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All Journal JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA INOVTEK Polbeng - Seri Informatika Jurnal Sistem Cerdas Building of Informatics, Technology and Science Malcom: Indonesian Journal of Machine Learning and Computer Science The Indonesian Journal of Computer Science
Rice Novita
Universitas Islam Negeri Sultan Syarif Kasim Ria
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

 1 
Implementasi Algoritma Fuzzy C-Means menggunakan Model LRFM untuk Mendukung Strategi Pengelolaan Pelanggan Aini, Delvi Nur; Afdal, M.; Novita, Rice; Mustakim, Mustakim
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7616

Abstract

The same treatment of all customers will cause customers who are not so valuable to become value destroyers in the concept of Customer Relationship Management. Providing discounts and promos to all customers without differentiating customer segments has not provided significant benefits for a company. These two things are being experienced by BC 4 HNI Pekanbaru, so changes are needed in evaluating the strategies taken to maintain relationships with customers and form segments according to customer characteristics. Customer segments can be analyzed from sales transaction data. The purpose of this study is to manage and group sales transaction data in determining customer segmentation so that the strategy is more targeted. The analysis of customer transaction data was carried out by grouping the data using the Fuzzy C-means algorithm and the length, recency, frequency, monetary (LRFM) model, and AHP weighting.  The formation of the number of validated clusters of the silhouette index and ranking is carried out by multiplying the weight of AHP to find the customer lifetime value (CLV) so that it can be known which customer groups provide high value to the company. The result of this study is that BC 4 HNI Pekanbaru customers are grouped into 2 segments, namely the potential customer group which has a fairly frequent transaction value with an average monetary value of Rp. 2,802,495.00 and a fairly high number of transactions contribute greatly to the Company and the new customer group which means a new customer segment with uncertain funds, an average monetary of Rp. 104,567.00. Based on the segment, BC 4 HNI Pekanbaru can carry out a strategy in managing its customers according to the type of segment generated from this research.
Penerapan Machine Learning Pada Analisis Sentimen Twitter Sebelum dan Sesudah Debat Calon Presiden dan Wakil Presiden Tahun 2024 Dwinnie, Zairy Cindy; Novita, Rice
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7504

Abstract

The 2024 Presidential Election has become the hottest topic in the past two years. The KPU has confirmed that there are 3 candidates for President and Vice President. For this reason, as a momentum for voters to assess the 2024 Presidential and Vice Presidential candidates, the KPU is holding the 2024 Presidential Choice Debate which is based on Law Number 7 of 2017 concerning General Elections. Based on the information presented on the kpu.go.id page, the debate will be held 5 times with 3 presidential candidate debates and 2 vice presidential candidate debates. For this reason, it is necessary to carry out an analysis to find out how public sentiment is positive, negative, and neutral on Twitter towards the three candidates for President and Vice President in 2024 before and after the debate was held. The aim is to estimate public support or disapproval of the three candidate pairs. This research uses three algorithms as a comparison of classification accuracy, namely the Support Vector Machine algorithm, Random Forest, and Logistic Regression. Where the data used is tweet data on Twitter related to before and after the debate as many as 30 datasets with a total of 9000 data. From the classification results, the average accuracy obtained for the three algorithms, namely SVM and Random Forest, was 78%, and Logistic Regression was 79%. The highest polarity obtained from the classification of the three algorithms is in the positive class. This indicates that the Logistic Regression algorithm provides better performance in classifying Twitter sentiment regarding the 2024 presidential and vice presidential candidate pairs.
Penerapan Algoritma K-Means Menggunakan Model LRFM Dalam Klasterisasi Nilai Hidup Pelanggan Afifah, Tiara Afrah; Novita, Rice; Ahsyar, Tengku Khairil; Zarnelly, Zarnelly
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7605

Abstract

In implementing customer relationship management, there are still many companies that have not utilized CRM optimally as part of their business strategy. As is the case with UD Sandeni. UD Sandeni still has problems in managing its relationships with customers because UD Sandeni does not fully understand the difference between customer information that is profitable and unprofitable for the company's sustainability. UD Sandeni has used a system to manage customer transaction data. However, this system is only used to calculate profits and create bookkeeping for registered agents so that UD Sandeni does not have an in-depth understanding of the characteristics of its customers. To overcome this problem, the solution that can be applied is to use customer grouping techniques, such as clustering. Customer transaction data is processed using a clustering process with K-Means and LRFM. Test the validity of cluster results using DBI and calculate CLV values using AHP weights to produce cluster rankings. The results of this research obtained customer clustering which consists of 2 segments, namely cluster 1 which has the highest CLV value of 0.3171156 with a total of 298 customers and includes the High Value Loyal Customers segmentation, and cluster 2 with a CLV value of 0.1434054 with a total of 72 customers. which is included in the segmentation of uncertain new customers (uncertain lost customers).
Co-Authors Abror, Naufal Afandi, Rival Afdal, M.Afdal Afifah, Tiara Afrah Ahsyar, Tengku Khairil Aini, Delvi Nur Akhiril Anwar Harahap Al-Qadr, Nola Ardelia Andriawan, Ahmad Rizky Anisa Putri Auliani, Sephia Nazwa Azzahra, Aura Dwinnie, Zairy Cindy F. Safiesza, Qhairani Frilla Hamzah, Muhammad Luthfi Khairil Ahsyar, Tengku Kurniawan, Viki M Afdal M. Afdal Muhammad Iqbal Munzir, Medyantiwi Rahmawati Munzir, Medyantiwi Rahmawita Mustakim Mustakim Mustakim Mustofa, Asdar Novansyah, Rizky nursalisah, febi Pangestu, Prayugo Pertiwi, Tata Ayunita Pulungan, Joliando Putri, Suci Maharani Rahayu, Dwi Sri Ramadani, Faradila Rozanda, Nesdi Evrilayan Sasmita, Indah Vamilina, Vina Wibisono, Yudistira Arya Yuda, Afi Ghufran Zarnelly Zarnelly Zarnelly