Rescollacomm (E-Journals)
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Actuarial Pension Fund Using the Projected Unit Credit (PUC) Method: Case Study at PT Taspen Cirebon Branch Office
Full text linkThe pension fund program is a program held by the government to ensure the welfare of Civil Servants (PNS) in retirement as old-age security. The pension program for civil servants is managed by a pension fund, PT Taspen (Persero). Actuarial calculations of pension funds need to be carried out to determine the amount of normal contributions and actuarial liabilities that must be paid by pension plan participants and companies. The actuarial calculation of pension funds used by PT Taspen in managing civil servant pension funds is the Accrued Benefit Cost which determines in advance the benefits that will be obtained by participants. The Projected Unit Credit (PUC) method is one part of the Accrued Benefit Cost. This study aims to determine normal contributions and actuarial liabilities using the Projected Unit Credit (PUC) method for civil servant pension program participants of PT Taspen (Persero) Cirebon Branch Office. The calculation results show that the PUC method provides a more accurate calculation of the estimated normal contributions and actuarial liabilities of the company. This study is expected to be a reference for other companies in managing employee pension funds using an actuarial approach
Comparative Analysis of Altman and Grover\u27s Methods in Predicting Bankruptcy Using the McNemar Test (Case Study: Vehicle Insurance Company in Indonesia)
Full text linkVehicle insurance is an important component of automotive financing and consumer protection, which includes various forms of protection that protect the vehicle and its owner. Predicting the bankruptcy of a vehicle insurance company is also very important for vehicle insurance companies to be able to identify potential financial problems as early as possible and take the necessary corrective actions. The Altman and Grover model can be a way to analyze bankruptcy in company. In this study, PT. Asuransi Astra Buana, PT. Allianz Utama Indonesia, PT. Sinar Mas Insurance, and PT. BCA Insurance are used as the analyzed company. The McNemar Test conducted in this study shows that the two methods do not have significant differences in result, so the two methods will relatively have same results
Feasibility Analysis of Establishing a Gudeg Jogja Business Using the Net Present Value (NPV) Method in the City of Jakarta
Full text linkThis research aims to analyze the feasibility of establishing a Jogja gudeg business in the city of Jakarta using the Net Present Value (NPV) method. Gudeg, as a typical Yogyakarta culinary specialty, has quite large market potential in Jakarta considering the high public interest in traditional and unique foods. This research will examine various aspects, including technical analysis, financial analysis, and sensitivity analysis. Financial analysis will focus on NPV calculations to measure the added value of investments in the long term. It is hoped that the results of the research will provide a clear picture of the potential success of the Jogja gudeg business in Jakarta and become a reference for prospective entrepreneurs who are interested in the culinary business
Comparison of Stock Price Forecasting with ARIMA and Backpropagation Neural Network (Case Study: Telkom Indonesia)
Full text linkThe growth of capital market investors in Indonesia is increasing every year. The most popular investment instrument is stocks. One of the stocks on the Indonesia Stock Exchange (IDX) is the Telkom Indonesia (TLKM). Through stock investment, investors can make a profit by utilizing stock prices in the market. However, stock price fluctuations are uncertain. Therefore, modeling is needed to be able to predict stock prices more accurately. The purpose of this study was to find an appropriate time series model and Neural Network model architecture, and to measure the accuracy of the two models in predicting future stock prices of TLKM. The study was conducted using the Autoregressive Integrated Moving Average (ARIMA) model and Backpropagation Neural Network (BPNN). For comparison, the Mean Absolute Percentage Error (MAPE) method was used. The data used in both models were the stock prices of Telkom Indonesia (TLKM) from September 1, 2023 to September 30, 2024. The result shows that the best ARIMA model, selected based on the least Akaike Information Criterion (AIC) value, is ARIMA(0,1,3) with a MAPE value of 1.20%. Meanwhile, the best BPNN model selected from the smallest testing Mean Squared Error (MSE) value, is BPNN(1,3,1) with a MAPE value of 1.17%. Among those two models, the BPNN model is more accurate because it has less MAPE value compared to the ARIMA one. The results of this research can be considered in forecasting TLKM stock price in the future
Modeling Queue Length at The Toll Gate Using Promodel Before and After Ramp-Off Construction
Full text linkIn everyday life, queues often occur. Waiting at the counter to get train or movie tickets, at the toll gate, at the bank, at the supermarket, and in other situations that we often encounter Queues occur when the need for services exceeds the capacity or capacity of the service facility. As a result, users of the facility cannot get immediate service due to the busyness of the service. The Amplas Toll Gate queue is the object of this research. The Amplas Toll Gate is one of the densest toll gates that is heavily traveled by vehicles both entering and exiting. This makes it often seen a fairly long queue, especially during peak hours in the late afternoon to evening. The Medan City Government built an off ramp at the Amplas flyover in 2016. This off ramp leads directly to the Amplas toll gate. The vehicle arrival rate increases along with the queue length because vehicles can arrive faster to the toll gate. This study aims to calculate the queue length at the Amplas toll gate before and after the construction of the ramp off. Data is obtained by recording the volume of vehicles at the research location. With an average service time of 7 seconds, the queuing method produces a queue length of 11.98 meters, while the results using Pro Model software are 11.98 meters. In addition, the queue length after the construction of the ramp off decreased to 6.67 meters from before the construction of the ramp off. Promodel is a windows-based simulation software used to simulate and analyze a system
Portfolio Optimization by Considering Return Predictions Using the ARIMA Method on Jakarta Islamic Index Sharia Stocks
Full text linkIn investment decision-making, accurate return projections are an important component in maximizing profits while minimizing risk. This study aims to construct an optimal stock portfolio in the Jakarta Islamic Index (JII) sharia stock sector by considering return predictions using the Autoregressive Integrated Moving Average (ARIMA) model. The ARIMA model is used to forecast future stock returns based on historical data. The prediction results are then utilized as input for expected returns in the Mean-Variance portfolio optimization model developed by Markowitz. This model considers the trade-off between expected return and risk (variance), with the goal of forming an optimal portfolio. The portfolio is evaluated to compare the performance of the prediction-based portfolio with the historical return-based portfolio. This study is expected to contribute to data-driven quantitative investment strategies and statistical predictions. The results of this study indicate that the ARIMA model is effective in predicting stock returns, which in turn improves the efficiency of portfolio construction. The prediction-based portfolio yields a higher average weekly return of 0.87% compared to 0.65% from the historical-based portfolio. Furthermore, the risk level, measured by standard deviation, is slightly lower in the prediction-based portfolio (1.46%) than in the historical one (1.50%). This leads to a significant improvement in the Sharpe ratio, rising from 0.43 to 0.60. These findings demonstrate that integrating ARIMA-based predictions into the portfolio optimization process enhances overall performance by increasing return per unit of risk. Therefore, the use of forecasting models such as ARIMA in portfolio selection provides a valuable tool for investors seeking to make more informed, data-driven investment decisions—particularly within the context of sharia-compliant equity markets such as the Jakarta Islamic Index
The Influence of Capital Structure on Profitability: Panel Regression Analysis of Indonesian State-Owned Enterprises in the Energy and Mining Sector from 2019 to 2023
Full text linkCapital structure is an important factor in financial decision-making that can influence a company\u27s profitability level. Indonesian state-owned enterprises (BUMN) in the energy and mining sector have high capital needs and significant exposure to external risks, making capital structure efficiency crucial. This study aims to analyze the impact of Debt to Asset Ratio (DAR) and Debt to Equity Ratio (DER) on Return on Equity (ROE) as a profitability indicator for Indonesian state-owned enterprises in the energy and mining sector in Indonesia during the period 2019–2023. This research uses six companies as samples, namely PT Aneka Tambang Tbk., PT Bukit Asam Tbk., PT Indonesia Asahan Aluminium, PT Pertamina (Persero), and PT Timah Tbk. The study employs a quantitative approach with a panel data regression method. Data was obtained from the annual financial statements of the company. The analysis process was conducted thoroughly using Eviews 12 software, including data processing, assumption testing, selection of the panel regression model, and final estimation. The results of the analysis indicate that the Random Effect Model is the most suitable approach. Simultaneously, DER and DAR have a significant effect on ROE. However, partially, only DER has a significant negative effect, while DAR is not significant. These findings indicate that the capital structure, specifically the proportion of debt to equity, plays an important role in determining the company\u27s profitability. Therefore, optimal management of the financing structure becomes an important strategy for the company in maintaining long-term financial performance
The Dynamic Impact of Foreign Debt-Based Education and Health Investment on Economic Growth in Asean-5 Countries
Full text linkThis study examines the use of external debt to finance health and education in order to promote economic growth in developing countries, focusing on five ASEAN member countries namely Cambodia, Indonesia, Laos, the Philippines and Thailand. The Johansen, Pedroni and Kao cointegration test results indicate the existence of a long-run relationship between the independent and dependent variables. The panel data Autoregressive Distributed Lag (ARDL) model is used to analyze the short-term and long-term effects using annual data for the period 2000-2022. The results of this study found that in the short run education financing has a positive effect while health, labor and capital financing have a negative impact on economic growth. The results in the long run found that education and health financing have a negative impact on economic growth in ASEAN-5 countries due to too high debt and inefficiency in allocation is also one of the reasons the long-term effect has not been realized. Labor and capital have a positive impact on economic growth this is due to high external debt in many ASEAN-5 countries is also high, although this is not proportional to external debt and the effect is very small. Based on the findings of this study, it is recommended that governments in ASEAN-5 countries continue to improve efficiency in managing and allocating foreign debt towards education and health. In addition, serious efforts are needed for more assertive and targeted policies related to the use of foreign debt
The Effect of Macroeconomic Variables on Indonesia\u27s Import Value Using the OLS Method
Full text linkThis study analyzes the factors influencing Indonesia’s import value during the period 2021–2025 using the Ordinary Least Squares (OLS) method. To ensure the validity of the model, a series of classical assumption tests were conducted in accordance with the Best Linear Unbiased Estimator (BLUE) criteria, including tests for normality, multicollinearity, heteroscedasticity, and autocorrelation. The data were obtained from official publications of the Central Statistics Agency (BPS) and other relevant sources. The estimation results demonstrate that the independent variables, namely the exchange rate (X₁), national income (X₂), foreign exchange reserves (X₃), inflation rate (X₄), and interest rate (X₅), exert varying effects on Indonesia’s import value, with certain variables exhibiting significant influence while others remain insignificant. The model is free from violations of the classical assumptions, thereby meeting the criteria of the Best Linear Unbiased Estimator (BLUE).
Keywords: Import Value, OLS, Classical Assumption Tests, Macroeconomic
Implementation of Machine Learning Model for Pest Classification in Rice Plants
Full text linkRice cultivation is a cornerstone of food security in agrarian countries like Indonesia, yet it remains highly vulnerable to pest infestations that can severely impact crop productivity. Manual identification of pests is time-consuming and error-prone, especially when pest species exhibit similar morphological characteristics. This study aims to implement and evaluate the performance of four classical machine learning algorithms Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and Logistic Regression (LR) for classifying rice pests based on image data. The dataset, derived from Kaggle’s “Rice Pest Detection Dataset,” includes 12 pest classes and underwent a series of preprocessing steps: grayscale conversion, image resizing to 128×128 pixels, feature extraction using Histogram of Oriented Gradients (HOG), label encoding, and class balancing via SMOTE. The experimental setup used 80% of the data for training and 20% for testing. Performance was evaluated using precision, recall, F1-score, and confusion matrices. Among the four models, SVM achieved the most consistent and robust performance, with F1-scores reaching up to 0.98 in several pest classes and an overall balanced classification across the dataset. Random Forest followed closely, particularly excelling in distinguishing classes such as Rice Water Weevil and Yellow Rice Borer, achieving F1-scores of 0.99 and 0.96 respectively. In contrast, KNN showed signs of overfitting, with extreme precision-recall imbalances, while LR was more stable but less accurate in separating visually similar classes like Rice Stem Fly and Thrips. Visual analysis of correct and incorrect predictions revealed that classes 7 (Rice Stem Fly) and 11 (Thrips) were consistently misclassified across all models, likely due to high visual similarity