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Umar Zaky
Universitas Teknologi Yogyakarta, Daerah Istimewa Yogyakarta
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Efektivitas Penggabungan Data Lintas Provinsi dan Augmentasi TimeGAN pada Prediksi Harga Beras Berbasis LSTM Sinta Dwi Lestari; Umar Zaky
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.11297

Abstract

Rice price fluctuations in each province are difficult to predict, although accurate forecasts are needed to anticipate price shocks, while the multivariate data available for several provinces are often short. This study compares two ways of addressing this limitation in a Long Short-Term Memory (LSTM) model: pooling data from three provinces (East Java, Central Java, and West Java) into a single model, and adding synthetic data from a Time-series Generative Adversarial Network (TimeGAN). Monthly data (March 2024 to March 2026) from four sources (weather, rice prices, food reserve stocks, and exchange rates) yielded 25 observations per province. The 84 scenarios under three training conditions (no augmentation, 50%, and 100% augmentation) made 252 experiments, assessed with MAPE, RMSE, and MAE. The pooled model K7 was more accurate than local models in all three provinces. Without augmentation, mean MAPE fell from 2.056% to 1.258%, RMSE from IDR 326.777/kg to IDR 223.018/kg, and MAE from IDR 306.063/kg to IDR 187.795/kg. In contrast, TimeGAN augmentation produced no detectable improvement, and at 100% it tended to raise the pooled models’ MAPE from 1.541% to 1.710%. The synthetic data were easily distinguishable from the real data (mean discriminative score 0.893). For food price monitoring in regions with short data, collecting real data from other provinces appears more promising than building synthetic data, so price-forecasting systems can prioritize pooling data across provinces before adding generative models.
Evaluasi Kritis Generalisasi Multidataset pada Deteksi Hoax Berbahasa Indonesia Menggunakan BiLSTM dan IndoBERT Ria Yunita; Umar Zaky
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.11298

Abstract

Machine learning-based Indonesian hoax detection often reports high scores on test data sharing the training data’s distribution, although such scores may misrepresent actual model capability. This critical evaluation, not a new method, analyzes how multidataset composition affects BiLSTM and IndoBERT performance and generalization, and identifies indications of shortcut learning arising from spurious correlations between labels and dataset origin. Three datasets were used: Mendeley (426 samples), Komdigi (3,951 samples), and sh1zuka (8,000 sampled), combined into 12,377 samples (8,256 Valid, 4,121 Hoax). Evaluation employed three schemes: Scheme 1 (stratified 70:15:15 split of the combined data), Scheme 2 (forward cross dataset), and Scheme 3 (reverse cross dataset). In Scheme 1, BiLSTM and IndoBERT achieved Macro F1 scores of 0.9803 and 0.9814, but these fugures cannot be read as successful hoax detection, since under cross dataset schemes Macro F1 dropped to 0.3642 and 0.5624 (Scheme 2) and 0.4231 and 0.4109 (Scheme 3), with accuracy below the majority class baseline. Dataset 1, comprising only 3.4% of the Scheme 1 test data, accounted for 87.9% of BiLSTM errors and 100% of IndoBERT errors, while the accuracy of the word “beredar” as a single label predictor fell from 96.30% on combined data to 42.96% on Dataset 1. These findings indicate that high scores under conventional evaluation reflect recognition of dataset origin more than comprehension of news content; therefore, cross dataset evaluation and source control tests should accompany random splitting on multisource data.
Co-Authors Ria Yunita Sinta Dwi Lestari