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Hajra Rasmita Ngemba
Information System Department, Faculty Of Engineering, Tadulako University, Jl. Soekarno Hatta No KM 9 Palu 94148, Central Sulawesi, Indonesia
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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.
Co-Authors - Ifandi A Djufri, Isdar Abd. Rahman Sholeh Abdillah Sani, Ilham Abdul Mahatir Najar Aimar Anand Alamsyah Alamsyah - Alda Nur Pradinda Amalia, Liza Angraeni, Dwi Shinta Anita Ahmad Kasim Anjany, Ni Luh Verha Anugrah Aidin Yotolembah Anwar, Asriani Azizah Azizah Chandra, Ferri Rama Deny Wiria Nugraha Dharmakirti, Dharmakirti Djohari, Riyandi Dwitama Dodu , Albertch Yordanus Erwin Dwi Shinta Angreni Dwi Wijaya, Kadek Agus Dwiwijaya, Kadek Agus Elyana Aulia Chandra Fahlevi, Mohammad Fazrin Fathul Wahid Habibu, Rahmawati Hamid, Odai Amer Hardianti Hardianti, Hardianti I Gusti Ngurah Agung Kade Dwi Arsana Iman Setiawan Indrajaya, Muhammad Aristo Junaidi Junaidi Junus Widjaja Kade Dwi Arsana, I Gusti Ngurah Agung Laila, Rahma Lakatjinda, Adiatma Lamadjido, Moh. Raihan Dirga Putra Lintine, Gabriella Bamba Ratih Liza Amalia Mohamad Irfan, Mohamad Mohammad Fajri Mohammad Yazdi Pusadan Muh Alif Alghifari Muh. Aristo Indrajaya Muhamad Aristo Indrajaya Muhammad Jindan Muhammad Nauval Daffa Ulhaq Muhammad Nur Alamsyah Muhammad Yazdi Nouval Trezandy Lapatta Nur Muliyana Nurpati Nurpati Nursalim P. Dominggo, Nenita Penidas Fodinggo Tanaem Pradinda, Alda Nur Pratama, Moh. Asry Eka Putra Ramadhan, Adjie Putra, Adhitya Pramana Putri Febrina, Annisa Rahmah Laila Raivandy, I Made Randhy Rifai, Muhammad Fajar Rina Marlina Risaldi Pata’Dungan, Adi Rizana Fauzi, Rizana Rizka Ardiansyah Rosmala Nur Ryfial Azhar, Ryfial Setijadi, Eko Sitti Ainul Yakin Smith, Jennifer Suryadi Hadi Suryani Putri, Fadiah Syahrullah Syahrullah Syahrullah Syaiful Hendra Ulhaq, Muhammad Naufal Daffa Uswary, Jonathan Albert Vivi Peggie Rantung Winarta, Ardhiansyah Wirdayanti Yudhaswana, Yuri Yuri Yudhaswana Joefrie Yusuf Anshori Zaharuddin