Riset

Bidang kajian dan riset Feri Sulianta dalam  arsip elektronik sebagai berikut:

1. Mining food industry's multidimensional data to produce association rules using apriori algorithm as a basis of business strategy


Abstract:
The food industry sell a range of product variations. The company want to take advantage to build business strategy from huge information which is stored in data warehouse. In this case, data mining technology needs to be implemented to explore valuable information on transactional data to assess customer's preferences for products sold as a business strategy.
Date of Conference: 20-22 March 2013
Date Added to IEEE Xplore: 05 August 2013
ISBN Information:
INSPEC Accession Number: 13697620
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2. Integrated model development of horticulture and forecast commodity using web based geographic information system and SMS Gateway technology

Abstract:
Agro industries are the industry's most promising and holds great promise for farmers in Indonesia. However, there is a pile of problems in the agricultural industry in Indonesia, namely fluctuations in prices when the implementation of the harvest. These constraints caused by increased yields while consumer demand has not changed, as a result of certain commodity farmers suffered losses. The major causes of products instability is because lack of information for the amount of the supply needs of the crop for farmers. For this reason, information management of agriculture (E-Agriculture) utilizing simulation production forecasts by combining GIS technology and SMS Gateway will be modeled as a basis to solve the problems of agriculture in province of West Java, especially in the district of Bandung and Garut in regulating the balance of plant production horticulture. Simulation web based Geographic Information System (GIS) is developed by considering simulation components, GIS and SMS Gateway Technology, and Spatial Data Layer.
Date of Conference: 26-27 April 2016
Date Added to IEEE Xplore: 29 September 2016
ISBN Information:
Publisher: IEEE 
  
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3. CUSTOMER PROFILING PADA SUPERMARKET MENGGUNAKAN ALGORITMA K-MEANS DALAM MEMILIH PRODUK BERDASARKAN SELERA KONSUMEN DENGAN DAYA BELI MAKSIMUM

Oleh: Feri Sulianta
Abstraksi: Sebuah supermarket dengan sistem informasi berbasiskan komputer memiliki data transaksi dan data master yang di kelola dengan baik. Manajemen ingin membuat strategi bisnis
berdasarkan penambangan historis data transaksi yang dimilikinya. Salah satunya dengan mencari tahu segmentasi pola perilaku pelanggan atau customer profiling yang memiliki daya beli tinggi terhadap barang-barang tertentu. Untuk itu akan dilakukan pencarian informasi berharga
terhadap analisa data mining dengan aturan klasterisasi menggunakan algoritma K-Means dalam mengelompokan pola belanja konsumen terhadap barang.
Kata kunci : Data Mining, K-Means, Clustering, Customer Profiling 

Jurnal Ilmiah Teknologi Informasi Terapan, Vol 1, No 1 (2014), ISSN: 2407-3911

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4. MEMBANGUN ATURAN ASOSIASI MENGGUNAKAN ALGORITMA APRIORI UNTUK MENGETAHUI HUBUNGAN KRIMINALITAS DENGAN FAKTOR DEMOGRAFI SEBAGAI PERTIMBANGAN MEMBUAT ATURAN KEPENDUDUKAN

Abstrak:



Pendekatan penambangan data digunakan sebagai teknik untuk mendapatkan informasi-informasi penting yang dapat dijadikan bahan penunjang dalam suatu pengambilan keputusan. Pada penelitian ini, metode asosiasi diimplementasikan untuk mendapatkan hubungan sebab akibat yang ada pada data kependudukan, terutama untuk mendapatkan pola hubungan kejadian kriminalitas dengan karakteristik penduduk. Dalam kasus ini, didapati data-data dengan nilai yang tidak valid dan kendala ketidaklengkapan data, yang harus ditangani dengan seksama sehingga layak untuk dibangunkan aturan asosiasi. Algoritma apriori diterapkan pada metode ini, karena algoritma ini terbukti mampu menghasilkan aturan dengan tingkat akurasi tinggi  dalam membangun pola keterhubungan antar atribut. Aturan asosiasi akan dijadikan dasar dalam membuat kebijakan sehubungan masalah kependudukan.  

Kata kunci :
apriori, aturan asosiasi, menambang data, demografi, kriminalitas, kependudukan

Abstract:
Data mining approach is used as a technique to obtain important information that can be used as supporting material in a decision . in this study, the association method is implemented to obtain relationship existing on population data , especially to get the relationship patterns of crime events related to characteristics of the population. In this case , invalid data and missing values must be handled carefully before building association rules. Apriori algorithm is applied to this method, since the algorithm is proven to generate rules with high degree of accuracy in establishing the pattern of connectivity between attributes . Association rules will be used as a basis for making policy related to the population problem .

Keywords :
apriori, association rule, data mining, , demographics, crime, populations

Seminar Nasional Telekomunikasi dan Informatika (SELISIK 2016) Bandung, 28 Mei 2016.ISSN : 2503-2844


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5. MINING TRANSACTIONAL DATA TO PRODUCE EXTENDED ASSOCIATION RULES USING COLLABORATIVE APRIORI, FSA-RED AND M5P PREDICTIVE ALGORITHM AS A BASIS OF BUSINESS ACTIONS

 AbstractThere are large amounts of transactional data which showed consumer shopping cart at a store that sells more than 150 types of products. In this case, the company is utilizing these data in making business action. In previous studies, the data that has a lot of attributes and record data reduction algorithms handled by the FSA Red (Feature Selection for Association Rules) are then mined using Apriori algorithm. The resulting association rules have high levels of accuracy and excellent test results, which rely more than 90%.
In this study, the association rules generated in previous research will be updated by using prediction algorithms M5P, so that the reliability of association rules can be updated for the next day forward.             Furthermore, some data mining technique such as: clustering and time series pattern will be implemented to examine the truth and to extend the validity of association rules which were built. It can be concluded that the association rules were established after will generate strong association rules with confidence equal or higher than 70% and the truth of the rules can be seen from the time series pattern on each group of goods which are then used as the basis of business actions.

Keyword- Association Rules, Apriori, Confidence, Clustering, Data Reduction, FSA-Red Algorithm, M5P, Time Series Patterns, Support

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