Knowledge Discovery in Databases (KDD) as Tools for Developing Customer Relationship Management as External Uncertain Environment: A Case Study with Reference to State Bank of India

Dash, Priyaranjan; Pattnaik, Sabyasachi; Rath, Biswaranjan

doi:10.17485/ijst/2016/v9i4/82902

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…Knowledge Discovery in Database (KDD) adalah proses mencari informasi yang berguna dari basis data besar menggunakan algoritma data mining dengan ukuran dan threshold yang ditentukan [13]. Hasil yang diperoleh dari proses ini digunakan sebagai knowledge base yang bertujuan untuk pengambilan keputusan.…”

Section: Knowledge Discovery In Database (Kdd)unclassified

Penerapan Data Mining Dalam Mencari Pola Asosiasi Data Tracer Study Menggunakan Equivalence Class Transformation (ECLAT)

Amri

Nazir

Haerani

et al. 2022

JNKTI

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Abstrak - Tracer study adalah sebuah pendekatan yang diterapkan universitas untuk memperoleh informasi tentang kemungkinan kelemahan dalam proses pendidikan dan proses pembelajaran yang menjadi dasar perencanaan aktivitas untuk penyempurnaan di masa mendatang. Pada Universitas Islam Negeri Sultan Syarif Kasim Riau belum pernah ada tracer study yang komprehensif dan terstruktur dalam ruang lingkup universitas. Tracer study yang dilakukan hanya dalam lingkup program studi dan hanya dilaksanakan menjelang proses akreditasi prodi. Tidak ada tracer study yang rutin dilakukan di tingkat program studi dan universitas setiap tahunnya. Pada penelitian ini akan berfokus kepada penerapan data mining untuk mencari pola asosiasi pada data tracer study menggunakan Equivalence Class Transformation (ECLAT). Dari hasil penelitian terdapat 4 pola yang memenuhi support 13% dan confidence 80% dengan pengujian lift rasio 1. Pola tersebut diantaranya Jika ipk antara 3 – 3,5 dan gaji pertama dibawah 3 juta dan laki-laki maka status kelulusan tidak tepat waktu dan “masa tunggu mendapatkan pekerjaan pertama kurang dari 6 bulan” dengan support 17% dan confidence 84%. Jika ipk antara 3 – 3,5 dan perempuan maka “masa tunggu mendapatkan pekerjaan pertama kurang dari 6 bulan” dan hubungan pekerjaan dengan jurusan sesuai dengan support 14 % dan confidence 100%.Kata kunci: Tracer Study, Data Mining, Asosiasi, Equivalence Class Transformation, Eclat Abstract - Tracer study is an approach applied by universities to obtain important information in the education and learning process which is the basis for planning activities for future improvement. At UIN SUSKA University there has never been a comprehensive and structured tracer study within the scope of the university. There is no routine tracer study conducted at the study program and university level every year. This research will focus on the application of data mining to find association rules in tracer study data using ECLAT. From the research results, there are 4 patterns that meet the support of 13% and 80% confidence with a lift ratio test 1. The patterns include If the ipk is between 3 - 3.5 and the first salary is below 3 million and male then the graduation status is not on time and the waiting period to get the first job is less than 6 months with 17% support and 84% confidence. If the ipk is between 3 - 3.5 and female, then the waiting period to get the first job is less than 6 months and the job relationship with the major is in accordance with the support of 14% and confidence 100%.Keywords: Tracer Study, Data Mining, Asosiasi, Equivalence Class Transformation, Eclat

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Section: Knowledge Discovery In Database (Kdd)unclassified

Penerapan Data Mining Dalam Mencari Pola Asosiasi Data Tracer Study Menggunakan Equivalence Class Transformation (ECLAT)

Amri

Nazir

Haerani

et al. 2022

JNKTI

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“…In the context of competing bankers who are practically performing with almost homogenous services, the customers of one bank are left with manifold options to switch over to other banks in search of better services. The banks have to perform their banking operations with the likelihood risk of the customer switching over at any given point of time which might result in a decline in revenue (Dash, Pattnaik & Rath, 2016). In order to stop or minimize this possibility of customer refraction; bankers have to come out with customer-centric strategic decision.…”

Section: Importance Of the Research Problemmentioning

confidence: 99%

Customer Relationship Management Practices and Employee Sensitivities of Private Sector Banks: An Analysis in Indian Context

Sultana

Mpanme

Ahmed

2022

BER

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This paper aims to scrutinize bank employees' perspectives on customer relationship management (CRM) practices in the banking sector, particularly private sector commercial banks operating in Goalpara District of Assam, India. The research plan consists of experimental in nature whereby different aspects of CRM in the banking sector have been extracted from the previous literature and tested on a sample size 24 number of employees working in the banks selected taking 3 each from 8 branches. Based on Bank Service Quality (BSQ) scale, variables were selected and analysed with the help of descriptive statistics and ranking analysis of identified parameters was utilized with the help of the Garret ranking technique. The study found that the loan facility and ATM facility along with service variety are found as the major product-related factors; timely services with trustworthy behaviour of the employees and the quality of services along with procedural simplicity and convenience is the most important service-related factors for evolving an effective CRM practice. The employee perceptions on the personal relationship of the bank officials with their customers’ staples in selecting the banks and retaining the customers.

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“…KDD refers to the extensive process of discovering knowledge in data with the application of DM methods such as statistics, machine learning, artificial intelligence, and data visualisation [14]. In overall, the KDD consists a total of nine steps, which are both iterative and interactive in essence [42].…”

Section: Kddmentioning

confidence: 99%

“…Interactive, on the other hand, refers to the end user being able to be involved in each step. The KDD steps involved include (1) Establishing the application domain (2) Building a target data set (3) Data cleaning and pre-processing (4) Data transformation (5) Selecting the suitable DM task [50] Selecting the DM algorithm (7) Applying the DM algorithm (8) Evaluation and interpretation of mined data (9) Consolidation of newly created knowledge [14].…”

Section: Kddmentioning

confidence: 99%

A review of data mining in knowledge management: applications/findings for transportation of small and medium enterprises

2020

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A core subfield of knowledge management (KM) and data mining (DM) constitutes an integral part of the knowledge discovery in database process. With the explosion of information in the new digital age, research studies in the DM and KM continue to heighten up in the business organisations, especially so, for the small and medium enterprises (SMEs). DM is crucial in supporting the KM application as it processes the data to useful knowledge and KM role next, is to manage these knowledge assets within the organisation systematically. At the comprehensive appraisal of the large enterprise in the transportation sector and the SMEs across various industries-it was gathered that there is limited research case study conducted on the application of DM-KM on the transportation SMEs in specific. From the extensive review of the case studies, it was uncovered that majority of the organisations are not leveraging on the use of tacit knowledge and that the SMEs are adopting a more traditional use of ICTs to its KM approach. In addition, despite DM-KM is being widely implemented-the case studies analysis reveals that there is a limitation in the presence of an integrated DM-KM assessment to evaluate the outcome of the DM-KM application. This paper concludes that there is a critical need for a novel DM-KM assessment plan template to evaluate and ensure that the knowledge created and implemented are usable and relevant, specifically for the SMEs in the transportation sector. Therefore, this research paper aims to carry out an in-depth review

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Knowledge Discovery in Databases (KDD) as Tools for Developing Customer Relationship Management as External Uncertain Environment: A Case Study with Reference to State Bank of India

Cited by 5 publications

References 17 publications

Penerapan Data Mining Dalam Mencari Pola Asosiasi Data Tracer Study Menggunakan Equivalence Class Transformation (ECLAT)

Penerapan Data Mining Dalam Mencari Pola Asosiasi Data Tracer Study Menggunakan Equivalence Class Transformation (ECLAT)

Customer Relationship Management Practices and Employee Sensitivities of Private Sector Banks: An Analysis in Indian Context

A review of data mining in knowledge management: applications/findings for transportation of small and medium enterprises

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