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All Journal Jurnal ICT : Information Communication & Technology Jurnal Teknologi Informatika dan Komputer
Chandra Dewi Lestari
STT Wastukancana
Computer Science & IT Electrical & Electronics Engineering

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Implementasi Sistem Prediksi Saham IDX80 Berbasis Berita Ekonomi Berbahasa Indonesia Menggunakan LSTM dan Framework Streamlit Muhamad Hernan Fauzan; Syariful Alam; Chandra Dewi Lestari
Jurnal ICT: Information Communication & Technology Vol. 26 No. 1 (2026): JICT-IKMI, July , 2026
Publisher : LPPM STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36054/jict-ikmi.v26i1.393

Abstract

The development of the Indonesian capital market, particularly for stocks included in the IDX80 index, generates a large volume of economic news information, making manual sentiment analysis challenging for investors. This study aims to build a sentiment classification system for Indonesian-language economic news articles related to IDX80 stocks using the Long Short-Term Memory (LSTM) method. The dataset consists of 3,000 articles obtained from trusted news portals. The research follows stages from data understanding to deployment. Text preprocessing includes case folding, cleansing, stopword removal, Sastrawi-based stemming, label encoding, tokenizing, and padding. The model architecture utilizes a 128-dimensional embedding layer, 128 LSTM units, 0.5 dropout, and Softmax activation. Experimental results show that the model achieved an accuracy of 92.67% in classifying positive, neutral, and negative sentiments. The model was evaluated using precision, recall, F1-score, and a confusion matrix. Furthermore, it was successfully implemented in a Streamlit-based application featuring single-article and batch CSV predictions. This system serves as a digital tool to assist investors in making objective investment decisions amidst market uncertainty.
Implementation of the BiLSTM Model for Detecting AI-Generated Indonesian Text Rafil Moehamad Alif; Syariful Alam; Chandra Dewi Lestari
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3551

Abstract

The rapid advancement of generative Artificial Intelligence (AI) presents challenges to academic integrity due to potential misuse like plagiarism. This study develops a text detection system specifically for the Indonesian language using a Deep Learning approach with a Bidirectional Long Short-Term Memory (Bi-LSTM) architecture. The research methodology follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. A dataset comprising 5,008 text rows was compiled via web scraping from journalism platforms and academic journals indexed in SINTA 4 for human-written texts, while AI-generated counterparts were engineered using ChatGPT and Google Gemini paraphrases. Text features were extracted using a Keras Tokenizer and Embedding Layer with 64 dimensions. Evaluation of the trained Bi-LSTM model on a 30% validation split demonstrated an overall accuracy of 78.24% and a Mean Absolute Error (MAE) of 0.3295. Specifically, the model achieved a 93.77% success rate in identifying human-written texts, though it logged a lower detection rate of 62.62% for academic AI text structures. The final model was successfully deployed as a web application using Streamlit.
Co-Authors Muhamad Hernan Fauzan Rafil Moehamad Alif Syariful Alam