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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Adang Suhendra
Gunadarma University
Computer Science & IT Electrical & Electronics Engineering

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A Preference Model on Adaptive Affinity Propagation Rina Refianti; Achmad Benny Mutiara; Asep Juarna; Adang Suhendra
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 3: June 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1767.959 KB) | DOI: 10.11591/ijece.v8i3.pp1805-1813

Abstract

In recent years, two new data clustering algorithms have been proposed. One of them isAffinity Propagation (AP). AP is a new data clustering technique that use iterative message passing and consider all data points as potential exemplars. Two important inputs of AP are a similarity matrix (SM) of the data and the parameter ”preference” p. Although the original AP algorithm has shown much success in data clustering, it still suffer from one limitation: it is not easy to determine the value of the parameter ”preference” p which can result an optimal clustering solution. To resolve this limitation, we propose a new model of the parameter ”preference” p, i.e. it is modeled based on the similarity distribution. Having the SM and p, Modified Adaptive AP (MAAP) procedure is running. MAAP procedure means that we omit the adaptive p-scanning algorithm as in original Adaptive-AP (AAP) procedure. Experimental results on random non-partition and partition data sets show that (i) the proposed algorithm, MAAP-DDP, is slower than original AP for random non-partition dataset, (ii) for random 4-partition dataset and real datasets the proposed algorithm has succeeded to identify clusters according to the number of dataset’s true labels with the execution times that are comparable with those original AP. Beside that the MAAP-DDP algorithm demonstrates more feasible and effective than original AAP procedure.
Predicting Indonesia's rice production using XGBoost, GAN-XGBoost, and BiLSTM-BiGRU with ENSO-IOD integration Reviana Siti Mardiah; Adang Suhendra; Fitrianingsih Fitrianingsih; Dian Kemala Putri; Saifudin Nasir; Muhammad Farhan Fathurrohman
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.11506

Abstract

Food security programs require accurate rice production predictions to meet demand and plan reserves. However, incomplete historical data, limited predictor integration, and insufficient representation of large-scale climate variability hamper these predictions. Missing data require appropriate imputation, while feature selection retains relevant predictors. Indonesia spans 6° N to 11°08' S and 95° to 141° E, with diverse provincial weather conditions requiring integration with global El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) signals. Previous studies have not combined regional weather variables, ENSO-IOD signals, data imputation, and feature selection into a rice production prediction framework. To address this gap, this research developed prediction models using extreme gradient boosting (XGBoost), generative adversarial network (GAN)-augmented XGBoost, and a hybrid bidirectional long short-term memory-bidirectional gated recurrent unit (BiLSTM-BiGRU) with explicit ENSO-IOD integration. Several imputation and feature selection strategies were evaluated. XGBoost with K-Nearest Neighbors imputation and without feature selection performed best, achieving a root mean square error (RMSE) of 0.0082, a mean absolute percentage error (MAPE) of 5.86%, and an R² of 0.9991. Neither GAN-augmented XGBoost nor BiLSTM-BiGRU outperformed XGBoost. Ablation tests showed that ENSO-IOD integration reduced RMSE from 0.0182 to 0.0082. These findings can support rice availability estimation, early production shortfall identification, and rice reserve requirement decisions.
Co-Authors Achmad Benny Mutiara Asep Juarna Dian Kemala Putri Fitrianingsih Fitrianingsih Muhammad Farhan Fathurrohman Reviana Siti Mardiah Rina Refianti Saifudin Nasir