scholarly journals Estudio de la gestión del presupuesto en el ISMMM utilizando minería de datos [Study of budget managementinISMMM using data mining]

2014 ◽  
Author(s):  
Yiezenia ROSARIO FERRER ◽  
Yanelis GÉ GUILARTE
Keyword(s):  
Data Mining ◽  
Analysis Tool ◽  
Standard Process ◽  
Budget Planning ◽  
Useful Knowledge ◽  
Industry Standard ◽  
Knowledge Analysis ◽  
Hidden Knowledge ◽  
Data Analysis Tool ◽  
Using Data

Resumen La elaboración del presupuesto permite a las empresas, los gobiernos e instituciones establecer prioridades y evaluar la consecución de sus objetivos. Su confección se basa en las instituciones de educación superior en sus procesos sustantivos.En los últimos años ha existido un gran crecimiento en las capacidades de generar y almacenar datos, de ahí que el volumen y la variedad de información hayan ido en aumento. Esto ha traído como consecuencia la incapacidad para analizar y transformar la información en conocimiento útil con las herramientas disponibles. En la presente investigación se realiza el descubrimiento de conocimiento oculto en los datos del presupuesto almacenados desde el 2004, registrados en documentos en formato Excel, por medio del uso de las técnicas de minería de datos: regresión lineal, asociación y clasificación; en la obtención de modelos para lograr mejoras en el proceso de elaboración del presupuesto en el ISMMM.El proceso de obtención de conocimiento fue realizado utilizando la metodología CRISP-DM (Cross – Industry Standard Processfor Data Mining) y a la herramienta de análisis de datos WEKA (WaikatoEnvironmentKnowledgeAnalysis). Palabras clave:CRISP-DM, gestión de presupuesto, minería de datos, WEKA.   Abstract The budget planning allows to enterprises, governments and institutions to states the priorities and to assesshow its objectives are carried out. In higher education institutions, the budget planning is based on its main processes. In the last years it had existed an accelerated growing of the capacities to generate and store data, so the volume and variety of information also grown. That’s why the disabilities to analyze and transform this information in useful knowledge with the available tools. This research aims to discover hidden knowledge from budget data stored in Excel files in the years from 2004 to 2011 using the data mining techniques: linear regression, association and classification, to improve the budget forecasting process at ISMMM. The knowledge discovering was made using the CRISP-DM (Cross – Industry Standard Process for Data Mining) methodology and the data analysis tool WEKA (Waikato Environment Knowledge Analysis). Keywords:Budget management, CRISP-DM, data mining, WEKA.

Evaluasi Telemarketing Kartu Kredit Bank Menggunakan Algoritma Genetika untuk Seleksi Fitur dan Naive Bayes

2020 ◽  
Vol 10 (1) ◽  
pp. 12
Author(s):  
Ekka Pujo Ariesanto Akhmad
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Standard Process ◽  
Industry Standard

<strong> </strong>Bagian pemasaran bank sudah menampung data dari nasabah atau pelanggan bank dengan cara memasarkan atau mensosialisasikan kartu kredit lewat telepon (telemarketing). Evaluasi telemarketing kartu kredit yang sudah dilakukan bank masih kurang membawa hasil dan berdaya guna. Salah satu cara yang tepat untuk evaluasi laporan telemarketing kartu kredit bank adalah menggunakan teknik data mining. Tujuan penggunaan data mining untuk mengetahui kecenderungan dan pola nasabah yang berpeluang untuk berlangganan kartu kredit yang ditawarkan bank. Metode penelitian menggunakan Cross Industry Standard Process for Data Mining (CRISP-DM) dengan Algoritma Genetika untuk Seleksi Fitur (GAFS) dan Naive Bayes (NB). Hasil penelitian menunjukkan jumlah atribut pada dataset telemarketing kartu kredit bank sejumlah 15 atribut terdiri dari 14 atribut biasa dan 1 atribut spesial. Dataset telemarketing bank mengandung data berdimensi tinggi, sehingga diterapkan metode GAFS. Setelah menerapkan metode GAFS diperoleh 7 atribut optimal terdiri dari 6 atribut biasa dan 1 atribut spesial. Enam atribut biasa meliputi pekerjaan, balance, rumah, pinjaman, durasi, poutcome. Sedangkan atribut spesial adalah target. Hasil penelitian menunjukkan algoritma NB mempunyai nilai akurasi <em>86,71</em>%. Algoritma GAFS dan NB meningkatkan nilai akurasi menjadi <em>90,27</em>% untuk prediksi nasabah bank yang mengambil kartu kredit.

scholarly journals Aplicación de los árboles de decisión en la identificación de patrones de lesiones fatales por causa externa en el municipio de Pasto, Colombia

2017 ◽  
Vol 19 (3) ◽  
pp. 388
Author(s):  
Ricardo Timaran-Pereira ◽  
Andrés Calderón-Romero ◽  
Arsenio Hidalgo-Troya
Keyword(s):  
Data Mining ◽  
Standard Process ◽  
Industry Standard ◽  
America Latina

Introducción: La Organización Panamericana de la Salud (OPS) desde el año 1993 y la Organización Mundial de la Salud (OMS) en 1996, aceptaron que la violencia es un problema de salud pública, situación que se corrobora en el Informe de Violencia y Salud, en el cual América Latina presentó una tasa de homicidios de 18 por cada 100.000 personas, y es considerada como una de las regiones más violentas del mundo. Objetivo: Detectar patrones delictivos con técnicas de minería de datos en el Observatorio del Delito del municipio de Pasto (Colombia). Materiales y métodos: Se aplicó Cross Industry Standard Process for Data Mining (CRISP-DM), una de las metodologías utilizadas en el desarrollo de proyectos de minería de datos en los ambientes académico e industrial. La fuente de información fue el Observatorio del Delito del municipio de Pasto, donde está almacenadas las cifras históricas, limpias y transformadas sobre las lesiones de causa externa (fatales y no fatales), registrados en 11 años. Resultados: Se construyó un modelo de clasificación basado en árboles de decisión que permitió descubrir patrones de muertes por causa externa. Para el caso de homicidios, estos sucedieron en su mayoría en la Comuna 5 de Pasto, los fines de semana, en la madrugada, en el segundo semestre del año, en la vía pública y las víctimas fueron hombres adultos, de oficios varios, la causa de los homicidios fueron riñas y se produjeron con arma de fuego. Conclusión: El conocimiento generado ayudará a los organismos gubernamentales y de seguridad a tomar decisiones eficaces en lo relacionado a la implementación de planes de prevención de delitos y seguridad ciudadana.

scholarly journals Osteoporosis Risk Prediction Using Data Mining Algorithms

2020 ◽  
Author(s):  
Efat Jabarpour ◽  
Amin Abedini ◽  
Abbasali Keshtkar
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
Industry Standard ◽  
Disease Information ◽  
Increased Risk ◽  
Using Data ◽  
Mining Algorithms

Introduction: Osteoporosis is a disease that reduces bone density and loses the quality of bone microstructure leading to an increased risk of fractures. It is one of the major causes of inability and death in elderly people. The current study aims at determining the factors influencing the incidence of osteoporosis and providing a predictive model for the disease diagnosis to increase the diagnostic speed and reduce diagnostic costs. Methods: An Individual's data including personal information, lifestyle, and disease information were reviewed. A new model has been presented based on the Cross-Industry Standard Process CRISP methodology. Besides, Support Vector Machine (SVM) and Bayes methods (Tree Augmented Naïve Bayes (TAN)) and Clementine12 have been used as data mining tools. Results: Some features have been detected to affect this disease. The rules have been extracted that can be used as a pattern for the prediction of the patients' status. Classification precision was calculated to be 88.39% for SVM, and 91.29% for  (TAN) when the precision of  TAN  is higher comparing to other methods. Conclusion: The most effective factors concerning osteoporosis are detected and can be used for a new sample with defined characteristics to predict the possibility of osteoporosis in a person.  

scholarly journals PENERAPAN ALGORITMA NAÏVE BAYES UNTUK MEMPREDIKSI KEPUTUSAN CALON NASABAH DAN NASABAH TETAPBANK BRI SYARIAH MENERIMA PENAWARAN PROGRAM DEPOSITO BERJANGKA

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Wahyu Nurjaya WK ◽  
Yusrina Adani
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Preparation ◽  
Standard Process ◽  
Industry Standard

Bank BRI Syariah memiliki banyak produk yang menarik untuk ditawarkan kepada calon nasabah maupun nasabah tetap berupa produk jangka panjang atau jangka pendek, yang menawarkan banyak keuntungan bagi nasabah itu sendiri. Salah satu produknya adalah Deposito berjangka yang merupakan produk investasi dengan menyimpan uang dan penarikanya hanya bisa dilakukan pada kurun waktu tertentu yang telah di janjikan oleh pihak bank dengan persetujuan nasabah. Dengan telemarketing yang baik oleh pihak bank maka diharapkan calon nasabah dan nasabah tetap mengetahui produk ini.Telemarketing adalah salah satu cara dalam mempromosikan produk-produk atau jasa layanan yang ada di bank. Seorang telemarketing bank harus dapat membuat target nasabah, nasabah mana yang berpotensi untuk meningkatkan deposito dengan melihat data-data nasabah bank yang telah tersimpan dalam database. Dikarenakan database nasabah sangat besar, maka tidak mungkin untuk mencari pola prediksi calon nasabah atau nasabah tetap yang berminat untuk program Deposito dengan cara konvensional.Berdasarkan hal tersebut, pengelolaan data yang sangat besar bisa diatasi dengan memanfaatkan Data Mining yaitu proses iteratif dan interaktif untuk menentukan pola atau model baru yang sempurna, bermanfaat dan dapat dimengerti dalam suatu database yang sangat besar. Data Mining berisi pencarian trend pola yang diinginkan dalam database besar untuk membantu pengambilan keputusan diwaktu yang akan datang. Dengan menggunakan Data Mining diharapkan dapat mengoptimasikan proses prediksi data nasabah oleh seorang telemarketing, sehingga dia mampu menawarkan deposito dengan target calon nasabah atau nasabah tetap yang tepat sasaran. Adapun Teknik Klasifikasi Data Mining menggunakan algoritma Naïve Bayes. Naïve Bayes bekerja sangat efektif saat diuji pada dataset yang besar untuk menentukan pola dimasa lalu dan mencari fungsi yang akan menjadi pola penilaian data dimasa yang akan datang. Untuk mencapai hasil yang diharapkan metode CRISP-DM (Cross Industry Standard Process for Data Mining) sangat cocok sebagai solusi, melalui proses business understanding, data understanding, data preparation, modeling, evaluation dan deployment. Dengan ini hasil prediksi akan lebih akurat, sehingga untuk target telemarketing produk Deposito Bank BRI Syariah akan tepat sasaran.

scholarly journals Desenvolvimento de um Sistema de Apoio a Decisão para priorização de Pedidos de Desembolso no Estado de Pernambuco

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Itallo Henrique de Santana Santos ◽  
Alexandre Magno Andrade Maciel
Keyword(s):  
Data Mining ◽  
Standard Process ◽  
Industry Standard

A Secretaria da Controladoria Geral do Estado (SCGE) analisa mensalmente despesas geradas pelos diferentes órgãos do Estado de Pernambuco com o objetivo de garantir os pagamentos daquelas que são mais sensíveis. Mais de 1 bilhão de reais em despesas ficaram pendentes no exercício de 2016, demonstrando importância de priorização dos pagamentos. Nesse contexto o artigo apresenta o processo de desenvolvimento de um Sistema de Apoio a Decisão (SAD) para a Secretaria da Controladoria Geral do Estado de Pernambuco. O sistema proposto tem a capacidade de classificar as despesas públicas por meio de árvore de decisão auxiliando o trabalho de análise dos gestores responsáveis na priorização de pagamentos. No artigo é detalhado a caracterização do problema, a fundamentação teórica usada no trabalho, a aplicação da metodologia CRISP-DM (Cross Industry Standard Process for Data Mining) e o sistema. A utilização de árvore de decisão na classificação das despesas teve como resultado uma acurácia de 99%, mostrando que o uso desse tipo de modelo atendeu satisfatoriamente na solução do problema encontrado.

Human Talent Forecasting using Data Mining Classification Techniques

2012 ◽  
pp. 261-274
Author(s):  
Hamidah Jantan ◽  
Abdul Ali Hamdan ◽  
Zulaiha Othman
Keyword(s):  
Data Mining ◽  
Human Resource ◽  
Talent Management ◽  
Past Experience ◽  
Classification Task ◽  
Close Attention ◽  
Classification Techniques ◽  
Useful Knowledge ◽  
Using Data ◽  
The Right

Talent management is a very crucial task and demands close attention from human resource (HR) professionals. Recently, among the challenges for HR professionals is how to manage organization’s talents, particularly to ensure the right job for the right person at the right time. Some employee’s talent patterns can be identified through existing knowledge in HR databases, which data mining can be applied to handle this issue. The hidden and useful knowledge that exists in databases can be discovered through classification task and has been widely used in many fields. However, this approach has not successfully attracted people in HR especially in talent management. In this regard, the authors attempt to present an overview of talent management problems that can be solved by using this approach. This paper uses that approach for one of the talent management tasks, i.e., predicting potential talent using previous existing knowledge. Future employee’s performances can be predicted based on past experience knowledge discovered from existing databases by using classification techniques. Finally, this study proposes a framework for talent forecasting using the potential Data Mining classification techniques.

Human Talent Forecasting using Data Mining Classification Techniques

2012 ◽  
pp. 486-499
Author(s):  
Hamidah Jantan ◽  
Abdul Razak Hamdan ◽  
Zulaiha Ali Othman
Keyword(s):  
Data Mining ◽  
Human Resource ◽  
Talent Management ◽  
Past Experience ◽  
Classification Task ◽  
Close Attention ◽  
Classification Techniques ◽  
Useful Knowledge ◽  
Using Data ◽  
The Right

Talent management is a very crucial task and demands close attention from human resource (HR) professionals. Recently, among the challenges for HR professionals is how to manage organization’s talents, particularly to ensure the right job for the right person at the right time. Some employee’s talent patterns can be identified through existing knowledge in HR databases, which data mining can be applied to handle this issue. The hidden and useful knowledge that exists in databases can be discovered through classification task and has been widely used in many fields. However, this approach has not successfully attracted people in HR especially in talent management. In this regard, the authors attempt to present an overview of talent management problems that can be solved by using this approach. This paper uses that approach for one of the talent management tasks, i.e., predicting potential talent using previous existing knowledge. Future employee’s performances can be predicted based on past experience knowledge discovered from existing databases by using classification techniques. Finally, this study proposes a framework for talent forecasting using the potential Data Mining classification techniques.

The Application of Data Mining Technology in Mechanical Fault Diagnosis

2011 ◽  
Vol 460-461 ◽  
pp. 821-826
Author(s):  
Yun Feng Lin ◽  
Xiao Ping Hu
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Technology Development ◽  
Development Trend ◽  
Advanced Technology ◽  
Mining Technology ◽  
Intelligent Diagnosis ◽  
Hidden Knowledge ◽  
Using Data ◽  
Development Tendency

This article first introduced the survey of mechanical fault diagnosis technology development and the data mining technology theory. Then its application situation at present and the main questions that exist are elaborated. Its development trend is analyzed. The application feasibility of using data mining technology in mechanical fault diagnosis is discussed. Next, the naissance, the development and the future development tendency of data mining technology are introduced. The four algorithms are analyzed and the framework is built too. Intelligent Diagnosis is a major development direction of the fault diagnosis. Knowledge acquisition is the bottleneck of Intelligent Diagnosis development. It comprehensive use of many kinds of advanced technology, discover valuable and hidden knowledge from the large amounts of data mining.

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