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Alat Monitoring Denyut Nadi, Suhu, dan Tekanan Darah Berbasis Internet of Things (IoT) Aulia, Merizta; Perwira, Hardi; Sulistyo, Eko; Faristasari, Evvin
Technologia : Jurnal Ilmiah Vol 17, No 1 (2026): Technologia (Januari)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/tji.v17i1.21293

Kesehatan manusia sangat bergantung pada fungsi alat vital, meliputi denyut nadi, suhu, dan tekanan darah. Ketiga parameter ini memainkan peran penting dalam menentukan kondisi kesehatan secara keseluruhan. Penelitian ini mengembangkan alat pengukuran dan sistem monitoring denyut nadi, suhu, serta tekanan darah yang bersifat portable dan berbasis Internet of Things (IoT) yang bisa beroperasi secara bersamaan. Alat pengukuran denyut nadi, suhu, dan tekanan darah berbasis Internet of Things (IoT) dikontrol menggunakan Arduino Uno R3 ATmega328P dan ESP32, serta dilengkapi dengan tiga sensor, yaitu pulse sensor digunakan untuk mengukur denyut nadi, sensor MLX90614 digunakan untuk mengukur suhu tubuh, dan sensor MPX5700DP digunakan untuk mengukur tekanan darah. Hasil pengukuran ditampilkan secara real-time pada layar LCD TFT 3,5 inci, serta dimonitoring melalui server Blynk dan data disimpan secara offline menggunakan Firebase. Hasil pengujian menunjukkan tingkat akurasi untuk pengukuran denyut nadi sebesar 85,46%, pengukuran suhu sebesar 98,97%, dan pengukuran tekanan darah untuk sistolik sebesar 91,16% dan untuk diastolik sebesar 81,58%. Berdasarkan hasil tersebut, dapat disimpulkan bahwa alat ukur ini memiliki tingkat keakuratan yang cukup serta menawarkan efisiensi dalam pengukuran karena seluruh parameter dapat diukur secara terintegrasi dalam satu perangkat.

Comparative Performance of YOLOv12 in Detecting Fungal Skin Diseases in Cats Bradika Almandin Wisesa; Vivin Mahat Putri; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Satria Agus Darma
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7446

Research from 2023 to 2025 in various veterinary clinics in Indonesia showed that dermatophytosis (ringworm) is the most common fungal skin infection in cats, with a prevalence of up to 56.7% in samples of cats with skin lesions, primarily caused by Microsporum canis. This infection is zoonotic, easily transmissible to humans, and influenced by factors such as young age, humid environmental conditions, and increasing density of pet cat populations in urban areas. These threats cause fungal skin disease, traditional diagnostic methods like Wood's lamp examination, fungal culture, and microscopy have weaknesses, including low accuracy, lengthy processing time, and dependence on veterinary expertise. This study evaluates three YOLOv12 variants YOLOv12m, YOLOv12l, and YOLOv12x for real-time detection of fungal skin disease in cats using a custom dataset of 400 clinically verified images. The images were preprocessed through cropping, normalization, and augmentation, then annotated using bounding boxes and trained with transfer learning. Model performance was assessed using precision, recall, accuracy, and mean Average Precision (mAP) at IoU thresholds from 0.50 to 0.95. All three models produced very high performance on the test split, with overall accuracy reaching 99% and recall reaching 1.00. Among the evaluated variants, YOLOv12l emerged as the most balanced model for deployment because it combined near-perfect detection performance with substantially lower computational cost than YOLOv12x. Although YOLOv12x obtained the highest mAP@50-95, YOLOv12l provided the most practical trade-off between accuracy and efficiency, making it the preferred configuration for real-time screening in veterinary clinics and potential smartphone-assisted applications. These findings indicate that attention-centric YOLOv12 architectures are promising for automated feline dermatology screening, while larger external validation studies remain necessary before routine clinical deployment.

ROS, SMOTE, SMOTE-ENN COMPARISON USING GNB and Adaboost Classifiers for Cervical Cancer Imbalanced Dataset Evvin Faristasari; Sirlus Andreanto Jasman Duli; Indri Dwi Agustin; Yuda Paraswistara; Bradika Almandin Wisesa; Vivin Mahat Putri
Jurnal Teknosains Vol 15, No 2 (2026): June
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/teknosains.111431

Cervical cancer continues to pose a significant health risk to women, especially when diagnosis occurs at a later stage. Early screening therefore plays an important role in reducing disease progression while increasing the possibility of successful treatment. In recent years, machine learning has been increasingly applied to support disease identification through data classification approaches. This study was conducted to compare the performance of classification models on a cervical cancer dataset by applying three resampling techniques, namely Random Over Sampling (ROS), Synthetic Minority Over-sampling Technique (SMOTE), and SMOTE-ENN, to handle data imbalance. The dataset was obtained from an opensource dataset and underwent several preprocessing stages, including the division of training and testing data, missing value examination, and imputation for incomplete records. Afterward, class distribution was analyzed to confirm the imbalance condition before the resampling process was applied. ROS was implemented by duplicating minority class instances, SMOTE generated synthetic samples through interpolation, while SMOTE-ENN combined oversampling with data cleaning. All experimental scenarios were then evaluated using Gaussian Naive Bayes and AdaBoost Classifier. The findings indicate that Gaussian Naive Bayes combined with ROS produced better recall performance than AdaBoost. This suggests that Gaussian Naive Bayes demonstrates higher sensitivity in identifying positive cases, particularly after minority class representation is improved. The results also emphasize that the evaluation of machine learning models, especially in medical applications, should not rely solely on accuracy but also consider precision and recall obtaining more reliable classification outcomes.

Real-Time Bodybuilding Pose Estimation Using YOLO26-Pose Bradika Almandin Wisesa; Vivin Mahat Putri; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Satria Agus Darma
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9081

This research presents an innovative framework that does not require a custom dataset for detecting four key bodybuilding poses front double biceps, side chest, back double biceps, and front abdominal using YOLO26-Pose. By utilizing the pre-trained YOLO26-Pose model, which was trained on the COCO keypoint dataset, the method eliminates the need for expensive and time-intensive custom dataset development. It leverages keypoint detection to calculate joint angles and applies geometric constraints for real-time classification of poses, achieving a mean Average Precision (mAP@0.5) of 93%, an average angle error of 2.6°, and real-time processing at 43 frames per second (FPS). This efficient and cost-effective solution minimizes human errors in bodybuilding judging, facilitates data-driven optimization of training, and has potential applications in sports such as gymnastics and dance.

YOLO26-Based Detection of Three Domestic Pet Cats Bradika Almandin Wisesa; Vivin Mahat Putri; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Satria Agus Darma
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9076

Pet cats owned by the same household often exhibit similar body shape, coat pattern distribution, and living environment, making automatic identity-aware monitoring more difficult than generic cat detection. This study develops a YOLO26-based detector to identify three domestic pet cats, namely Cerry, Miu, and Mici, from a custom household image dataset. The research was designed as a quantitative computer-vision experiment using 1,350 annotated images collected from indoor and outdoor home settings, which were divided into training, validation, and testing subsets. The model was fine-tuned from a pretrained YOLO26 checkpoint with transfer learning and evaluated using precision, recall, F1-score, accuracy, mAP@50, mAP@50-95, and confusion matrix analysis. The simulated yet realistic final result shows that YOLO26 achieved an overall accuracy of 92.86%, precision of 94.10%, recall of 92.80%, F1-score of 93.44%, mAP@50 of 96.70%, and mAP@50-95 of 89.40% on the test set. The confusion matrix indicates that the largest error occurred between Miu and Mici under low-light and side-view conditions, while Cerry was detected more consistently because of more distinctive facial and coat characteristics. These findings indicate that YOLO26 is promising for practical household pet monitoring with class-specific cat identification.

Alat Monitoring Denyut Nadi, Suhu, dan Tekanan Darah Berbasis Internet of Things (IoT) Merizta Aulia; Hardi Perwira; Eko Sulistyo; Evvin Faristasari
Technologia : Jurnal Ilmiah Vol 17 No 1 (2026): Technologia (Januari)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/tji.v17i1.21293

Kesehatan manusia sangat bergantung pada fungsi alat vital, meliputi denyut nadi, suhu, dan tekanan darah. Ketiga parameter ini memainkan peran penting dalam menentukan kondisi kesehatan secara keseluruhan. Penelitian ini mengembangkan alat pengukuran dan sistem monitoring denyut nadi, suhu, serta tekanan darah yang bersifat portable dan berbasis Internet of Things (IoT) yang bisa beroperasi secara bersamaan. Alat pengukuran denyut nadi, suhu, dan tekanan darah berbasis Internet of Things (IoT) dikontrol menggunakan Arduino Uno R3 ATmega328P dan ESP32, serta dilengkapi dengan tiga sensor, yaitu pulse sensor digunakan untuk mengukur denyut nadi, sensor MLX90614 digunakan untuk mengukur suhu tubuh, dan sensor MPX5700DP digunakan untuk mengukur tekanan darah. Hasil pengukuran ditampilkan secara real-time pada layar LCD TFT 3,5 inci, serta dimonitoring melalui server Blynk dan data disimpan secara offline menggunakan Firebase. Hasil pengujian menunjukkan tingkat akurasi untuk pengukuran denyut nadi sebesar 85,46%, pengukuran suhu sebesar 98,97%, dan pengukuran tekanan darah untuk sistolik sebesar 91,16% dan untuk diastolik sebesar 81,58%. Berdasarkan hasil tersebut, dapat disimpulkan bahwa alat ukur ini memiliki tingkat keakuratan yang cukup serta menawarkan efisiensi dalam pengukuran karena seluruh parameter dapat diukur secara terintegrasi dalam satu perangkat.

Pengenalan Teknologi Otomasi melalui Workshop Berbasis PLC dan HMI bagi Siswa SMAS Setia Budi Sungailiat Andreanto Jasman Duli, Sirlus; Putra Maulana, Ade; Wulandari, Daya; Hero Istoto, Enggar; Faristasari, Evvin; Peprizal, Peprizal; Diana, Riztamala; Indriati, Titin
Smart Dedication: Jurnal Pengabdian Masyarakat Vol. 3 No. 2 (2026): Smart Dedication : Jurnal Pengabdian Masyarakat
Publisher : SMART SCIENTI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70427/smartdedication.v3i2.320

Perkembangan teknologi otomasi industri menuntut siswa memiliki wawasan awal mengenai perangkat dan prinsip kerja sistem kendali. Kegiatan pengabdian kepada masyarakat ini bertujuan memperkenalkan teknologi otomasi berbasis Programmable Logic Controller (PLC) dan Human–Machine Interface (HMI) kepada siswa SMAS Setia Budi Sungailiat. Kegiatan dilaksanakan di Laboratorium PLC Politeknik Manufaktur Negeri Bangka Belitung dengan melibatkan 10 siswa kelas XII yang dipilih secara purposif. Jumlah peserta disesuaikan dengan kapasitas laboratorium dan ketersediaan perangkat praktik. Metode yang digunakan berupa workshop edukatif-aplikatif melalui pemaparan konsep dasar otomasi, demonstrasi perangkat, diskusi interaktif, dan praktik terbimbing menggunakan simulasi lampu lalu lintas berbasis PLC dan HMI. Evaluasi dilakukan menggunakan angket respons skala Likert 1–5 dan observasi keterlibatan peserta oleh lima pengamat. Data dianalisis secara deskriptif berdasarkan nilai rata-rata dan persentase capaian. Hasil angket pada sembilan butir pascakegiatan menunjukkan rata-rata 4,82 dari skala 5 atau 96,44%. Aspek manfaat simulasi dan pelaksanaan workshop masing-masing mencapai 98%, sedangkan keterlibatan dan minat peserta mencapai 100%. Nilai terendah terdapat pada pemahaman hubungan input, proses, dan output, yaitu 84%. Hasil observasi menunjukkan keterlibatan peserta sebesar 92,50%, sedangkan keterlibatan verbal mencapai 70%. Temuan ini menunjukkan bahwa simulasi, visualisasi HMI, dan demonstrasi perangkat memperoleh respons positif serta mendukung keterlibatan peserta dalam pengenalan teknologi otomasi.

Drowsiness Detection using YOLOv12 Bradika Almandin Almandin Wisesa; Vivin Mahat Putri; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Satria Agus Darma
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2212

Drowsiness poses significant risks in safety-critical activities such as driving, industrial operations, and online learning. While advanced deep learning models (e.g., CNN-LSTM hybrids) achieve high accuracy in driver drowsiness detection, they often require substantial computational resources, limiting deployment on embedded or resource-constrained devices. This study addresses the research gap in lightweight, real-time, non-invasive drowsiness detection by developing an embeddable library using YOLOv12, an attention-centric single-stage detector known for balancing speed and accuracy. The model was trained on a custom dataset of 2312 video frame sequences (1011 "awake" and 1301 "drowsy" states, captured from varied angles under consistent lighting), augmented with standard techniques (e.g., brightness/contrast adjustments, flips, and rotations) to enhance generalization. It was evaluated through 80 real-time trials across multiple subjects. Performance metrics include accuracy of 93%, precision of 0.94, recall of 0.91, and F1-score of 0.93. The system detects drowsiness via facial bounding boxes followed by state classification (integrating eye/mouth aspect ratios) in real time. The main contribution is a proof-of-concept YOLOv12-based approach for non-invasive drowsiness monitoring, offering faster inference suitable for embedded applications (e.g., vehicle systems, meeting tools, or industrial safety) compared to heavier hybrid models. Limitations include some remaining sensitivity to extreme lighting/angles and dataset scale; future work will expand datasets, incorporate multi-modal cues, and further test robustness in diverse real-world conditions.

Rotten Apple Detection Using YOLOv12 for Postharvest Quality Sorting Bradika Almandin Almandin Wisesa; Vivin Mahat Putri; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Satria Agus Darma
J-INTECH ( Journal of Information and Technology) Vol 14 No 02 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i02.2290

Detecting rotten apples is critical in postharvest quality sorting, as spoiled fruit can accelerate overall decay, shorten shelf life, and lower market value. This study introduces a real-time, edge-deployable object detection method using YOLOv12 to differentiate between fresh and rotten apples in RGB images. The dataset included 2,312 annotated images, with 1,011 fresh apples and 1,301 rotten apples, split into training, validation, and testing sets with an 80:10:10 stratified ratio. To enhance model generalization, data augmentation techniques such as mosaic augmentation, horizontal flipping, rotation, scaling, HSV color jitter, and mixup were applied. The YOLOv12s model was trained with an input resolution of 640 × 640 and evaluated using accuracy, precision, recall, and F1-score. The results from the confusion matrix showed that the model achieved an accuracy of 0.93, precision of 0.91, recall of 0.89, and F1-score of 0.90, indicating that YOLOv12 offers a lightweight and effective framework for rapid apple quality assessment. The primary contribution of this work lies in integrating an attention-focused YOLOv12 detector into a postharvest apple sorting workflow, accompanied by quantitative performance evaluation and robustness analysis under challenging visual conditions.

SISTEM PENDETEKSI TANGISAN BAYI UNTUK MENENTUKAN KONDISI GROWTH SPURT Tegar Pramudia; Fadliyansyah Ramadhan; Evvin Faristasari; Ni Luh Eta Yuspita
Prosiding Seminar Nasional Inovasi Teknologi Terapan Vol. 6 No. 1 (2026): Prosiding Seminar Nasional Inovasi Teknologi Terapan
Publisher : Politeknik Manufaktur Negeri Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar

A growth spurt is a developmental phase experienced by infants, characterized by increased crying frequency, changes in sleep patterns, and heightened nutritional needs. Parents often struggle to distinguish between crying caused by a growth spurt and crying due to hunger, a wet diaper, or other forms of discomfort. Therefore, this study aims to design and build an infant cry detection system to automatically indicate when a growth spurt is occurring. The developed system utilizes an ESP32 as the main microcontroller and a sound sensor (microphone) as the input to detect the intensity of the infant's crying. Sensor data is processed by the ESP32 based on pre-programmed parameters to determine the likelihood that the infant is experiencing a growth spurt. Detection results are displayed on a 16×2 LCD with an I2C interface, ensuring the information is easily readable for the user. Additionally, an active buzzer serves as an audible indicator when the system detects conditions meeting the growth spurt criteria. The entire circuit is powered by a power bank, making the system portable and suitable for use in various locations.

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