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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) Systemic: Information System and Informatics Journal J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Terapan (J-TIT) Journal Zetroem Journal of Community Development Archive: Jurnal Pengabdian Kepada Masyarakat
Dianni Yusuf
Politeknik Negeri Banyuwangi
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Nursing Public Health Social Sciences

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Prediksi Harga Ikan Koi Berbasis Analisis Morfometrik Menggunakan Algoritma Random Forest Regressor Sepyan Purnama Kristanto; Lutfi Hakim; Dianni Yusuf; Moh. Erdda Habiby
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8617

Abstract

The lack of clarity in koi fish (Cyprinus rubrofuscus) pricing within the Indonesian ornamental fish market, driven by subjective valuation practices and information asymmetry, remains a primary challenge creating significant price disparities. The primary objective of this research is to address this challenge by designing and evaluating an objective predictive model. As its main contribution, this study develops the first Random Forest Regressor (RFR) based price prediction model. This model is specifically designed to handle complex non-linear relationships by integrating three main feature groups: morphometric parameters (species, size) and phenotypic characteristics (color patterns). Using a dataset of 800 samples collected from koi breeding centers in East Java, the optimized model achieved solid predictive performance, indicated by a Coefficient of Determination (R²) of 0.85 and a Root Mean Squared Error (RMSE) of IDR 265,000. Feature importance analysis revealed the significant finding that fish variety (one of the three analyzed feature groups) is the most dominant price determinant (62% contribution). The model quantitatively validates that rare varieties (such as Tancho/Utsuri) are valued 3 to 5 times higher than common varieties of the same size. Comparative analysis with traditional linear regression models (R² 0.61) also demonstrated the RFR's superiority in capturing complex morphological feature interactions. A critical finding indicates that the model's accuracy, already strong in the non-premium segment, can be improved by up to 15% through the quantification of qualitative aesthetic attributes (such as kiwa or gradation) using computer vision. The implementation of this model has the potential to standardize koi valuation, reduce market information asymmetry by up to 40%, and serve as a foundation for the development of the first AI-based price recommendation system in Indonesia's aquaculture industry.
Analisis Kinerja Multimodal Dense Neural Network untuk Deteksi Hipoksia Janin pada Dataset Tidak Seimbang Subono Subono; Dianni Yusuf
ZETROEM Vol 7 No 2 (2025): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v7i2.6204

Abstract

This study aims to develop a Multimodal Dense Neural Network (MDNN) for detecting fetal hypoxia using an imbalanced Cardiotocography (CTG) dataset. The primary challenges in fetal hypoxia diagnosis include the imbalance between Normal, Suspect, and Hypoxia classes and the limited interpretability of conventional deep learning models. To address these issues, a robust preprocessing pipeline was designed, consisting of Physiological Clipping (50–200 bpm), Median Absolute Deviation (MAD) normalization, SMOTETomek balancing, and Gaussian noise augmentation. The MDNN architecture integrates two parallel branches: Fetal Heart Rate (FHR) signals and clinical parameters (pH, Apgar score, and base deficit), fused through a Dense Fusion Layer to generate compact multimodal representations. Experimental results demonstrate that the proposed MDNN achieved 99.7% accuracy, 99.5% F1-score, and 0.993 AUC, outperforming CNN (84.6%), ResNet18 (82.3%), and MLP (87.5%). The confusion matrix showed good generalization capability with per-class accuracies of 69% (Normal), 56% (Suspect), and 67% (Hypoxia). SHAP feature importance analysis identified FHR pattern (0.45) and pH level (0.25) as the most influential features in classification. These findings confirm that the proposed MDNN is robust, computationally efficient, and clinically interpretable, making it a promising framework for real-time fetal hypoxia diagnosis in modern clinical environments.
Analisis Efektivitas Metode Responsible, Accountable, Consulted, Informed (RACI) dalam Sistem Manajemen Process Approval Dianni Yusuf; subono subono
ZETROEM Vol 7 No 2 (2025): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v7i2.6450

Abstract

The approval management process plays an essential role in improving efficiency and accountability in organizational decision-making. PT Asta Berkah Autonomous, a company specializing in automation system development, faces challenges in transparency and efficiency due to manual approval procedures conducted through Google Forms and email. This study aims to design and implement a web-based approval management system integrated into the Asta Project application using the Responsible, Accountable, Consulted, Informed (RACI) method. The RACI method is applied to clearly define the roles and responsibilities of each stakeholder, ensuring a structured and transparent approval workflow. The system development process adopts the Rapid Application Development (RAD) approach, emphasizing iterative design and user involvement. System testing was conducted using Blackbox Testing and User Acceptance Testing (UAT) based on ISO 9126 quality standards. The results demonstrate that the implementation of the RACI method enhances role clarity, process efficiency, and transparency among participants. The developed system successfully reduces submission time, simplifies approval tracking, and supports faster and more accurate decision-making. This implementation significantly contributes to improving productivity and governance of the approval process within PT Asta Berkah Autonomous. System testing using Blackbox Testing and User Acceptance Testing (UAT) based on ISO 9126 quality standards. The results show that all system functions operated successfully (100% valid), with an average user satisfaction score of 84.44%, categorized as excellent. The application of the RACI method significantly improved efficiency, transparency, and accountability in the company’s approval process. Overall, the developed system contributes to digital transformation efforts and enhances corporate governance effectiveness.
Co-Authors Aditya Roman Asyari Agung Bahtiar Azizatur Rohma Endi Sailul Haq Farizqi Panduardi Fitri Nur Afia Hadi Rizaldi Rahmanto I Putu Dody Lesmana Kusuma, Dedy Hidayat Lutfi Hakim Mistiko Rini, Eka Moh. Erdda Habiby Muhammad Fuad Alharis Nurul Hidayati Novitasari Purwoko, Agus Ruth Ema Febrita Sepyan Purnama Kristanto Subono Subono