scholarly journals Penerapan Agglomerative Hierarchical Clustering Untuk Segmentasi Pelanggan

2020 ◽  
Vol 18 (1) ◽  
pp. 75
Author(s):  
Widyawati Widyawati ◽  
Wawan Laksito Yuly Saptomo ◽  
Yustina Retno Wahyu Utami
Keyword(s):  
Hierarchical Clustering ◽  
Cluster Formation ◽  
Customer Segmentation ◽  
Marketing Strategies ◽  
Manhattan Distance ◽  
Agglomerative Hierarchical Clustering ◽  
Customer Group ◽  
Silhouette Coefficient ◽  
Customer Type ◽  
The Right

As more businesses emerge, companies need to have the right marketing strategy to provide the best service to customers. The first step is to know the type of customer and make appropriate marketing strategies according to the type of customer. In this research, it is proposed for clustering customers so that an appropriate strategy for that customer group can be determined. The method used for cluster formation uses Agglomerative Hierarchical Clustering with Average Linkage approach and distance determination using Manhattan Distance. The variables in this research are Recency, Frequency, and Monetary (RFM). The results of testing using the Silhouette coefficient show that the results of 7 clusters are the best results when compared with 2 clusters up to 20 clusters because they have the smallest minus value. Based on the results of the Silhoutte coefficient, customer segmentation uses 7 clusters with each cluster representing the existing customer type.

Implementation of Clustering Algorithm Method for Customer Segmentation

2020 ◽  
Vol 17 (2) ◽  
pp. 1388-1395
Author(s):  
Nurmalasari ◽  
Anna Mukhayaroh ◽  
Siti Marlina ◽  
Sari Hartini ◽  
Sri Muryani ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Customer Segmentation ◽  
Marketing Strategies ◽  
Goods And Services ◽  
Customer Group ◽  
Digital Era ◽  
The Everyday ◽  
The Right ◽  
Online Shops ◽  
Potential Customers

The intense competition in the sale of goods and services in the digital era of e-commerce requires to manage customers optimally. Some online shops try to improve their marketing strategies by classifying their customers. This study aims to determine potential customers, namely loyal customers. Potential customers can be determined by customer segmentation. Sampling from several online shops in Indonesia. The model used for segmentation is RFM (Recency, Frequency, and Monetary) and data mining techniques, namely clustering method with the K-Means algorithm. The results of this segmentation research divide the customer into 2 clusters. The best number of clusters is determined based on the Davies Bouldin index. The first cluster is cluster 0 consisting of 261 customers with RFM Score between 111–543. The first cluster includes the Everyday Shopper group. The second cluster, cluster 1 consists of 102 customers with RFM Score 443–555. The second cluster includes the Golden Customer group. With the existence of research on customer segmentation, it is expected to help in grouping customers so that companies can determine the right strategy for each group of customers.

scholarly journals Pemodelan Cluster Loyalitas Customer Menggunakan Algoritma K-Means Dengan Parameter LRIFMQ

Jurnal INFORM ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 54
Author(s):  
Aloysius Matz Teguh Utomo
Keyword(s):  
Calculated Data ◽  
Optimal Number ◽  
Customer Segmentation ◽  
Number Of Clusters ◽  
Silhouette Coefficient ◽  
Customer Lifetime ◽  
One Year ◽  
The Right ◽  
Hierarchy Process ◽  
A Company

Loyal customers are one of the factors that determine the development of a business. Therefore, businesses need a strategy to keep customers loyal, even making customers who were previously less loyal to become more loyal. The strategy used must be right on target according to customer segmentation. The purpose of this paper is to model a cluster of customer loyalty to help businesses in making the right decisions of marketing strategy. Segmentation is done using the k-means algorithm with LRIFMQ (length, recency, interval, frequency, monetary, quantity) as parameters, and the CLV (customer lifetime value) of each cluster is calculated. Data obtained from PT. XYZ (a company engaged in food processing) for one year (1 January 2019 - 31 December 2019), with 337.739 transactions, and 26.683 customers. AHP (analytical hierarchy process) method is used for LRIFMQ weighting because this method has a consistency index calculation. The silhouette coefficient is used to calculate the cluster quality and determine the optimal number of clusters. The best results are obtained with the silhouette coefficient value of 0,632904 with the number of clusters 6.

scholarly journals PENERAPAN METODE K-MEANS DENGAN METODE ELBOW UNTUK SEGMENTASI PELANGGAN MENGGUNAKAN MODEL RFM(Recency, Frequency, & Monetary)

Repositor ◽  
2020 ◽  
Vol 2 (7) ◽  
pp. 945
Author(s):  
Adnan Burhan Hidayat Kiat ◽  
Yufiz Azhar ◽  
Vinna Rahmayanti
Keyword(s):  
Euclidean Distance ◽  
Motor Vehicle ◽  
Cluster Formation ◽  
Optimal Number ◽  
Customer Segmentation ◽  
Manhattan Distance ◽  
Automotive Company ◽  
Rfm Model ◽  
Coefficient Method

Segmentasi pelanggan pada perusahaan merupakan tindakan yang dapat mempermudah perusahaan dalam mengambil keputusan ke depan. Pada penelitian ini data yang digunakan berasal dari perusahaan otomotif, PT Hasjrat Abadi Ambon. Data yang dipakai terdiri dari data transaksi dan pelanggan kendaraan bermotor. Penerapan model RFM dapat mengelompokkan pelanggan-pelanggan berdasarkan nilai variabel Recency, Frequency dan Monetary. Hasil dari model RFM akan memperoleh status baru pada tiap pelanggan dari skala terbaik sampai terburuk. Pelanggan yang telah memiliki status akan dikelompokkan menggunakan metode K-Means menjadi beberapa Cluster(kelompok). Dalam menentukan jumlah Cluster yang optimal maka diterapkan metode Elbow. Algoritma yang digunakan dalam pembentukan Cluster terdiri dari Euclidean Distance dan Manhattan Distance. Kedua algoritma akan dibandingkan kualitas pembentukan Clusternya menggunakan metode Silhoutte Coefficient. Hasil yang diberikan pada penelitian ini berupa data yang terbagi atas 5 kelompok dengan dilakukannya lima kali pengujian untuk menentukan centroid yang unggul. Cluster yang unggul akan dibuatkan visualisasi datanya untuk memudahkan perusahaan dalam mengambil keputusan. Berdasarkan penerapan Silhoutte Coefficient, algoritma yang lebih unggul yaitu Manhattan Distance dengan nilai s(i) sebesar 0.152695. Customer segmentation at the company is an action that can facilitate the company in making decisions going forward. In this study the data used came from an automotive company, PT Hasjrat Abadi Ambon. The data used consists of transaction data and motor vehicle customers. The application of the RFM model can classify customers based on the value of the Recency, Frequency and Monetary variables. The results of the RFM model will obtain a new status on each customer from the best to the worst scale. Customers who already have status will be grouped using the K-Means method into several Clusters (groups). In determining the optimal number of Clusters, the Elbow method is applied. The algorithm used in Cluster formation consists of Euclidean Distance and Manhattan Distance. The two algorithms will be compared the quality of the Cluster formation using the Silhoutte Coefficient method. The results given in this study are in the form of data divided into 5 groups by conducting five tests to determine superior centroids. Excellent clusters will be made of data visualization to facilitate the company in making decisions. Based on the application of Silhoutte Coefficient, a superior algorithm is Manhattan Distance with value s(i) : 0.152695.

scholarly journals Analisa Hasil Pengelompokan Wilayah Kejadian Non-Kebakaran Menggunakan Agglomerative Hierachical Clustering di Semarang

2021 ◽  
Vol 15 (2) ◽  
pp. 63
Author(s):  
Desy Exasanti ◽  
Arief Jananto
Keyword(s):  
Hierarchical Clustering ◽  
Manhattan Distance ◽  
Agglomerative Hierarchical Clustering ◽  
Single Linkage ◽  
Bottom Up ◽  
Environment Analysis ◽  
Complete Linkage ◽  
Average Linkage

Abstrak−Klasterisasi merupakan metode pengelompokan dari data yang sudah diketahui label kelasnya untuk menemukan klaster baru dari hasil observasi. Dalam klasterisasi banyak metode yaitu metode terpusat, hirarki, kepadatan dan berbasis kisi, namun dalam penelitian yang dilakukan ini dipilih metode berbasis hirarki. Metode hirarki ini bekerja melakukan pengelompokan objek dengan membentuk hirarki klaster namun bukan berarti selalu digambarkan dengan hirarki dalam organsasi. Dipilihnya Agglomerative Hierarchical Clustering dimana merupakan jenis dari bawah ke atas atau biasa disebut (bottom-up) dalam metode ini objek yang akan diuji dianggap sebagai objek tunggal sebagai klaster dan lalu dilakukan iterasi untuk menemukan klaster-klaster yang lebih besar. Data yang akan digunakan adalah data non-kebakaran pada Dinas Pemadam Kebakaran Kota Semarang ynng mana akan dilakukan pengelompokan wilayah penanganan non-kebakaran. Dinas Pemadam Kebakaran melakukan penanganan bukan hanya kebakaran saja namun ada banyak hal yang sebenarnya dapat ditangani oleh petugas pemadam kebakaran, kejadian non-kebakaran ada beberapa seperti evakuasi reptil, evakuasi kucing, penyelamatan korban kecelakaan dan lain sebagainya. Dari data non-kebakaran dari 16 kecamatan di Kota Semarang pada tahun 2019 akan dilakukan uji menggunakan tiga algoritma yaitu Single Lingkage, Average Linkage dan Complete Linkage . Adapun dari algoritma Single Linkage dilakukan prosedur pemusatan dari jarak terkecil antar objek data, algoritma Average Linkage dilakukan prosedur dari jarak rata-rata objek data, sedangkan jika algoritma Complete Linkage dilakukan prosedur pemusatan dari jarak yang terbesar. Implementasi dan visualiasi dari data uji coba yang dilakukan di penilitian ini menggunakan tools WEKA 3.8.4, Wakaito Environment Analysis for Knowledge atau yang biasa dikenal dengan WEKA ini merupakan software yang menggunakan bahasa pemrograman java. Dari dataset 380 data diambil sampel 100 data untuk diuji mengunakan WEKA menggunakan metode perhtungan jarak Manhattan Distance dengan 3 cluster. Hasil dari data uji coba dapat divisualisasikan dengan visualisasi dendogram pada fitur visualize tree  dan jika dilakukan visualisasi dalam bentuk grafik dapat dilakukan menggunakan fitur visualize clusters assignment.

scholarly journals ANALISIS SEGMENTASI PELANGGAN MENGGUNAKAN KOMBINASI RFM MODEL DAN TEKNIK CLUSTERING

2018 ◽  
Vol 2 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Beta Estri Adiana ◽  
Indah Soesanti ◽  
Adhistya Erna Permanasari
Keyword(s):  
Small And Medium Enterprises ◽  
Cluster Formation ◽  
Optimal Number ◽  
Customer Segmentation ◽  
Marketing Strategies ◽  
Monetary Analysis ◽  
Data Mining Approach ◽  
Rfm Model ◽  
Customer Services ◽  
Medium Enterprises

Intense competition in the business field motivates a small and medium enterprises (SMEs) to manage customer services to the maximal. Improve of customer royalty by grouping cunstomers into some of groups and determining appropriate and effective marketing strategies for each group. Customer segmentation can be performed by data mining approach with clustering method. The main purpose of this paper is customer segmentation and measure their loyalty to a SME’s product. Using CRISP-DM method which consist of six phases, namely business understanding, data understanding, data preparatuin, modeling, evaluation and deployment. The K-Means algorithm is used for cluster formation and RapidMiner as a tool used to evaluate the result of clusters. Cluster formation is based on RFM (recency, frequency, monetary) analysis. Davies Bouldin Index (DBI) is used to find the optimal number of clusters (k). The customers are divided into 3 clusters, total of customer in first cluster is 30 customers who entered in typical customer category, the second cluster there are 8 customer whho entered in superstar customer and 89 customers in third cluster is dormant cluster category.

scholarly journals Penerapan Penerapan Data Mining dalam Mengelompokkan Data Riwayat Akademik Sebelum Kuliah dan Data Kelulusan Mahasiswa menggunakan Metode Agglomerative Hierarchical Clustering (AHC)

2021 ◽  
Vol 3 (1) ◽  
pp. 62-71
Author(s):  
BANU HARLI TRIMULYA SUANDI AS
Keyword(s):  
Data Mining ◽  
Hierarchical Clustering ◽  
Euclidean Distance ◽  
Data Cleaning ◽  
Data Transformation ◽  
Agglomerative Hierarchical Clustering ◽  
Silhouette Coefficient

Fakultas Teknologi Industri Universitas Ahmad Dahlan setiap tahun menerima mahasiswa baru dalam jumlah besar. Disisi lain, kelulusan mahasiswa yang lulus tepat waktu masih rendah sehingga rasio dosen dan mahasiswa semakin besar. Akibat lainnya adalah pengguna fasilitas kampus melebihi kapasitas dan kegiatan belajar mengajar menjadi tidak efektif, sehingga diperlukan tahapan berupa pengelompokan data mahasiswa berdasarkan data akademik sebelum kuliah dan data kelulusan mahasiswa yang dilakukan dengan teknik data mining untuk mengetahui kelompok-kelompok mahasiswa yang lulus tepat waktu di Fakultas Teknologi Industri.                 Penelitian ini menggunakan metode pengelompokan hierarki (Hierarchical Clustering). Tahapan dalam penelitian ini dari Load Data, Cleaning Data, Transformation Data dengan metode One Hot Encoding, Euclidean Distance, dan pengelompokan Agglomerative Hierarchical Clustering. Pengujian hasil cluster menggunakan Silhouette Coefficient, serta dilakukan evaluasi pola, dan representasi pengetahuan.                 Penelitian menghasilkan 158 mahasiswa yang direkomendasikan dan semuanya berasal dari Pulau Jawa dan rata-rata nilai matematika >= 80 pada dataset Informatika, Industri, dan Elektro, dan >=67 untuk Kimia. Diperoleh data yang direkomendasikan dengan jumlah data berturut-turut 43, 24, 19, dan 72 data. Pengujian Silhouette Coefficient memperoleh hasil yang sangat bagus dengan nilai berturut-turut sesuai prodi sebesar 0,868, 0,883, 0,879, dan 0,873.

scholarly journals Unsupervised Twitter Sentiment Analysis on The Revision of Indonesian Code Law and the Anti-Corruption Law using Combination Method of Opinion Word and Agglomerative Hierarchical Clustering

2020 ◽  
Vol 8 (1) ◽  
pp. 200-220
Author(s):  
Nur Restu Prayoga ◽  
Tresna Maulana Fahrudin ◽  
Made Kamisutara ◽  
Angga Rahagiyanto ◽  
Tahegga Primananda Alfath ◽  
...  
Keyword(s):  
Social Media ◽  
Hierarchical Clustering ◽  
Confusion Matrix ◽  
Combination Method ◽  
Agglomerative Hierarchical Clustering ◽  
Single Linkage ◽  
Silhouette Coefficient ◽  
Error Ratio ◽  
Points Of View ◽  
Twitter Users

The rejection on ratification of the revision of Indonesian Code Law or known as RKUHP and Corruption Law raises several opinions from various perspectives in social media. Twitter as one of many platforms affected, has more than 19.5 million users in Indonesia. Twitter is one of many social media in Indonesia where people can share their views, arguments, information, and opinions from all points of view. Since Twitter has a great diversity of users, it needs a system which is designed to determine the opinion tendency towards the problems or objects. The purpose of this study is to analyze the sentiment of Twitter users' tweets to reject the revision of the Law whether they have positive or negative sentiments using the Agglomerative Hierarchical Clustering method. The data that being used in this study were obtained from the results of crawling tweets based on hashtag (#) (#ReformasiDikorupsi). The next stage is pre-processing which consists of case folding, tokenizing, cleansing, sanitizing, and stemming. The extraction features Opinion words and Term Frequency (TF) which performs the process automatically. In the clustering stage, two clusters use three approaches; single linkage, complete linkage and average linkage. In the accuracy calculation phase, the writer uses the error ratio, confusion matrix, and silhouette coefficient. Therefore, the results are quite good. From 2408 tweets, the highest accuracy results are 61.6%.

scholarly journals GIS implementation and classterization of potential blood donors using the agglomerative hierarchical clustering method

2021 ◽  
Vol 3 (2) ◽  
pp. 44-54
Author(s):  
Pratama Ryan Harnanda ◽  
Natalia Damastuti ◽  
Tresna Maulana Fahrudin
Keyword(s):  
Hierarchical Clustering ◽  
Blood Donors ◽  
Research Data ◽  
Blood Type ◽  
Red Cross ◽  
Agglomerative Hierarchical Clustering ◽  
City Center ◽  
Silhouette Coefficient ◽  
The One ◽  
Cluster Quality

The blood needs of PMI (Indonesian Red Cross) in the Surabaya City area are sometimes erratic, the problem occurs because the amount of blood demand continues to increase while the blood supply is running low. As the main objective of this research, data mining was applied to able to cluster the blood donor data in UTD-PMI Surabaya City Center which was to determine both potential and no potential donors and also visualize the pattern of donor distribution in Geographic Information System (GIS). Agglomerative Hierarchical Clustering was applied to obtain the clustering result from the existing of 8757 donors. The experiment result shown that the cluster quality was quite good which reached 0.6065410 using Silhouette Coefficient. We concluded the one interesting analysis that private male employees with blood type O, and live in the eastern part of Surabaya City are the most potential donors.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

Influence of social media marketing towards purchase of residential property

2019 ◽  
Vol 118 (6) ◽  
pp. 145-149
Author(s):  
A. Ekanthalingam ◽  
Dr. A. Gopinath
Keyword(s):  
Social Media ◽  
High Speed ◽  
Word Of Mouth ◽  
Marketing Strategies ◽  
Social Media Marketing ◽  
Purchase Decision ◽  
Residential Property ◽  
Customer Delight ◽  
The Right ◽  
Potential Customers

‘Marketing’ is not just an activity. It is a process, a philosophy and a phenomenon. The evolution of marketing has produced tremendous benefits to business and end consumers. The innovation in this field has been steady and yet at high speed. From ‘word of mouth advertising’ which was the only option earlier we are now at the mercy of what consumers are sharing about their experience on the internet. Social Media has become more powerful than what we think and this article shows how we can leverage this to benefit the top-line and customer delight. We dive deep to understand the influence Social Media can create towards purchase of residential property. As much complex it is to make the purchase decision of a property, it is equally difficult for marketers to send the right message to their target audience. Through this article, we are trying to see how marketers have transformed their traditional marketing strategies to address the needs of the millennial population, who are the most potential customers for property purchase.

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