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All Journal Journal of Innovation Information Technology and Application (JINITA) Journal of English Language and Education Jurnal Algoritma
Welly Desriyati
Institut Teknologi dan Bisnis Riau Pesisir
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Languange, Linguistic, Communication & Media Other

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English Language Needs Analysis of Industrial Engineering Students at the Institute of Technology and Business Riau Pesisir Julanos Julanos; Welly Desriyati; Hanifatul Rahmi; Azhari Siregar; Fariz Kharisma
Journal of English Language and Education Vol 11, No 3 (2026)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jele.v11i3.2459

Abstract

English language skills play a vital role in supporting students’ academic and professional development, particularly in Industrial Engineering. Therefore, a needs analysis is essential to identify the specific language competencies required in industrial contexts. This study employed a qualitative descriptive design to examine students’ English language needs, focusing on four main skills—writing, reading, speaking, and listening—as well as relevant language components. Data were collected through questionnaires distributed to 242 randomly selected from a population of 619 active Industrial Engineering students at the Institute of Technology and Business Riau Pesisir. The findings indicate that reading and writing skills in Occupational Health and Safety (OHS) and environmental contexts considered as primary subject, and office administration, technical production, and logistics are the secondary important subject. Meanwhile, speaking and listening skills are in office and OHS contexts considered as primary subjects, while production and logistics as secondary. Overall, students emphasize mastering language components to support workplace communication.
Evaluasi Komparatif Neural Network dan Random Forest untuk Prediksi Produktivitas Tandan Buah Segar Kelapa Sawit Berbasis Fitur Musiman Gellysa Urva; Welly Desriyati
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3352

Abstract

Fresh Fruit Bunch (FFB) productivity in oil palm exhibits seasonal patterns that pose challenges for predictive modeling, particularly given the limited amount of data. This study aims to compare the performance of Neural Networks and Random Forests in predicting FFB productivity based on temporal features, including lag, rolling mean, and cyclical encoding. Evaluation was conducted using time-series validation with MAE, RMSE, and R² metrics. The results indicate that Neural Networks face generalization limitations with limited data, reflected in poor performance on the test data. Conversely, Random Forest delivers more stable and accurate performance with an MAE of 0.2581, an RMSE of 0.3325, and an R² of 0.9675. These findings confirm the superiority of tree-based ensemble approaches in handling seasonal data with small sample sizes. The contribution of this research is to provide empirical evidence and recommendations for more reliable models for TBS productivity prediction as a basis for developing decision support systems in the plantation sector.
Hybrid Genetic Algorithm and XGBoost Model for Optimizing Sharia Compliant Stock Portfolios in the Indonesian Capital Market Gellysa Urva; Welly Desriyati
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.2999

Abstract

This study proposes a hybrid portfolio optimization model that integrates Genetic Algorithm (GA) and Extreme Gradient Boosting (XGBoost) to optimize Sharia-compliant stock portfolios in the Indonesian capital market. The model leverages the predictive capability of XGBoost and the adaptive optimization mechanism of GA to achieve an optimal balance between return and risk under Sharia investment principles.  The dataset consists of historical daily stock price data from six Sharia-compliant stocks (ADRO, INDF, KLBF, TLKM, UNVR, and ICBP) during 2023. XGBoost was employed to predict stock returns using selected technical indicators, and its performance was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The predicted returns were then used as inputs for portfolio optimization using GA.  The results show that the hybrid model produces an optimized portfolio with KLBF (22.7%) and ICBP (21.3%) receiving the highest allocations, indicating favorable risk–return characteristics. The model achieved RMSE values ranging from 0.0081 to 0.0142 and MAPE values between 4.24% and 6.11%, demonstrating high predictive accuracy. Furthermore, the hybrid portfolio outperformed benchmark strategies, achieving an expected return of 6.42% with a Sharpe Ratio of 0.30.  These findings confirm that the integration of GA and XGBoost enhances both prediction reliability and portfolio optimization performance. This study contributes to the development of artificial intelligence-based portfolio optimization in Islamic finance and provides a practical framework for ethical and data-driven investment decision-making.
Co-Authors Azhari Siregar Fariz Kharisma Gellysa Urva Gellysa Urva Hanifatul Rahmi Julanos Julanos