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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Bayu Taruna Widjaja Putra
Jember University
Computer Science & IT

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Dental caries detection and treatment cost prediction using YOLOv11 Salsabila Qotrunnada; Bayu Taruna Widjaja Putra; Mei Syafriadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 1: February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i1.27507

Abstract

Dental caries is one of the most prevalent oral diseases, progressively damaging tooth structure and often leading to significant treatment costs. Variations in dental service fees across clinics can become a financial barrier, discouraging timely and appropriate care. This study introduces an artificial intelligence (AI)-based framework that utilises smartphone camera images to detect dental caries and predict treatment costs. A total of 1,200 images of carious and normal teeth were collected from dental clinics in Denpasar, Bali, Indonesia, and classified by three dental experts. Data augmentation expanded the dataset twentyfold to 23,060 images to address variation and class imbalance. The you only look once version 11 (YOLOv11) deep learning algorithm was employed for caries detection, and its performance was evaluated using mean average precision (mAP), precision, and recall metrics. The model demonstrated high accuracy, achieving an mAP of 96.1%, a precision of 95.5%, and a recall of 93.0%. This study provides the first integration of YOLOv11 with RGB-intensity-based cost prediction in digital dentistry. The proposed system offers a fast, accessible, and cost-efficient approach for early caries detection and treatment cost estimation. These findings highlight its potential to support real-time, AI-assisted preventive dentistry and contribute to more equitable access to oral healthcare.
Leveraging technology to improve tuberculosis patient adherence: a comprehensive review Almas Fahrana; Sri Hernawati; Saiful Bukhori; Al Munawir; Bayu Taruna Widjaja Putra
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26176

Abstract

Tuberculosis (TB) is a chronic disease that requires long-term treatment, generally for at least 6 to 9 months. Patients should follow the recommended treatment scheme regularly and completely. Poor adherence to treatment can cause patients to remain a source of infection for others. Patients with TB need additional support during treatment, in terms of information, motivation, and emotional support. Compliance monitoring helps ensure that patients take drugs according to a predetermined schedule. Comprehensive approach review method, careful selection of relevant data from various sources. This aims to provide overview of modern technology used to optimize the success of TB treatment. This paper aims to provide various methods that have existed in conventional and technology-enhanced approaches to monitoring and evaluating the treatment of TB patients. The existing studies only focus on making tools as a reminder to take medication but do not evaluate whether the drug is consumed. In addition, this paper describes prospective ideas involving advanced technology by using the internet of things (IoT)-based smart medicine bottle to accommodate the problem and become an effective communication solution in TB medication.
Co-Authors Al Munawir Almas Fahrana Mei Syafriadi Saiful Bukhori Salsabila Qotrunnada Sri Hernawati