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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Abdurrahman, Raka Admiral
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Computer Science & IT Engineering

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Enhancing E-Commerce Customer Segmentation with Fuzzy C-Means Soft Clustering Probabilities Putra, Muhamad Iqbal Januadi; Alexander, Vincent; Chusyairi, Ahmad; Abdurrahman, Raka Admiral; Pratama, Alexander Daniel
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10652

Abstract

Customer segmentation is of paramount importance in the e-commerce industry, enabling businesses to improve marketing strategies and customer engagement. This study compares the performance of two clustering algorithms, K-Means and Fuzzy C-Means (FCM), using Walmart’s public e-commerce dataset of 550,068 transactions. After preprocessing and normalization, the elbow method was applied to determine the optimal number of clusters, yielding seven clusters for K-Means and eight for FCM. Experimental evaluation based on the silhouette score shows that FCM achieved 0.48, outperforming K-Means which scored 0.36, indicating that FCM generated clusters with stronger cohesion and separation. However, this improvement comes at a computational cost. K-Means consistently required less than 0.02 seconds per run, while FCM averaged 0.3 seconds and peaked at 1.38 seconds when the number of clusters increased, making it approximately 20–30 times slower. Cluster distribution analysis further revealed that K-Means produced an uneven segmentation dominated by a single large cluster, whereas FCM generated a more balanced distribution across its clusters. This demonstrates the advantage of FCM in capturing overlapping and multidimensional customer behaviors through partial memberships, in contrast to the rigid and oversimplify assignments of K-Means. These findings highlight the benefit of adopting FCM for e-commerce segmentation, as it provides more interpretable and actionable insights for personalized marketing. At the same time, the trade-off between clustering quality and computation time suggests that future research should explore optimization techniques such as parallelization, approximate fuzzy clustering, or hybrid models that combine the efficiency of hard clustering with the interpretability of soft clustering.
Sistem Automasi Perkebunan dan Pemantauan Cuaca Menggunakan AWS Berbasis Raspberry Pi Rohadi, Erfan; Abdurrahman, Raka Admiral; Ekojono, Ekojono; Asmara, Rosa Andrie; Siradjuddin, Indrazno; Ronilaya, Ferdian; Setiawan, Awan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 6: Desember 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (117.188 KB) | DOI: 10.25126/jtiik.2018561121

Abstract

AbstrakInternet of Things (IoT) mengalami perkembangan yang sangat pesat dan menjadi topik yang layak untuk diperbincangkan dan dikembangkan saat ini. IoT merupakan sebuah metode yang bertujuan untuk memaksimalkan manfaat dari konektivitas internet untuk melakukan transfer dan pemrosesan data- data atau informasi melalui sebuah jaringan internet secara nirkabel, virtual dan otonom. Salah satu pemanfaatan IoT adalah sistem automasi. Sistem automasi pada umumnya menggunakan pengatur waktu (timer) untuk proses penyiraman tanaman. Penggunaan timer bertujuan agar penyiraman tanaman berjalan secara rutin tanpa bantuan manusia.Pengembangan sistem automasi ini dimulai dengan pembuatan prototype lahan tanaman cabai rawit di lahan 5 x 2.5 meter, kemudian menyusun komponen-komponen yang dibutuhkan serta cara kerjanya. Selanjutnya dilakukan pemrograman sensor-sensor terhadap Raspberry Pi sebagai pengontrol dalam sistem tersebut berdasarkan kondisi yang telah diatur dan perubahan temperatur yang diterima oleh sensor. Setelah semua dilakukan, maka dilakukan pengujian terhadap sistem tersebut.Berdasarkan pengujian yang telah dilakukan, diketahui telah berhasil dilakukan penyiraman otomatis, baik secara reguler (pukul 06.00 dan 18.00) maupun penyiraman pendinginan. Pendinginan dilakukan jika suhu lebih dari 30 derajat celcius. Sistem automasi yang dikembangkan dengan uji tanaman cabai rawit menjanjikan untuk diterapkan pada pemanfaatan lahan di sekitar rumah. AbstractRecently, The Internet of Things (IoT) has been implemented and become an interesting topic for discussion. IoT is a method that aims to maximize the benefits of Internet connectivity to transfer and process data or information through an internet network wirelessly, virtual and autonomous. One of the IoT's utilization is automation system. The automation system generally uses a timer for the plant watering process. The use of timers aims to water the plants routinely without human assistance.The development of this automation system begins with the making of the prototype of chili land in the field 5 x 2.5 meters, then compile the required components and how it works. Further programming of sensors to Raspberry Pi as a controller in the system based on the conditions that have been set and changes in temperature received by the sensor.As a result, the system has been successfully done automatic watering, both on a regular basis (at 06.00 and 18.00) and cooling watering. Cooling is done if the temperature exceeds more than 30 degrees Celsius. The automation system promises to be applied to the utilization of land around the house.
Co-Authors Alexander, Vincent Awan Setiawan Chusyairi, Ahmad Ekojono, Ekojono Erfan Rohadi Pratama, Alexander Daniel Putra, Muhamad Iqbal Januadi Ronilaya, Ferdian Rosa Andrie Asmara Siradjuddin, Indrazno