Browsing by Author "Injante Ore, Richard Enrique"

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    Density-Based Unsupervised Learning Algorithm to Categorize College Students into Dropout Risk Levels
    (2022-11) Valles Coral, Miguel Angel; Salazar Ramírez, Luis; Injante Ore, Richard Enrique; Hernandez Torres, Edwin Augusto; Juárez Díaz, Juan; Navarro Cabrera, Jorge Raul; Pinedo Tuanama, Lloy Pool; Vidaurre Rojas, Pierre
    Compliance with the basic conditions of quality in higher education implies the design of strategies to reduce student dropout, and Information and Communication Technologies (ICT) in the educational field have allowed directing, reinforcing, and consolidating the process of professional academic training. We propose an academic and emotional tracking model that uses data mining and machine learning to group university students according to their level of dropout risk. We worked with 670 students from a Peruvian public university, applied 5 valid and reliable psychological assessment questionnaires to them using a chatbot-based system, and then classified them using 3 density-based unsupervised learning algorithms, DBSCAN, K-Means, and HDBSCAN. The results showed that HDBSCAN was the most robust option, obtaining better validity levels in two of the three internal indices evaluated, where the performance of the Silhouette index was 0.6823, the performance of the Davies–Bouldin index was 0.6563, and the performance of the Calinski–Harabasz index was 369.6459. The best number of clusters produced by the internal indices was five. For the validation of external indices, with answers from mental health professionals, we obtained a high level of precision in the F-measure: 90.9%, purity: 94.5%, V-measure: 86.9%, and ARI: 86.5%, and this indicates the robustness of the proposed model that allows us to categorize university students into five levels according to the risk of dropping out.
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    Método para recomendar factores de posicionamiento personalizados en el motor de búsqueda de Google
    (2020-03) Injante Ore, Richard Enrique; Mauricio, David
    El considerable aumento de sitios web en Internet con temáticas de diversa índole ha hecho que los usuarios utilicen este medio para buscar y conseguir información. De todos los motores empleados para esta tarea, la mayoría de personas emplea Google como su motor de búsqueda preferido. Teniendo esto en consideración, se vuelve fundamental alcanzar las mejores posiciones en los resultados de búsqueda para poder promocionar un sitio web. Este trabajo ofrece un método basado en 6 fases para recomendar factores de posicionamiento personalizados a los propietarios de páginas web con el fin de que mejoren la clasificación de sus páginas web en el buscador de Google. El método se aplicó en una página web y se logró alcanzar mejoras considerables en su posicionamiento.