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All Journal Jurnal ABDINUS : Jurnal Pengabdian Nusantara Jurnal Telematics and Information Technology (TELEFORTECH) Management of Information System Journal
Setyani, Tria
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering

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PENERAPAN DATA MINING MENGGUNAKAN METODE K-MEANS CLUSTERING PADA PENJUALAN HANDPHONE DAN ELEKTRONIK (STUDI KASUS : CV REY GASENDRA) Setyani, Tria; Setiawansyah, Setiawansyah; Octaviansyah, A. Ferico; Andika, Rio
TELEFORTECH : Journal of Telematics and Information Technology Vol 5, No 1 (2024): TELEFORTECH VOL 5, NO 1 (JULI 2024)
Publisher : Fakultas Teknik dan Ilmu Komputer, Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/tft.v5i1.4586

Abstract

Abstrak CV Rey Gasendra merupakan salah satu toko handphone dan elektronik yang bergerak dalam penjualan berbagai macam merk handphone, mesin cuci, kulkas, tv, kipas angin dan barang elektronik lainnya. Pengelompokan data penjualan pada CV Rey Gasendra masih dilakukan dengan cara manual di excel. Cara pengelompokkan tersebut membutuhkan waktu dan memungkinkan data hilang. Dalam melakukan pengelompokan data diperlukan histori data penjualan, apabila data penjualan besar maka di perlukan teknik data mining, salah satu teknik data mining yaitu dengan menggunakan metode K-means Clustering. Clustering merupakan salah satu metode data mining yang bersifat unsupervised dan K-Means adalah salah satu metode clustering non-hirarki yang berusaha membagi data yang ada ke dalam bentuk satu atau lebih kelompok. Metode K-Means clustering dapat diterapkan untuk mengelompokan suatu data penjualan berdasarkan jenis barang, tipe pelanggan, atau jumlah barang. Data yang digunakan adalah data penjualan pada bulan Desember-Februari sebanyak 2027 data. Hasil pengujian dilakukan dengan menggunakan aplikasi RapidMiner dimana hasilnya terdapat 2 cluster yaitu cluster 0 berjumlah 102 data traksaksi dan cluster 1 berjumlah 1925 data transaksi.
Perbandingan Kinerja Algoritma K-Means Dan Dbscan Dalam Segmentasi Nasabah Berdasarkan Data Pemasaran Bank Indriani, Yulia; setyani, Tria; Sulistiani, Heni
Management of Information System Journal Vol 3 No 3: Juli 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v3i3.2119

Abstract

Customer segmentation is one of the important approaches in the banking industry to improve the effectiveness of marketing strategies and risk management. This study aims to compare the performance of two commonly used clustering algorithms, namely K-Means and DBSCAN, in segmenting customers based on bank marketing data. The data used comes from the bank marketing dataset available on Kaggle, including attributes such as age, type of employment, marital status, education level, savings balance, and previous campaign history. The analysis process includes data pre-processing, feature extraction, data standardization, and implementation of the clustering algorithm. Evaluation of the results using the Silhouette Score metric shows that the K-Means algorithm produces a higher Silhouette Score value and a lower Davies-Bouldin Index (DBI) than DBSCAN indicating that K-Means is able to produce more solid (compact) clusters and has clearer boundaries between data groups. However, DBSCAN shows advantages in the ability to detect noise and group data with irregular shapes. Thus, the selection of the best algorithm is highly dependent on the characteristics of the data and the purpose of the segmentation. For bank marketing data with a relatively uniform distribution, K-Means is a more optimal choice.
Peningkatan Kemampuan Guru SMK Kridawisata di Masa Pandemi Covid-19 Melalui Pengelolaan Sistem Pembelajaran Daring Ahdan, Syaiful; Sucipto, Adi; Priandika, Adhie Thyo; Setyani, Tria; Safira, Wilga; Sari, Kevinda
Jurnal ABDINUS : Jurnal Pengabdian Nusantara Vol 5 No 2 (2021): Volume 5 Nomor 2 Tahun 2021
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/ja.v5i2.15591

Abstract

Today's online learning technology has created a new paradigm in the process of implementing learning. Face-to-face activities between teachers and students are no longer a necessity to gain knowledge in school. SMK Kridawisata has adequate facilities and infrastructure to support the learning process such as classrooms and laboratories, but there is no system that is able to apply the learning process in networks that can overcome the problems of the standardized learning process during the Covid-19 pandemic. The solution for implementing online learning systems is expected to increase productivity, especially in the learning process, and to optimize the knowledge and ability of teachers in utilizing online-based learning systems in order to overcome problems that occur when teachers are unable to attend. Online learning systems are built using a learning management system (LMS) platform with the availability of features needed in the learning process online.
Co-Authors Adi Sucipto, Adi Ahdan, Syaiful Andika, Rio Heni Sulistiani Octaviansyah, A. Ferico Priandika, Adhie Thyo Safira, Wilga Sari, Kevinda Setiawansyah Setiawansyah Yulia Indriani