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All Journal Journal of Applied Data Sciences Jurnal Minfo Polgan (JMP)
Manja, Erni
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Library & Information Science Mathematics Social Sciences

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A Hybrid LSTM–Stacking–SMOTE Model for Weather-Aware Palm Oil Price Prediction Addressing Data Imbalance and Forecast Accuracy Kusmanto, Kusmanto; Subagio, S; Manja, Erni
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.922

Abstract

Accurate forecasting of palm oil prices is crucial for agribusiness decision-making due to high market volatility influenced by dynamic weather conditions. This study proposes a novel hybrid deep learning model combining Long Short-Term Memory (LSTM), Stacking Ensemble, and Synthetic Minority Over-sampling Technique (SMOTE) to improve predictive accuracy and handle class imbalance in price trend classification. The model was trained using a multivariate time-series dataset sourced from Kaggle, consisting of daily records of temperature, humidity, rainfall, and palm oil prices. A binary classification scheme was applied by labeling instances as either price increase (class 1) or price stable/decrease (class 0), based on a 0% price change threshold. Four experimental configurations were evaluated: standard LSTM, LSTM + SMOTE, LSTM + Stacking, and the proposed LSTM + SMOTE + Stacking. The proposed model outperformed all baselines, achieving the highest accuracy of 83.12%, an F1-score of 0.8466, MAE of 0.1688, RMSE of 0.4109, and a perfect recall of 1.0000, indicating excellent sensitivity to minority class trends. In contrast, the standard LSTM achieved only 77.32% accuracy and an F1-score of 0.7224, showing limited ability in handling imbalanced data. Visualization of loss curves and confusion matrices confirmed the model’s learning stability and classification effectiveness. This study contributes a novel integration of ensemble learning and oversampling in time-series commodity forecasting and demonstrates the effectiveness of this approach in capturing weather-driven price patterns, offering a robust framework for predictive analytics in agriculture.
Pengaruh Lingkungan dan Beban Kerja terhadap Kinerja Pegawai PDAM Tirta Silaupiasa Kisaran Manja, Erni
Jurnal Minfo Polgan Vol. 13 No. 2 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i2.14586

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh lingkungan kerja dan beban kerja terhadap kinerja pegawai PDAM Tirta Silaupiasa Kisaran. Penelitian ini menggunakan metode kuantitatif dengan sifat asosiatif, di mana populasi yang digunakan adalah pegawai PDAM Tirta Silaupiasa Kisaran. Analisis data dilakukan menggunakan regresi linier berganda dengan alat uji SPSS 27.0. Hasil penelitian menunjukkan bahwa lingkungan kerja memiliki pengaruh yang signifikan terhadap kinerja pegawai, dengan nilai t-hitung sebesar 7,606 dan signifikansi 0,000. Setiap peningkatan satu satuan dalam lingkungan kerja akan meningkatkan kinerja pegawai sebesar 1,671 satuan. Sementara itu, beban kerja tidak memiliki pengaruh yang signifikan terhadap kinerja pegawai, dengan nilai t-hitung sebesar 1,841 dan signifikansi 0,072. Uji F menunjukkan bahwa lingkungan kerja dan beban kerja secara simultan berpengaruh signifikan terhadap kinerja pegawai dengan nilai F-hitung sebesar 37,145 dan signifikansi 0,000. Nilai Adjusted R Square sebesar 0,616 menunjukkan bahwa 61,6% variasi dalam kinerja pegawai dapat dijelaskan oleh lingkungan kerja dan beban kerja, sedangkan sisanya 38,4% dipengaruhi oleh faktor lain di luar model penelitian. Dengan hasil penelitian ini diharapkan manajemen PDAM Tirta Silaupiasa Kisaran lebih meningkatkan kualitas lingkungan kerja untuk mendorong kinerja pegawai. Selain itu, penelitian selanjutnya dapat memasukkan variabel lain yang berpotensi memengaruhi kinerja pegawai guna memperoleh hasil yang lebih komprehensif.  
Co-Authors Kusmanto Kusmanto Subagio, S