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All Journal Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Malcom: Indonesian Journal of Machine Learning and Computer Science PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Aini, Delvi Nur
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Other

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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.
Implementation of Convolutional Neural Network (CNN) for Image Classification of Leaf Disease In Mango Plants Using Deep Learning Approach Rinanda, Puji Dwi; Aini, Delvi Nur; Pertiwi, Tata Ayunita; Suryani, Suryani; Prakash, Allam Jaya
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 2: PREDATECS January 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i2.872

Abstract

Plant diseases pose a serious threat to a country's economy and food security. One way to identify diseases in plants is through the visible features on their leaves. Farmers need to conduct an active examination of the condition of the leaves of plants to eradicate this disease. In this case, automatic recognition and classification of diseases of leaf crops is required in order to obtain an accurate identification. Digital image processing technology can be used to solve this problem. One effective approach is the Convolutional Neural Network (CNN). The trial image used a dataset consisting of 4000 images of mango leaf disease, namely Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, and Sooty Mould. This study aims to compare the accuracy of CNN, VGG16 and InceptionV3.  Architectural modeling uses these drawings to train and test models in recognizing and classifying mango leaf diseases. The results of modeling trials in the three scenarios were most optimally obtained by VGG16 with an accuracy of 96.87%, then InceptionV3 with an acquisition of 96.50% and CNN by 81%.
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.
Fuzzy Clustering-Based Grouping for Mapping the Distribution of Student Success Data Mustakim, Mustakim; Aini, Delvi Nur; Batubara, Ana Uzla; Erkamim, Moh.; Legito, Legito
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 3 No. 2 (2023): MALCOM October 2023
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v3i2.1227

Abstract

Learning activities are the main activity in the overall teaching and learning process in schools. This is because whether or not the achievement of educational goals depends on how the learning process is carried out by students. The uneven level of student success in learning is one of the problems in the school's efforts to realize the vision and mission of SMKN 5 Pekanbaru in preparing skilled graduates to be able to work in certain sectors by the public interest and the industrial world. In this study, mapping and grouping student grade data was carried out using the Fuzzy C-Means algorithm to provide information to the school in making the right decisions and optimizing the learning process. Furthermore, clustering was carried out in several experiments K=3 to K=7, and obtained the best validity value tested with the Silhouette Index of 0.4277 located at K=5. Then the distribution of cluster 5 on student score data was obtained with details, namely cluster 1 with a capacity of 1 student, cluster 2 with a capacity of 27 students, cluster 3 with a capacity of 1 student, cluster 4 with a capacity of 10 students, cluster 5 with a capacity of 23 students.
ANALISIS SEGMENTASI PELANGGAN MENGGUNAKAN MODEL LRFM DAN TEKNIK CLUSTERING Aini, Delvi Nur; Ramadhani, Indah; Erlangga, Dwi
Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Vol 9, No 2 (2023): Agustus
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/rmsi.v9i2.24311

Abstract

Di era teknologi seperti sekarang ini, perkembangan teknologi informasi tak hanya mempengaruhi banyak bidang salah satunya didunia bisnis. Faktor terpenting dalam mencapai kesuksesan bisnis yaitu dengan mengelola hubungan yang baik dengan pelanggan. Twin Cake Pekanbaru merupakan salah satu toko retail dimana pencatatan datanya masih dilakukan secara manual. Data transaksi yang terjadi setiap hari ini cukup banyak yang menyebabkan banyaknya tumpukan data yang seharusnya dapat diolah lebih lanjut agar menghasilkan informasi yang berguna.  Length, recency, frequency, monetary atau biasa dikenal sebagai LRFM, adalah teknik yang digunakan untuk menghitung nilai pelanggan berdasarkan riwayat transaksi mereka. Tujuan dari penelitian ini adalah untuk mengelompokkan data pelanggan berdasarkan model LRFM dan mengestimasi nilai Customer Lifetime Value (CLV) menggunakan algoritma clustering K-Means. Hasil penelitian menunjukkan bahwa dalam pengelompokan data pelanggan dengan model LRFM, algoritma K-Means menunjukkan performa yang baik dalam melakukan klasterisasi data pelanggan. Hal ini terbukti dari nilai validitas DBI terbaik yang diperoleh, yaitu 0,2500 pada cluster 3. Selanjutnya, cluster 3 memiliki peringkat CLV tertinggi dalam penelitian ini, dengan nilai CLV sebesar 0,918. Cluster ini ditandai dengan simbol LRFM L↑R↓F↑M↑, yang mengindikasikan bahwa pelanggan dalam cluster ini memiliki tingkat loyalitas yang tinggi. Peringkat CLV kedua ditempati oleh cluster 2 dengan nilai CLV sebesar 0,213, dan peringkat CLV ketiga ditempati oleh cluster 1 dengan nilai CLV sebesar 0,074.
Co-Authors Batubara, Ana Uzla Erlangga, Dwi legito, Legito M. Afdal Moh. Erkamim Mustakim Mustakim Pertiwi, Tata Ayunita Prakash, Allam Jaya Ramadhani, Indah Rice Novita Rinanda, Puji Dwi Suryani Suryani