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All Journal Journal Of Information System And Artificial Intelligence Abdi Cendekia: Jurnal Pengabdian Masyarakat
Syarifah Atika
Fakultas Sains Dan Teknologi, Teknik Informatika, Universitas Prima Indonesia, Medan, Indonesia
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Penerapan Mikrokontroler dalam Meningkatkan Kreativitas dan Keterampilan Teknologi Siswa SD, SMP, dan SMA Melalui Lomba Robotika Berbasis Lego Despaleri Perangin Angin; Christnatalis HS; Riski Titian Ginting; Dewi Sholeha; Puji Syukran; Delima Sitanggang; Syarifah Atika; Uni Pratama Pebrina Br Tarigan; N P Dharsinni
Abdi Cendekia : Jurnal Pengabdian Masyarakat Vol 5 No 2 (2026): Juni
Publisher : Yayasan Zia Salsabila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61253/abdicendekia.v5i2.618

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan kreativitas dan keterampilan teknologi siswa jenjang SD, SMP, dan SMA melalui penerapan mikrokontroler dalam lomba robotika berbasis Lego sebagai respon terhadap dominasi pendekatan teoritis dalam pembelajaran teknologi saat ini. Menggunakan pendekatan deskriptif kuantitatif dengan desain Project -Based Learning (PjBL), kegiatan ini melibatkan 70 siswa di Kota Medan yang mengikuti tahapan pelatihan materi, praktik perakitan pemrograman, hingga kompetisi pada 20 Februari 2026. Hasil evaluasi menunjukkan peningkatan signifikan pada kompetisi siswa dengan capaian tertinggi pada kreativitas desain robot (75%) dan logika (70%), meskipun aspel algotitma pemrograman dasar masih menjadi tantangan dengan tingkat pemahaman sebesar (58%). Pelaksanaan kegiatan memperoleh respons sangat positif dengan tingkat kepuasan tertinggi pada kejelasan alur kegiatan (90%) dan kualitas perangkat (88%). Secara keseluruhan, integrasi mikrokontroler melalui lomba robotika berbasis Lego terbukti efektif dalam mengembangkan pemahaman sistem kendali, kemampuan problem solving, dan kolaborasi tim siswa guna menghadapi tantangan teknologi masa depan. 
Optimization of the K-Means Algorithm Using PCA Dimensionality Reduction for E-Commerce Customer Segmentation Mahara Bengi; Syarifah Atika; Chici Rizka Gunawan; Chica Rizka Gunawan
Journal Of Information System And Artificial Intelligence Vol. 6 No. 2 (2026): Vol.6 No. 2 (2026): Journal of Information System and Artificial Intelligence V
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v7i2.306

Abstract

The rapid growth of the e-commerce industry in recent years has generated increasingly large and complex volumes of customer data. This data holds strategic potential to be analyzed in order to understand customer behavior patterns and to support data-driven decision-making. This study aims to identify customer segmentation through an unsupervised learning approach using Principal Component Analysis (PCA) and the K-Means algorithm. The dataset used in this research demonstrates good quality with no missing values, making it suitable for further analysis. Initial exploratory findings indicate that Total Spending, Number of Items Purchased, and Average Rating are the most significant variables in representing customer characteristics. The application of PCA successfully reduced data dimensionality while retaining 79.41% of the total variance, thus producing a more concise representation without compromising essential information. The clustering process using K-Means grouped customers into three clearly distinguishable clusters. The first cluster represents customers with high activity levels, the second cluster reflects customers with moderate activity, and the third cluster corresponds to customers with lower engagement intensity. Validation using the Elbow Method and Silhouette Score confirmed that k = 3 is the most optimal number of clusters. Cluster visualizations show strong separation between groups and consistent relationships among variables. This study demonstrates that the combination of PCA and K-Means is effective in producing informative and interpretable customer segmentation. These findings provide a foundation for subsequent analyses and support data-driven decision-making in e-commerce customer management.
Co-Authors Chica Rizka Gunawan Chici Rizka Gunawan Christnatalis HS Delima Sitanggang Despaleri Perangin Angin Dewi Sholeha Mahara Bengi N P Dharsinni Puji Syukran Riski Titian Ginting Uni Pratama Pebrina Br Tarigan