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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Journal of Student Research Exploration
Septiana, Dina Wachidah
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Etika Shopee Affiliator dalam Meningkatkan Traffic Penjualan Prameswari, Della Egyta; Septiana, Dina Wachidah; Wahyudiantoro, Rizky Tri; Alaida, Salma Keysha; Santoso, Tony Budi; Arini S.Kom., M.Cs., Ph.D., Florentina Yuni
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 11 No 1 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i1.6495

Abstract

Artikel ini membahas tentang fenomena afiliasi shopee di Indonesia, dimana shopee bekerja sama dengan individu atau pihak ketiga untuk mempromosikan produk dan memperluas jangkauan pasar. Melalui metode tinjauan literatur, penelitian ini menganalisis etika Afiliator Shopee, mengidentifikasi kode etik yang harus dipatuhi, prinsip etika dalam pemasaran afiliasi, tanggung jawab Afiliator Shopee, dan dampaknya terhadap lalu lintas penjualan. Hasilnya menunjukkan pentingnya kejujuran, transparansi, kesehatan, keselamatan, legalitas, praktik yang sadar, perilaku pribadi, komunikasi yang jujur, dan mengatasi kekhawatiran publik dalam pemasaran afiliasi. Tanggung jawab Afiliasi Shopee mempengaruhi lalu lintas penjualan dengan pemilihan produk, konten berkualitas, distribusi yang efektif, dan analisis kinerja. Strategi pemasaran afiliasi Afiliasi Shopee melibatkan penggunaan media sosial, khususnya Instagram, dengan fokus pada video promosi produk. Pengaruh Afiliator Shopee terhadap trafik penjualan di Shopee cukup signifikan, meningkatkan popularitas produk dan kesadaran merek, meski menghadapi tantangan persaingan yang semakin ketat. Program Afiliator Shopee memberikan peluang besar bagi individu atau perusahaan untuk memanfaatkan platform e-commerce seperti Shopee untuk meningkatkan kesuksesan penjualan dan pemasaran.
Classification of travel class with k-nearest neighbors algorithm using rapidminer Septiana, Dina Wachidah; Pastika, Puan Bening
Journal of Student Research Exploration Vol. 2 No. 2: July 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v2i2.357

Abstract

he tourism industry in Indonesia plays an important role in the national economy. The selection of travel class according to the needs and budget of tourists is an important aspect in the tourism industry. This research aims to develop a travel class classification model using dummy datasets and the K-Nearest Neighbors (KNN) algorithm with RapidMiner software. The travel class dummy data set was obtained from the internet and modified according to research needs. The KNN algorithm was used to classify new travel classes based on previously classified dummy data. These dummy data were preprocessed and analyzed using RapidMiner software. The performance of the KNN model was evaluated using accuracy, precision, recall and F1-score. The results showed that the KNN algorithm with the values k = 1-2, k = 3-6, k = 8-10, k = 11-14 and k = 15 resulted in accuracy of 35.71%, 39.29%, 48.26%, 46.43% and 50.00%, respectively. This shows that the KNN algorithm with a value of k=15 produces the highest accuracy that can be effectively used to classify new travel classes based on dummy data.
Co-Authors Alaida, Salma Keysha Arini S.Kom., M.Cs., Ph.D., Florentina Yuni Pastika, Puan Bening Prameswari, Della Egyta Santoso, Tony Budi Wahyudiantoro, Rizky Tri