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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Abdi Ilmu International Journal of Advances in Data and Information Systems Indonesian Journal of Education, Social Sciences and Research (IJESSR) Jurnal Ilmiah MADIYA: Masyarakat Mandiri Berkarya International Journal of Research in Vocational Studies (IJRVOCAS) Jurnal Sains dan Teknologi Majalah Iptek Politeknik Negeri Medan Polimedia Internasional Journal of Data Science, Computer Science and Informatics Technology (InJODACSIT) Journal of Artificial Intelligence and Digital Business Journal Global Technology Computer Jurnal Pengabdian Barelang Komposit : Jurnal Pengabdian Dan Pemberdayaan Masyarakat Journal of Computer Science Advancements
Husna, Meryatul
Politeknik Negeri Medan
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Multi-Detection System Using Faster R-CNN for Fish Species Classification and Quality Assessment on Android Faza, Sharfina; Lubis, Arif Ridho; Meryatul Husna; Rina Anugrahwaty; Muhammad Rafif Rasyidi; Romi Fadillah Rahmat
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16374

Abstract

Species identification and quality assessment of fish in trade still rely on manual visual observation, which is subjective and requires specialized expertise. This method's limitations make it difficult for consumers to distinguish species with similar morphology and accurately assess fish quality, which can lead to inappropriate purchasing decisions. This research develops a multi-detection system based on Faster R-CNN with VGG16 backbone for fish species classification and quality assessment simultaneously on Android platform. The system uses convolutional layers to extract visual features from input images, Region Proposal Network for fish object detection and localization, and fully connected layers for simultaneous classification of species and quality levels. The research dataset consists of 3,000 images of five fish species (gourami, tilapia, nile tilapia, snapper, and pomfret) with four quality levels, divided into 2,400 training images and 600 testing images. The trained model is converted to TensorFlow Lite format for implementation on Android devices. Test results show the multi-detection system achieves 92% accuracy in fish species classification and quality assessment, demonstrating the effectiveness of the Faster R-CNN approach for multi-detection applications in the Android-based fisheries sector.
Sistem Absensi Berbasis Deteksi Wajah dengan Pendekatan Eksperimen Meryatul Husna; Kinarta Ketaren; Sharfina Faza; Orli Binta Tumanggor; Aprilza Aswani
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 6 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i6.6700

Abstract

This study compares the accuracy of two face detection algorithms, Haar Cascade and Multi-task Cascaded Convolutional Networks (MTCNN), to determine the most suitable method for implementation in a facial recognition–based attendance system. The evaluation was conducted through a series of tests under common real-world conditions, including variations in distance, lighting intensity, and face orientation. Each algorithm was assessed using performance metrics such as Precision, Recall, F1-Score, and processing time to provide a comprehensive understanding of their strengths and limitations. The results indicate that MTCNN consistently achieves higher accuracy across nearly all tested scenarios, particularly under low-light conditions and when the face is not oriented frontally. In contrast, Haar Cascade demonstrates faster processing time but experiences significant decreases in accuracy under non-ideal conditions typically found in practical applications. Based on these findings, MTCNN is considered more suitable for attendance systems that require high accuracy and robustness to environmental variations, while Haar Cascade may be preferred in applications where computational efficiency and speed are the primary considerations.
Adoption of Explainable Artificial Intelligence in Decision Support Systems under Complex Data Environments Adam, Hikmah Adwin; Lopulalan, Pierre Marcello; Husna, Meryatul
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 1 (2026): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.7793

Abstract

The increasing complexity of data in the digital banking industry in Indonesia has encouraged the use of Decision Support Systems (DSS) based on Artificial Intelligence (AI), especially in the credit scoring process. However, the black-box characteristics of conventional AI models pose problems related to transparency, trust, and regulatory compliance, especially in credit decision-making that has a direct impact on customers. This study aims to analyze the adoption of Explainable Artificial Intelligence (XAI) in DSS in credit scoring systems in Indonesian digital banking, with an emphasis on how XAI can improve model interpretability in complex and heterogeneous data environments. The research approach used is mixed-methods, which combines quantitative analysis of the performance of the XAI model with a qualitative study through case studies on digital banking institutions. The results show that the implementation of XAI is able to improve the clarity of credit decisions without sacrificing predictive accuracy significantly, especially through the use of post-hoc explainability techniques and hybrid models. In addition, organizational readiness, compliance with Financial Services Authority (OJK) regulations, and data governance maturity are the main determinants in the success of XAI adoption. This research contributes theoretically through the development of a conceptual framework that integrates the dimensions of model clarity, data complexity, and decision quality in the context of digital banking. In practical terms, these findings provide strategic implications for the development of a transparent, accountable, and trust-oriented DSS, thereby supporting fairer and more accountable credit decision-making in Indonesia.
Co-Authors Adam, Hikmah Adwin Ajulio Padly Sembiring Ajulio Padly Sembiring Andam Lukcyhasnita Andi Supriadi Chan, Andi Supriadi Aprilza Aswani Dani Gunawan Demora Ari Destiadi, Rezha Fawwaz, Insidini Faza, Sharfina Hasibuan, Annalisa Sonaria Hikmah Adwin Adam Janferson Panggabean Julham Julham Julham Julham Kinarta Ketaren Lopulalan, Pierre Marcello Lubis, Arif Ridho Lukcyhasnita, Andam Marliana Sari Muhammad Rafif Rasyidi Pandu Pratama Putra, Pandu Pratama Poppy Andriyani Prayudani, Santi Purba, Lampson Pindahaman Putri, Maharani Rina Anugrahwaty Rina Anugrahwaty, Rina Rinaldy, Muhammad Eri Rizka, Ade Rizki Syahputra Romi Fadillah Rahmat Sembiring, Ajulio Padly Silmi Silmi Silmi Simarmata, Anggi C Simarmata Sinambela, Lamtiur Siregar, Mhd. Ikhsan P. Tumanggor, Orli Binta Winda Syafitri Wiwin Sry Adinda Banjarnahor Yusnida, Yuyun Yusuf, Kadri Yuyun Yusnida Lase