Lilis Laome

Introduction/Main Objectives: This study aims to forecast the Farmer Exchange Rate (FER) in Southeast Sulawesi Province for 2024 as a basis for short-term economic assessment and policy-related analysis. Background Problems: FER is a key indicator of farmers’ purchasing power and agricultural welfare; however, its monthly dynamics are characterized by fluctuations and uncertainty, making conventional forecasting methods less effective in capturing its behavior. Novelty: This study contributes by implementing the the Fuzzy Time Series (FTS) Cheng approach for FER forecasting in Southeast Sulawesi, emphasizing its suitability for handling vagueness and nonlinear patterns inherent in agricultural economic indicators. Research Methods: The analysis utilizes monthly secondary FER data obtained from BPS-Statistics of Southeast Sulawesi Province, covering the period from January 2014 to December 2023. Forecast accuracy is evaluated using the Mean Absolute Percentage Error (MAPE). Finding/Results: The forecasting results indicate that the FER values for January, February, and March 2024 are each estimated at 105.93. The model achieved a MAPE of 0.3027%, corresponding to an accuracy level of 99.6973%, which places the forecasting performance in the “excellent” category.

Perpustakaan modern berperan penting dalam meningkatkan kualitas pendidikan masyarakat. Untuk mengoptimalkan pengelolaan dan pelayanan, diperlukan peramalan jumlah pengunjung perpustakaan. Penelitian ini menggunakan metode Autoregressive Integrated Moving Average (ARIMA) dengan prosedur iteratif untuk menangani data yang mengandung outlier. Model terbaik yang diperoleh adalah ARIMA(0,1,2) dengan nilai Mean Squared Error (MSE) sebesar 149404,2 dan Mean Absolute Percentage Error (MAPE) sebesar 21,63%, yang menunjukkan bahwa akurasi peramalan masih dalam batas wajar. Hasil peramalan menunjukkan jumlah pengunjung cenderung stabil dengan rata-rata 877 pengunjung per minggu, namun interval kepercayaan yang semakin lebar mengindikasikan meningkatnya ketidakpastian dalam prediksi. Prosedur iteratif dalam deteksi outlier terbukti efektif dalam mengidentifikasi dan mengoreksi pencilan, sehingga meningkatkan akurasi model. Penelitian ini merekomendasikan eksplorasi model alternatif seperti ARIMAX atau metode machine learning untuk menangkap pola yang lebih kompleks dan meningkatkan akurasi peramalan.

The issue of gender equality in Southeast Sulawesi still needs further attention, as indicated by the uneven value of the Gender Development Index (GDI) in each district/city in the region. Therefore, an in-depth analysis is needed to identify factors that affect the GDI. One method that can be used is semiparametric regression with the Nadaraya-Watson estimator, which allows modeling the relationship between variables with more flexibility. This study aims to build a semiparametric regression model to identify factors that contribute to HDI in Southeast Sulawesi Province. The results of the analysis showed that the optimal bandwidth values obtained were h1= 1.57, h2=0.49, h3=2.50 and h4=4.61. The resulting model has an R2 and MSE values of 99.8% and 0.14% respectively, indicating that the model has high accuracy in explaining the overall variation in GDI.

Penelitian ini bertujuan untuk membandingkan metode ARIMA, Single Moving Average, dan Single Exponential Smoothing dalam meramalkan jumlah penumpang DAMRI Rute Kendari-Mawasangka. Data yang digunakan adalah jumlah penumpang DAMRI dari Januari 2021 hingga Juni 2024. Hasil penelitian menunjukkan bahwa metode Single Exponential Smoothing dengan parameter α = 0,3 merupakan model terbaik berdasarkan nilai MSE terkecil sebesar 141.883, dibandingkan dua metode lainnya. Berdasarkan model terbaik tersebut, diperoleh peramalan jumlah penumpang DAMRI enam bulan ke depan sebesar 1.935, 1.354, 948, 663, 464, dan 325. Hasil ini mengindikasikan adanya tren penurunan jumlah penumpang DAMRI setiap bulan, yang dapat menjadi bahan pertimbangan dalam perencanaan dan pengelolaan layanan transportasi.

Multiple linear regressions using spatial data are developed as Geographically Weighted Regression (GWR). It is used to solve the problem of regression mod Multiple linear regressions using spatial data are developed as Geographically Weighted Regression (GWR). It is used to solve the problem of regression mod

Geographically Weighted Regression (GWR) is the development of multiple linear regression models used in spatial data. The assumption of spatial heterogeneity results in each location having different characteristics and allows the relationships between the response variable and each predictor variable to be unknown, hence nonparametric regression becomes one of the alternatives that can be used. In addition, regression functions are not always the same between predictor variables. This study aims to use the Geographically Weighted Nonparametric Regression (GWNR) model with a mixed estimator of truncated spline and Fourier series. Both estimators are expected to overcome unknown data patterns in spatial data. The mixed GWNR model estimator is then determined using the Weighted Maximum Likelihood Estimator (WMLE) technique. The estimator’s characteristics are then determined. The results of the study found that the estimator of the mixed GWNR model is an estimator that is not biased and linear to the response variable y.

The aim of this study is to propose appropriate models to forecast Return on Asset (ROA) and financing of Indonesia Islamic Commercial Banks during COVID-19 pandemic. In particular, we study the models which involve reciprocal relation between ROA and financing and incorporate COVID-19 pandemic’s impact. It is crucial because the government would benefit from forecasting results to formulate the policy for the banks related to ROA and financing. We consider two models: Vector Autoregressive with exogenous variable (VARX) and spline regression, since both models are able to exploit the multivariate structure of ROA and financing and to include COVID-19 impact as predictor. The results show that the VARX outperforms spline regression in terms of RMSE. Using VARX, we deduce that ROA and financing have a positive reciprocal relationship, meaning that when ROA increases, financing would increase, and vice versa. In addition, the pandemic has significant impact on the decline of the ROA. We recommend that banks conduct an in-depth analysis to determine the appropriate form of restructuring for debtors so that it does not have a significant impact on the decrease in ROA.

The aim of the study was to determine the amount of electrical energy generated from methane gas produced from the volume of waste. The research method is carried out by literature review and field surveys, as well as theoretical calculations. Data was collected at the Tempat Pembuangan Akhir (TPA) and Kendari City Sanitation Office, by meeting and interviewing people directly related to the landfill management process, communities in energy independent settlements, as well as in the municipal sanitation office. The results of the study revealed that the energy produced from the Tempat Pembuangan Akhit (TPA) Puuwatu in the daily average was 288,466.5332 kWh. The amount of electrical energy that has been utilized by the Pemukiman Mandiri Energi is 1,080 kWh. The untapped energy is 287,386.5332 kWh.