La presente tesis se titula “Sistema informático en plataforma web para el proceso de venta de pasajes en la empresa Transzela”. El objetivo principal consiste en determinar el efecto del proceso de venta de pasajes en la empresa Transzela, utilizando un sistema informático en plataforma web con la finalidad de disminuir el tiempo promedio y el número de errores en el proceso de venta manual. El tiempo se emplea en realizar la búsqueda de la disponibilidad en la programación del viaje que el cliente solicita y en el registro de los datos del mismo en el boleto de viaje; el número de errores está conformado por: la emisión nueva, la anulación, la postergación, el cambio de datos y la duplicidad de los boletos. Para la realización del proceso de venta de pasajes con el sistema informático previamente, se registra la programación del viaje: la fecha, la hora, el bus, y la r...

La tesis se titula Implementación de un software integrado de tecnología web y móvil para la mejora proceso de venta de pasajes en una empresa de transportes. El objetivo principal consistió en demostrar la mejora del proceso de venta de pasajes en una empresa de transportes con la implementación de un software integrado de tecnología web y móvil. La venta con el software integrado de tecnología web y móvil consiste en realizar búsqueda de la disponibilidad en la programación del viaje, el registro de datos del cliente y la emisión del boleto. De esta forma se mejora el tiempo del proceso gracias a la combinación de las tecnologías web y móvil. El tipo de estudio es un cuasi experimental, con una muestra de 208 unidades de boletos del proceso de venta de pasajes. Por consiguiente, el Grupo control y el grupo experimental es de 104 elementos respectivamente. Los resultados ...

“With the onset of the COVID-19 pandemic, online education has become one of the most important options available to students around the world. Although online education has been widely accepted in recent years, the sudden shift from face-to-face education has resulted in several obstacles for students. This paper, aims to predict the level of adaptability that students have towards online education by using predictive machine learning (ML) models such as Random Forest (RF), KNearest-Neighbor (KNN), Support vector machine (SVM), Logistic Regression (LR) and XGBClassifier (XGB).The dataset used in this paper was obtained from Kaggle, which is composed of a population of 1205 high school to college students. Various stages in data analysis have been performed, including data understanding and cleaning, exploratory analysis, training, testing, and validation. Multiple parameters, such as ...

“With the onset of the COVID-19 pandemic, online education has become one of the most important options available to students around the world. Although online education has been widely accepted in recent years, the sudden shift from face-to-face education has resulted in several obstacles for students. This paper, aims to predict the level of adaptability that students have towards online education by using predictive machine learning (ML) models such as Random Forest (RF), KNearest-Neighbor (KNN), Support vector machine (SVM), Logistic Regression (LR) and XGBClassifier (XGB).The dataset used in this paper was obtained from Kaggle, which is composed of a population of 1205 high school to college students. Various stages in data analysis have been performed, including data understanding and cleaning, exploratory analysis, training, testing, and validation. Multiple parameters, such as ...

With the onset of the COVID-19 pandemic, online education has become one of the most important options available to students around the world. Although online education has been widely accepted in recent years, the sudden shift from face-to-face education has resulted in several obstacles for students. This paper, aims to predict the level of adaptability that students have towards online education by using predictive machine learning (ML) models such as Random Forest (RF), K-Nearest-Neighbor (KNN), Support vector machine (SVM), Logistic Regression (LR) and XGBClassifier (XGB).The dataset used in this paper was obtained from Kaggle, which is composed of a population of 1205 high school to college students. Various stages in data analysis have been performed, including data understanding and cleaning, exploratory analysis, training, testing, and validation. Multiple parameters, such as ac...

Abstract—In recent years, computer science has advanced exponentially, helping significantly to identify and classify text extracted from social networks, specifically Twitter. This work identifies, classifies, and analyzes tweets related to real natural disasters through tweets with the hashtag #Nat-uralDisasters, using Machine learning (ML) algorithms, such as Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF). First, tweets related to natural disasters were identified, creating a dataset of 122k geo-located tweets for training. Secondly, the data-cleaning process was carried out by applying stemming and lemmatization techniques. Third, exploratory data analysis (EDA) was performed to gain an initial understanding of the data. Fourth, the training and testing process of the BNB, MNB, ...

Identifying and classifying text extracted from social networks, following the traditional method, is very complex. In recent years, computer science has advanced exponentially, helping significantly to identify and classify text extracted from social networks, specifically Twitter. This work aims to identify, classify and analyze tweets related to real natural disasters through tweets with the hashtag #NaturalDisasters, using Machine learning (ML) algorithms, such as Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF). First, tweets related to natural disasters were identified, creating a dataset of 122k geolocated tweets for training. Secondly, the data-cleaning process was carried out by applying stemming and lemmatization techniques. Third, exploratory data analysis (EDA) was performed...