Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : bit-tech

UI/UX Development of a Boarding House Reservation Application: A Design Thinking Approach in Surabaya Azizatul Fara Dibah; Abdul Rezha Efrat Najaf; Prasasti Karunia Farista Ananto
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3320

Abstract

Fragmented, inconsistent, and frequently outdated information about boarding-house availability, facilities, and pricing remains a persistent usability problem in existing digital platforms. Current UI/UX and reservation-system research has not sufficiently addressed these issues within the specific context of urban rental ecosystems, creating gap in designing solutions that respond to the needs of both tenants and property owners. This study addresses that gap by developing and evaluating a user-centered interface for a boarding-house reservation application using the five-stage Design Thinking framework: empathize, define, ideate, prototype, and test. Insights were gathered from 30 participants representing owners, tenants, and administrators in Surabaya, forming the basis for personas, information architecture, user flows, and low- to high-fidelity prototypes designed in Figma. Usability and interface quality were examined through task-based testing, the System Usability Scale (SUS), and Nielsen’s heuristic evaluation to integrate both user perception and normative usability standards. Initial testing produced SUS scores of 74.5 (owners), 76.5 (tenants), and 66 (administrators), indicating acceptable but improvable usability and several interface issues. Iterative refinement led to marked enhancements, with second-round SUS scores of 90, 87, and 89, accompanied by high learnability (96–97%), strong memorability (95–96%), and low error rates (0.0306–0.0800). A minor efficiency decrease was attributed to unstable network conditions rather than design flaws. Overall, the findings demonstrate that structured, iterative UI/UX development supported by heuristic auditing effectively resolves core information and interaction challenges in boarding-house reservation systems. The final prototype demonstrates high usability and provides a replicable design rationale for future implementation and scaling.
Performance Analysis of Reasoning Models in RAG-Based Question Answering System for University Admission Services Setiawan, Muhammad Surya Adhi; Pratama, Arista; Ananto, Prasasti Karunia Farista
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3707

Abstract

Access to accurate, relevant, and timely information is crucial for prospective university students; however, conventional information services often struggle with high query volumes and the risk of generative hallucinations in automated systems. This study investigates whether reasoning-oriented large language models provide measurable improvements in response quality within a Retrieval-Augmented Generation (RAG) architecture for university admission services. The study hypothesizes that internal chain-of-thought reasoning improves factual grounding compared with non-reasoning models under identical retrieval conditions. A vector-based institutional knowledge base was constructed from 30 official admission sources using VoyageAI embeddings and evaluated on a multilingual dataset of 353 real-world inquiries in Indonesian, English, and Javanese dialects. To isolate the effect of reasoning capabilities, retrieval outputs and prompt configurations were controlled across all models. Performance was evaluated using the RAGAS framework across six models categorized as reasoning (DeepSeek-R1, Gemini-2.5-Flash, o4-mini) and non-reasoning (DeepSeek-V3, Gemini-2.0-Flash, GPT-4o-mini). The results show that reasoning models achieved a higher average RAGAS score (0.7772) than non-reasoning models (0.7289), representing a 6.63% improvement, with the largest gain observed in factual correctness (+15.95%). Additional multilingual benchmarking confirmed that reasoning models maintain more stable performance across languages. Gemini-2.5-Flash achieved the highest composite score (0.8207) while maintaining favorable cost efficiency. These findings indicate that reasoning-enabled models significantly improve factual reliability in domain-specific RAG systems, although overall system performance remains strongly dependent on retrieval quality.