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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) AMMA : Jurnal Pengabdian Masyarakat
Saeful Amri
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Pelatihan Pembuatan Website dan Pengelolaan Media Sosial Untuk Promosi Produk Lokal Cep Lukman Rohmat; Dadang Sudrajat; Saeful Amri; Selvi Andini
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 03 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Improving the competitiveness of local products in the digital era requires micro, small, and medium enterprises (MSMEs) to be able to utilize information technology in their marketing processes. However, many local business actors still rely on conventional promotional methods and have not yet optimized the use of digital media. This Community Service Program (PKM) aims to assist partner MSMEs in increasing their product visibility through website creation and social media management as promotional tools. The program was carried out in several stages: identifying partner needs, training in the creation and management of websites using WordPress, training in content design and marketing strategies via social media (Instagram, Facebook, and WhatsApp Business), and mentoring in content creation and account management. During the program, partners were equipped with basic skills to build a simple online store website, copywriting techniques, product photography using smartphones, and the use of free design tools such as Canva. The results of the activity show that partners who previously had no online presence now have active websites displaying product catalogs, business profiles, and contact information. In addition, the partners’ social media accounts became more active and strategically managed, featuring consistent and engaging promotional content. This activity also increased partners’ understanding of the importance of digital identity and content-based marketing strategies. The PKM provided direct impact in the form of increased consumer trust in local brands, enhanced sales potential, and better preparedness of MSMEs to compete in the digital market. Moving forward, this training can be replicated in various local business communities as part of a technology-based community economic empowerment effort.
Optimizing the Classification Model for Plant Medicine Supplies Using the Decision Tree Algorithm at the Anugrah Tani Shop, Brebes Regency: Inggris Saeful Amri; Rudi Kurniawan; Saeful Anwar
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.825

Abstract

Retail businesses in the agricultural industry often face difficulties in estimating inventory needs, especially plant medicines which are important for protecting plants from pests and diseases. The lack of an accurate inventory prediction system can cause stock discrepancies, as happened at the Anugrah Tani Store, Brebes Regency, thereby disrupting operations and customer satisfaction. This research uses the Decision Tree classification technique to increase the accuracy of predicting the need for plant medicine supplies, with a clustering approach using the K-Means algorithm to determine the optimal K value through the Davies-Bouldin Index (DBI) calculation. A DBI value of -0.065 indicates good cluster quality with an optimal K of 2, where Cluster 0 has high inventory needs (1138 data) and Cluster 1 has low needs (4 data). The analysis results show that the accuracy level of the Decision Tree model is 98.25%, which is quite high. This model is not only able to predict inventory patterns accurately but also provides in-depth insights to support stock decision making. This research proves that the Decision Tree algorithm can help inventory management with a faster response to customer needs, while contributing to the development of machine learning-based classification models for the agricultural and retail sectors.
Co-Authors Cep Lukman Rohmat Dadang Sudrajat Rudi Kurniawan Saeful Anwar Selvi Andini