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All Journal Jurnal Teknosains Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIKO (Jurnal Informatika dan Komputer) International Journal of Informatics and Computation Jurnal Pengembangan Sistem Informasi dan Informatika Journal of Dinda : Data Science, Information Technology, and Data Analytics Emerging Information Science and Technology J-Intech (Journal of Information and Technology) Kharisma Tech Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Journal Of Artificial Intelligence And Software Engineering Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Journal of Information Technology Dharma Nusantara: Jurnal Ilmiah Pemberdayaan dan Pengabdian kepada Masyarakat
Bradika Almandin Wisesa
Politeknik Manufaktur Negeri Bangka Belitung
Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Sistem Pakar Hibrida Deteksi Keterlambatan Bicara pada Anak Menggunakan Forward Chaining dan Naïve Bayes Putri, Vivin Mahat; Wisesa, Bradika Almandin; Edyyul, Ilham Akerda ; Darma, Satria Agus
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 3 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i3.1323

Abstract

Penelitian ini bertujuan untuk mengembangkan dan memvalidasi sebuah metode inferensi hibrida yang mengintegrasikan penalaran berbasis aturan (rule-based reasoning) dari Forward Chaining dengan klasifikasi probabilistik dari Naïve Bayes. Sistem ini dirancang sebagai alat skrining (penapisan) dini terhadap risiko keterlambatan bicara dan bahasa pada anak. Sistem dikembangkan dengan pendekatan hibrida. Metode Forward Chaining diimplementasikan untuk merepresentasikan pengetahuan klinis yang pasti. Sementara itu, metode klasifikasi Naïve Bayes digunakan sebagai sistem probabilistik yang dilatih menggunakan dataset yang telah divalidasi. Proses optimasi model Naïve Bayes melibatkan serangkaian teknik, termasuk penyederhanaan masalah menjadi klasifikasi biner, penyeimbangan data latih menggunakan Synthetic Minority Over-sampling Technique (SMOTE), dan hyperparameter tuning. Pengujian model Naïve Bayes menunjukkan bahwa proses optimasi yang komprehensif berhasil meningkatkan performa secara signifikan. Dengan menggunakan model Bernoulli Naïve Bayes pada data biner yang telah diseimbangkan, performa model berhasil mencapai akurasi sebesar 72.22%. Secara khusus, model menunjukkan nilai recall yang tinggi sebesar 84% untuk kelas 'Terindikasi', yang sangat krusial untuk alat skrining. Sistem pakar hibrida yang diusulkan menunjukkan validitasnya sebagai instrumen skrining yang fungsional. Sinergi antara penalaran logis dari Forward Chaining dan inferensi probabilistik dari Naïve Bayes yang telah dioptimalkan menghasilkan sistem dengan keandalan yang tinggi. Implementasi metode ini telah berhasil divalidasi melalui sebuah prototipe aplikasi web yang fungsional.
Aplikasi Mobile Padi Kita Berbasis Rapid Application Development Untuk Digitalisasi Pertanian Desa Rias Muhammad Raihan Pasha; Linda Fujiyanti; Bradika Almandin Wisesa
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/zan4db85

Abstract

The agricultural sector, especially the rice commodity in Rias Village, is a strategic pillar still facing significant challenges, namely the manual nature of harvest recording and data management processes, limited access to accurate agroclimatology information, and suboptimal market access. This research aims to overcome these constraints through the development of an integrated agricultural information system. The proposed solution is the Padi Kita mobile application, which was designed to support agricultural digital transformation. The development method applied is the Rapid Application Development (RAD) model, chosen for its effectiveness in accelerating the design cycle and rapidly accommodating functional adjustments based on user needs. The application facilitates digital harvest recording, provides real-time weather information, and enables more transparent monitoring and marketing of sales results, including a direct ordering feature for buyers. The results of the study indicate that the implementation of the Padi Kita application successfully realized the digitalization of the agricultural business flow, providing time efficiency and improving data accuracy at the farmer and milling administrator levels. It is concluded that this RAD-based application development is capable of creating a more integrated agricultural ecosystem, directly contributing to increased productivity and potential welfare for farmers in Rias Village
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

Abstract

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

Abstract

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

Abstract

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

Abstract

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.
YOLO26-Based ASL Sign Language Gesture Detection on Android Isnaeni, Nenen; Wisesa, Bradika Almandin; Febrianto, Dany Candra
Emerging Information Science and Technology Vol. 7 No. 1 (2026): May
Publisher : Universitas Muhammadiyah Yogyakarta

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

Abstract

This study proposes a real-time Android-based American Sign Language (ASL) gesture detection system using YOLO26. The model recognizes 26 static gesture classes consisting of 24 alphabet gestures (a-i and k-y) and two common expressions, namely “I love you” and “thank you”, from images captured by a smartphone camera. A custom dataset of 7,800 images was prepared and divided into 5,460 training images, 1,560 validation images, and 780 testing images. Preprocessing included resizing, normalization, horizontal flipping, random rotation, brightness adjustment, contrast variation, and zoom augmentation to improve robustness under different acquisition conditions. Three YOLO26 variants, namely YOLO26s, YOLO26n, and YOLO26m, were trained and evaluated using precision, recall, F1-score, mAP@50, mAP@50-95, latency, frames per second, and lighting robustness. Experimental results show that YOLO26n provided the most balanced deployment performance with 96.4% precision, 95.8% recall, 96.1% F1-score, 97.8% mAP@50, and 84.2% mAP@50-95. Real-time testing on an Infinix X6726 device produced an average latency of 118 ms per frame or 8.47 FPS. Robustness testing under high, medium, and low lighting produced detection success rates of 98.3%, 96.7%, and 93.3%, respectively. The findings indicate that YOLO26 is feasible as an exploratory architecture for Android-based ASL gesture detection, although broader cross-device and cross-dataset validation is still required before claiming general real-world superiority.
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

Abstract

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

Abstract

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.
Analisis Sentimen pada Ulasan Produk dengan SVM dan Word2Vec WIDYASTUTI ANDRIYANI; Yuli Astuti; Bradika Almandin Wisesa; Hengki Hengki
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 1 (2025): Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i1.1498

Abstract

Analisis sentimen adalah salah satu cabang pemrosesan bahasa alami (NLP) yang bertujuan untuk mengidentifikasi opini dalam teks. Penelitian ini mengusulkan model analisis sentimen dengan menggunakan kombinasi Word2Vec sebagai teknik representasi fitur dan Support Vector Machine (SVM) sebagai algoritma klasifikasi. Dataset yang digunakan adalah Amazon Customer Reviews, dengan 500 ribu sampel ulasan produk yang dilabeli sebagai sentimen positif atau negatif. Model yang diusulkan dibandingkan dengan baseline seperti Naive Bayes dan Logistic Regression, yang menggunakan representasi fitur berbasis TF-IDF.Hasil evaluasi menunjukkan bahwa SVM dengan Word2Vec menghasilkan akurasi 91.3\%, precision 90.8\%, recall 92.1\%, dan F1-score 91.4\%, lebih unggul dibandingkan model baseline. Grafik Precision-Recall Curve dan ROC Curve memperkuat temuan bahwa Word2Vec memberikan representasi fitur yang lebih informatif, yang secara signifikan meningkatkan performa SVM dalam tugas klasifikasi teks.Penelitian ini membuktikan efektivitas kombinasi Word2Vec dan SVM untuk analisis sentimen pada dataset besar dan kompleks. Pendekatan ini relevan untuk berbagai domain, seperti e-commerce dan analisis opini di media sosial, serta membuka peluang untuk pengembangan lebih lanjut menggunakan model berbasis transformer.
Co-Authors Andriyani, Widyastuti Better Swengky Dany Candra Febrianto Darma, Satria Agus Edyyul, Ilham Akerda Evvin Faristasari Evvin Faristasari Fadila, Resma Hengki Hengki Indra Irawan Indri Dwi Agustin Irwan Linda Fujiyanti LINDA FUJIYANTI Lionza, Rahmat Lisda Lisda M. Galvin Prihardi Putra M. Hizbul Wathan Muhammad Raihan Pasha Nenen Isnaeni Pasha, Muhammad Raihan Peprizal Putri Armilia Prayesy Rahmat Lionza Satria Agus Darma Silvia Agustin Silvia Agustin Silvia Agustin Sirlus Andreanto Jasman Duli Vivin Mahat Putri Yuda Paraswistara Yuli Astuti Zain, M Syafrizal