as Value at Risk (VaR). This research focuses on estimating VaR for four top general insurance companies in Indonesia: PT Lippo General Insurance Tbk (LPGI), PT Asuransi Tugu Pratama Indonesia Tbk (TUGU), PT Victoria Insurance Tbk (VINS), and PT Asuransi Dayin Mitra Tbk (ASDM). These companies were selected due to their leading positions in the industry. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, an extension of the ARIMA method designed to handle volatility clustering, is utilized for VaR estimation. Results at confidence levels of 90%, 95%, and 99% reveal that VINS carries the highest risk, with a maximum VaR of IDR 2,848,710 at 99% confidence, while LPGI shows the lowest risk, with a maximum VaR of IDR 22,677. For TUGU, the maximum possible loss is IDR 517,589, and for ASDM, it is IDR 1,532,267. Backtesting confirms the reliability of the models, with some accepted at specific significance levels. Based on this analysis, the results can help investors make investment decisions that minimize potential losses, specifically in the four stocks analyzed
Perencanaan Sistem Pengukuran Kinerja Menggunakan Metode Balanced Score Card dan Performance Prism Melalui Analytical Hierarchy Process Sebagai Pertimbangan Keputusan Strategis (Studi Kasus: Pengukuran Kinerja Organisasi Pada STT MIGAS Balikpapan)
Get PDFDesigning the most effective performance measurement system used in an educational organization is not easy. Given that there are not many other studies that use the BSC and Performance prism methods simultaneously. Performance prism has several advantages including identifying stakeholders from many interested parties, such as owners and investors, suppliers, customers, workers, government, and the surrounding community. While BSC identifies stakeholders only from the stakeholder and customer side. The purpose of this study will be to design a performance measurement system by integrating the three methods, namely BSC and Performance prism for stakeholder satisfaction, as a consideration of strategic decisions will use the Analytical Hierarchy Process (AHP) method. The results of the study showed that the achievement of organizational performance was in the yellow category because it was in the index range of 4-8, which means that the organization's performance has not yet achieved the expected performance/not optimal. The achievement in the green category, it is in the index range of 8-10, which means that the organization's performance shows that STT Migas Balikpapan has good performance results so it needs to be maintained