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All Journal Zero : Jurnal Sains, Matematika, dan Terapan
Jaharuddin Jaharuddin
School of Data Science, Mathematics, and Informatics, IPB University, Bogor, 16680, Indonesia
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics

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Dual-Fairness Nurse Scheduling via the Double Direct Progressive Filling Algorithm under Qualification and Contract Constraints Tita Putri Redytadevi; Toni Bakhtiar; Jaharuddin Jaharuddin
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.29388

Abstract

Nurse scheduling requires balancing workload distribution while satisfying qualification and employment contract constraints. This study implements a hybrid scheduling framework integrating Goal Programming (GP), the Double Direct Progressive Filling Algorithm (DDPFA), and the CP-SAT solver to generate feasible nurse schedules under actual and workforce-reduction scenarios in inpatient and emergency departments. Performance is evaluated using four indicators: inter-shift fairness, inter-nurse fairness, soft-constraint compliance, and computation time. The results show that the proposed approach achieves lower standard deviation values (0.15–0.42), satisfies all soft constraints, and generates feasible schedules in under 3 seconds. Compared with the evaluated manual scheduling and goal programming approaches, the framework produced more balanced workload allocation across shifts and nurses under the evaluated scenarios. These findings suggest that the proposed framework may provide a practical approach for fairness-oriented and cost aware workforce planning under the evaluated hospital conditions.
Analysis of Online Marketplace user Satisfaction using SERVQUAL, Quality Function Deployment, and Mixed Integer Linear Programming Nayla Nur Alifah; Toni Bakhtiar; Jaharuddin Jaharuddin
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.30035

Abstract

This study analyzes and optimizes user satisfaction in online marketplaces by integrating SERVQUAL, the Kano Model, Quality Function Deployment (QFD), and Mixed Integer Linear Programming (MILP). Data were collected from 167 Shopee users, with 107 valid responses analyzed. SERVQUAL measured service quality dimensions, the Kano Model derived satisfaction and dissatisfaction parameters, QFD translated user requirements into internal service measures, and MILP selected optimal improvement alternatives under an IDR 90,000,000 budget constraint. The results show that assurance had the highest SERVQUAL weight (0.207), followed by tangibility (0.201), reliability (0.200), empathy (0.198), and responsiveness (0.195). The Pearson correlation based HoQ approach selected customer service training, automatic delivery update features, and feedback completion incentives, with a total cost of IDR 85,000,000 and a deviation value of 0.191. These findings provide scenario based decision support to improve the quality of marketplace services.
Co-Authors Nayla Nur Alifah Tita Putri Redytadevi Toni Bakhtiar