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Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB
Published by Universitas Pelita Bangsa
ISSN : 24073903     EISSN : 28291891     DOI : https://doi.org/10.37366/sigma.v16i1
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB merupakan jurnal ilmiah yang diterbitkan oleh Program Studi Teknik Informatika Universitas Pelita Bangsa (UPB) Cikarang dengan no p-ISSN 2407-3903 (Media Cetak). Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB adalah sebagai salah satu wadah publikasi bagi dosen-dosen yang memiliki penelitian ilmiah di bidang Teknik Informatika, Ilmu Komputer, Sistim Informasi, Artificial Inteligent, Data Mining, Image Processing, Rekayasa Perangkat Lunak. Setiap artikel yang diterbitkan oleh Jurnal Ilmiah SIGMA: Informatics Engineering of UPB telah melalui proses review dan editorial yang ketat serta menghormati ketentuan hukum hak cipta, privasi, dan etika publikasi ilmiah. Jurnal Ilmiah SIGMA : Information Technology Journal of UPB terbit dua kali dalam setahun, yaitu bulan Maret, Juni, September dan Desember.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 13 Documents
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Search results for , issue "Vol 9 No 4 (2019): Juni 2019" : 13 Documents clear
Penerapan Data Mining Untuk Menentukan Penerima Jaminan Kesehatan Daerah (Jamkesda) Dengan Metode Decision Tree (C4.5) Ismasari Nawangsih; Fazri Setyawan
Jurnal SIGMA Vol 9 No 4 (2019): Juni 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

The Regional Health Insurance (JAMKESDA) effort is a public health maintenance whose financing is managed in an integrated manner. In addition, the Regional Health Insurance (JAMKESDA) is a guarantee program for payment of health service costs provided by the Regional Government. The method applied in determining the Regional Health Insurance recipient (JAMKESDA) is a decision tree method and using the C.45 Decission tree algorithm is a decision tree classification method that is mostly used because it has a major disability that can produce decision pchons that are easy to implement, have a high degree of accuracy acceptable, efficient in handling discrete and numeric type attributes. The results of this study indicate that the application of the decision tree method with the C4.5 algorithm makes selection faster, more precise and accurate in selecting prospective regional health insurance (JAMKESDA). Keywords : Jamkesda, data mining, decision tree, c4.5
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

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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
Klasifikasi Status Gizi Orang Dewasa Menggunakan Algoritma Naïve Bayes (Studi Kasus Klinik Bhakti Mulia Cikarang) Wahyu Hadikristanto1; Tiara Deswara Pungkas
Jurnal SIGMA Vol 9 No 4 (2019): Juni 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

Nutritional status is a condition caused by a balance between nutrient intake from food and nutritional needs by the body. The nutritional status of adults is sometimes less stable and there are many irregular eating patterns. To find out the nutritional status of adults, research on nutritional status in adults is carried out. Therefore, this study was made with a naïve bayes algorithm to determine the nutritional status of adults. Naïve Bayes is a simple probability-based prediction technique that is based on the application of the Bayes theorem, and the end result will be seen from the most appropriate or accurate value, so that this algorithm is considered to be good enough in performing probabilities to determine results. In this study, a naïve bayes algorithm was used to determine the nutritional status of adults. Data are classified into three, namely Normal, Less, and Obesity. This trial was conducted with 150 data, and the results of the accuracy obtained were 88.67%. Keyword : Nutritional Status, Classification, Data Mining, Naïve Bayes Algorithm
Implementasi Sistem Pendukung Keputusan Untuk Pemilihan Objek Wisata Di Majalengka Menggunakan Metode Naive Bayes Wiyanto Wiyanto; Aida Ratnasari
Jurnal SIGMA Vol 9 No 4 (2019): Juni 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

This study aims to assist tourists in choosing tourist objects, one of the famous tourist objects in Indonesia is in Majalengka. There are 3 famous objects in Majalengka, namely Paragliding, Tirta Indah, and Muara Jaya Waterfall. To determine the right choice, three criteria are used, namely: distance from the city center, visitor rates, and visitor convenience facilities. To determine the right choice, the approach is to use a decision support system through the Naïve Bayes Algorithm method which is one of the applications of the Bayes theorem in classification, Naive Bayes is based on a simplifying assumption that attribute values ​​are conditional independent of each other if an output value is given. To make it easier for visitors to make the right choice, a simple application was made using the PHP and My SQL programming languages ​​from the results of data processing using the Naïve Bayes method. The results of the classification of distance, rates and visitor convenience, the underarm typed one of the tourist attractions in Majalengka, namely Tirta Indah tourism, "far" city center distance, "cheap" tourist rates, and "comfortable" facilities with a decision result of 0.024321 (Quite satisfied). Keywords : Decision Support System, Tourism Objects, Naive Bayes, PHP programming language and My SQL as Database
Penerapan Algoritma Apriori Untuk Menentukan Penjualan Produk Pada Minimarket Studi Kasus Indomaret Ruko Ventura Nurhadi Surojuddin1; Khoerrudin Khoerrudin
Jurnal SIGMA Vol 9 No 4 (2019): Juni 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

The Indomaret shop at the Ventura shop is one of the shops that sells various daily necessities. Every day there are sales transactions of various items. However, Indomaret management does not know the pairs of items that consumers often buy simultaneously, so there is often a lack of stock on items that consumers often buy simultaneously. Data mining techniques have been widely used to overcome existing problems, one of which is the application of the Apriori algorithm to obtain information about associations between products from a transaction database. Shop transaction data can be reprocessed using a data mining application so as to produce strong association rules between sales items so that members can recommend the stocking of goods in stores, especially goods purchased simultaneously by consumers and an increase in goods sold in stores. Keywords: Minimarket stores, sales transactions, data mining, Apriori algorithm.Pendahuluan
Penerapan Algoritma C4.5 Untuk Klasifikasi Kepuasan Pelanggan Jasa Vidio Shotting Garasi Potret Purbalingga Wiyanto Wiyanto; Anggit Prasetyo Utomo
Jurnal SIGMA Vol 9 No 4 (2019): Juni 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

At present there are many companies that set up businesses in the field of video shooting, one of which is the Purbalingga Portrait Garage. The increasingly fierce competition in today's business world requires employers to be quick and responsive in making decisions so that established companies can survive amid such situations and conditions. One method that can be used for this is the data mining algorithm C4.5 method. The advantages of using this decision tree classification model are that the results of the tree are simple and easy to understand. The learning and classification process is simple and fast. In general, the decision tree algorithm classification model has a high degree of accuracy. From the calculation of customer satisfaction data training data with C4.5 algorithm using training data with confusion matrix has a value that is accuracy of 90.00%, precision 86.98%, and recall 96.98% and ROC curve optimistic with excellent classification accuracy of 0.980. This shows that the results of this prediction can be used for new quality test data. From the analysis of training data obtained a decision tree that has 20 rule models that can be used as a reference in making satisfaction in portrait garage customers. Keywords : Satisfaction, Service, C4.5 Algorithm, Data mining, Decision Tree
Penerapan Data Mining Untuk Klasifikasi Kualitas Pipa Pvc Menggunakan Metode Algoritma C4.5 Studi Kasus Pt Cipta Aneka Agung Asep Muhidin; Ading Bagus Saputra
Jurnal SIGMA Vol 9 No 4 (2019): Juni 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

The research was aimed to determine the results of the prediction of PVC pipe quality test by looking at the accuracy of the C4.5 Algorithm so as to facilitate QC in determining the quality of PVC pipes. The research was carried out on the PVC pipe quality test data, from the data was carried out the distribution of training data and testing data. Data mining extracts data to find information and patterns in determining the quality of PVC pipes. Classification method is carried out on training data to find a rule that can be applied to classify PVC pipe quality test categories in new data. The learning process using the C.45 decision tree technique transforms facts into decision trees that represent rules that are easier to understand. Variables used in this method are: Flattening Test, Tensile Strength Test, Elongation Test, Hydrostatic Pressure Test, and Impact Test. Based on the classification results using C4.5 algorithm shows that the accuracy reached 92.92%, which shows that the C4.5 algorithm is suitable for measuring the quality of PVC pipe tests. Keyworad: Quality, C4.5 Algorithm, Data mining, Decision Tree
Prediksi Produk Laris Mobil Honda Dengan Metode Klasifikasi Menggunakan Algoritma C4.5 (STUDI KASUS : DATA PENJUALAN SALES PT PROSPECT MOTOR, CIKARANG) Aswan Sunge; Heri Fidiawan2
Jurnal SIGMA Vol 9 No 4 (2019): Juni 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

The amount of competition in the business world, especially in the car sales industry requires developers to find a strategy that can increase sales and marketing of products sold. Companies must pay attention to the type of product sales transactions both new products and old products which are marketed in various ways so as to improve the effectiveness of the company's performance in processing sales transaction data. Knowing the prediction results by looking at the accuracy of the C4.5 algorithm so that Honda car sales can obtain targets according to the planning that has been determined by the company. Secondary data used in this study are sales data of PT Prospect Motor Cikarang's sales executive. Forming a prediction model using the C4.5 method. In C4.5 algorithm, entropy and information gain calculations are performed where the best-selling attribute is the destination attribute, while the class, model, transmission, income, leasing, tenor and discount as source attributes to obtain root nodes and other nodes. Based on the results of the classification using the C4.5 algorithm shows that the accuracy reached 67.5%, which shows that the C4.5 algorithm is suitable for measuring sales predictions for sales of Honda cars. Keywords : Data Mining, Product Sale, Decision Tree, Algoritma C4.5.
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
Sistem Pakar Diagnosis Penyakit Ginjal Dengan Menggunakan Metode Dempster Shafer Suherman Suherman; Zaenur Rozikin
Jurnal SIGMA Vol 9 No 4 (2019): Juni 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

Expert systems are computer-based systems that use knowledge, facts, and reasoning in solving problems that are usually only solved by an expert in a particular field. Expert systems provide added value to technology to help deal with an increasingly sophisticated information age. This expert system application produces an output in the form of the possibility of kidney disease suffered based on symptoms felt by the user. This system also displays the magnitude of the symptom's belief in the possibility of kidney disease suffered by the user. The amount of the trust value is the result of calculations using the Dempster Shafer method. Keywords : Kidney, Expert System, Dempster-Shafer Method

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