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All Journal Jurnal Teknosains Journal of Islamic Pharmacy Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Islamic Medicine Technologia: Jurnal Ilmiah International Journal of Informatics and Computation Jurnal Pengembangan Sistem Informasi dan Informatika Jurnal Listrik, Instrumentasi, dan Elektronika Terapan J-Intech (Journal of Information and Technology) Kharisma Tech Paradigma Prosiding Seminar Nasional Inovasi Teknologi Terapan Journal Of Artificial Intelligence And Software Engineering Jurnal Inovasi Teknologi Terapan Smart Dedication: Jurnal Pengabdian Masyarakat Journal of Technology and Engineering Journal of Information Technology
Evvin Faristasari
Politeknik Manufaktur Negeri Bangka Belitung
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences

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SISTEM KLASIFIKASI SAMPAH ORGANIK, ANORGANIK, DAN BAHAN BERBAHAYA BERACUN (B3) Bradika Almandin Wisesa; Vivin Mahat Putri; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Rahmat Lionza
KHARISMA Tech Vol 21 No 2 (2026): KHARISMATech Journal
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/kharismatech.v21i2.695

Abstract

Penelitian ini mengembangkan sistem klasifikasi sampah real-time untuk sampah organik, anorganik, dan bahan berbahaya beracun (B3) di Jalan Timah Sungailiat, Bangka Belitung, Indonesia menggunakan YOLOv11 untuk mengatasi pencemaran lingkungan akibat pengelolaan sampah yang tidak tepat. Dataset khusus sebanyak 5.000 gambar yang seimbang pada kondisi siang, senja, dan malam dikumpulkan untuk melatih YOLOv11 sehingga mampu menangani variasi pencahayaan. Metodologi meliputi pra-pemrosesan (ubah ukuran 640×640 piksel, normalisasi, augmentasi data), pelatihan YOLOv11 pada dataset seimbang, serta evaluasi menggunakan metrik mean Average Precision (mAP@0.5), presisi, recall, dan F1-score. Sistem mencapai mAP@0.5 sebesar 70%, presisi 69%, recall 70%, dan F1-score 0,70 dengan kecepatan 43 frame per detik (FPS) serta 102 GFLOPs sehingga dapat dijalankan pada GPU kelas menengah. Meskipun akurasi masih moderat karena variasi pencahayaan dan oklusi, kerangka kerja ini menawarkan solusi hemat biaya dan skalabel untuk pengelolaan sampah kota pintar, mengurangi tenaga sorting manual serta mendukung inisiatif daur ulang. Perbaikan mendatang akan fokus pada peningkatan performa malam hari dan penanganan.
Implementation of YOLOv5 for Fingerling Detection under Turbid Conditions in Abandoned Tin Mining Ponds Indra Dwisaputra; Sirlus Andreanto Jasman Duli; Eko Sulistyo; Evvin Faristasari; Ali Rospawan; Fadillah Aslammy Pratama Sayti; Muhammad Putra Deswalki
Jurnal Listrik, Instrumentasi, dan Elektronika Terapan Vol 7, No 2 (2026)
Publisher : Departemen Teknik Elektro dan Informatika Sekolah Vokasi UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/juliet.v7i2.121918

Abstract

The utilization of abandoned tin mining ponds for freshwater aquaculture in Bangka Belitung requires reliable monitoring technology, particularly for fingerling counting under variable water clarity conditions. Because in-situ underwater counting within large, deep, and unconfined pond waters is technically impractical, this study developed an on-bank portable inspection station integrating YOLOv5-based image processing on an embedded Raspberry Pi unit with multi-point turbidity sensing using water extracted directly from abandoned tin mining ponds. A dataset of 80 original images was strictly partitioned prior to augmentation into training (70%), validation (20%), and independent test (10%) sets to ensure zero data leakage, followed by training set expansion to 225 images via offline augmentations. Trained at 640×640 resolution across 50 epochs, the YOLOv5 model achieved 98% accuracy, 100% precision, 96% recall, 100% specificity, and an F1-score of 0.98 at an IoU threshold of 0.5. While vision-only detection remained stable at low densities, severe under-counting (up to 26%–30% loss) occurred under high-density conditions (40–50 fingerlings) when turbidity exceeded 85 NTU due to light scattering and boundary occlusion. To address this limitation, an Adaptive Turbidity Correction Factor (α = 1.36) was introduced, successfully restoring biomass estimation accuracy to over 96%. The embedded prototype maintained an average inference speed of 10.5 FPS with a peak CPU temperature of 67.2°C during a continuous 2-hour stress test. These findings confirm that combining edge vision with turbidity-aware calibration substantially improves fingerling counting reliability in challenging aquatic environments of abandoned tin mining ponds.
Comparative Machine Learning for Real-Time Hand Gesture Recognition of Common Cold Symptoms in Healthcare Communication  Duli, Sirlus Andreanto Jasman; Wisesa, Bradika Almandin; Faristasari, Evvin; Putri, Vivin Mahat; Peberiyan, Rizki
Paradigma - Jurnal Komputer dan Informatika Vol. 28 No. 2 (2026): September 2026 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v28i2.12960

Abstract

Effective medical anamnesis for patients with hearing or speech impairments requires accessible communication channels. This study evaluates a vision-based hand gesture recognition pipeline to interpret gestures representing Common Cold complaints during healthcare triage. Hand landmark data comprising 63 spatial coordinates per frame were captured across three human subjects using a single RGB webcam powered by the MediaPipe Hand Landmarker under varying distances (1 m and 2 m) and ambient lighting conditions. The dataset covers seven gesture classes based on standard Indonesian Sign Language (SIBI): Saya (I), Sakit (Sick), Flu, Batuk (Cough), Demam (Fever), Pusing (Headache), and Nyeri Menelan (Painful Swallowing). Three supervised classifiers—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB)—were comparatively evaluated using a 5-fold Stratified Cross-Validation scheme. Experimental results demonstrate that Gaussian Naive Bayes achieved the highest classification performance with an overall accuracy of 0.81 and a macro F1-score of 0.81, followed by SVM (accuracy 0.80, F1-score 0.80) and KNN (accuracy 0.76, F1-score 0.76). These findings indicate that the continuous Gaussian distribution assumption aligns effectively with normalized knuckle coordinate features. Rather than serving as an automated disease diagnostic instrument, this framework functions as a reliable assistive interpreter for symptom-related sign language in clinical anamnesis.
Co-Authors ., Yudhi Abd. Rasyid Syamsuri Ade Putra Maulana Ali Rospawan Amin, Fita Avrista Vilusi Aulia, Merizta Better Swengky Bradika Almandin Wisesa Daniel, Rodo Eko Sulistyo Enggar Hero Istoto Fadila, Resma Fadillah Aslammy Pratama Sayti Fadliyansyah Ramadhan Hardi Perwira Hero Istoto, Enggar Indra Dwisaputra Indra Irawan Indri Dwi Agustin Lionza, Rahmat M. Hizbul Wathan Merizta Aulia Muhammad Putra Deswalki Ni luh Eta Yuspita Peberiyan, Rizki Peprizal Perwira, Hardi Rahmat Lionza Riztamala Diana Satria Agus Darma Silvia Agustin Silvia Agustin Sirlus Andreanto Jasman Duli Sukma, Shafril Muliawan Tegar Pramudia Titin Indriati Vivin Mahat Putri Yuda Paraswistara