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All Journal IT JOURNAL RESEARCH AND DEVELOPMENT West Science Business and Management
Andreas, Prima
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering

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Analysis of Competitive Advantage Strategy at QU Laundry in Tuah Madani District, Pekanbaru City Nadila Putri Atdika; Jushermi, Jushermi; Alvionita, Agnes; Andreas, Prima
West Science Business and Management Vol. 3 No. 04 (2025): West Science Business and Management
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsbm.v3i04.2369

Abstract

This study aims to analyze the competitive advantage strategy of QU Laundry in Tuah Madani District, Pekanbaru City. The increasing competition in the laundry business requires entrepreneurs to apply the right strategies to survive and grow. This research uses a descriptive method with a qualitative approach. Data were collected through interviews, observation, and documentation. Data analysis employed the SWOT matrix to identify strengths, weaknesses, opportunities, and threats, followed by the IFE and EFE matrices to determine the company’s strategic position. The results show that the total IFE score is 3.28 and the EFE score is 3.06, placing QU Laundry in the Grow and Build strategy position. Recommended strategies include improving promotion, utilizing digital technology in services, and developing innovations to strengthen competitiveness and expand market share in Pekanbaru City.
Job Classification Based on Skills and Qualifications Using Natural Language Processing and Ensemble Learning Methods Oktasia Nasution, Hafiza; Ramadhani, Dian; Aprilina Tarigan, Mida; Andreas, Prima; Suryati Ningsih, Dewita; Pramadewi, Arwinence
IT Journal Research and Development Vol. 10 No. 2 (2025)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2025.25550

Abstract

This study proposes a job classification framework using Natural Language Processing (NLP) and Ensemble Learning to classify job roles based on required skills and qualifications. A large-scale open-source dataset containing 1.048.576 job postings was utilized, with attributes such as job title, qualifications, skills, company profile, and role. Only relevant attributes were used: skills and qualifications as input features, and role as the target label. Data were filtered to focus on three major job roles—Management, IT, and Digital—resulting in 489,651 relevant entries. Skills were extracted and standardized using GROK AI before feature transformation with MultiLabelBinarizer for one-hot encoding. The XGBoost algorithm was applied for classification under multiple data split configurations (70:15:15, 80:10:10, 70:30, 80:20, 90:10) with random_state=42 and multi-class log loss evaluation. Results showed that the 90:10 configuration achieved the highest accuracy (74.18%), followed by 80:20 with 68.44%. This research demonstrates that ensemble learning effectively handles high-dimensional categorical job data and provides a foundation for automated job classification systems and labor market analysis.
Co-Authors Alvionita, Agnes Aprilina Tarigan, Mida Arwinence Pramadewi Dewita Suryati Ningsih Jushermi Jushermi, Jushermi Nadila Putri Atdika Oktasia Nasution, Hafiza Ramadhani, Dian