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All Journal Sistemasi: Jurnal Sistem Informasi Infotekmesin
Maldini, Naufal
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Computer Science & IT Electrical & Electronics Engineering Mechanical Engineering

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Early Detection of Mental Health Disorders based on Sentiment using Stacking Method Maldini, Naufal; Utomo, Danang Wahyu; Tresyani, Rahmadika Putri
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i1.4842

Abstract

Mental health disorders are a serious and growing global concern, including in Indonesia. This study aims to predict mental health disorders through sentiment analysis using the Stacking Classifier approach, which combines Random Forest, Gradient Boosting Classifier, and Logistic Regression algorithms. The dataset was sourced from various social media platforms, consisting of textual data classified into seven mental health categories, such as depression, anxiety, and personality disorders. The data underwent preprocessing steps, including cleaning, balancing, and dimensionality reduction using the TF-IDF algorithm. The study results indicate that the Stacking Classifier method achieved an accuracy of 95.66%, with a precision of 95.63%, recall of 95.66%, and F1-Score of 95.64%. These results outperform the individual algorithms tested in the research. The findings demonstrate the significant potential of sentiment analysis powered by machine learning for early detection of mental health disorders, making it a valuable tool to enhance diagnosis and intervention in mental health care more effectively.
Deteksi Dini Gangguan Kesehatan Mental dengan Model Bert dan Algoritma Xgboost Rahmadika Putri Tresyani; Wahyu Utomo, Danang; Maldini, Naufal
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2535

Abstract

Mental health disorders are severe conditions that affect a person's thoughts, feelings, behavior, and well-being. Data from the World Health Organization (WHO) shows that more than 264 million people worldwide experience depression, one of the most common forms of mental health disorders. However, limited access to psychological services, such as lack of professionals and high costs, are major challenges in providing adequate support. Therefore, innovative technology-based solutions are needed for efficient and affordable psychological support. Efforts to improve research results to develop a mental health chatbot model by combining BERT (Bidirectional Encoder Representations from Transformers) and XGBoost (Extreme Gradient Boosting) models. The BERT model is used to understand the context of the conversation, while the XGBoost algorithm is used for text classification. The dataset used comes from Kaggle, which consists of 312 question patterns with several patterns or classes, namely 79 classes. The results of the program implementation test produced a percentage of 93.05% and output in the form of a program in the execution of the model on Google Colab..
Deteksi Dini Gangguan Kesehatan Mental dengan Model Bert dan Algoritma Xgboost Rahmadika Putri Tresyani; Wahyu Utomo, Danang; Maldini, Naufal
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2535

Abstract

Mental health disorders are severe conditions that affect a person's thoughts, feelings, behavior, and well-being. Data from the World Health Organization (WHO) shows that more than 264 million people worldwide experience depression, one of the most common forms of mental health disorders. However, limited access to psychological services, such as lack of professionals and high costs, are major challenges in providing adequate support. Therefore, innovative technology-based solutions are needed for efficient and affordable psychological support. Efforts to improve research results to develop a mental health chatbot model by combining BERT (Bidirectional Encoder Representations from Transformers) and XGBoost (Extreme Gradient Boosting) models. The BERT model is used to understand the context of the conversation, while the XGBoost algorithm is used for text classification. The dataset used comes from Kaggle, which consists of 312 question patterns with several patterns or classes, namely 79 classes. The results of the program implementation test produced a percentage of 93.05% and output in the form of a program in the execution of the model on Google Colab..
Co-Authors Rahmadika Putri Tresyani Tresyani, Rahmadika Putri Utomo, Danang Wahyu