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All Journal Jurnal Teknik Industri : Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri Techno Nusa Mandiri : Journal of Computing and Information Technology
Kafunda Katalay, Pierre
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering

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ARTIFICIAL LEARNING BASED ON KERNEL SVM FOR THE PREDICTION OF CARDIOVASCULAR DISEASE HYPERTENSION MUSUBAO SWAMBI, Patient; Ntumba Nkongolo, Albert; Kafunda Katalay, Pierre; Mabela Matendo Makengo, Rostin; Mbuyi Mukendi, Eugène
Jurnal Techno Nusa Mandiri Vol. 22 No. 1 (2025): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v22i1.6011

Abstract

Hypertension, a critical risk factor for cardiovascular diseases, requires accurate early detection for effective management. This study examines the application of kernel-based Support Vector Machines (SVM) for predicting hypertension, utilizing advanced machine learning techniques to address the complex, non-linear relationships inherent in healthcare data. By employing various kernel functions, such as the radial basis function (RBF) and polynomial kernels, the study aims to enhance the model's ability to capture and interpret the nuanced patterns associated with hypertension risk. The research utilizes a diverse dataset that includes demographic, physiological, and lifestyle variables, applying kernel SVM to predict hypertension outcomes. Performance is evaluated through rigorous cross-validation, with metrics including accuracy, precision, recall, and F1-score. The findings indicate that kernel SVMs significantly outperform traditional linear models, offering superior prediction accuracy and robustness. This study highlights the potential of advanced machine learning methods in improving early detection and personalized risk assessment for hypertension, ultimately supporting more effective management strategies and better cardiovascular health outcomes.
DIGITALIZATION OF HR AT ONIP: INFORMATION SYSTEMS URBANIZATION AND STRATEGIC ALIGNMENT AS KEY LEVERS SINDANI, Evariste; Kafunda Katalay, Pierre; Ntumba Badibanga, Simon; Mbuyi Mukendi, Eugène
Jurnal Techno Nusa Mandiri Vol. 22 No. 1 (2025): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v22i1.6149

Abstract

This article examines the digitalization of human resources (HR) at the Office National d’Identification de la Population (ONIP) in the Democratic Republic of Congo (DRC), emphasizing the pivotal role of information systems (IS) urbanization and strategic alignment as key levers. Using a qualitative methodology that combines semi-structured interviews with 15 stakeholders (HR managers, IT specialists, directors) and process analysis, we demonstrate the following outcomes: 40% reduction in HR processing time (from 7 to 4.2 days), 30% decrease in data entry errors through administrative task automation, 29% optimization of annual IT expenditures (from 120,000 to 85,000 USD), Increase in employee satisfaction scores from 58% to 82% (based on an internal survey of 200 employees). These results, derived from the implementation of a secure and modular HR information system (HRIS), underscore the efficacy of a structured approach in a fragile context. The article contributes to the literature on HR digital transformation in the African public sector by proposing a reproducible framework grounded in IS interoperability and collaborative governance.
Digitalization and Optimization of HR at ONIP (DRC): An Integrated Mathematical Approach Sindani, Evariste; Ntumba Badibanga, Simon; Kafunda Katalay, Pierre; Mbuyi Mukendi, Eugène
JTI: Jurnal Teknik Industri Vol 11, No 1 (2025): JUNI 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jti.v11i1.36185

Abstract

This study proposes an integrated approach to digitalizing human resources (HR) in African public institutions by developing a performance optimization model. Based on five key variables—processing time, operational cost, service quality, degree of automation, and employee satisfaction—this model aims to enhance the overall efficiency of HR processes. The study is applied to the case of the National Office for Population Identification (ONIP) in the Democratic Republic of Congo and highlights substantial improvements in human resource management. Theoretically, the approach contributes to the digital transformation field through modeling, and practically, by offering a reproducible and adaptable framework for other public organizations with limited resources.Keywords: Digitalization, HR process optimization, ONIP, HR performance, HRIS.
Co-Authors Mabela Matendo Makengo, Rostin Mbuyi Mukendi, Eugène MUSUBAO SWAMBI, Patient Ntumba Badibanga, Simon Ntumba Nkongolo, Albert SINDANI, Evariste