JMBI (Journal of Marketing and Business Intelligence)

FORECASTING COOPERATIVE MEMBERSHIP GROWTH: A COMPARATIVE STUDY OF THREE TREND METHODS

Dwi Herinanto, Bernadhita Herindri Samodera Utami — Mon, 02 Nov 2026 00:00:00 +0000

Forecasting membership growth is important for savings and loan cooperatives to establish membership targets and plan operational requirements systematically. This study aims to compare the forecasting performance of three trend methods: the least squares method, the semi-average method, and the time-series least squares method, in estimating the number of members of Mekar Sai Savings and Loan Cooperative (KSP Mekar Sai) in Bandar Lampung, Indonesia. This study employs a quantitative approach using time-series analysis based on annual membership data for the 2011–2024 period obtained from the cooperative’s official records. Each method is applied to the historical data to construct a trend equation and generate an estimate of membership in 2025. Forecasting performance is evaluated using the mean squared error (MSE) and mean absolute percentage error (MAPE), where lower values indicate smaller forecasting errors. The results show that the least squares method estimates 24,123 members, the semi-average method estimates 24,344 members, and the time-series least squares method estimates 23,956 members for 2025. Based on the reported evaluation results, the time-series least squares method produces the lowest MAPE of 2.393%, whereas the least squares method yields the lowest MSE of 124,263. These findings indicate that the assessment of forecasting performance depends on the accuracy measure employed. In practical terms, the forecasts can provide a reference for KSP Mekar Sai’s membership planning and operational resource allocation. However, the findings should be interpreted in light of the study’s limitations, particularly its reliance on historical membership data from a single cooperative without incorporating external factors that may influence membership growth.

AN INTELLIGENT DEEP LEARNING FRAMEWORK FOR MULTI-CROP PRICE FORECASTING AND AGRICULTURAL SELLING DECISION SUPPORT: A CASE STUDY OF TEN COMMODITIES IN LAMPUNG, INDONESIA

Agus Suryana, Shabil Avito Mazyanda, Thanh Nguyen — Thu, 05 Nov 2026 00:00:00 +0000

Agricultural commodity price volatility presents significant challenges to farmers, traders, and policymakers, particularly in determining appropriate selling prices and managing market uncertainty. This study develops an intelligent deep learning-based Decision Support System (DSS) integrating a Long Short-Term Memory (LSTM) architecture for forecasting ten agricultural commodity prices in Lampung Province, Indonesia. Historical price data were collected from agricultural markets across several districts and processed through data cleaning, outlier handling, normalization, and time-series sequence generation. A multi-output LSTM architecture incorporating stacked recurrent layers, dropout regularization, and fully connected layers was implemented to capture nonlinear temporal dependencies and commodity-specific price fluctuations. Exploratory data analysis, correlation matrices, and temporal visualizations were employed to investigate market characteristics. Experimental results demonstrated promising forecasting performance, achieving an overall Root Mean Squared Error (RMSE) of approximately 0.09 on normalized price data. However, the model exhibited limited sensitivity to abrupt price fluctuations, indicating challenges in predicting highly volatile commodities. The proposed DSS integrates predictive analytics and price visualization to support informed selling decisions, market monitoring, and agricultural risk management. These findings demonstrate the potential of multi-output LSTM forecasting for developing scalable, data-driven agricultural decision support systems in regional commodity markets.

ENHANCING BUSINESS DECISION-MAKING THROUGH ACCOUNTING INFORMATION SYSTEMS: THE ROLE OF BUSINESS ENVIRONMENTAL UNCERTAINTY IN CULINARY MSMEs

Sat, 07 Nov 2026 01:51:26 +0000

This study aims to examine the influence of Accounting Information Systems (AIS) on Business Decision-Making, the effect of Business Environmental Uncertainty (BEU) on Business Decision-Making, and the moderating role of BEU in the relationship between AIS and Business Decision-Making among culinary MSMEs in Bandar Lampung City. A quantitative approach was employed using a survey method, with a sample of 120 culinary MSME owners selected through purposive sampling. Data were collected via structured questionnaires and analyzed using Structural Equation Modeling based on Partial Least Squares (SEM‑PLS) with the assistance of SmartPLS software. The findings reveal that AIS has a positive and significant effect on Business Decision-Making. Similarly, BEU exerts a positive and significant effect on Business Decision-Making. However, BEU does not moderate the relationship between AIS and Business Decision-Making. These results indicate that greater utilization of AIS enhances the quality of business decisions, while BEU directly influences decision-making but does not alter the strength of the AIS–decision-making relationship. This study contributes to the literature on MSME management by clarifying the role of AIS and BEU in decision-making under uncertain business conditions. Practically, the findings provide implications for MSME owners to optimize the use of AIS as a reliable information source to support strategic decision-making, while simultaneously developing adaptive strategies to manage environmental uncertainty.