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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi IJEBD (International Journal Of Entrepreneurship And Business Development) Informatik : Jurnal Ilmu Komputer Journal of Information Systems and Informatics bit-Tech International Journal of Advances in Data and Information Systems Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences International Journal of Robotics and Control Systems International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Impresi Indonesia Literasi Nusantara Journal of International Conference Proceedings Jurnal Ilmiah Edutic : Pendidikan dan Informatika Signal and Image Processing Letters
Diyasa, I Gede Susrama Mas
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Physics Social Sciences Other

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Journal : Literasi Nusantara

 1 
Identification of Abnormal Spermatozoa Motility Using the SVM Algorithm Karim, Mohammad Daniel Sulthonul; Puspaningrum, Eva Yulia; Diyasa, I Gede Susrama Mas
Literasi Nusantara Vol. 5 No. 1 (2025): Literasi Nusantara: November 2024- February 2025
Publisher : Yayasan Citra Dharma Cindekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56480/jln.v5i1.1324

Abstract

Spermatozoa motility is one of the key indicators in determining male fertility quality. Manual assessment of motility abnormalities often requires significant time and effort, thus necessitating a more efficient and accurate automated approach. This study aims to identify abnormalities in spermatozoa motility using the Support Vector Machine (SVM) algorithm, utilizing microscopic video data analyzed through TrackPy for spermatozoa trajectory tracking. The analysis process involves data acquisition, spermatozoa detection in each frame, sperm trajectory construction, and trajectory classification into normal or abnormal categories. The SVM model was trained using a dataset derived from spermatozoa trajectories classified based on parameters such as average velocity and trajectory linearity. The results show that the method achieved the highest accuracy of 89 percent in identifying spermatozoa motility abnormalities in HD resolution videos with a frame rate of 30 fps.
Implementation of Convolutional Neural Network for Road Damage Detection and Classification in Surabaya City Kusuma, Nugraha Varrel; Diyasa, I Gede Susrama Mas; Anggraeny, Fetty Tri
Literasi Nusantara Vol. 5 No. 1 (2025): Literasi Nusantara: November 2024- February 2025
Publisher : Yayasan Citra Dharma Cindekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56480/jln.v5i1.1357

Abstract

Road damage is a significant infrastructural problem that impacts the safety of road users and economic efficiency. The current road damage detection system, which relies on manual inspection, has limitations in speed and accuracy. Therefore, this study proposes the use of a conventional Convolutional Neural Network (CNN) to enhance accuracy and efficiency in the detection and classification of road damage in Surabaya City. The methods applied include data preprocessing and basic data augmentation techniques such as rotation and flipping. The dataset used comes from CV. Wastu Kencana Teknik, consisting of four road damage classes: potholes, surface delamination, cracks, and edge cracks. The implementation of the CNN model with standard configurations shows potential for application in an AI-based road infrastructure monitoring system. The model evaluation was performed using a confusion matrix and ROC-AUC, indicating that the model has stable and accurate classification performance. With these results, the model has the potential to enhance the effectiveness of detection and decision-making in road maintenance.
Co-Authors A, Denisa Septalian A. A. Ngurah Gunawan Achmad Junaidi, Achmad Adila, Ma’ratul Agussalim, Agussalim Ali, Munawar Ani Dijah Rahajoe Awang, Wan Suryani Wan Bambang Trigunarsyah, Bambang Basuki Rahmat Masdi Siduppa Damaliana, Aviolla Terza Dewi , Deshinta Arrova Dwi Arman Prasetya Erik Iman Heri Ujianto Erma Suryani Eva Yulia Puspaningrum Fahrudin, Tresna Maulana Faikul Umam Fatullah, Ryan Reynickha Fetty Tri Anggraeny Henki Bayu Seta Humairah, Sayyidah I Made Merdana Ibrahim, Mohd Zamri Bin idhom, Mohammad Karim, Mohammad Daniel Sulthonul Kartika Maulida Hindrayani Kusuma, Nugraha Varrel Kuswardana, Dendy Arizki Kuswardhani , Hajjar Ayu Cahyani Made Hanindia Prami Swari Mudjahidin Najma P., Safira NYOMAN DITA PAHANG PUTRA, NYOMAN P, Made Hanindia Permana, Eriko Indra Permanasari, Wahyu Melinda Prayoga, Aji Putra, Handie Pramana Riyantoko, Prismahardi Aji Rizky Mahendra, Rakha Sampurno, Ilham Ade Widya Saputra, Wahyu Syaifullah Jauharis Sari, Allan Ruhui Fatmah Sari, Refika Ayuna Sibarani, Michael Alexander Justin Audison Sugiarto S Trenggono, Brahmantio Widyo Trimono Trimono, Trimono Ulayya, Yasmin Viet Ha, Nguyen Yisti Vita Via Zahrah, Fathima Zamri, Mohd