Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Insyst : Journal of Intelligent System and Computation
Devi Putri Febriyanti
Politeknik Indonusa Surakarta
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Stability Evaluation of the @akhyar11/ml-v1 Library for Training Recurrent Neural Networks on Indonesian Sentiment Classification Muhammad Akhyar; Devi Putri Febriyanti; Norma Puspitasari
Intelligent System and Computation Vol 8 No 1 (2026): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v8i1.498

Abstract

The Python ecosystem currently dominates machine learning development, while machine learning libraries in TypeScript remain relatively limited, particularly for deep learning and sequence modeling tasks. This study evaluates the stability of @akhyar11/ml-v1, a TypeScript-native machine learning library, in training recurrent neural network architectures for Indonesian sentiment classification. Rather than focusing solely on identifying the best-performing model, this research positions RNN, LSTM, and GRU as evaluation instruments to assess the library training reliability, consistency, and robustness. Each architecture was trained independently across 10 repeated runs for 30 epochs using the same sentiment classification dataset and experimental configuration. The evaluation focuses on training stability indicators, including accuracy consistency, macro F1-score, weighted F1-score, performance variation across runs, convergence behavior, and the occurrence of training failures such as NaN values or extreme performance drops. The results show that @akhyar11/ml-v1 can run recurrent neural network training processes functionally and relatively stable on the tested configuration. Among the evaluated architectures, LSTM demonstrated the most consistent performance, while RNN showed stable but lower predictive performance. GRU exhibited higher sensitivity across repeated runs, indicating greater variance in training outcomes. These findings suggest that @akhyar11/ml-v1 has practical potential as a non-Python machine learning library for sequence-based natural language processing experiments, although further evaluation on broader datasets, architectures, and training configurations is still required.
Co-Authors Muhammad Akhyar Norma Puspitasari