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All Journal IJISTECH (International Journal Of Information System & Technology) Journal of Intelligent Decision Support System (IDSS) IJISTECH
Anggi Puspita Sari
Universitas Bina Sarana Informatika, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Multivariate Long Short-Term Memory (LSTM) Algorithm for Spatial-Temporal Agricultural Productivity Time Series Forecasting Embun Fajar Wati; Anggi Puspita Sari; Tuslaela Tuslaela
Journal of Intelligent Decision Support System (IDSS) Vol 9 No 2 (2026): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v9i2.356

Abstract

Accurate forecasting of crop productivity is fundamental to contemporary food security planning, yet conventional predictive models frequently underperform when confronted with the multivariate, spatial, and temporal intricacies inherent in agronomic datasets. This study presents a robust deep learning framework leveraging a multivariate Long Short-Term Memory (LSTM) network to forecast yields of principal food crops. The model was developed using a panel dataset from 12 districts in Chhattisgarh and Madhya Pradesh, India (2010–2017), comprising area, production, and yield observations for multiple competing crops. Rigorous preprocessing protocols included the application of separate StandardScalers to mitigate matrix inversion issues, and the derivation of land-allocation features to capture spatial interactions among crops. A lightweight LSTM architecture stabilized by gradient clipping was employed to enhance convergence and prevent exploding gradients. Empirical results demonstrate that the multivariate LSTM notably outperforms simple baseline estimators by effectively modeling non-linear relationships and district-level yield heterogeneity, attaining an RMSE of 494.70 Kg/ha and an R² of 0.8031. These findings suggest that spatial anthropogenic indicators—particularly the allocation of land across commodities—serve as informative proxies for reliable yield prediction in contexts lacking comprehensive weather-sensor data
Customer Loyalty Classification With Random Forest Algorithm Anggi Puspita Sari; Astrid Noviriandini; Sifa Fauziah
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.393

Abstract

Customer loyalty is very important for the survival of the company. Because with customers who have customer loyalty, they will make purchases regularly. Customer loyalty needs to be maintained to increase profits. The method is to classify loyal customers with non-loyal ones, in order to retain loyal customers and set strategies for non-loyal customers. The method used is classification with random forest with cleaning stages that can clean data from noise or empty data or data that does not match, selection that can select some data to be processed for classification, transformation that can change data into two or three formats, classification with random forest with split validation using testing data and training data and with rapidminer software. Evaluation by checking the results of the classification with random forest in the form of accuracy, precision, recall, and AUC. The results of the classification show from the accuracy table that the prediction of loyal and true loyal customers is 129 more than the prediction of not loyal and true not loyal customers which is 32. The accuracy result is 96.41% which shows that the data is really accurate with very high results. The recall result is 98.47%, while the precision result is 96.99%.
Customer Loyalty Classification With Random Forest Algorithm Anggi Puspita Sari; Astrid Noviriandini; Sifa Fauziah
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.393

Abstract

Customer loyalty is very important for the survival of the company. Because with customers who have customer loyalty, they will make purchases regularly. Customer loyalty needs to be maintained to increase profits. The method is to classify loyal customers with non-loyal ones, in order to retain loyal customers and set strategies for non-loyal customers. The method used is classification with random forest with cleaning stages that can clean data from noise or empty data or data that does not match, selection that can select some data to be processed for classification, transformation that can change data into two or three formats, classification with random forest with split validation using testing data and training data and with rapidminer software. Evaluation by checking the results of the classification with random forest in the form of accuracy, precision, recall, and AUC. The results of the classification show from the accuracy table that the prediction of loyal and true loyal customers is 129 more than the prediction of not loyal and true not loyal customers which is 32. The accuracy result is 96.41% which shows that the data is really accurate with very high results. The recall result is 98.47%, while the precision result is 96.99%.
Co-Authors Astrid Noviriandini Embun Fajar Wati Sifa Fauziah Tuslaela Tuslaela