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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) International Journal of Computer Technology and Science SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
Yulio Ferdinand
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Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Peran Manajemen Pengetahuan dalam Pengembangan Kompetisi dan Kinerja Pegawai di Perguruan Tinggi : Pendekatan Study Literature Review Possumah, Mercy Kristina; Yulio Ferdinand; Neca Aqila; Muharman Lubis
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15783922

Abstract

Dalam era transformasi digital, perguruan tinggi dituntut untuk meningkatkan kinerja institusional melalui optimalisasi pengelolaan pengetahuan dan sumber daya manusia. Studi ini bertujuan untuk mengevaluasi kontribusi integratif antara manajemen pengetahuan dan praktik manajemen sumber daya manusia terhadap peningkatan kinerja pegawai di lingkungan perguruan tinggi. Melalui pendekatan Systematic Literatur Review (SLR), penelitian ini menganalisis 26 artikel dari database scopus yang diterbitkan dalam rentang waktu 2019 – 2025. Hasil sintesis menunjukkan bahwa penerapan strategi Knowledge Management (KM) seperti knowledge sharing, knowledge contribution dan knowledge transfer, serta praktik Human Resource Management (HRM) seperti training development, performance evaluation dan talent management  berkontribusi positif terhadap pengembangan kompetensi pegawai dan pencapaian performansi kinerja. Studi ini juga mengusulkan model konseptual yang mengintegrasikan proses KM dan HRM sebagai fondasi dalam membangun budaya organisasi yang kolaboratif inovatif dan berkelanjutan. Implikasi praktis dari temuan ini memberikan arahan strategis bagi pengelola perguruan tinggi dalam merancang kebijakan pengembangan pegawai berbasis pengetahuan untuk mendukung keunggulan institusi
Classification of University IT Helpdesk Tickets Using Support Vector Machine with Hyperparameter Optimization Yulio Ferdinand; Lubis, Muharman; Pratiwi, Oktariani Nurul
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2413

Abstract

The classification of IT helpdesk tickets is crucial for improving response efficiency in service management systems, particularly within academic institutions. However, the process is still mostly manual, increasing the risk of misclassification. This study explores the use of the Support Vector Machine (SVM) algorithm with four kernel functions — RBF, Linear, Polynomial, and Sigmoid — to automate the classification of user-submitted service tickets. The dataset was sourced from the Telkom University service desk application database, covering 2023 and 2024, and comprises 13,508 records across nine service categories. Preprocessing steps such as stemming, stopword removal, and TF-IDF feature extraction were applied before model training and evaluation. The RBF kernel achieved the highest accuracy at 85.04%, followed by Linear at 80.64%, Sigmoid at 75.94%, and Polynomial at 63.69%. The internet access category had the best classification performance across all kernels, with RBF and Linear achieving F1-scores of 90% and 89%, respectively. The request data category also showed consistently strong results with F1-scores above 80%. Misclassifications were mainly due to overlapping vocabulary, data imbalance, and limited semantic variation in ticket descriptions. The results indicate that the RBF kernel is most suitable for this multi-class classification task. This study highlights the effectiveness of machine learning in improving helpdesk automation and provides a basis for future enhancements, such as incorporating semantic-rich features and addressing class imbalance. Notably, this research contributes a comparative analysis of different SVM kernel performances, which has not been extensively explored in previous research.
A Systematic Literature Review on AI and NLP Applications for Customer Support Automation and Digital Service Yulio Ferdinand; Muharman Lubis; Oktariani Nurul Pratiwi
International Journal of Computer Technology and Science Vol. 2 No. 4 (2025): International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v2i4.317

Abstract

This study presents a Systematic Literature Review on Artificial Intelligence (AI) and Natural Language Processing (NLP) applications for customer support automation and digital service optimization. The review follows the PRISMA framework to ensure methodological rigor and transparency, focusing on literature published between 2020 and 2025 from the Scopus database. The findings reveal that AI-driven technologies, including Machine Learning, Deep Learning, and Large Language Models, have significantly improved efficiency, response time, and customer satisfaction in customer support and digital service. Common NLP applications include sentiment analysis, ticket classification, and automated response generation. Among these, hybrid and transformer-based models demonstrate superior accuracy and contextual understanding compared to traditional algorithms. However, several challenges persist, including data quality limitations, privacy and security concerns, algorithmic bias, and linguistic ambiguities such as sarcasm and negation. Moreover, issues related to trust and ethical adoption continue to influence user acceptance of AI systems. This review provides a comprehensive synthesis of current methodologies, trends, and research gaps, offering insights for future studies to develop explainable, secure, and human-centered AI systems that enhance the sustainability and transparency of digital customer support services.
Co-Authors Muharman Lubis Neca Aqila Oktariani Nurul Pratiwi Possumah, Mercy Kristina