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All Journal Jurnal Informatika Jurnal Ilmu Komputer dan Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Information Systems Engineering and Business Intelligence Media Jurnal Informatika Journal of Integrated and Advanced Engineering (JIAE)
Priyanto Hidayatullah, Priyanto
Politeknik Negeri Bandung, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering

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PENGUKURAN TINGKAT MOTILITAS SPERMA BERDASARKAN LINEARITAS SPERMA MENGGUNAKAN METODE ADAPTIVE LOCAL THRESHOLD DAN ELLIPSE DETECTION Hidayatullah, Priyanto; Nuriyadi, M.; Awaludin, Iwan; Kusumo, Reyhan Damar
Jurnal Informatika Vol 13, No 2 (2015): NOVEMBER 2015
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (627.347 KB) | DOI: 10.9744/informatika.13.2.44-50

Abstract

Technology that can be used toassist sperm examination is Computer-Aided Sperm Analysis (CASA). The problems with this technology are expensive and the methods are not open for public. A lot of researches have been done to have an altenative CASA’s method to examine sperm quality accurately, inexpensively, and fast. This research is focused on measuring the level of sperm motility. Visual observations of sperm motility greatly depend on each andrologists which are subjective and also the possibility of repetition for the examined sample does not exist. Moreover, visual observation cannot provide precise values for parameters that affect the sperm motion patterns which are Velocity Straight Line, Velocity Curvilinear Line, and Linearity. Focus of this research is to create an application using sperm detection method, Local Adaptive Threshold and Ellipse Detection to measure sperm linearity to determine the level of sperm motility based on the WHO standard. Experiment produces result with the smallest difference 5.7333% and the biggest difference 10.4667% compared to the result of visual analysis by andrologist.
Bull Sperm Motility Measurement Improvement Using Sperm Head Direction Angle Akbar Akbar; Eros Sukmawati; Dwi Utami; Muhammad Nuriyadi; Iwan Awaludin; Priyanto Hidayatullah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.8685

Abstract

It is well known that sperm motility is the most important parameter in measuring sperm quality. Sperm motility can be measured manually by a medical veterinarian or using Computer Aided Sperm Analysis (CASA) system. Measuring bull sperm motility manually is the most popular method nevertheless it has major drawbacks which are subjective and high variability. CASA gives consistent results, hence it is an expensive system. Therefore, an alternative method is required. In this paper, a method to measure bull sperm motility was proposed, It was using ellipse detection and sperm head direction angle. The video samples were taken using a digital microscope. Compared with the manual assessment by medical veterinarians, the proposed method gave the average absolute margin of 5.16% which surpassed the previous method which gave 10.69%.
Early Stopping Effectiveness for YOLOv4 Afif Rana Muhammad; Hamzah Prasetio Utomo; Priyanto Hidayatullah; Nurjannah Syakrani
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.11-20

Abstract

Background: YOLOv4 is one of the fastest algorithms for object detection. Its methods, i.e., bag of freebies and bag of specials, can prevent overfitting, but this can be combined with early stopping as it could also prevent overfitting. Objective: This study aims to identify the effectiveness of early stopping in preventing overfitting in the YOLOv4 training process. Methods: Four datasets were grouped based on the training data size and object class, These datasets were tested in the experiment, which was carried out using three patience hyperparameters: 2, 3, and 5. To assess the consistency, it was repeated eight times. Results: The experimental results show that early stopping is triggered more frequently in training with data below 2,000 images. Of the three patience hyperparameters used, patience 2 and 3 were able to halve the training duration without sacrificing accuracy. Patience 5 rarely triggers early stopping. There is no pattern of correlation between the number of object classes and early stopping. Conclusion: Early stopping is useful only in training with data below 2,000 images. Patience with a value of 2 or 3 are recommended. Keywords: Early Stopping, Overfitting, Training data, YOLOv4
Counting Various Vehicles using YOLOv4 and DeepSORT Alfan Pahreza Kusumah; Dena Djayusman; Galih Rizki Setiadi; Ade Chandra Nugraha; Priyanto Hidayatullah
Journal of Integrated and Advanced Engineering (JIAE) Vol 3, No 1 (2023)
Publisher : Asosiasi Staf Akademik Perguruan Tinggi Seluruh Indonesia (ASASI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51662/jiae.v3i1.68

Abstract

The Ministry of Public Works and Public Housing (PUPR) conducted a traffic survey to determine the total number of vehicles and classify them according to the Bina Marga vehicle categorisation. The survey has thus far been carried out manually. As a result, surveys take a lot of time and money to perform. Additionally, as the survey scope grows, so will the requirement for surveyors. Therefore, a substitute that can execute the survey procedure automatically and with tolerable accuracy is required. One solution is to utilise deep learning technology to detect and categorise vehicles that can be used in apps. The program is designed as a web application that provides a summary of vehicle calculations and receives video data from traffic recordings. The deep learning model used is YOLOv4 which is trained to recognise vehicle classes following Bina Marga vehicle types. The model was trained and tested using the Python programming language and the Darknet framework on the Google Colab platform. The YOLOv4 and DeepSORT method with custom dataset reached a decent accuracy of 67.94%, considering the limited 1000 images used for training the model.
YOLOv7 Tiny improvement for bull sperm detection Nashirin, Wafi Khoerun; Zaman, Azzam Badruz; Hidayatullah, Priyanto; Ekawijana, Ardhian
Journal of Integrated and Advanced Engineering (JIAE) Vol 4, No 2 (2024)
Publisher : Akademisi dan Saintis Indonesia (ASASI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51662/jiae.v4i2.154

Abstract

YOLO (You Only Look Once) is a prominent deep learning model used in object detection due to its high detection accuracy and speed. Nonetheless, in detecting bull sperm, YOLOv7 Tiny performance suffers because of the unique characteristics of bull sperm: its tiny size and the large quantity of sperm. YOLOv7 Tiny's performance can be improved by adjusting based on its unique characteristics. This study proposes a modified YOLOv7 Tiny model to detect bull sperm with higher accuracy. The main objective of this research is to increase the accuracy of YOLOv7 Tiny in detecting and counting bull sperm. The YOLOv7 Tiny architecture will be modified based on the characteristics of the object to be detected, specifically bull sperm. Several architectural parts were deleted, the anchor box's size was changed, and the grid cell's size was changed. The omitted architecture parts are the ones used for detecting large and medium-sized objects. The anchor box and grid cell sizes will be altered to fit the size of the object. Accuracy is measured using mean average precision (mAP). The modified YOLOv7 Tiny will be evaluated in comparison to the original YOLOv7 Tiny. In our experiment, we produced 65.8 mAP with an inference time of 14.4 ms on the test dataset. When detecting bull sperm, the modified model is 1.3 points more accurate and 1.23x faster than YOLOv7 Tiny. The size of the modified model file is likewise decreased by 84.2%.
Penggunaan MediaPipe untuk Pengenalan Gesture Tangan Real-Time dalam Pengendalian Presentasi Agustiani, Amelia Dewi; Sholahuddin, Muhammad Rizqi; Putri, Salsabila Maharani; Hidayatullah, Priyanto
Media Jurnal Informatika Vol 16, No 2 (2024): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v16i2.4788

Abstract

Penelitian ini membahas masalah pengendalian presentasi yang terbatas pada penggunaan perangkat fisik seperti mouse atau keyboard, yang sering mengurangi fleksibilitas pengguna. Untuk mengatasi hal ini, penelitian ini mengusulkan implementasi MediaPipe, sebuah framework pengolahan citra dan video, untuk pengenalan gestur tangan secara real-time. Metode ini memungkinkan pengguna mengontrol presentasi PowerPoint secara intuitif melalui gerakan tangan tanpa kontak fisik dengan perangkat. Pengendalian dilakukan dengan mendeteksi dan menginterpretasikan gestur tangan menggunakan teknologi pengenalan pola berbasis jaringan saraf tiruan. Studi ini bertujuan meningkatkan efisiensi dan kenyamanan dalam mengendalikan presentasi, khususnya dalam situasi yang membutuhkan interaksi jarak jauh. Hasil penelitian menunjukkan implementasi ini mampu memberikan respons cepat terhadap perubahan gestur dalam berbagai kondisi penggunaan. Model pengenalan gestur tangan yang diusulkan menunjukkan performa sangat baik, dengan nilai macro average precision, recall, dan F1-score masing-masing mencapai 97%, yang berkontribusi pada pengembangan antarmuka pengguna yang lebih intuitif dan efisien.
Preprocessing Impact on SAR Oil Spill Image Segmentation Using YOLOv8 Syakrani, Nurjannah; Kurniawan, Dimas; Nugraha, Wili Akbar; Hidayatullah, Priyanto; Firdaus, Lukmannul Hakim; Sholahuddin, Muhammad Rizqi
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 1 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i1.1380

Abstract

Synthetic Aperature Radar (SAR) is a sensory equipment used in marine remote sensing that emits radio waves to capture a representation of the target scene. SAR images have poor quality, one of which is due to speckle noise. This research uses SAR images containing oil spills as objects that are detected using machine learning with the YOLOv8 model. The dataset was obtained from MKLab by preprocessing to improve the quality of SAR images before processing. Preprocessing involves annotating the dataset, augmenting it with flip augmentation, and filtering it using threshold and median filters in addition to a sharpen kernel that finds the optimal midway value. The default value of the YOLOv8 hyperparameter is used with addition of delta as well as subtraction of the same delta. The implementation of preprocessing and combination of hyperparameters is examined to optimize the YOLOv8 model in detecting oil spills in SAR images. Based on 10 experimental scenarios, initial results with the original MKLab image provide an mAP50 of 49.7%. Implementing Flip augmentation alone on the data set increases the mAP50 value by 18.8%. Followed by the sharpen 1.2 kernel filter increasing the mAP50 value to 68.89%, while the median and thresholding filters tend to reduce the mAP50 value. The combination of experiments with the best results was preprocessing with flip augmentation and sharpen 1.2 kernel filter with hyperparameters: epoch 200, warmup 4.0, momentum 0.9, warmup bias lr 0.01, weight decay 0.005, and learning rate 0.000714, resulting in an mAP50 value of 68.89%. In addition, it was found that the sharpening kernel with a real number midpoint of 1.2 and combination with flipping augmentation had the greatest impact on increasing the MAP50 value in SAR oil spill image segmentation by YOLOv8.
Real-time Convolutional Neural Networks untuk Klasifikasi Emosi Wajah yang Terintegrasi dengan Rekomendasi Konten YouTube Berdasarkan Emosi Secara Rule-Based Gantini, Annisa Dinda; Malika, Adinda Faayza; Elfada, Berliana; Hidayatullah, Priyanto; Sholahuddin, Muhammad Rizqi
Media Jurnal Informatika Vol 17, No 1 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i1.5100

Abstract

Kemampuan sistem untuk mendeteksi emosi pada citra wajah manusia dan memberikan rekomendasi konten YouTube berdasarkan hasil deteksi tersebut dapat meningkatkan interaksi manusia dengan teknologi. Penelitian ini mengusulkan integrasi baru antara deteksi emosi wajah secara real-time dengan sistem rekomendasi multimedia berbasis emosi, menggunakan kerangka kerja ringan berbasis Convolutional Neural Networks (CNN). Dua arsitektur CNN yang efisien—Sequential Fully-CNN dan Mini-Xception—dibandingkan untuk mengklasifikasikan tujuh emosi pada dataset FER-2013. Kontribusi utama dari penelitian ini adalah penggabungan klasifikasi emosi secara real-time dengan sistem pemetaan berbasis aturan untuk merekomendasikan konten YouTube yang relevan secara emosional, menciptakan pengalaman pengguna yang lebih personal dan adaptif. Hasil pengujian menunjukkan bahwa model CNN yang dikembangkan mampu mendeteksi emosi dengan akurasi tinggi sekaligus menjaga efisiensi komputasi untuk aplikasi waktu nyata. Pendekatan ini diharapkan dapat meningkatkan kualitas interaksi manusia-komputer melalui umpan balik multimedia yang responsif dan relevan secara emosional.
Co-Authors Ade Chandra Nugraha Afif Rana Muhammad Agustiani, Amelia Dewi Akbar Akbar Alfan Pahreza Kusumah Ardhian Ekawijana Dena Djayusman Dimas Kurniawan Dwi Utami Elfada, Berliana Eros Sukmawati Firdaus, Lukmannul Hakim Galih Rizki Setiadi Gantini, Annisa Dinda Hamzah Prasetio Utomo Iwan Awaludin M. Nuriyadi, M. Malika, Adinda Faayza Muhammad Rizqi Sholahuddin Nashirin, Wafi Khoerun Nugraha, Wili Akbar Nuriyadi, Muhammad Nurjannah Syakrani Putri, Salsabila Maharani Reyhan Damar Kusumo, Reyhan Damar Syakrani, Nurjannah Zaman, Azzam Badruz