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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 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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Pelatihan Membuat Aplikasi Tanpa Coding Bagi Siswa SMK Yapensu Sungailiat Bradika Almandin Wisesa; Vivin Mahat Putri; Indra Irawan; M. Syafrizal Zain; Putri Armilia Prayesy
Dharma Nusantara: Jurnal Ilmiah Pemberdayaan dan Pengabdian kepada Masyarakat Vol. 3 No. 2 (2025): Dharma Nusantara: Jurnal Ilmiah Pemberdayaan dan Pengabdian kepada Masyarakat
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/dharma.v3i2.2154

Abstract

Kegiatan pengabdian masyarakat berbentuk pelatihan pembuatan aplikasi mobile berbasis no-code sukses digelar untuk siswa SMK Yapensu Sungailiat dengan memanfaatkan Glide Apps—platform yang dipilih berkat kemudahan penggunaan dan fitur gratisnya yang cukup untuk aplikasi sederhana tanpa perlu coding. Prosesnya meliputi persiapan administratif, survei pra-pelatihan melalui kuesioner, penyuluhan disertai demonstrasi dan praktik langsung, hingga evaluasi pasca-pelatihan; peserta berhasil mengembangkan Aplikasi Siswa Yapensu menggunakan Glide Tables dengan antarmuka mirip Excel. Evaluasi menunjukkan rata-rata 80 % responden memberikan penilaian “sangat baik” dan “sangat sesuai”, menandakan respons positif serta kemampuan peserta menyelesaikan seluruh tahap pengembangan aplikasi secara mandiri.
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
Drowsiness Detection using YOLOv12 Wisesa, Bradika Almandin Almandin; 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.
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): Juni 2026 (in progress)
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.
Co-Authors Andriyani, Widyastuti Better Swengky Dany Candra Febrianto Darma, Satria Agus Edyyul, Ilham Akerda  Evvin Faristasari Evvin Faristasari Evvin Faristasari Fadila, Resma Hengki Hengki Indra Irawan Indri Dwi Agustin Linda Fujiyanti LINDA FUJIYANTI Lionza, Rahmat Lisda Lisda M. Hizbul Wathan Muhammad Raihan Pasha Nenen Isnaeni Pasha, Muhammad Raihan Peprizal, Peprizal Putri Armilia Prayesy Satria Agus Darma Satria Agus Darma Silvia Agustin Silvia Agustin Silvia Agustin Sirlus Andreanto Jasman Duli Sirlus Andreanto Jasman Duli Vivin Mahat Putri Vivin Mahat Putri Yuda Paraswistara Yuli Astuti Zain, M Syafrizal