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INDONESIA
Technomedia Journal
Published by Pandawan Sejahtera Indonesia
ISSN : 26203383     EISSN : 25286544     DOI : 10.33050/tmj
Core Subject : Science, Education,
Computer Science & IT Languange, Linguistic, Communication & Media
Technomedia Journal (TMJ) adalah jurnal yang didedikasikan untuk pertukaran hasil penelitian berkualitas tinggi di semua aspek Informatika, Teknologi Informasi, dan Ilmu Data. TMJ ini merupakan bagian dari Pandawan Sejahtera Indonesia, serta didukung oleh Alphabet Incubator yang merupakan diseminasi hasil penelitian para ilmuwan dan insinyur di berbagai bidang ilmu pengetahuan dan teknologi. TMJ mengikuti kebijakan akses terbuka yang memungkinkan artikel yang diterbitkan tersedia secara online tanpa berlangganan.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Sistem Informasi Menejemen
Articles 2 Documents
 1 
Search results for , issue "Vol 10 No 3 (2026): February" : 2 Documents clear
Implementation of Naive Bayes for Optimizing Asset Condition Classification in a Web-Based Information System Putra, Adhitya Pramana; Lapatta, Nouval Trezandy; Ngemba, Hajra Rasmita
Technomedia Journal Vol 10 No 3 (2026): February
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/686nnx47

Abstract

Improving the quality of work performance is an essential aspect for employees at the Office of Investment and Integrated One-Stop Services of Central Sulawesi Province. Many challenges remain in managing asset data, especially because the recording and monitoring processes are still performed manually. This manual approach often leads to inconsistencies, inefficiencies, and difficulties in determining asset eligibility. Therefore, an information system capable of supporting accurate and efficient data management is highly needed. The main objective of this study is to apply the Naive Bayes algorithm to classify asset conditions in a web-based system, enabling faster decision-making and improving the accuracy of asset feasibility assessments within government institutions. The dataset used in this study consists of three key attributes asset functionality, asset age, and physical condition. These attributes serve as indicators for classification using the Naïve Bayes probabilistic approach. The developed web-based application was evaluated through black-box testing to ensure that all system functions performed according to expectations and produced consistent outputs. Black-box testing results show that the system successfully provides correct outputs for each test scenario, verifying that the classification and data management processes operate properly. The application is able to classify assets into feasible or non-feasible categories based on calculated probabilities. Findings indicate that implementing the Naïve Bayes algorithm significantly improves the efficiency of asset data processing and enhances data management quality. The system also supports more objective decision-making regarding asset feasibility. This study demonstrates that probabilistic classification can be effectively integrated into governmental asset management systems to optimize operational performance.
AI Agent Based Service Innovation to Enhance Efficiency and User Experience Cahyono, Dwi; Atmaja, Hanung Eka; Zainarthur, Henry
Technomedia Journal Vol 10 No 3 (2026): February
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/bw4vra55

Abstract

The innovation of services based on Artificial Intelligence (AI) Agent has become a key strategy in improving operational efficiency, service quality, and user experience across various digital business sectors. AI Agent, utilizing natural language processing, machine learning, and realtime data analysis, can automate service processes that previously required manual interaction, such as customer responses, recommendations, and processing complex information. This study aims to analyze how the application of AI Agent can accelerate service responses, improve information accuracy, and create more personalized interactions for users. The research method used is a literature review from reputable journals, academic books, and industry reports, which are then analyzed descriptively to identify the adoption patterns of AI Agent across various digital platforms such as e-commerce, financial services, education, and creative industries. The results of the literature synthesis show that AI Agent can reduce operational workload by up to 40%, accelerate service response time by up to 60%, and enhance user satisfaction through adaptive interactions tailored to individual preferences and behaviors. Additionally, the implementation of AI Agent also proves to improve service consistency, expand operational scalability, and reduce the risk of human error in service processes. These findings emphasize that the integration of AI Agent not only enhances the efficiency and effective- ness of digital business processes but also plays a key role in creating strategic innovation, strengthening competitiveness, and building a more responsive and valuable service experience for users in the digital era.

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