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All Journal Jurnal Matematika: MANTIK Pedagogik Journal of Islamic Elementary School Jurnal Matematika UNAND Journal of Science and Technology Mathematics and Applications (MAp) Journal Counseling and Humanities Review
Mutia Yollanda
Department of Mathematics and Data Science, Universitas Andalas, 25163, Indonesia
Humanities Computer Science & IT Education Mathematics Neuroscience Other

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Model Indeks Harga Saham Gabungan menggunakan Artificial Neural Network dan Multivariate Adaptive Regression Spline Mutia Yollanda; Dodi Devianto; Putri Permathasari
Jurnal Matematika MANTIK Vol. 5 No. 2 (2019): Mathematics and Applied Mathematics
Publisher : Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15642/mantik.2019.5.2.112-122

Abstract

The Indonesian Composite Stock Price Index is an indicator of changes in stock prices are a guide for investors to invest in reducing risk. Fluctuations in stock data tend to violate the assumptions of normality, homoscedasticity, autocorrelation, and multicollinearity. This problem can be overcome by modelling the Composite Stock Price Index uses an artificial neural network (ANN) and multivariate adaptive regression spline (MARS). In this study, the time-series data from the Composite Stock Price Index starting in April 2003 to March 2018 with its predictor variables are crude oil prices, interest rates, inflation, exchange rates, gold prices, Down Jones, and Nikkei 225. Based on the coefficient of determination, the determination coefficient of ANN is 0.98925, and the MARS determination coefficient is 0.99427. While based on the MAPE value, MAPE value of ANN was obtained, namely 6.16383 and MAPE value of MARS, which was 4.51372. This means that the ANN method and the good MARS method are used to forecast the value of the Indonesian Composite Stock Index in the future, but the MARS method shows the accuracy of the model is slightly better than ANN.
MODEL NON-LINIER PADA JARINGAN SARAF TIRUAN Mutia Yollanda; Dodi Devianto; Hazmira Yozza
Jurnal Matematika UNAND Vol 7, No 3 (2018)
Publisher : Jurusan Matematika FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmu.7.3.110-118.2018

Abstract

Jaringan Saraf Tiruan merupakan model yang meniru cara kerja jaringan saraf secara biologi. Algoritma pembelajaran Jaringan Saraf Tiruan digunakan untuk melatih jaringan secara iterasi sehingga bobot antar unit dapat disesuaikan dengan galat yang ditentukan. Metode Backpropagation didesain untuk operasi pada jaringan feedforward dengan banyak lapisan sehingga memperoleh bobot jaringan dengan galat terkecil. Bobot tersebut digunakan untuk memodelkan data. Fungsi sigmoid digunakan pada jaringan feedforward sehingga menghasilkan bobot yang berbentuk tidak linear. Bobot yang berbentuk tidak linear membentuk model non-linear pada Jaringan Saraf Tiruan.Kata Kunci: Jaringan Saraf Tiruan, Metode Backpropagation, Sigmoid, Feedforward
MODEL NON-LINEAR PADA JARINGAN SARAF TIRUAN Mutia Yollanda; Dodi Devianto; Hazmira Yozza
Jurnal Matematika UNAND Vol 7, No 2 (2018)
Publisher : Jurusan Matematika FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmu.7.2.89-97.2018

Abstract

Abstrak. Jaringan Saraf Tiruan merupakan model yang meniru cara kerja jaringansaraf secara biologi. Algoritma pembelajaran Jaringan Saraf Tiruan digunakan untukmelatih jaringan secara iterasi sehingga bobot antar unit dapat disesuaikan dengan galatyang ditentukan. Metode Backpropagation didesain untuk operasi pada jaringan feedfor-ward dengan banyak lapisan sehingga memperoleh bobot jaringan dengan galat terke-cil. Bobot tersebut digunakan untuk memodelkan data. Fungsi sigmoid digunakan padajaringan feedforward sehingga menghasilkan bobot yang berbentuk tidak linear. Bobotyang berbentuk tidak linear membentuk model non-linear pada Jaringan Saraf Tiruan.Kata Kunci: Jaringan Saraf Tiruan, Metode Backpropagation, Sigmoid, Feedforward
Penerapan Model Regresi Logistik Terhadap Indeks Pembangunan Manusia (IPM) di Provinsi Sumatera Barat Tahun 2019 – 2021 Fitri Rahmah Ul Hasanah; Mutia Yollanda
Journal of Science and Technology Vol 2, No 2: September 2022
Publisher : UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/jostech.v2i2.4383

Abstract

The human development index is an indicator that can measure the success of a region in developing human quality. The success of human development can be measured by how fundamental problems in society can be overcome, such as poverty, unemployment, and lack of access to public facilities. The increase in HDI in an area can be determined by several factors, including life expectancy (UHH) and the unemployment rate. One of the models that can be used to determine the factors that significantly affect the human development index is logistic regression, where logistic regression is an approach to making a predictive model in the form of the probability of a variable.The data used in this study are HDI, UHH, and unemployment rates in West Sumatra Province in 2019–2021. Based on the multicollinearity test, there is no relationship between UHH and the unemployment rate. This study was conducted to determine the factors that significantly affect the human development index of districts/cities in West Sumatra Province. Based on the results obtained, UHH has dramatically affected the HDI of districts/cities in West Sumatra Province over the last three years.
Analisis Pengaruh Variabel Moneter Terhadap Perkembangan Ekonomi Negara ASEAN Mutia Yollanda; Fitri Rahmah Ul Hasanah
JOSTECH Journal of Science and Technology Vol 3, No 1: Maret 2023
Publisher : UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/jostech.v3i1.5741

Abstract

This study aims to determine the effect of inflation and exchange rates on ASEAN countries' gross domestic product (GDP). The data used is secondary data accessed through the world bank from 2019 to 2021, which consists of 30 data. The independent variables used in this data are exchange rates and inflation, while the dependent variable used is gross domestic product (GDP). The panel data regression used in this study includes the common effect model, the fixed effect model and the random effect model. Based on the Chow and Hausman tests conducted, the best model in this study was the random effect model (REM). The best equation in this research is given by the equation . his study uses robustness because the normality test on the classical assumption is not fulfilled. Based on the test results, the USD exchange rate and inflation variables have a significant effect on the GDP variable. While the inflation variable has no significant effect on the GDP variable.
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.
SARIMA MODELING FOR RAILWAY FREIGHT TRANSPORTATION FORECASTING ON SUMATRA Putri Fauziahtul Asri; Mutia Yollanda; Windry Novalia Jufri; Jonni Mardizal
MAp (Mathematics and Applications) Journal Vol 8, No 1 (2026)
Publisher : Universitas Islam Negeri Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/map.v8i1.13774

Abstract

This study develops a time series model to forecast railway freight volume in Sumatra using monthly data from January 2013 to June 2025. A seasonal autoregressive integrated moving average (SARIMA) model with a drift component captures both trend and seasonal patterns in the data. Model selection is based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The results show that the SARIMA(0,1,2)(1,0,0)[12] with drift provides the best performance, yielding a log-likelihood value of 158.93 and a mean absolute percentage error (MAPE) of 0.76%. These findings indicate that the model can accurately represent freight dynamics in Sumatra and may serve as a quantitative reference for regional rail freight planning and infrastructure development.
Co-Authors Ade Herdian Putra Ade Herdian Putra Asdi, Yudiantri Dahlia Misrika Devianto, Dodi Fitri Rahmah Ul Hasanah Hazmira Yozza Jonni Mardizal Nova Noliza Bakar Pratiwi, Lidya Putra, Ade Herdian PUTRA, ROBI JAYA Putri Fauziahtul Asri Putri Permathasari Weisha, Ghea Windry Novalia Jufri Yaser, Mishbah El Zadrian Ardi