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Bambang Hermanto
STT Pelita Bangsa
Computer Science & IT Decision Sciences, Operations Research & Management

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Penerapan Data Mining Untuk Prediksi Penerima Bantuan Pangan Non Tunai (Bpnt) Di Desa Wanacala Menggunakan Metode Naïve Bayes Bambang Hermanto; Achmad Jaelani2
Jurnal SIGMA Vol 9 No 4 (2019): Juni 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

The Non-Cash Food Assistance Program held by the government is often not on target due to many factors, one of which is the number of criteria that must be considered to be a decision of beneficiaries. Of the eleven criteria set requires the right algorithm to perform calculations so that the results given are more accurate. Naïve Bayes algorithm is a method for classification using probability theory that has a high degree of accuracy. Naïve Bayes algorithm testing uses Rapid Miner tools that produce an accuracy rate of 96% of the 50 data provided. This algorithm is right for the selection of recipients of non-cash food assistance. There are 2 classes that are needed, namely Worthy and Not Eligible. Keywords: Classification, Naïve Bayes, Rapid Miner, Non-Cash Food Aid
Penerapan Data Mining Untuk Menentukan Tingkat Ketersedian Minimum Atas Material On-Hand Pada Pt. Dhl Supply Bambang Hermanto; Nissan Situmeang
Jurnal SIGMA Vol 9 No 4 (2019): Juni 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (501.389 KB)

Abstract

Mining data or efforts to explore valuable information and knowledge in the database a very large one is called data mining or Knowledge Discovery in Database abbreviated KDD. One of the most popular algorithm in data mining techniques is the Apriori algorithm. Whereas in the discovery Patterns of relationship combinations between itemssets are used Association Rules. Data Mining has been implemented in various fields, including business or trade, education, and telecommunications. In the business sector, for example, the results of data mining implementation using Apriori algorithms can help business people in decision-making policies what is related to inventory. For example the importance of the inventory system at a company and what types of goods are the top priority that must be in stock to anticipate void of goods. Because the lack of stock can affect consumer service and income company. Therefore, the availability of various types of materials or products in a company as one of the food suppliers, is absolutely necessary to support the smooth distribution to consumers, so that the activity customer service goes well. Keywords: Data Mining, Association Rules, Apriori Algorithms, Inventory Level.
Penerapan Metode Naïve Bayes Untuk Prediksi Kepuasan Pelanggan ( Studi Kasus Bengkel Win Motor ) Bambang Hermanto; Ahmad Romadhoni
Jurnal SIGMA Vol 9 No 4 (2019): Juni 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (466.787 KB)

Abstract

The tight competition in the workshop business makes a company engaged in service or maintenance of motorized vehicles compete to attract its customers in buying spare parts and services offered. To maintain its customers, a company must be able to understand carefully the expectations - any expectations of its customers so that the company must know the level of satisfaction of each customer. At WIN MOTOR Workshop which is located in the residential area of East Cikarang Graha, it is difficult to determine whether the customer feels satisfied or not in terms of the services provided. By using questionnaire techniques, the results of questionnaire data are obtained. Processing data using the application of naïve Bayes methods to predict customer satisfaction. Based on the data from the questionnaire that was processed using the naïve bayes method to predict customer satisfaction in the WIN MOTOR Workshop, the accuracy rate was 90.00%. After testing cases to determine customer satisfaction predictions using manual calculations and using the Rapidminer application, the same customer satisfaction predication results were obtained.. Keywords : Prediction, Naïve Bayes, Customer Satisfaction
Co-Authors Achmad Jaelani2 Ahmad Romadhoni Nissan Situmeang