Article Per Year (5 Year)
p-Index From 2021 - 2026
0.983
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This Author published in this journals
All Journal Jurnal Teknik Informatika (JUTIF) Jurnal Algoritma Journal of Elektronik Sistem InformasI
Budy Santoso, Cahyono
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Underwriting Technology Trends: A Systematic Literature Review Budy Santoso, Cahyono; Ghaniy, Rajib
JESII: Journal of Elektronik Sistem InformasI Vol. 2 No. 1 (2024): JournaI of Elektronik Sistem InformasI - JESII (JUNE)
Publisher : Departement Information Systems Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/jesii.v2i1.3420

Abstract

This study systematically reviews trends in underwriting technology to enhance the precision and personalization of insurance companies' risk assessment and decision-making processes. Using the Kitchenham method, we conducted a systematic review of scientific publications indexed by Scopus from 2011 to 2021. Our findings reveal the extent of research activity in this field, the leading contributing countries, the methodologies employed, the technologies utilized, and the specific areas investigated. The results indicate significant advancements in the application of machine learning, blockchain, and other technologies in underwriting, providing a comprehensive overview of current trends and future directions. This study offers valuable insights for researchers and practitioners aiming to improve underwriting technology, highlighting potential areas for further research and development. These insights are crucial for advancing the field and enhancing the efficiency and effectiveness of underwriting practices.
Underwriting Technology Trends: A Systematic Literature Review Budy Santoso, Cahyono; Ghaniy, Rajib
JESII: Journal of Elektronik Sistem InformasI Vol 2 No 1 (2024): JournaI of Elektronik Sistem InformasI - JESII (JUNE)
Publisher : Departement Information Systems Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/jesii.v2i1.3420

Abstract

This study systematically reviews trends in underwriting technology to enhance the precision and personalization of insurance companies' risk assessment and decision-making processes. Using the Kitchenham method, we conducted a systematic review of scientific publications indexed by Scopus from 2011 to 2021. Our findings reveal the extent of research activity in this field, the leading contributing countries, the methodologies employed, the technologies utilized, and the specific areas investigated. The results indicate significant advancements in the application of machine learning, blockchain, and other technologies in underwriting, providing a comprehensive overview of current trends and future directions. This study offers valuable insights for researchers and practitioners aiming to improve underwriting technology, highlighting potential areas for further research and development. These insights are crucial for advancing the field and enhancing the efficiency and effectiveness of underwriting practices.
Implementasi Business Intelligence untuk Prediksi Produksi Perikanan Budidaya Berbasis Web Dashboard Visualisasi Vistiyawati, Vanessa; Budy Santoso, Cahyono
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2902

Abstract

Aquaculture plays an essential role in supporting food security and meeting the protein needs of the population, particularly in urban areas such as Jakarta. However, data management in aquaculture production is often still performed manually, making analysis and prediction difficult. This study aims to design a web-based visualization dashboard integrated with Business Intelligence implementation to predict aquaculture production in the Jakarta region. The research employs the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, which consists of six main stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Aquaculture production data were processed through cleaning and integration stages, followed by the application of predictive models using Random Forest and Linear Regression algorithms, with Python as the data processing tool. The prediction and analysis results are visualized in an interactive web-based dashboard for easy access and interpretation. Evaluation results indicate that the predictive models used were able to provide an overview of production trends with a satisfactory level of accuracy. The contribution of this research lies in the integration of predictive methods with interactive web-based visualization, which has rarely been applied in the context of urban aquaculture, offering a new approach to supporting strategic decision-making. Through this dashboard, stakeholders can obtain more comprehensive information to enhance strategic decisions in aquaculture management in Jakarta.
Pengembangan Dashboard Prediksi Penggunaan Transportasi Umum Berbasis Business Intelligence dan Random Forest di Jakarta Ahmad, Alifio Fikra Ahmad; Budy Santoso, Cahyono
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2941

Abstract

This study applies the concept of Business Intelligence (BI) to predict and visualize trends in public transportation usage in Jakarta to support data-driven decision making. Secondary data from the Satu Data Jakarta portal was analyzed using the Random Forest algorithm due to its ability to process complex variables with accurate prediction results (R² = 0.978). The results show that TransJakarta, MRT, and KRL have stable passenger trends, while LRT, KCI Commuter Bandara, ships, and school buses are more volatile. These results are visualized in a web-based dashboard that facilitates fleet planning and public transportation operational policies. This research contributes to the application of BI in the transportation sector by presenting a prediction model that supports data-driven policy formulation.
Deep Learning-Based Detection of Potato Leaf Diseases Using ResNet-50 with Mobile Application Deployment Budy Santoso, Cahyono; Effendi, Rufman Iman Akbar; Siregar, Johannes Hamonangan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5186

Abstract

Plant diseases significantly reduce agricultural productivity, especially in developing regions with limited access to early detection tools. This research presents a deep learning-based approach for detecting potato leaf diseases, focusing on Early blight, Late blight, and healthy conditions. A modified ResNet-50 architecture was employed and trained using a publicly available potato leaf image dataset. Preprocessing steps included data augmentation and normalization to enhance model generalization. The model achieved a high accuracy of 99.31%, with precision, recall, and F1-score all exceeding 99%, indicating excellent classification performance. This study introduces a novel approach that improves classification performance through an optimized deep learning architecture, achieving higher accuracy compared to existing models. In addition to enhancing predictive capability, the study also addresses the practical need for accessibility by integrating the trained model into an Android-based mobile application. The application allows users to upload or capture leaf images and receive real-time predictions. The interface was designed for simplicity and usability in field conditions, making it accessible to farmers and agricultural workers. The findings demonstrate that combining deep learning with mobile technology can offer an effective and scalable solution for early disease detection in agriculture. Future work may explore cross-crop adaptability and lightweight model optimization for real-time performance on low-resource devices.
Co-Authors Ahmad, Alifio Fikra Ahmad Effendi, Rufman Iman Akbar Ghaniy, Rajib Johannes Hamonangan Siregar Vistiyawati, Vanessa