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All Journal INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Knowbase : International Journal of Knowledge in Database
Sundari, Agus
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Computer Science & IT Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering

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Named Entity Recognition for Uncovering Clinical and Emotional Entities from Breast Cancer Patient Interviews Alias, Norma; Sundari, Agus
Knowbase : International Journal of Knowledge in Database Vol. 5 No. 1 (2025): June 2025
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v5i1.10192

Abstract

This study aims to develop a Named Entity Recognition (NER) system capable of identifying clinical and emotional entities within interview transcripts of breast cancer patients. The corpus was manually annotated using the BIO scheme across seven main entity categories: Social Support (Dukungan Sosial), Medical Actions (Tindakan Medis), Diagnosis, Negative Emotions (Emosi Negatif), Positive Emotions (Emosi Positif), Symptoms (Gejala), and Spiritual. The annotation process was followed by the implementation of a rule-based method supported by entity dictionaries and word normalization, and the model was evaluated using precision, recall, and F1-score metrics. The analysis results revealed that Dukungan Sosial was the most dominant entity with 347 occurrences, followed by Tindakan Medis and Diagnosis. The rule-based NER model achieved an F1-score of 0.50 for the Diagnosis entity, although its performance on emotional and social entities remained low due to data imbalance. These findings highlight the importance of integrating clinical and emotional aspects in natural language processing to gain a more comprehensive understanding of patient narratives. The proposed approach has potential applications in healthcare text mining for detecting emotional experiences and medical contexts, and it can be further enhanced through the integration of transformer-based models such as IndoBERT to improve entity recognition accuracy.
Application of The RFM Model and K-Means Clustering for Customer Segmentation in E-Wallet Top-Up Services Sundari, Agus; Putra, Indra Syah; Sibuea, Nuraini
INFOMATEK Vol 28 No 1 (2026): Juni 2026 (In Progress)
Publisher : Fakultas Teknik, Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/infomatek.v28i1.42246

Abstract

The implementation of digital payment technology through e-wallet top-up services requires financial institutions to understand user characteristics and behavior comprehensively The objective of this study is to segment customers based on their e-wallet top-up behavior by analyzing 143,836 bill payment transaction records using the RFM (Recency, Frequency, Monetary) model combined with the K-Means clustering algorithm. The dataset contains more than one hundred thousand transaction entries, with RFM parameters representing the time since the last transaction, the frequency of top-ups, and the monetary value spent by users. The RFM scoring process is applied to quantify user activity levels before entering the clustering stage. The K-Means clustering model successfully grouped customers into three distinct segments. The first segment represents low-activity users, the second consists of moderately active customers with stable transaction behavior, while the third segment captures highly engaged users with the highest transaction frequency and value. Evaluation metrics, including a silhouette score of 0.64, a Calinski-Harabasz index of 21690.50, and a Davies-Bouldin score of 0.70, demonstrate strong clustering performance and reliable separation between groups. The findings provide valuable insights for designing service strategies, improving mobile banking system performance, and developing targeted marketing approaches tailored to each customer segment. This research highlights the potential of RFM based clustering as a decision-support tool for enhancing digital payment service optimization and customer engagement.
Co-Authors Alias, Norma Putra, Indra Syah Sibuea, Nuraini