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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal Ilmiah Sistem Informasi
Kurnia, Dennis Ma'rifatul
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Transportation Other

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Segmentasi Pelanggan Berdasarkan Volume Pembelian Produk Menggunakan Algoritma K-Means Untuk Efektivitas Pemasaran Kurnia, Dennis Ma'rifatul; Aulia, Nurun Nihayatur Rifqiya; Hidayah, Yulistiya Nur; Saputri, Cindy Avitaselly Bambang
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 4 No. 2 (2025): OKTOBER
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v4i2.9042

Abstract

The rapid development of information technology has changed the way products are sold, especially through online platforms that are increasingly in demand. In increasingly tight business competition, companies need to understand the differences in customer needs and behavior. Inability in this regard can make it difficult to design effective marketing strategies. Therefore, customer segmentation based on transaction data is an important solution to group customers based on similar purchasing patterns. This study aims to examine customer segmentation based on sales transactions to help companies understand customer characteristics and develop more targeted and adaptive marketing strategies. A quantitative approach is used by applying the K-Means Clustering algorithm and PCA dimension reduction to a dataset from Kaggle containing 3,900 entries with 9 attributes. Determination of the optimal number of clusters was carried out using the Elbow and Silhouette Score methods. The segmentation results show five optimal clusters with the highest Silhouette Score of 0.81. Cluster 0 is the most dominant. PCA visualization shows a fairly clear cluster separation although there is little overlap. This study has succeeded in grouping customers based on purchase volume. Limitations of the study include the uneven distribution of clusters and it is recommended to add demographic attributes and evaluate other algorithms such as DBSCAN.
Segmentasi Produk Berdasarkan Penjualan Menggunakan Algoritma K-Means Clustering(Studi Kasus: Shopee) Kurnia, Dennis Ma'rifatul; Hidayah, Yulistiya Nur; Daniati, Erna Daniati
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 9 No. 3 (2025): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/2tmxrk65

Abstract

Dalam lingkungan e-commerce seperti Shopee, terdapat ribuan produk dengan variasi harga dan performa penjualan yang berbeda. Untuk membantu penjual dan manajemen marketplace dalam pengambilan keputusan strategis, diperlukan pendekatan analisis data yang mampu mengelompokkan produk berdasarkan karakteristik relevan. Metode yang dinilai paling efektif dalam penelitian ini adalah analisis K-Means Kluster yaitu salah satu teknik clustering non-hierarkis yang bekerja dengan cara membagi sekumpulan objek atau data ke dalam beberapa kelompok (cluster) berdasarkan tingkat kemiripan karakteristik yang dimilikinya. Produk yang memiliki karakteristik yang sama akan tergolong ke dalam cluster yang sama, sementara produk dengan karakteristik yang berbeda akan dikelompokkan ke dalam cluster yang berbeda. Penelitian ini bertujuan untuk melakukan pengelompokan produk berdasarkan daya jual serta melihat keterkaitan antar variabel produk. Hasil segmentasi ini diharapkan memberikan informasi yang berguna untuk rekomendasi strategi penjualan, meningkatkan efisiensi promosi, dan memberikan insight bisnis yang lebih tajam bagi penjual maupun pihak pengelola marketplace dalam mengembangkan strategi pemasaran berbasis data.
Deteksi Kantuk Mahasiswa pada Pembelajaran Online Menggunakan YOLOv8 dengan Pendekatan CRISP-DM pada Dataset Roboflow Kurnia, Dennis Ma'rifatul; Nugroho, Arie; Wardani, Anita Sari
JURNAL FASILKOM Vol. 16 No. 2 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i2.11532

Abstract

The quality of online learning in higher education is highly influenced by the level of student alertness and concentration during the lecture process. However, the phenomenon of drowsiness resulting from physical fatigue and prolonged screen time often reduces learning effectiveness. The limitations of lecturers in visually monitoring students' conditions on online learning platforms drive the need for an automated monitoring system capable of operating in real-time. This study aims to develop a student drowsiness detection system using the YOLOv8 algorithm with a CRISP-DM approach. The dataset used originates from two public datasets on the Roboflow platform, namely SleepyDetect and Yawn, which then underwent curation, labeling, and augmentation processes to produce 8,573 images across three classes: open-eyes, pre-drowsy (yawning), and drowsy (eyes closed). The evaluation results show that the model achieves an mAP50 value of 0.992, Precision of 0.973, Recall of 0.979, and an F1-Score of 0.976, with an inference speed of 4.1 ms per image, demonstrating high detection performance on the utilized dataset. The model's implementation was carried out through the AwakeLens software integrated with a monitoring dashboard to support the online learning process. Nevertheless, because this research relies on a public dataset, the obtained results still require further validation using a primary dataset collected from Indonesian students to enhance the external validity and generalization capability of the model.
Co-Authors Arie Nugroho, Arie Aulia, Nurun Nihayatur Rifqiyah Daniati, Erna Daniati Hidayah, Yulistiya Nur Saputri, Cindy Avitaselly Bambang Wardani, Anita Sari