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All Journal International Journal Software Engineering and Computer Science (IJSECS) PASCIDEV: Pasundan Social Science Development
Waramena, Shella Sukma Dewi
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Computer Science & IT Social Sciences

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Implementation of Automation Technology in Enhancing Digital Marketing Efficiency for Small Businesses Waramena, Shella Sukma Dewi
Pasundan Social Science Development Vol. 6 No. 1 (2025): Pasundan Social Science Development (PASCIDEV)
Publisher : Doctoral Program of Social Science Pasundan University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56457/pascidev.v6i1.251

Abstract

The rapid advancement of automation technology has significantly impacted digital marketing strategies, particularly for small businesses aiming to enhance efficiency and optimize operations. This study explores the implementation of automation tools in digital marketing efforts, focusing on their potential to streamline processes, improve customer engagement, and increase overall marketing effectiveness for small businesses. Automation technologies, such as customer relationship management (CRM) systems, email marketing automation, and social media management tools, allow small enterprises to reach their target audience more effectively while saving time and resources. Through an in-depth analysis of case studies and real-world applications, this research highlights how automation can assist in lead generation, customer retention, and data-driven decision-making. The findings suggest that by adopting automation, small businesses can not only boost their marketing efficiency but also gain a competitive edge in a crowded digital marketplace. Challenges such as initial setup costs, integration with existing systems, and the need for ongoing maintenance are also addressed. However, the long-term benefits, including improved ROI, enhanced customer experience, and increased scalability, make automation a valuable investment for small business owners. This paper provides actionable insights for small businesses looking to leverage automation in their digital marketing strategies, offering recommendations for successful implementation and optimization.
Analysis and Implementation of a Hybrid Case-Based Reasoning and K-Nearest Neighbor Approach for Chronic Kidney Disease Prediction Larasati, Hananing Sumaningdiah; Waramena, Shella Sukma Dewi; Pahira, Wulan
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6894

Abstract

Chronic Kidney Disease (CKD) is a progressive deterioration of kidney function that frequently goes undetected in its early stages, posing a growing clinical concern — particularly among productive-age individuals whose diagnosis is often delayed until irreversible damage has occurred. Early and accurate prediction remains a pressing challenge, especially given the rising CKD incidence in this demographic linked to hypertension, diabetes, and shifting lifestyle patterns. This study developed a hybrid method combining Case-Based Reasoning (CBR) with weighted similarity and K-Nearest Neighbor (KNN) to improve prediction accuracy while preserving model interpretability. The dataset was obtained from the UCI Machine Learning Repository and filtered for productive-age individuals aged 15–64 years, yielding 288 instances after preprocessing. Attribute weighting was performed using Information Gain to reflect the varying diagnostic relevance of each variable, and inter-case similarity was measured through a weighted similarity approach. Classification was then carried out using KNN across multiple K values. At K = 2, the proposed method achieved an accuracy of 98.26%, with precision, recall, and F1-score each recorded at 0.983 — results that suggest the hybrid CBR-KNN approach is well-suited for deployment as a clinical decision support system for early CKD detection.
Co-Authors Larasati, Hananing Sumaningdiah Pahira, Wulan