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All Journal Information Technology Education Journal Journal of Information System Exploration and Research
Alamsyah
Department of Computer Science, Universitas Negeri Semarang, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Ensemble Learning-based Potato Leaf Disease Classification Using DenseNet201 and MobileNetV2 Burhan Ahmad; Alamsyah
Journal of Information System Exploration and Research Vol. 4 No. 1 (2026): January 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i1.597

Abstract

Early and late blight are major threats to potato crops and can cause significant losses for farmers. Early disease classification is essential for quick and appropriate treatment. This study proposes an ensemble learning approach by combining DenseNet201 and MobileNetV2 architectures to classify potato leaf diseases from digital images. The dataset used consists of 2,152 potato leaf images and is processed through normalization, augmentation, and image resizing stages. The ensemble model was trained with optimized parameters and evaluated using accuracy, precision, recall, and F1-score. The test results showed an accuracy of 99.56%, with precision, recall, and F1- score values of 99.56% each. Demonstrated improved performance compared to single CNN models on the evaluated dataset, and offers an accurate and efficient solution for disease detection in the agricultural sector.
Customer Segmentation Using RFM Analysis and the K-Means Clustering Algorithm to Support Data-Driven Marketing Strategies Rifat Naufal; Alamsyah
Information Technology Education Journal Vol. 5, No. 3, August (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i3.12821

Abstract

Purpose – Customer segmentation plays an important role in supporting data-driven marketing decision-making. This study aimed to analyze and classify customers according to their buying pattern characteristics by implementing the RFM (Recency, Frequency, Monetary) technique in the K-Means Clustering algorithm.  Design/methods/approach– This study used the public Marketing Campaign dataset from Kaggle through several phases, covering data preprocessing, RFM model construction, logarithmic conversion, information standardization, identification of the most suitable number of clusters through the elbow technique and assessment through the Silhouette Score. Findings – The results showed that the most suitable number of clusters was k=3 with a Silhouette Score measurement of 0.503, indicating moderate clustering quality with acceptable cluster cohesion and separation. The resulting segmentation consisted of three main clusters, namely Loyal Customers, Need Attention Customers and At Risk Customers, where each cluster had different contribution characteristics and potential for the company. Research implications – The segmentation results provide practical recommendations that may help companies maintain Loyal Customers, improve engagement among Need Attention customers, and reduce customer churn risk through more targeted marketing strategies. This study used a single public dataset; therefore, the segmentation results may differ when applied to other datasets or industrial sectors. Originality/value – The originality of this study lies in the application of RFM-based customer value transformation prior to K-Means clustering on the Marketing Campaign dataset. This approach provides interpretable customer segmentation and behavioral insights that support data-driven marketing decision-making. The findings suggest that integrating RFM analysis with K-Means clustering can generate meaningful customer segments and provide actionable insights for customer retention, re-engagement, and loyalty management.
Co-Authors Burhan Ahmad Rifat Naufal