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All Journal Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Journal of Computer Networks, Architecture and High Performance Computing Jurnal Informatika Teknologi dan Sains (Jinteks)
Ahmad Faisol
Institut Teknologi Nasional Malang
Computer Science & IT Control & Systems Engineering Education

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Researchers Productivity Level Clustering Based On H-Index and Citation Using The Fuzzy C-Means Algorithm Mira Orisa; Ahmad Faisol
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 1 (2023): Article Research Volume 5 Issue 1, January 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i1.1984

Abstract

The fuzzy C-Means algorithm is a partition-based clustering algorithm.Fuzzy C-Means is very helpful in modeling data whose distribution has outliers. Outliers are where there is a data object that is far apart from the existing clusters. Fuzzy C-Means groups data by minimizing the membership function of a data set. so that each piece of data can be a member of more than one group. In this study, the dataset used was the paper citation vs. H-index dataset in the Kaggle.com repository. This dataset is known to have outliers in fuzzy C-Means and has better performance compared to the K-Means and K-Medoid algorithms in modeling datasets that have outliers.
PERANCANGAN WEBSITE COMPANY PROFILE MENGGUNAKAN DESIGN SCIENCE RESEARCH METHODOLOGY (DSRM) Mira Orisa; Ahmad Faisol; Mochammad Ibrahim Ashari
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 5 No 1 (2023): EDISI 15
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v5i1.2576

Abstract

The development of software and hardware in the field of information technology makes access to information unlimited. A website is an internet application that can be accessed by almost all people today. The benefit of a contractor company like PT. Dinamika Indonesia Presisi using the company profile website is that it notifies service users of the company's existence while also increasing the company's attractiveness to potential users.The company profile website contains a menu about us, clients, services, a portfolio, and a contact us menu. Information system design uses the design science research methodology (DSRM) method. This method combines principles, practices, and procedures so that it can make it easier for researchers to focus on solutions to problems that exist at PT. Dinamika Indonesia Presisi and help design the creation of the company profile web application for the company. Web applications are built using the HTML and CSS programming languages to make the website look more attractive.
Penerapan Algoritma K-Means Clustering Untuk Sistem Segmentasi Pelanggan Berdasarkan Model RFM (Studi Kasus: PT Sehati Bangunan Abadi) Lusi Damayanti; Ahmad Faisol; Nurlaily Vendyansyah
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 9, No 1 (2026): Januari
Publisher : Akademi Ilmu Komputer Ternate

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v9i1.414

Abstract

Abstrak: Penelitian ini bertujuan untuk melakukan segmentasi pelanggan berdasarkan data  transaksi menggunakan algoritma K-Means Clustering pada PT Sehati Bangunan Abadi (SBA) tahun 2024. Data yang digunakan mencakup tiga variabel utama, yaitu Recency, Frekuensi, dan Total Nominal, yang sebelumnya telah melalui proses pembersihan serta normalisasi agar setiap variabel memiliki skala yang seimbang. Proses pengelompokan dilakukan dengan menghitung jarak menggunakan rumus Euclidean Distance untuk menentukan kesamaan antar data. Hasil penelitian menunjukkan bahwa pelanggan dapat dikelompokkan menjadi tiga kategori, yaitu Prioritas, VVIP, dan VIP, dengan nilai Silhouette Score masing-masing sebesar 0.5884 untuk KAI, 0.5101 untuk End User, dan 0.6274 untuk Singres Member. Nilai tersebut menunjukkan bahwa hasil pengelompokan memiliki kualitas yang cukup baik.Kata kunci: K-Means Clustering, Segmentasi Pelanggan, Data MiningAbstract: This study aims to perform customer segmentation based on transaction data using the K-Means Clustering algorithm at PT Sehati Bangunan Abadi (SBA) in 2024. The data used includes three main variables, namely Recency, Frequency, and Total Amount, which have previously undergone a cleaning and normalization process to ensure that each variable has a balanced scale. The clustering process was carried out by calculating the distance using Euclidean Distance to determine the similarity between data points. The results show that customers can be grouped into three categories: Priority, VVIP, and VIP, with Silhouette Score values of 0.5864 for KAI, 0.51017 for End User, and 0.6274 for Singres Member. These values indicate that the clustering results have a fairly good quality.Keywords: K-Means Clustering, Customer Segmentation, Data Mining
Co-Authors Lusi Damayanti Mira Orisa Mochammad Ibrahim Ashari Nurlaily Vendyansyah