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All Journal Jurnal Ilmiah Informatika Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Jurnal Transformatika Jurnas Nasional Teknologi dan Sistem Informasi E-Dimas: Jurnal Pengabdian kepada Masyarakat Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JPM (Jurnal Pemberdayaan Masyarakat) Jurnal BALIRESO ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer KOMPUTIKA - Jurnal Sistem Komputer Celebes Abdimas: Jurnal Pengabdian Kepada Masyarakat Buletin Ilmiah Sarjana Teknik Elektro G-Tech : Jurnal Teknologi Terapan Journal of Information Technology and Its Utilization Indian Journal of Forensic Medicine & Toxicology International Journal of Engineering, Science and Information Technology Jurnal PkM (Pengabdian kepada Masyarakat) Caraka : Indonesia Journal of Communication Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat Celebes Journal of Community Services KOPEMAS
Dolly Indra
Universitas Muslim Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Optimizing YOLO-Based Algorithms for Real-Time BISINDO Alphabet Detection Under Varied Lighting and Background Conditions in Computer Vision Systems Hayati, Lilis Nur; Handayani, Anik Nur; Gunawan Irianto, Wahyu Sakti; Asmara, Rosa Andrie; Indra, Dolly; Damanhuri, Nor Salwa
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.948

Abstract

This research explores the optimization of YOLO-based computer vision algorithms for real-time recognition of Indonesian Sign Language (BISINDO) letters under diverse environmental conditions. Motivated by the communication barriers faced by the deaf and hearing communities due to limited sign language literacy, the study aims to enhance inclusivity through advanced visual detection technologies. By implementing the YOLOv5s model, the system is trained to detect and classify correct and incorrect BISINDO hand signs across 52 classes (26 correct and 26 incorrect letters), utilizing a dataset of 3,900 images augmented to 10,920 samples. Performance evaluation employs k-fold cross-validation (k=10) and confusion matrix analysis across varied lighting and background scenarios, both indoor and outdoor. The model achieves a high average precision of 0.9901 and recall of 0.9999, with robust results in indoor settings and slight degradation observed under certain outdoor conditions. These findings demonstrate the potential of YOLOv5 in facilitating real-time, accurate sign language recognition, contributing toward more accessible human-computer interaction systems for the deaf community.
Generative adversarial networks (GANS) for generating face images Indra, Dolly; Hidayat, Muh Wahyu; Umar, Fitriyani
Jurnal Ilmiah Kursor Vol. 13 No. 1 (2025)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i1.422

Abstract

The advancement of artificial intelligence technology, particularly deep learning, presents significant potential in facial image processing. Generative Adversarial Networks (GANs), a type of deep learning model, have demonstrated remarkable capabilities in generating high-quality synthetic images through a competitive training process between a generator, which creates new data, and a discriminator, which evaluates its authenticity. However, the use of public facial datasets such as CelebA and FFHQ faces limitations in representing global demographic diversity and raises privacy concerns. This study aims to generate realistic synthetic facial datasets using the StyleGAN2-ADA architecture, a specialized variant of GAN, with two training approaches: training from scratch on two types of datasets (private and public), each containing 480 images. The public dataset used is FFHQ (Flickr-Faces-HQ), known for its broader facial variation and high-quality images. Evaluation is conducted using the Frechet Inception Distance (FID), a metric that assesses image quality by comparing the feature distributions of real and generated images. Results indicate that training from scratch with the public dataset (FFHQ) using a batch size of 16 and a learning rate of 0.0025 achieves an FID score of 85.67 and performance of 86.46% at Tick 100, whereas the private dataset, under the same conditions, results in an FID score of 98.59 with a performance of 18.54%.. The training from scratch approach with the public dataset proves more effective in generating high-quality synthetic facial images compared to the private dataset. In conclusion, this approach supports the optimal generation of realistic synthetic facial data.
Improving Indonesian Sign Alphabet Recognition for Assistive Learning Robots Using Gamma-Corrected MobileNetV2 Hayati, Lilis Nur; Handayani, Anik Nur; Irianto, Wahyu Sakti Gunawan; Asmara, Rosa Andrie; Indra, Dolly; Damanhuri, Nor Salwa
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13300

Abstract

Sign language recognition plays a critical role in promoting inclusive education, particularly for deaf children in Indonesia. However, many existing systems struggle with real-time performance and sensitivity to lighting variations, limiting their applicability in real-world settings. This study addresses these issues by optimizing a BISINDO (Bahasa Isyarat Indonesia) alphabet recognition system using the SSD MobileNetV2 architecture, enhanced with gamma correction as a luminance normalization technique. The research contribution is the integration of gamma correction preprocessing with SSD MobileNetV2, tailored for BISINDO and implemented on a low-cost assistive robot platform. This approach aims to improve robustness under diverse lighting conditions while maintaining real-time capability without the use of specialized sensors or wearables. The proposed method involves data collection, image augmentation, gamma correction (γ = 1.2, 1.5, and 2.0), and training using the SSD MobileNetV2 FPNLite 320x320 model. The dataset consists of 1,820 original images expanded to 5,096 via augmentation, with 26 BISINDO alphabet classes. The system was evaluated under indoor and outdoor conditions. Experimental results showed significant improvements with gamma correction. Indoor accuracy increased from 94.47% to 97.33%, precision from 91.30% to 95.23%, and recall from 97.87% to 99.57%. Outdoor accuracy improved from 93.80% to 97.30%, with precision rising from 90.33% to 94.73%, and recall reaching 100%. In conclusion, the proposed system offers a reliable, real-time solution for BISINDO recognition in low-resource educational environments. Future work includes the recognition of two-handed gestures and integration with natural language processing for enhanced contextual understanding.
Sentiment Analysis of Public Opinion on Deforestation in Papua on YouTube Platform Using Long Short-Term Memory (LSTM) Method Indra, Dolly; Ramdaniah, Ramdaniah; Ode, Nada Kayatri
G-Tech: Jurnal Teknologi Terapan Vol 9 No 4 (2025): G-Tech, Vol. 9 No. 4 October 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i4.7855

Abstract

Deforestation in Papua has emerged as a significant environmental concern, attracting considerable attention due to its effects on biodiversity and the livelihoods of indigenous communities. This study seeks to examine public sentiment toward the issue by analyzing comments posted on the YouTube platform, employing the Long Short-Term Memory (LSTM) method. A dataset of 3,000 comments was gathered and processed through several stages, including text cleaning, tokenization, normalization, and Term Frequency–Inverse Document Frequency (TF-IDF) weighting. Subsequently, an LSTM model was developed and assessed using accuracy, precision, recall, and F1-score as evaluation metrics. The results reveal that the LSTM model achieved an accuracy of 88.43%, a precision of 90.01%, a recall of 97.49%, and an F1-score of 93.60%. Nevertheless, signs of overfitting were observed, indicated by lower validation performance compared to training results. These findings demonstrate that the LSTM approach is effective for identifying public opinion regarding deforestation and can serve as a valuable reference in decision-making and the formulation of environmental policies.
Color Feature Extraction for Grape Variety Identification: Naïve Bayes Approach Jafar, Putri; Indra, Dolly; Umar, Fitriyani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3823

Abstract

The problem addressed in this research is the lack of an efficient and accurate method for automatically identifying grape varieties. Accurate identification is crucial for quality control in the agricultural and food industries, impacting product labeling, pricing, and consumer trust. The aim of this research is to develop an automated system to classify green, black, and red grapes using digital image processing technology. This research method employs Naïve Bayes classification combined with color feature extraction. Testing was conducted under two scenarios: a database scenario with predefined grape image datasets and an out-of-database scenario with images resembling grape colors. Image processing includes resizing images to 200x200 pixels, Gamma Correction, Gaussian filtering, conversion to Lab* color space, K-Means Clustering for segmentation, followed by feature extraction and Naïve Bayes classification. The results of this research are that in the database scenario, the system achieved accuracies of 98.33% with an 80:20 data split and 98.89% with a 70:30 split. In the out-of-database scenario, accuracies were 96.67% with an 80:20 split and 97.78% with a 70:30 split. The conclusion of this research is the proposed method provides a reliable and efficient solution for automatic grape variety identification, benefiting quality control in agriculture and food industries.
Comparative Analysis of User Satisfaction Levels of Threads and X Applications Using the PIECES Method Rahmayani, Nurul; Indra, Dolly; Asis, Muhammad Arfah
Jurnal Nasional Teknologi dan Sistem Informasi Vol 10 No 1 (2024): April 2024
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v10i1.2024.17-26

Abstract

Technology and information systems are high-speed in their application in helping community activities in the digital era. People's desire to access the Internet has increased to provide opportunities for companies to provide technology services, namely the Internet. One of the latest platforms to gain popularity among young users is Threads, which Instagram launched. X is an online social networking and microblogging service that allows users to send and receive text-based messages or posts. To determine whether Threads and X can be used to the best of their ability to access information, an analysis of the performance of the applications was conducted. This study uses the PIECES method to determine user satisfaction with the Threads and X applications. This research uses the PIECES analysis method, which consists of several assessment indicators (Performance, Information and Data, Economics, Control, Security, Efficiency, and Service) by distributing questionnaires to active users of Threads and X from various regions to get 1002 respondents. The results of this study show that Threads receives an average score of 3.61, indicating that users are satisfied, while X gets an average score of 4.37, indicating that users are satisfied. So, the results of this study show that the average level of satisfaction of X users is more significant than Threads. This shows that X's average level of user satisfaction is superior to that of Threads.
Co-Authors Abdul Rauf Tuasikal Agung, Riski Dewa Ahmad Toni, Ahmad Aldri Frinaldi Amir, Nur Hikmah Anik Nur Handayani Arfan Zainuddin As'ad, Ihwana Astuti, Wistiani Damanhuri, Nor Salwa Daris, Mega Asfirawati Erick Irawadi Alwi Erick Irawadi Alwi Fadly Achmad Farniwati Fattah Fery Setyo Aji Firman Shantya Budi, Firman Shantya Hadyan Mardhi Fadlillah Haerdiansyah Syahnur, Muhammad Harlinda Lahuddin Hayudin Hasnanda Maila Herman Herman Hi. Talib, Juraiz Hidayat, Muh Wahyu Huzain Azis Ihwana As’ad Irawati Irawati Irja, Mulianty Cipta Jafar, Putri Jufriadif Na`am, Jufriadif Julius Santony Kadri Rahmat Suat, Wahyu Kasman Kasman Lilis Nur Hayati lilis nurhayati Lukman Syafie Lutfi Budi Ilmawan Manga, Abdul Rachman Mansyur, St. Hajrah Mardiyyah Hasnawi Muh. Ridwan Rahim Muhammad Al Mubarak Muhammad Arfah Asis Muhammad Farhan Hermansyah Mukarramah, Rifqatul Novanto, Achmad Nur Hayati, Lilis Nurhalima Nurhalima Ode, Nada Kayatri Purnawansyah Purnawansyah Rahmayani, Nurul Ramadan, Syahril Ramdan Satra Ramdaniah Ramdaniah Ramdaniah, Ramdaniah Rezky Anugrah Rifky Saputra Scania, Muhammad Rosa Andrie Asmara Satma, Satma St. Hajrah Mansyur Subhan Ardhiman Sugiarti, Sugiarti Sukur, Widianti Syahnur, Muh. Haerdiansyah Syahrul Mubarak Abdullah Tasmil Tasmil Tasrif Hasanuddin Taufik, Muhammad Asrai Umar Mansyur Umar, Fitriyani Veithzal Rivai Zainal Wahyu Sakti Gunawan Irianto Yuhandri Yuhandri, Yuhandri Yundari, Yundari