The Cross-Industry Standard Process for Data Mining (CRISP-DM), despite being the most popular data mining process more than two decades, is known to leave those organizations lacking operational experience puzzled and unable start their projects. This especially apparent in first phase of Business Understanding, at conclusion which, goals project hand should be specified, which arguably requir...

The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely accepted framework in production and manufacturing. This data-driven knowledge discovery provides an orderly partition of the often complex data mining processes to ensure practical implementation analytics machine learning models. However, application robust industry-specific models faces multiple data- model developmen...

This research report showcases various data mining (DM) techniques such as Classification, Regression, and Clustering in brief also discusses the aptest framework method for healthcare industry, CRISP-DM. explores applications industry. DM is utilized to extract from a lot of information. includes two models, predictive descriptive. Classifying form classes either with final objective learning ...

Data siswa merupakan database yang sangat penting karena data ini dapat digunakan untuk menganalisis prestasi akademik siswa, seperti halnya indeks siswa. Di Fakultas Teknik dan Sains Universitas Muhammadiyah Bangka Belitung terdiri dari program studi Ilmu Komputer, Sipil Konservasi Sumber Daya Alam memiliki mahasiswa. tersebut analisis. Pada diterapkan metode mining klasifikasi dengan Algoritm...

Freedom of expression on social media Twitter not always give positive value, because sometimes can contains negative things such as fake news, spreads hate speech, and racism, where these kinds tweet be categorized an act Cyberbullying. Where this cyberbullying tends to increase every time. The aim study is use the Naïve Bayes method in classifying types sentiment Twitter. keyword used Saipul ...

This paper presents a framework for process mining in critical care. The framework uses the CRISP-DM model, extended to incorporate temporal and multidimensional aspects (CRISP-TDM), combined with the Patient Journey Modeling Architecture (PaJMa), to provide a structured approach to knowledge discovery of new condition onset pathophysiologies in physiological data streams. The approach is based...

<p>Memperkirakan data saham telah menjadi salah satu isu penting terutama bagi orang yang memilih investasi sebagai sarana manajemen keuangan. Banyak penelitian sudah dilakukan untuk membantu memperkirakan dimasa depan menggunakan machine learning. Namun kebanyakan model learning digunakan kompleks dan hanya memprediksi variabel deret waktu. Pada ini berfokus pada membuat algoritma VAR be...

We propose an extension of the Cross Industry Standard Process for Data Mining (CRISPDM) which addresses specific challenges of machine learning and data mining for context and model reuse handling. This new general context-aware process model is mapped with CRISP-DM reference model proposing some new or enhanced outputs.