Identification of Mental Health Workers in Lamongan with Machine Learning - Early Identification Model for Medical Personnel

Aug 2, 2026

COVID-19 triggered a global health crisis that increased both physical illness and psychological strain. To support balanced management of physical and mental health, this work develops a machine learning–based analysis system for identifying the mental health of medical personnel during the pandemic. Using 24-question questionnaires, multiple models (Naive Bayes, Decision Tree, k-NN, SVM, Backpropagation, and Logistic Regression) are trained and evaluated. The SVM model achieves the highest accuracy, reaching 100%.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Abstract

Introduction

Literature Reviews

IDENTIFICATION OF MENTAL HEALTH WORKERS IN LAMONGAN WITH MACHINE LEARNING Retno Wardhani1), Nur Nafiiyah2)* 1, 2) Teknik Informatika, Universitas Islam Lamongan Veteran 53A Lamongan e-mail: [retzno@yahoo.com](retzno@yahoo.com1)[1](retzno@yahoo.com1)), [mynaff@unisla.ac.id](mynaff@unisla.ac.id2)[2](mynaff@unisla.ac.id2)) *[e-mail korespondensi: mynaff@unisla.ac.id](e-mail korespondensi: mynaff@unisla.ac.id) ABSTRACT COVID-19 has caused a global health crisis, with increasing numbers of people being infected and dying every day. Various countries have tried to control its spread by applying the basic principles of social aggregation and testing. Experts agree that physical and mental health are interrelated and must be managed and balanced. The government must pay attention to balancing physical and mental health during a pandemic. The Ministry of Health has issued a guidebook for Mental Health and Psychosocial Support (DKJPS) during the COVID-19 pandemic. Based on the mental health conditions of the community or medical personnel, we are trying to create a system for mental health analysis for medical professionals based on the results of questionnaires using the machine learning method (Naive Bayes, Decision Tree, k-NN, SVM, Backpropagation, and Logistic Regression). A total of 24 question questionnaires were submitted to respondents. This study aimed to create a machine learning model (Naive Bayes, Decision Tree, k-NN, SVM, Backpropagation, and Logistic Regression) to identify the mental health of medical personnel during the COVID-19 pandemic. The results of this study are machine learning models that have the highest accuracy in identifying health workers' mental health and are 100% SVM. Keywords: identification, mental health, medical personnel, machine learning. I. INTRODUCTION Some research that has been done before [1] using a questionnaire to determine the factors that affect mental health during the COVID-19 pandemic and how to analyze it using correlation statistics. Research [2] statistically analyzed questionnaire data shows that gender can affect mental health during the COVID- 19 pandemic. Previous studies processed questionnaire data by looking for correlations to find out the factors that affect mental health during the COVID-19 pandemic [3], [4], [5], [6], [7], [8], [9], and using binary logistic regression to determine the correlation between variables [10], used multivariate logistic regression to determine the factors that influence mental health [11], [11], [12] . Research using Twitter data to analyze mental health during the COVID-19 pandemic, namely modelling text to produce a mental health index system using machine learning [13], [14] . We will use machine learning (Naive Bayes, Decision Tree, k-NN, SVM, Backpropagation, and Logistic Regression) to analyze questionnaire data from medical personnel. Therefore we need an analysis of features that affect the mental health of medical personnel during the COVID-19 pandemic and a model for early identification of the mental health of medical personnel. II. LITERATURE REVIEWS Coronavirus Disease-19 (COVID-19) is a respiratory disease caused by the SARS-CoV-2 virus (World Health Organization (WHO), 2020a) . This disease was first detected in China and has now spread globally, including in Indonesia. As of July 4 2021, the World Health Organization (WHO) reported an increase in the number of positive cases of COVID-19 by 3 per cent (2,668,561 points) globally. WHO says that in the Southeast Asia region, as of July 4 2021, there was an increase in positive cases of 7 per cent (612,933 points) compared to the previous week's data (World Health Organization (WHO), 2020a) . The graph of the distribution of positive cases ofCOVID-19 in Indonesia per day compiled by the COVID-19 Distribution Map shows that until July 10 2021, there were the most additional cases in the province of the Special Capital Region (DKI) Jakarta, namely 649,302

FAQs

What goal does the study pursue regarding mental health during COVID-19?

The study aims to create a machine learning model that identifies the mental health of medical personnel during the COVID-19 pandemic using questionnaire results.

What data and methods are used to build the machine learning models?

The research uses a 24-item questionnaire submitted to respondents and trains several algorithms: Naive Bayes, Decision Tree, k-NN, SVM, Backpropagation, and Logistic Regression.

Which machine learning model produces the best results?

The SVM model achieves the highest accuracy, reported as 100%, for identifying medical workers' mental health.

Aug 2, 2026

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