Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Applied Information Technology and Computer Science (AICOMS)
Ghufron Ghufron
Universitas Islam Sultan Agung
Computer Science & IT

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Prediksi Harga Saham Sektor Energi di BEI Menggunakan Model Multimodal LSTM dengan Integrasi Fitur Numerik dan Sentimen Berita Pasar Modal di Indonesia Naufal Lathifan Yumna; Ghufron Ghufron
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/jm8dtf93

Abstract

The movement of energy sector stock prices exhibits high volatility, which is influenced by historical data and news sentiment. This research evaluates the performance of stock price prediction by integrating numerical data and economic news texts using a multimodal Long Short-Term Memory (LSTM) architecture. The data utilized includes the stock prices of ADRO, PGAS, and INDY from the 2021 to 2026 period, alongside Indonesian-language economic news. Sentiment extraction from the news texts was conducted using the IndoBERT model. The results indicate that the IndoBERT model achieved an accuracy and F1-score of 83%. The evaluation of the unimodal model (historical data only) yielded a Mean Absolute Percentage Error (MAPE) of 3.62% for ADRO, 3.17% for PGAS, and 5.90% for INDY. Meanwhile, the multimodal model, which combined numerical and sentiment features, resulted in a MAPE of 4.00% (ADRO), 5.46% (PGAS), and 8.47% (INDY). In conclusion, the unimodal LSTM model proved to be effective; however, the integration of sentiment features in the multimodal scheme did not provide a significant improvement in accuracy due to the highly volatile nature of the stocks.
Perbandingan Metode Temporal Fusion Transformer (TFT) dan Long Short-Term Memory (LSTM) Dalam Prediksi Harga Saham Indonesia Berbasis Data Teknikal dan Fundamental Nurhasan Nurhasan; Ghufron Ghufron
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/jmhe8c20

Abstract

The Indonesian capital market exhibits a high level of volatility, requiring stock price prediction models that are accurate and adaptive. Conventional forecasting models have limitations in capturing nonlinear patterns and multivariate relationships within stock time series data. This study compares the performance of Long Short-Term Memory (LSTM) and Temporal Fusion Transformer (TFT) models in predicting stock prices of LQ45 index companies, namely BBRI, TLKM, and ADRO, with a 7-day forecasting horizon. Both models were trained using 12 combined technical and fundamental features with a data split ratio of 70:15:15. Model evaluation was conducted using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that the TFT model achieved better performance on BBRI with a MAPE of 0.74% and TLKM with a MAPE of 0.91%, while also demonstrating faster training convergence compared to LSTM. In contrast, the LSTM model outperformed TFT on ADRO with a MAPE of 2.71%, which exhibited a relatively consistent trend pattern. Overall, TFT proved to be more effective for stocks with complex multivariate dynamics, whereas LSTM remained competitive for stocks with more stable trend patterns. The selection of prediction models should therefore consider the volatility characteristics and movement patterns of each stock issuer
Analisis Sentimen menggunakan IndoBERT dan Tren Topik Keluhan Pasien pada Ulasan Google Maps Rumah Sakit Menggunakan Latent Dirichlet Allocation Naufal Muhammad Afif; Ghufron Ghufron
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/m86x0x75

Abstract

Patient satisfaction is a crucial indicator of hospital quality, yet management often focuses solely on star ratings that fail to explain the root causes of issues. This study develops a hybrid Natural Language Processing (NLP) model using IndoBERT for sentiment classification of Google Maps reviews. Reviews classified as negative sentiment are then filtered and processed using the Latent Dirichlet Allocation (LDA) method to uncover hidden themes within patient complaints. The test results show that the IndoBERT model achieves exceptionally high performance, with an accuracy of 95.23%, precision of 95.22%, recall of 95.23%, and an F1-score of 95.22%. The LDA analysis successfully identifies 10 optimal topics, which are categorized into five main complaint categories: time efficiency, medical services, facilities/parking, administrative procedures, and specialist services. The integration of IndoBERT and LDA proves effective in transforming raw digital reviews into strategic information for the automated evaluation of hospital service quality.
Co-Authors Naufal Lathifan Yumna Naufal Muhammad Afif Nurhasan Nurhasan