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All Journal Proceedings of KNASTIK Semantik Jurnal Simetris Jurnal Sains dan Teknologi Prosiding SNATIF Jurnal Informatika dan Teknik Elektro Terapan E-Dimas: Jurnal Pengabdian kepada Masyarakat Sistemasi: Jurnal Sistem Informasi JURNAL ILMIAH INFORMATIKA Jurnal Pengabdian Masyarakat AbdiMas Jurnal DISPROTEK EDUMATIC: Jurnal Pendidikan Informatika Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal SITECH : Sistem Informasi dan Teknologi Journal of Information Systems and Informatics Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat JATI (Jurnal Mahasiswa Teknik Informatika) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Technology, Informatics and Science (IJTIS) Jurnal UNITEK Jurnal Pendidikan dan Teknologi Indonesia Muria Jurnal Layanan Masyarakat Jurnal Locus Penelitian dan Pengabdian Jurnal Sistem Informasi dan Manajemen
Pratomo Setiaji
Universitas Muria Kudus
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

 1 
K-Means Clustering untuk Segmentasi Pelanggan: Mengungkap Pola Pembelian Strategi Pemasaran pada Sektor Ritel Artiarno, Andrean Maulana; Setiaji, Pratomo; Nugraha, Fajar
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30336

Abstract

Digital transformation has posed new challenges for retail companies in understanding consumer behavior due to the increasing volume of data and continuously changing preferences. This study aims to uncover purchasing patterns among retail customers and to provide data-driven marketing strategies through customer segmentation using the K-Means Clustering algorithm. This research adopts a quantitative exploratory approach using 3,900 synthetic entries from the Kaggle platform, representing retail transactions. The analysis focuses on variables such as age, gender, product category, location, purchase amount, and transaction frequency. The analytical process includes data preprocessing, dimensionality reduction using PCA, and segmentation with the K-Means algorithm. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, while the quality of the clustering was evaluated using internal metrics, namely the Calinski-Harabasz Score (491.47) and the Davies-Bouldin Score (2.02). These values indicate a well-structured and reliable clustering result. Our findings reveal five distinct customer segments with varying characteristics, ranging from teenagers with small and periodic purchases to high-value adult customers who transact infrequently. These insights serve as the foundation for developing marketing strategies such as loyalty programs, seasonal promotions, and exclusive approaches.
Sistem Klasifikasi Kematangan Apel Fuji berdasarkan Warna menggunakan KNN untuk Sortasi Otomatis Maula, Ahmad Inzul; Triyanto, Wiwit Agus; Setiaji, Pratomo
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.31243

Abstract

Manual fruit sorting typically relies on workers' visual observation to assess ripeness. This assessment is heavily influenced by individual experience and lighting conditions, often leading to inaccuracies. Furthermore, manual methods are time-consuming, increase the risk of misclassification, and reduce operational efficiency. Our research aims to develop a color-based Fuji apple ripeness classification application using the K-Nearest Neighbor algorithm that combines RGB and HSV features. Our research is developmental research using the Waterfall model, consisting of requirements analysis, design, implementation, testing, and maintenance. We used 240 fuji apple images sourced from images taken in the Kudus area. Our findings are an automatic classification application capable of classifying apple images into three ripeness levels: unripe, semi-ripe, and ripe. The evaluation results showed an accuracy of 93.75% with balanced precision, recall, and f1-score across all classes, confirming the system's stable performance without any indication of bias. Testing results using the black-box method in three scenarios opening the application, uploading an image, and reclassifying proved that all features performed as expected. The implication is that this application is ready for use in camera-based sorting in horticultural production lines and can be developed for other fruit classifications, supporting widespread post-harvest digitalization.
Co-Authors - Supriyono Adi Nugroho Aditiya, Natasya Aditya, Ahsanu 'Amala Adiyono, Soni Andy Prasetyo Utomo Anteng Widodo Aprilia Damayanti Arif Setiawan Artiarno, Andrean Maulana Aynullutfihana, Nanda Azzahro, Erina Diana Laily Fithri Eko Darmanto Fadhilah, Bayu Samudro Fajar Nugraha Fakhriyyah, Anis Ferianti, Lydya Ayu Hanif Rizqullah, Aufa Irfan Santiko Kurniasari, Nita Kusumawardani, Nada Laily Fithri, Diana Lisa Rachmawati Maula, Ahmad Inzul Muhammad Arifin Nia Zuliyana, Nia Noor Latifah Nurhaliza, Maulin Pambudi, Satrio Prayogo, Adhie Putri Kurnia Handayani Putri, Rizka Ferbriliana R Rhoedy Setiawan Rahmawati, Yulinda Ramadlan As'ari, Wahyu Ramandani, Fitri Rohmah, Putri Anjilis Romadhon, Zainur Semit, Danial Setiawan, Faris Apri Setyanto, Rangga Setyawan, Moh Dodi Suciningtyas - Suciningtyas - Suciningtyas Suciningtyas Suciningtyas Suciningtyas, Suciningtyas Suku Rahayu, Sri Intan Supriyono Supriyono Supriyono Supriyono Syafiul Muzid Tamaulina Br Sembiring Tengku Riza Zarzani N Widodo, Wahyu Kurniawan Ade Nur Wiwit Agus Triyanto Yoanas, Sahenda Yudie Irawan Zahra, Fatimah Az