Mutia Yollanda
Department of Mathematics and Data Science, Universitas Andalas, 25163, Indonesia

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

Examining Counseling Search Interest Through Mental Health-Related Search Trends and Machine Learning Approaches Mutia Yollanda; Ghea Weisha; Ade Herdian Putra
Counseling and Humanities Review Vol 6, No 1 (2026): Counseling and Humanities Review
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/0001408chr2026

Abstract

Mental health-related concerns have become increasingly visible in digital environments, where online search behavior reflects public awareness and information-seeking patterns. Examining temporal changes in mental health-related searches may provide valuable insights into emerging public concerns and support evidence-based planning. This study analyzes monthly Google Trends data on counseling and related mental health topics from January 2011 to June 2026. An XGBoost regression model was developed using historical counseling search interest, lagged variables, and related mental health indicators, including depression, anxiety, stress, suicide, and mental health search trends. Temporal analysis, feature importance evaluation, and SHAP interpretation were applied to investigate trend patterns and explain model predictions. The findings revealed a substantial increase in counseling-related search interest, particularly after 2018, with several periods showing notable fluctuations. The XGBoost model demonstrated satisfactory predictive performance, achieving a mean absolute percentage error of 18.78 percent and a coefficient of determination of 0.658. Historical counseling search variables were identified as the most influential predictors, indicating that previous search behavior plays a dominant role in forecasting future trends. The study highlights the potential of interpretable machine learning approaches for monitoring digital mental health trends and supporting data-driven public health strategies.
Investigating the Influence of Working Hours, Unemployment, Inflation, and the COVID-19 Pandemic on Self-Harm Trends in Indonesia Through ARIMAX Models Mutia Yollanda; Ghea Weisha; Ade Herdian Putra; Dahlia Misrika; Nova Noliza Bakar
Counseling and Humanities Review Vol 6, No 1 (2026): Counseling and Humanities Review
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/0001406chr2026

Abstract

Self-harm is a significant public mental health concern influenced by complex interactions between psychological, social, and environmental factors. Understanding long-term patterns and identifying potential determinants of self-harm are essential for developing effective prevention strategies and counseling interventions in Indonesia. This study analyzed annual self-harm attempt rates in Indonesia from 1991 to 2025 using an autoregressive integrated moving average with exogenous variables (ARIMAX) approach. The explanatory variables included unemployment rate, inflation rate, average working hours, and a COVID-19 pandemic indicator. Several ARIMA specifications were evaluated based on model performance, parameter significance, and information criteria. Residual diagnostic tests were conducted to assess model adequacy, including examinations for autocorrelation and heteroscedasticity. The ARIMAX(1,2,1) model was selected as the preferred specification based on its statistical performance and the complete estimation of its parameters. The findings showed that average working hours (? = 0.1908, p = 0.024) and the COVID-19 pandemic period (? = 0.2275, p < 0.001) were significant predictors of self-harm rates, whereas unemployment and inflation were not statistically significant. Forecasting results indicated a gradual decline in self-harm rates, from 13.06 in 2026 to 12.34 in 2028, with increasing uncertainty over longer forecasting horizons. The findings suggest that self-harm trends in Indonesia are closely associated with psychosocial stressors, particularly occupational burden and major social disruptions. Integrating time-series evidence with counseling and public mental health strategies may support more targeted prevention efforts, early identification, and improved psychological support systems.