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All Journal Media Statistika Counseling and Humanities Review
Weisha, Ghea
Universitas Andalas
Humanities Decision Sciences, Operations Research & Management Education Neuroscience Other

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A-OPTIMAL DESIGN IN NON-LINEAR MODELS TO INCREASE SILICON DIOXIDE PURITY LEVELS Weisha, Ghea; Erfiani, Erfiani; Irzaman, Irzaman; Syafitri, Utami Dyah
MEDIA STATISTIKA Vol 17, No 1 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.1.36-44

Abstract

Silica is the most mineral found on earth and is widely used in industry. Silica used in industry is usually silicon dioxide with a purity ≥ 95% and its often sold at a higher cost. To obtain the silica at a lower cost, silica extraction from biomass such as rice husk can be conducted. The purity of silica extracted from biomass tends to be lower than that of mineral silica. Silica with low purity can be increased by adjusting the temperature and the rate of temperature rise. This research aims to obtain the best design to determine the purity of silicon dioxide. The design of this study was generated based on the A-optimality criterion using the DETMAX algorithm. The A-optimality criterion is minimizing the trace of the variance-covariance of the parameter estimation. The best design points obtained using A-optimal design consist of three temperature groups: the minimum temperature of 800°C, the middle temperature of 850°C, and the maximum temperature of 900°C, with varying rates of temperature rise. Points were repeated at the temperature of 850°C, with rates of temperature rise of 1.67°C/min and 3.34°C/min. 
Exploring School Enrollment Trends in Indonesia Through Time Series Analysis to Inform Counselling and Communication Strategies Yollanda, Mutia; Weisha, Ghea; Pratiwi, Lidya; Putra, Ade Herdian; Putra, Robi Jaya; Yaser, Mishbah El
Counseling and Humanities Review Vol 5, No 1 (2025): Counseling and Humanities Review
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/0001299chr2025

Abstract

A time series analysis of School Enrollment Rates across different age groups in Indonesia from 2003 to 2024 was conducted using ARIMA modelling. Data were segmented into four age groups: 7 to 12, 13 to 15, 16 to 18, and 19 to 24 years. Stationarity testing required first-order differencing, and ARIMA models were selected based on autocorrelation and partial autocorrelation structures. The ARIMA(1,1,0) model showed the best fit for the younger groups, capturing the gradual and predictable participation trends influenced by long-term education policies and stable school enrollment patterns. Forecast accuracy was evaluated using Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE), revealing excellent accuracy for ages 7 to 12 with MAPE 0.036 percent and MSE 0.001, and for ages 13 to 15 with MAPE 0.089 percent and MSE 0.008. Forecasts for ages 16 to 18 showed moderate accuracy, while results for 19 to 24 indicated greater variability. These findings inform the development of age-specific guidance counselling and public communication strategies to address distinct educational challenges. The study underscores the utility of interpretable forecasting models in supporting evidence-based education policy and planning.
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.
Psychosocial Factors Associated with Depressive Symptom Severity among Indonesian Adults: An Ordinal Logistic Regression Analysis of IFLS-5 Data Ghea Weisha; Mutia Yollanda; Yudiantri Asdi
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: 10.24036/0001412chr2026

Abstract

Depression is a major public health concern and one of the most prevalent mental health disorders in Indonesia. However, evidence regarding psychosocial factors associated with depressive symptom severity among Indonesian adults remains limited. This study examined the associations of educational attainment, marital status, and emotional problems with depressive symptom severity using data from the fifth wave of the Indonesia Family Life Survey (IFLS-5). A cross-sectional analysis was conducted among 21,416 adults using descriptive statistics, Chi-square tests, and ordinal logistic regression. The results showed that educational attainment, marital status, and emotional problems were significantly associated with depressive symptom severity. Higher educational attainment and being married were associated with lower odds of more severe depressive symptoms, whereas emotional problems were associated with higher odds. These findings underscore the importance of psychosocial factors in developing targeted mental health interventions for Indonesian adults.
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.
Co-Authors Ade Herdian Putra Asdi, Yudiantri Dahlia Misrika Erfiani Erfiani Irzaman, Irzaman Mutia Yollanda Nova Noliza Bakar Pratiwi, Lidya Putra, Ade Herdian PUTRA, ROBI JAYA Utami Dyah Syafitri Yaser, Mishbah El