Multidimensi Pada Data Warehouse Dengan Menggunakan Rumus Kombinasi

Multidimensional in data warehouse is a compulsion and become the most important for information delivery, without multidimensional data warehouse is incomplete. Multidimensional give the able to analyze business measurement in many different ways. Multidimensional is also synonymous with online analytical processing (OLAP)

Analisis Implementasi Sistem OLAP dan Klasifikasi Ketepatan Waktu Lulus dan Undur Diri Mahasiswa Teknik Informatika Universitas Telkom Menggunakan Random forest

Informasi kelulusan dan undur diri mahasiswa merupakan salah satu tolak ukur untuk mengevaluasi keberhasilan sebuah universitas. Begitu pula dengan program studi S1 Teknik Informatika, Universitas Telkom, yang memanfaatkan informasi kelulusan dan undur diri sebagai salah satu pendukung dalam kegiatan perencanaan dan evaluasi dalam mempertahankan kualitas kelulusan dan akreditasi program studi. Pada kenyataanya, pihak prodi memiliki permasalahan dalam melakukan evaluasi kelulusan, dikarenakan prodi belum bisa mendapatkan informasi yang lengkap, cepat dan akurat, padahal setiap tahunnya permasalahan mengenai kelulusan yaitu jumlah mahasiswa lulus tidak tepat waktu yang lebih besar dibanding dengan jumlah mahasiswa yang lulus tepat waktu dapat mempengaruhi kualitas kelulusan dan akreditasi prodi. Pada tugas akhir, dilakukan pembangunan sistem OLAP yang meliputi ektraksi data operasional ke dalam sebuah data warehouse untuk kemudian dilanjutkan dengan kegiatan analisis data menggunakan teknik klasifikasi data mining dengan random forest untuk menganalisis pola dari penyebab ketepatan waktu lulus dan undur diri mahasiswa. Hasil klasifikasi dievaluasi menggunakan micro average f1-score untuk mengetahui performansi sistem. Berdasarkan data akademik yang digunakan untuk klasifikasi menggunakan Random forest, nilai micro average f1-score tertinggi yang diperoleh sebesar 77%. Kata Kunci:Data mining, random forest, Online Analytical Processing (OLAP), data warehouse

SISTEM INFORMASI EVALUASI TRACERSTUDY UNTUK PENGUKURAN KEY PERFORMANCE INDICATORS ALUMNI MENGGUNAKAN METODE FUZZY

Information Systems Evaluation Tracer Study was conducted to determine the parameters of alumni KPI and success rate of alumni at University. The purpose of this study is create of information systems evaluation tracer study for key performance indicators measured of alumni as a parameter of success. Data processing and analysis methods using OLAP and Takagi-Sugeno fuzzy. Data processing and analysis using OLAP and Takagi-Sugeno fuzzy method for determining the success and quality of graduates parameter Higher Education. OLAP analysis using KPIs of alumni that is IPK alumni, long studies, waiting time and suitability fields of work. The results of OLAP analysis done process fuzzification, inference, defuzzification for a produce quality indeks of alumni. This research resulted of Information System Tracer Study Evaluation web-based which can be accessed online as an indicator of the performance of graduates. This system produces graduates KPIs value and quality on college graduates in the form of tables, graphs and dashboards, from case studies conducted in Kupang State Polytechnic of alumni for a KPIs show that the parameters of alumni success rate is very good overall

Principal Component Analysis Untuk Analisa Pola Tangkapan Ikan Di Indonesia

Different kinds of fish in Indonesia is very much known to exist more than 80 species of fish caught in the waters of Indonesia. To find out which type of fish caught necessary analysis of the data pattern catches so as to know what kind of fish are caught. Search pattern or associative relationships of large-scale data that are closely related to data mining. Analysis of the association or the association rule mining is a data mining technique to discover the rules of associative between a combination of items. In the association rule method, there are two processes, namely the process of generating Frequent Itemset and trenching association rules. Frequent Itemset Generation is a process to get itemset interconnected and has a value of association based on the value of support and confidence. The algorithm used to generate the frequent itemset is Apriori Algorithm.Apriori algorithm has a weakness in the appropriate feature extraction that is used to attribute causing rule that formed a research banyak.dalam bebasis applying apriori algorithm principal component analysis to obtain a more optimal rule. After experiments using apriori algorithm with a magnitude Φ = 30, min Support 80% and 80% Confidence min rule formed results totaled 82 rules. While the second experiment was done by using an algorithm based on principal component analysis priori the magnitude Φ = 30, min Support 80% and 80% Confidence min formed results amounted to 12 rules to fully lift the ratio of

Penerapan Data Mining Untuk Mengetahui Minat Siswa Pada Pelajaran IPA Mengunakan Metode K-Means Clustering

The purpose of this research is to build a data mining application to find out students' interest in science subjects at SMPN 18 Bengkulu City. With data collection techniques by way of observation and interviews, this study used a sample of data on grades IX.8 science subjects with a total of 30 students at SMPN 18 Bengkulu City. The research method used is the K-Means Clustering method because this method separates data into groups that have objects with similar characteristics. In building this application using Xampp server, PHP programming language and MySQL database. This datamining application is used to make it easier for a teacher or admin in this application to manage student data to determine student interest in science subjects. The results of this study were 3 groups of students' interest in science subjects, namely 12 students with high interest, 17 students with moderate interest and 1 student with low interest in SMPN 18 Bengkulu City

Model Sistem Intelejensia Bisnis Untuk Perbaikan Pelayanan E-Service Pada PT. X

PT. X is one of the best airlines in Indonesia. The business intelligence system can participate as a tool to provide accurate and useful information for decision makers within the time limit that is determined to support decision making in the company's E-service services. The purpose of this study is to identify the factors that are attributes of E-Service services at PT. X. and proposed the adoption of the Business Intelligence system model for PT.X airline E-service services as a proposed service improvement. The data used are customer comment data on the Jakarta route to Singapore and Singapore to Jakarta for the period January to December 2015. The research method used is a combination to develop business intelligence research is the Pareto Diagram, Unified Modeling Language (UML), Naïve Bayes Data Mining algorithm, On Line Analytical Processing (OLAP), Extract, Transform, Loading (ETL), and Data Warehousing. From the results of data processing that has been done, it can be seen the factors attributes of E-service services are case origin, comment type, flight number, root case, and unit to charge. From the results of the calculation of the second stage of Naïve Bayes data mining, it is obtained that the greatest probability of the highest probability on the Jakarta route to Singapore is the prior probability between the customer care classification class and the suggestion form with a prior probability value of 0.92, between the inflight service classification class and the customer care priority value. equal to 1, class classification comment type compliment and customer care with prior values probability of 0.76. The prior probability of the greatest probability on the Singapore route to Jakarta is the prior probability between the customer care classification class and the suggestion form with the prior probability value of 0.92, between the inflight service classification class and the customer care with a probability prior value of 1, and between class classifications comment type compliment and customer care with a prior value of probability of 0.78. Based on the results of the largest posterior calculations, the proposed improvements were prioritized more on divisions or units to charge

Sistem Informasi Eksekutif Universitas Jenderal Achmad Yani dengan Pendekatan Online Analytical Processing

Executive Information System (EIS) adalah sebuah konsep untuk menyajikan informasi yang berasal dari data dalam jumlah besar pada sebuah organisasi agar menjadi informasi yang berguna dan sesuai dengan kebutuhan pengguna. Online Analytical Processing (OLAP) dapat melakukan penyaringan data dengan memberikan perintah query dan mengubahnya menjadi informasi yang sesuai dengan kebutuhan. EIS dapat memudahkan manajemen eksekutif perguruan tinggi untuk mendapatkan informasi akademik mahasiswa yang dibutuhkan secara efektif dan efisien tanpa harus melakukan analisis data agar menjadi sebuah informasi