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All Journal JSAI (Journal Scientific and Applied Informatics) Jurnal Abdimas Mandiri International Journal of Health, Engineering and Technology AnoaTIK: Jurnal Teknologi Informasi dan Komputer
Agustina Heryati
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Dentistry Education Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary

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Journal : JSAI (Journal Scientific and Applied Informatics)

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Optimasi Strategi Pemasaran E-Commerce Melalui Prediksi Konversi Berbasis Machine Learning Agustina Heryati; Terttiaavini, Terttiaavini; Septa Cahyani; K.Ghazali; Harsi Romli; Iski Zaliman
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7553

Abstract

The research identifies the problem of enhancing e-commerce sales conversion through TikTok amidst intense content competition. The objective of the study is to develop a machine learning-based marketing strategy to analyze user behavior and categorize them into Non-Purchasers and Purchasers.The method employed includes clustering using K-Means, K-Medoids, and Fuzzy C-Means algorithms, with K-Means demonstrating the best performance, achieving the highest Silhouette Coefficient (0.1857) and the lowest Davies-Bouldin Index (1.9991). Following clustering, classification is performed using Naïve Bayes, Decision Tree, and Random Forest algorithms. The Random Forest model yields the best results with an accuracy of 0.9945, showcasing its effectiveness in predicting sales conversions.The conclusion of this study indicates that K-Means and Random Forest are the optimal methods for clustering and classification, respectively, in understanding user behavior on TikTok. These findings can assist e-commerce players in tailoring their marketing strategies, improving sales conversion rates, and enhancing advertising efficiency
Analisis Tren Penjualan dan Prediksi Produk CV. Sentosa Menggunakan Regresi Linier Dona Marcelina; Indah Pratiwi Putri; Evi Yulianti; Agustina Heryati
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7649

Abstract

This study analyzed sales trends and forecasted the sales of CV Sentosa's products, namely Ater 360 New (X1), Bon Bon (X2), Mini Peanut Crackers (X3), and Marie Susu Int (X4), during the period of January 2019 to August 2023. Monthly sales data were processed using exploratory data analysis (EDA) and linear regression to predict sales trends. The linear regression analysis results indicated that X2 and X3 experienced sales growth with a slope of m=0.01, representing an average increase of 0.01 units per month. Conversely, X4 showed a slight decline with m=−0.01, while X1 remained stable with m=−0.00, indicating minimal changes in sales volume. The accuracy evaluation of the predictions based on MAE, MSE, and RMSE showed that X2 had the best performance with MAE 0.14, MSE 0.03, and RMSE 0.19, followed by X1 and X3, which had similar prediction errors. Although X4 initially showed significant growth, its model exhibited higher prediction errors (MAE 0.17, MSE 0.04, RMSE 0.21). This study provides valuable insights for CV Sentosa's business strategies, highlighting X2 and X3 as promising products due to their consistent growth trends and accurate predictions. This research provides a strong foundation for CV Sentosa in formulating more effective marketing strategies and product development in the future
Co-Authors Abdul Kholik Ajeng Oktaviyani Aldri Frinaldi Arum Adisha Putra Anandez Dio Resta Permana Dona Marcelina EVI YULIANTI Fauzia Afriyani Harsi Romli Indah Pratiwi Putri Iski Zaliman K.Ghazali M. Fadhiel Alie Alie Septa Cahyani Terttiaavini, Terttiaavini Try Wulandari