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All Journal Jurnal Teknik Informatika (JUTIF)
Nurhadi Nurhadi
Magister of Information Systems, University of Dinamika Bangsa, Indonesia
Computer Science & IT

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Laboratory Assistant Selection Using Fuzzy BWM and Fuzzy TOPSIS with Sensitivity Analysis and Monte Carlo Simulation Dwi Jatmiko; Benni Purnama; Effiyaldi Effiyaldi; Nurhadi Nurhadi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.4.5686

Abstract

This study addresses the challenge of laboratory assistant selection at Universitas Nurdin Hamzah (UNH) Jambi, where manual assessment can introduce subjectivity and limited transparency. To improve objectivity and traceability, this research develops a web-based Decision Support System (DSS) by integrating the Fuzzy Best–Worst Method (Fuzzy-BWM) to derive criteria weights and the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy-TOPSIS) to rank candidates. The evaluation uses four criteria: Competency Test (C1), Certification (C2), Grade Point Average (GPA) (C3), and Interview (C4), with assessments represented by linguistic fuzzy scales to accommodate uncertainty in human judgment. The system generates ranking outputs along with the Closeness Coefficient (CC) as an interpretable decision indicator. Robustness is further examined using sensitivity analysis and Monte Carlo simulation under varying criteria weights to evaluate ranking stability. Results show that the proposed DSS produces measurable and explainable rankings and provides additional evidence of decision robustness under weight perturbations. From an Informatics and Computer Science perspective, this work demonstrates the practical integration of fuzzy-MCDM algorithms into a reliable computerized DSS that supports transparent, reproducible, and auditable decision-making in academic operational management.
User Experience Evaluation of the Hear Me Sign Language Learning Application in a Special Education Setting Using the UEQ Framework Nurrohmi Gita Permata; Nurhadi Nurhadi; Joni Devitra
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.4.5712

Abstract

The increasing adoption of mobile learning technologies in inclusive education highlights the need for systematic user experience (UX) evaluation, particularly for accessibility-oriented applications designed for students with hearing impairments. This study evaluates the UX of the Hear Me sign language learning application in a special education (SLB) context using the User Experience Questionnaire (UEQ). A quantitative evaluative design was applied to 27 respondents, including data transformation, reliability verification using Cronbach’s Alpha, dimensional mean analysis, group comparison, and benchmark interpretation based on the official UEQ dataset. The results indicate that all six UX dimensions fall within the lower benchmark range, reflecting limited experiential differentiation. Mean scores ranged from −0.065 to 0.176 and clustered near neutrality. Efficiency achieved the highest score (0.176), while stimulation (−0.065) and novelty (−0.056) indicated limited motivational engagement. Reliability coefficients across dimensions (α = 0.821–0.880) demonstrated acceptable internal consistency. A descriptive comparison between students and teachers revealed perceptual differences in pragmatic and hedonic evaluations. Despite a relatively high public rating (4.5/5) on the Google Play Store platform, benchmark analysis revealed a divergence between public satisfaction indicators and structured UX measurement. These findings emphasize the importance of standardized UX benchmarking for accessibility-oriented educational applications. Improvements in interactive feedback, animation stability, and gamification features are recommended to enhance engagement and experiential quality in inclusive mobile learning systems.
Decision Support System for Prioritizing PKH Social Assistance Beneficiaries using Fuzzy-AHP, MOORA and Sensitivity Analysis Mychele Salsabila; Nurhadi Nurhadi; Dodo Zaenal Abidin
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.4.5714

Abstract

Prioritizing beneficiaries of the Program Keluarga Harapan (PKH) at the urban-village level is often conducted manually, making the process prone to subjectivity, inconsistency, and limited traceability. This study aims to develop a transparent and auditable Decision Support System (DSS) to prioritize PKH candidates objectively under multi-criteria conditions and assessment uncertainty. The proposed DSS combines AHP to verify judgment consistency, Fuzzy-AHP using Triangular Fuzzy Numbers to model linguistic uncertainty and derive criterion weights, and MOORA to compute preference values and generate candidate rankings. The approach is evaluated through a case study in Pasir Putih Urban Village involving 50 prospective beneficiaries and 18 regulation-aligned evaluation criteria. The consistency test yields a Consistency Ratio (CR) of 0.09, indicating acceptable consistency. The ranking results show that alternative A12 achieves the highest preference value, followed by A50. To assess recommendation reliability, a sensitivity analysis is performed by varying criterion weights by ±25% with proportional normalization. While several rank shifts occur, seven alternatives (A14, A32, A10, A26, A7, A12, and A50) remain relatively stable, indicating more robust outcomes. From an informatics perspective, this work contributes a reproducible MCDM-based DSS framework that integrates uncertainty modeling and robustness evaluation to improve accountability and decision transparency in public-sector social assistance prioritization.
Co-Authors Benni Purnama Dodo Zaenal Abidin Dwi Jatmiko Effiyaldi Effiyaldi Joni Devitra Mychele Salsabila Nurrohmi Gita Permata