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All Journal Jurnal Ilmiah Teknik Informatika dan Komunikasi Jurnal Intelek Dan Cendikiawan Nusantara Repeater: Publikasi Teknik Informatika dan Jaringan
Rezki Abdillah
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Computer Science & IT Electrical & Electronics Engineering Other

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Transformasi Digital Marketing untuk Meningkatkan Minat Beli Konsumen di Shopee Rezki Abdillah
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 3 No. 1 (2025): Januari: Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v3i1.377

Abstract

The purpose of this study was to determine whether digital marketing media can influence consumer interaction with the Shopee marketplace. Online communication and sales are new things in the realm of marketing thanks to the rapid development of technology that allows individuals to make transactions through digital platforms. Currently, buying and selling transactions in the marketplace are in great demand and are popular with the public. One of the applications most often used by the public is Shopee. By using a digital marketing strategy in the Shopee marketplace, selling products without having to meet face to face with consumers becomes very easy. Business actors also try to apply efficient tactics to influence the level of consumer buying interest. In this study, the researcher applied a qualitative method that was explained descriptively by utilizing books, websites, and related journals. The quality and unique characteristics of the products being promoted
Penentuan Kelayakan Kredit Menggunakan Metode Fuzzy Mamdani Berdasarkan Penghasilan dan Riwayat Kredit Fahri Finanda Rizki; Rezki Abdillah; Khairul Saleh
Jurnal Intelek Dan Cendikiawan Nusantara Vol. 2 No. 6 (2025): Desember 2025 - Januari 2026
Publisher : PT. Intelek Cendikiawan Nusantara

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Abstract

Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem pendukung keputusan dalam menentukan kelayakan kredit menggunakan metode inferensi Fuzzy Mamdani berdasarkan variabel penghasilan dan riwayat kredit. Penilaian kelayakan kredit pada praktiknya sering dihadapkan pada ketidakpastian serta subjektivitas karena tingkat penghasilan dan riwayat pembayaran debitur tidak selalu dapat dikelompokkan secara tegas ke dalam kategori tertentu. Metode Mamdani dipilih karena memiliki karakteristik rule-based sehingga keputusan yang dihasilkan dapat dijelaskan secara logis. Penelitian ini menerapkan fungsi keanggotaan fuzzy berbentuk trapesium dan segitiga untuk merepresentasikan variabel linguistik seperti penghasilan rendah, sedang, tinggi serta riwayat kredit buruk, cukup, baik. Mekanisme inferensi fuzzy mencakup proses fuzzifikasi, evaluasi aturan, agregasi, dan defuzzifikasi centroid untuk menghasilkan nilai tegas kelayakan kredit. Pengujian menggunakan 25 data sampel menunjukkan bahwa sistem mampu memberikan rekomendasi keputusan yang konsisten, yakni tidak layak, dipertimbangkan, dan layak. Model ini dapat menjadi alternatif yang fleksibel bagi analis kredit serta dapat ditingkatkan akurasinya dengan menambahkan variabel dan dataset riil.
Penerapan Learning Vector Quantization (LVQ) Untuk Klasifikasi Data Citra Digital Bambang Irwansyah; Delyanti Putri Sitorus; Rezki Abdillah; Rizky Febriansyah; Harry Ardian; Syahrul Syahrul; Ferry Cahyadi; Fahri Finanda Rizki
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 6 No. 1 (2026): Maret : Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v6i1.2072

Abstract

The rapid development of information technology has increased the utilization of digital images in various fields, creating a need for classification methods that are accurate and efficient. One method that can be applied to classify numerical data obtained from image feature extraction is Learning Vector Quantization (LVQ). This study aims to implement the LVQ method for digital image classification based on numerical features and to evaluate its performance in terms of accuracy. The data used in this study consist of grayscale digital images that have undergone a feature extraction process and are represented as numerical vectors. The dataset is divided into two classes, namely Class A and Class B. The research stages include data collection, grayscale conversion, feature extraction, LVQ training, and classification testing. The classification results are evaluated using a confusion matrix and accuracy measurement. The experimental results show that the LVQ method successfully classified all test data correctly, achieving an accuracy rate of 100%. These results indicate that Learning Vector Quantization is an effective method with good performance for classifying digital image data based on numerical features.
Co-Authors Delyanti Putri Sitorus Fahri Finanda Rizki Ferry Cahyadi Harry Ardian Irwansyah, Bambang Khairul Saleh Rizky Febriansyah Syahrul Syahrul