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All Journal Journal of English Language and Education Jurnal Algoritma
Welly Desriyati
Institut Teknologi dan Bisnis Riau Pesisir
Computer Science & IT 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.
Co-Authors Azhari Siregar Fariz Kharisma Gellysa Urva Hanifatul Rahmi Julanos Julanos