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Bambang Susanto
Universitas Kristen Satya Wacana
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Journal : International Journal of Advances in Intelligent Informatics

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Human Capital Decision Intelligence (HCDI) architecture in microbiology laboratory based on machine learning and operations research models Trihandaru, Suryasatriya; Susetyo, Yosia Adi; Parhusip, Hanna Arini; Susanto, Bambang
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.1676

Abstract

The Human Capital Decision Intelligence (HDCI) system integrates human-computer interaction in a microbiology laboratory that uses machine learning and operational research to classify new tasks and then recommend assignments to each person. The models evaluated in building this system are Support Vector Machine, Gaussian Naive Bayes, Multinomial Logistic Regression, and Artificial Neural Network. The results of the research show that the ANN model is the most consistent and reliable across various training ratios, as indicated by the model's goodness parameters. The selected ANN model is combined with a linear programming approach to optimize workload distribution. The integrated system successfully manages new job scenarios and recommends staff based on competencies and availability. It also ensures assignments do not exceed maximum workload limits and finds alternatives when key staff are unavailable. The implementation of the HDCI system has a positive impact on various factors, including the fair distribution of tasks, enhanced staff performance monitoring, and significantly improved operational efficiency and human resource management in the microbiology laboratory. The system is designed to be easy to use and support collaboration between laboratory staff and computational models. The system is not only advanced in supporting personnel management decision-making, but it can also demonstrate how artificial intelligence and operations research systems can be combined to address the needs of the microbiology laboratory environment.
Co-Authors Adi Setiawan Adrianus Herry Heriadi Agus Priyono Alfagustina, Yumita Cristin Alfida Tegar Nurani Algunadi, Muhammad Alicia Anggelia Lumbantoruan Alz Danny Wowor Asido Saragih Awik Hidayati Carolina Febe Ronicha Putri Denny Indrajaya Didit B Nugroho Didit Budi Nugroho Dini Kurniawati Dudi Rudianto Dwi Tristianto Egidius Saut Poltak Situmorang Elsa Septyana Endang Ruswanti Eva Rachmawati Faldy Tita Faundra, Alvi Haay, Happy Alyzhya Hanna Arini Parhusip Hanna Arini Parhusip Herdiyanti, Gita Hersusetiyati, Hersusetiyati Johanes Dian Kurniawan Kezia Natalia Putri Prasetia Khalingga, M Ariq Khalingga, Muhammad Ariq Kholil, Zaini Kurniawan, Johanes Dian Laurentia Nindya Sari Prameswara Lenox Larwuy Leopoldus Ricky Sasongko Lestari, Dina Aulia Lilik Linawati Maria Anita masipupu, Frangky Aristiadi Mince M. M. Rosely Modjo, Marchella Ellena Nugraha, Rian Nanda Nuryani, Dwi Panjaitan, Lam Peter Petrus Priyo Santosa Prasetia, Kezia Natalia Putri PURWANTI PURWANTI Putri, Zefania Sasongko Ratnawati, Aryanti Rebecca Rorimpandey Ricky Agusiady Rima Dwijayanty Rinadi, Geraldus Anggoro Rorimpandey, Rebecca Saepudin Saepudin Saragah R Pratama SARI, EMMA NOVITA Setiawan, Audita Sukadwilinda Suroto, Agung Suryasatriya Trihandaru Susetyo, Yosia Adi Syafi’i, Syafi’i Syarifah harahap Tanujaya, Lukhia Britanthia Christina Tri Pujadi Susilo Tundjung Mahatma Vania Beatrice Liwandouw Visher Laja Jaja wahyu ngestisari Wulandari, Nadya Putri Yohanes Sardjono Yohanes Sardjono, Yohanes Yusria, Anna Zaenal Aripin