PERAN SISTEM INTELIJENSIA BISNIS DALAM MANAJEMEN PENGELOLAAN PELANGGAN DAN MUTU UNTUK AGROINDUSTRI SUSU SKALA USAHA MENENGAH

Business intelligence systems participate to deliver an accurate and useful information to appropriate decision makers within the necessary timeframe to support an effective decision making in dairy agro- industry. The objective of this study was to determine the role of business intelligence systems to support customer relationship management and quality in dairy-agro industry medium enterprise. The combine methods to develop this research included Unified Modeling Language (UML), Fuzzy, Failure Mode Effect Analysis (FMEA), Cube, On Line Analytical Processing (OLAP), Extract, Transform, Loading (ETL) and Data warehouse. Data warehouse model is supported by ETL process. Business Intelligence Model is an integration of Cube, Data warehouse Model and Fuzzy system and it helps for a fast and efficient transaction in the system. The design consisted of quality and CRM (Customer Relationship Management) sub model. The Role of In Quality Sub Model based on Fuzzy, FMEA, the highest Fuzzy Risk Priority Number (FRPN) was 692 with type of failure, sum of Total Plate Control, bigger than 1 million/mL. The CRM (Customer Relationship Management) Model with RFM (Recency Frequency Monetory) and Customer Life Value (CLV) methods with OLAP Cube, the highest rank CLV for dairy processing industry to get potential customer was at PT FFI. The integration quality and CRM models into BI System would make it quickly anticipate, adapt, and react to the changing business conditions.   Keywords: Business Intelligence (BI), Unified Modelling Language (UML), OLAP, cub

Knowledge Generation through Web Mining Techniques for e-Business Recommendation System

World Wide Web plays a vital role in global information service center.There is the paradigm shift in the e-Business computing for the data collection and knowledge generation. The online-business is growing very rapidly by creating website for the business.Due to exponential growth of dynamic information over the internet by the websites, information overloadcreate bigchallenges for the researchers in this area. Web data engineering is the best concept for dealing these challenges. Web business is very easy and efficient way to do the business but it creates the competitive environment.This paper discusses theidea about thewebmining techniques, knowledgegeneration frame work used in recommended system for both consumers and severalweb business operations of any organization

PERANCANGAN MODEL SISTEM INTELIJENSIA BISNIS UNTUK MENGANALISIS PEMASARAN PRODUK ROTI DI PABRIK ROTI MENGGUNAKAN METODE DATA MINING DAN CUBE

Business intelligence systems participate to deliveran accurate and useful information to decision makers in marketing division of bakeries manufacture. The purpose of this study was to design business intelligence model to analyze the marketing product, design the data mining model,  measure and analyze the marketing process of the product they sell. The methodology of this research wasto analyze system requirements, design unified modeling language, make process extract, transform, and load, designdata warehouse, and data mining that integrated with the on line analytical process cube webbased. The business intelligence model produced was a marketing data mining model and on line analytical process cube. The result from on line analytical process cube was the data warehouse of transaction in R Bakery. In designing the data mining, K-means clustering method was used. The results from data mining k-means clustering were there were 83% cluster 1 and 17% cluster 2. Cluster 1 wasthecategorize for low leftover breads and cluster 2 was the categorize for high leftover breads. The model cube recency, frequency, and monetary and customer lifetime value resulted ranked out of the most amount of sales in R Bakery. Keywords: business intelligence system, data mining, extract transform load, on line analitical process cub

PERANCANGAN MODEL SISTEM INTELIJENSIA BISNIS UNTUK MENGANALISIS PEMASARAN PRODUK ROTI DI PABRIK ROTI MENGGUNAKAN METODE DATA MINING DAN CUBE

Business intelligence systems participate to deliveran accurate and useful information to decision makers in marketing division of bakeries manufacture. The purpose of this study was to design business intelligence model to analyze the marketing product, design the data mining model, measure and analyze the marketing process of the product they sell. The methodology of this research wasto analyze system requirements, design unified modeling language, make process extract, transform, and load, designdata warehouse, and data mining that integrated with the on line analytical process cube webbased. The business intelligence model produced was a marketing data mining model and on line analytical process cube. The result from on line analytical process cube was the data warehouse of transaction in R Bakery. In designing the data mining, K-means clustering method was used. The results from data mining k-means clustering were there were 83% cluster 1 and 17% cluster 2. Cluster 1 wasthecategorize for low leftover breads and cluster 2 was the categorize for high leftover breads. The model cube recency, frequency, and monetary and customer lifetime value resulted ranked out of the most amount of sales in R Bakery

PERANCANGAN MODEL SISTEM INTELIJENSIA BISNIS UNTUK MENGANALISIS PEMASARAN PRODUK ROTI DI PABRIK ROTI MENGGUNAKAN METODE DATA MINING DAN CUBE

Business intelligence systems participate to deliveran accurate and useful information to decision makers in marketing division of bakeries manufacture. The purpose of this study was to design business intelligence model to analyze the marketing product, design the data mining model,  measure and analyze the marketing process of the product they sell. The methodology of this research wasto analyze system requirements, design unified modeling language, make process extract, transform, and load, designdata warehouse, and data mining that integrated with the on line analytical process cube webbased. The business intelligence model produced was a marketing data mining model and on line analytical process cube. The result from on line analytical process cube was the data warehouse of transaction in R Bakery. In designing the data mining, K-means clustering method was used. The results from data mining k-means clustering were there were 83% cluster 1 and 17% cluster 2. Cluster 1 wasthecategorize for low leftover breads and cluster 2 was the categorize for high leftover breads. The model cube recency, frequency, and monetary and customer lifetime value resulted ranked out of the most amount of sales in R Bakery. Keywords: business intelligence system, data mining, extract transform load, on line analitical process cub

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