Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Sistem Informasi dan Informatika (SIMIKA) Journal of Applied Data Sciences
Hadi Asnal
Universitas Sains Dan Teknologi Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
MODEL PREDIKSI PRODUKTIVITAS PADI MENGGUNAKAN XGBOOST DAN RANDOM FOREST Yoga Safitra Anugraha; Helda Yenni; Wirta Agustin; Hadi Asnal
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i1.4169

Abstract

Rice is a strategic commodity in ensuring national food security in Indonesia. Predicting rice productivity is a critical issue due to the decreasing harvest area and fluctuating production. This study aims to develop and compare the performance of two machine learning algorithms, namely Extreme Gradient Boosting (XGBoost) and Random Forest, in predicting rice productivity based on harvest area and total production data. The dataset consists of rice productivity data from 38 provinces in Indonesia over the period 2018 to 2024. The models were evaluated using three data splitting ratios (70:30, 80:20, and 90:10) and four evaluation metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). The results show that both models perform well, with Random Forest achieving the highest R² value of 0.887 and the lowest RMSE of 2.939 on the 90:10 split, indicating higher accuracy. XGBoost, while slightly lower in accuracy (R² = 0.781), produced more stable predictions across varying input scales. When tested on new data, both models showed consistent performance, demonstrating generalization capabilities. These findings indicate that machine learning models are effective in modeling and forecasting agricultural productivity and can serve as decision-support tools for policymakers and agricultural stakeholders. The models can be utilized for strategic planning, resource allocation, and improving agricultural productivity in the future.
Adaptive Neural Collaborative Filtering with Textual Review Integration for Enhanced User Experience in Digital Platforms Lusiana Efrizoni; Edwar Ali; Hadi Asnal; Junadhi Junadhi
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.v6i4.944

Abstract

This research proposes a hybrid rating prediction model that integrates Neural Collaborative Filtering (NCF), Long Short-Term Memory (LSTM), and semantic analysis through Natural Language Processing (NLP) to enhance recommendation accuracy. The main objective is to improve alignment between system predictions and actual user preferences by leveraging multi-source information from the Amazon Movies and TV dataset, which includes explicit user–item ratings and textual reviews. The core idea is to combine three complementary processing paths—(1) user–item interaction modeling via NCF, (2) temporal dynamics capture through LSTM, and (3) semantic understanding of reviews using NLP—into a unified deep learning-based adaptive architecture. Experimental evaluation demonstrates that this multi-input approach outperforms the baseline collaborative filtering model, with the Mean Absolute Error (MAE) reduced from 1.3201 to 1.2817 (a 2.91% improvement) and the Mean Squared Error (MSE) reduced from 2.2315 to 2.1894 (a 1.89% improvement). Training metrics visualization further shows a stable convergence pattern, with the MAE gap between training and validation consistently below 0.03, indicating minimal overfitting. The findings confirm that integrating cross-dimensional signals significantly enhances predictive performance and can contribute to increased user satisfaction and engagement in recommendation platforms. The novelty of this work lies in the simultaneous integration of interaction, temporal, and semantic dimensions into a single adaptive recommendation framework, a configuration not jointly explored in prior studies. Moreover, the flexible architecture enables adaptation to other domains such as e-commerce, music, or online learning, broadening its practical applicability.
Co-Authors Edwar Ali Helda Yenni Junadhi Junadhi Lusiana Efrizoni Wirta Agustin Yoga Safitra Anugraha