HUMAN COMPUTER INTERACTION DESIGN ENHANCING USABILITY IN MOBILE COMPUTING APPLICATIONS

Afen Prana Utama Sembiring (1), Filbert Ivander (2), Erlanie Sufarnap (3)
(1) Universitas Mikroskil, Indonesia,
(2) Institut Teknologi dan Bisnis Sabda Setia, Indonesia,
(3) Universitas Mikroskil, Indonesia

Abstract

The rapid growth of mobile computing applications has intensified the need for effective Human Computer Interaction design to ensure high usability and positive user experience. Mobile applications are used in diverse contexts characterized by limited screen space, touch-based interaction, and frequent interruptions, making usability a critical determinant of system acceptance and sustained use. This study aims to examine how Human Computer Interaction design principles contribute to enhancing usability in mobile computing applications. The research employed a mixed-methods approach combining usability testing, standardized usability questionnaires, and qualitative user interviews to capture both performance-based and perceptual usability outcomes. Quantitative data focused on task completion time, error rates, task success, and perceived usability, while qualitative data explored user interaction experiences and design-related challenges. The results indicate that mobile applications designed with clear navigation structures, consistent visual elements, and effective feedback mechanisms demonstrate significantly higher usability scores, faster task completion, and lower error frequency. Qualitative findings further reveal increased user confidence, reduced cognitive load, and higher satisfaction when interacting with well-designed interfaces. The study concludes that Human Computer Interaction design plays a central role in enhancing usability in mobile computing applications. Systematic integration of user-centered design principles throughout the development process is essential for creating efficient, effective, and satisfying mobile applications across various domains.

Full text article

Generated from XML file

References

Alzubi, T. M., Alzubi, J. A., Singh, A., Alzubi, O. A., & Subramanian, M. (2025). A Multimodal Human-Computer Interaction for Smart Learning System. International Journal of Human–Computer Interaction, 41(3), 1718–1728. https://doi.org/10.1080/10447318.2023.2206758

Balhara, S., Gupta, N., Alkhayyat, A., Bharti, I., Malik, R. Q., Mahmood, S. N., & Abedi, F. (2025). A survey on deep reinforcement learning architectures, applications and emerging trends. IET Communications, 19(1), e12447. https://doi.org/10.1049/cmu2.12447

Barzegar Gerdroodbary, M., & Salavatidezfouli, S. (2025). A predictive surrogate model based on linear and nonlinear solution manifold reduction in cardiovascular FSI: A comparative study. Computers in Biology and Medicine, 189, 109959. https://doi.org/10.1016/j.compbiomed.2025.109959

Borghi, S., Ruo, A., Sabattini, L., Peruzzini, M., & Villani, V. (2025). Assessing operator stress in collaborative robotics: A multimodal approach. Applied Ergonomics, 123, 104418. https://doi.org/10.1016/j.apergo.2024.104418

Chen, Y., Hao, Y., Feng, L., Meng, J., Yang, Z., Wu, H., Li, P., Zhu, Z., Zhao, B., & Wei, Q. (2025). A flexible multifunctional triboelectric nanogenerator based on bio-inspired nanocellulose/tannic acid@MXene-composited hydrogel for human healthcare. International Journal of Biological Macromolecules, 306, 141261. https://doi.org/10.1016/j.ijbiomac.2025.141261

Cheng, A., Li, Xin, Li, D., Chen, Z., Cui, T., Tao, L.-Q., Jian, J., Xiao, H., Shao, W., Tang, Z., Li, Xinyue, Dong, Z., Liu, H., Yang, Y., & Ren, T.-L. (2025). An intelligent hybrid-fabric wristband system enabled by thermal encapsulation for ergonomic human-machine interaction. Nature Communications, 16(1), 591. https://doi.org/10.1038/s41467-024-55649-1

Choudhary, S., Kaushik, N., Sivathanu, B., & Rana, N. P. (2025). Assessing Factors Influencing Customers’ Adoption of AI-Based Voice Assistants. Journal of Computer Information Systems, 65(5), 592–609. https://doi.org/10.1080/08874417.2024.2312858

Cohn, C., Snyder, C., Fonteles, J. H., T. S., A., Montenegro, J., & Biswas, G. (2025). A multimodal approach to support teacher, researcher and AI collaboration in STEM +C learning environments. British Journal of Educational Technology, 56(2), 595–620. https://doi.org/10.1111/bjet.13518

Emami, M., Bayat, A., Tafazolli, R., & Quddus, A. (2025). A Survey on Haptics: Communication, Sensing and Feedback. IEEE Communications Surveys & Tutorials, 27(3), 2006–2050. https://doi.org/10.1109/COMST.2024.3444051

Fan, K., Li, K., Wang, Z., Men, W., Wu, X., Cheng, J., & Zhang, J. (2025). An unprecedented strategy with electric double layer and adaptive ionic liquid in fully ionogel fiber-based TENG for enhanced output and dynamic stability. Nano Energy, 135, 110658. https://doi.org/10.1016/j.nanoen.2025.110658

Fu, X., Mo, S., Buendia, A., Laurent, A. P., Shao, A., Alvarez-Torres, M. D. M., Yu, T., Tan, J., Su, J., Sagatelian, R., Ferrando, A. A., Ciccia, A., Lan, Y., Owens, D. M., Palomero, T., Xing, E. P., & Rabadan, R. (2025). A foundation model of transcription across human cell types. Nature, 637(8047), 965–973. https://doi.org/10.1038/s41586-024-08391-z

Halkiopoulos, C., Gkintoni, E., Aroutzidis, A., & Antonopoulou, H. (2025). Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations. Diagnostics, 15(4), 456. https://doi.org/10.3390/diagnostics15040456

Hamdani, R., & Chihi, I. (2025). Adaptive human-computer interaction for industry 5.0: A novel concept, with comprehensive review and empirical validation. Computers in Industry, 168, 104268. https://doi.org/10.1016/j.compind.2025.104268

Huang, T., Gao, B., Li, M., Zhou, X., He, W., Yan, J., Luo, X., Lai, W., Li, J., Luo, S., Yue, Y., Ma, Y., & Gao, Y. (2025). Cathode?Free Aqueous Micro?battery for an All?in?One Wearable System with Ultralong Stability. Advanced Energy Materials, 15(4), 2402871. https://doi.org/10.1002/aenm.202402871

Li, L., Xu, H., Li, Z., Zhong, B., Lou, Z., & Wang, L. (2025). 3D Heterogeneous Sensing System for Multimode Parrallel Signal No?Spatiotemporal Misalignment Recognition. Advanced Materials, 37(6), 2414054. https://doi.org/10.1002/adma.202414054

Li, W., Luo, F., Liu, Y., Zou, Y., Mo, L., He, Q., Lin, P., Xu, Q., Liu, A., Zhang, C., Cheng, J., Cheng, L., & Ji, L. (2025). Bioinspired Smart Triboelectric Soft Pneumatic Actuator?Enabled Hand Rehabilitation Robot. Advanced Materials, 37(9), 2419059. https://doi.org/10.1002/adma.202419059

Li, X., Wang, Y., Tian, Y., Zhang, L., & Ma, J. (2025). Biomimetic multiscale structure with hierarchically entangled topologies of cellulose-based hydrogel sensors for human-computer interaction. Carbohydrate Polymers, 348, 122825. https://doi.org/10.1016/j.carbpol.2024.122825

Lin, J., Chen, J., Yang, K., Roitberg, A., Li, Siyu, Li, Z., & Li, Shutao. (2025). AdaptiveClick: Click-Aware Transformer With Adaptive Focal Loss for Interactive Image Segmentation. IEEE Transactions on Neural Networks and Learning Systems, 36(3), 5759–5773. https://doi.org/10.1109/TNNLS.2024.3378295

Linh, V. T. N., Han, S., Koh, E., Kim, S., Jung, H. S., & Koo, J. (2025). Advances in wearable electronics for monitoring human organs: Bridging external and internal health assessments. Biomaterials, 314, 122865. https://doi.org/10.1016/j.biomaterials.2024.122865

Omidian, H. (2025). AI-powered breakthroughs in material science and biomedical polymers. Journal of Bioactive and Compatible Polymers, 40(2), 161–174. https://doi.org/10.1177/08839115241308202

Parasa, S., Berzin, T., Leggett, C., Gross, S., Repici, A., Ahmad, O. F., Chiang, A., Coelho-Prabhu, N., Cohen, J., Dekker, E., Keswani, R. N., Kahn, C. E., Hassan, C., Petrick, N., Mountney, P., Ng, J., Riegler, M., Mori, Y., Saito, Y., … Sharma, P. (2025). Consensus statements on the current landscape of artificial intelligence applications in endoscopy, addressing roadblocks, and advancing artificial intelligence in gastroenterology. Gastrointestinal Endoscopy, 101(1), 2-9.e1. https://doi.org/10.1016/j.gie.2023.12.003

Pillalamarri, R., & Shanmugam, U. (2025). A review on EEG-based multimodal learning for emotion recognition. Artificial Intelligence Review, 58(5), 131. https://doi.org/10.1007/s10462-025-11126-9

Qi, W., Xu, X., Qian, K., Schuller, B. W., Fortino, G., & Aliverti, A. (2025). A Review of AIoT-Based Human Activity Recognition: From Application to Technique. IEEE Journal of Biomedical and Health Informatics, 29(4), 2425–2438. https://doi.org/10.1109/JBHI.2024.3406737

Shah, R., Doss, A. S. A., & Lakshmaiya, N. (2025). Advancements in AI-enhanced collaborative robotics: Towards safer, smarter, and human-centric industrial automation. Results in Engineering, 27, 105704. https://doi.org/10.1016/j.rineng.2025.105704

Song, M., Liu, Q., Xu, X., Wang, B., Lu, Y., Yang, L., Liu, X., Wang, Y., Li, M., & Wang, D. (2025). A Fabric?Based Multimodal Flexible Tactile Sensor With Precise Sensing and Discrimination Capabilities for Pressure?Proximity?Magnetic Field Signals. Advanced Functional Materials, 35(19), 2420445. https://doi.org/10.1002/adfm.202420445

Suarez-Amaran, L., Song, L., Tretiakova, A. P., Mikhail, S. A., & Samulski, R. J. (2025). AAV vector development, back to the future. Molecular Therapy, 33(5), 1903–1936. https://doi.org/10.1016/j.ymthe.2025.03.064

Tariq, M. U. (2024). Bridging Generations: Digital Transformation and Its Multifaceted Impact Across Societal Sectors. In M. Anshari, M. N. Almunawar, & P. Ordóñez De Pablos (Eds.), Advances in Human and Social Aspects of Technology (pp. 1–22). IGI Global. https://doi.org/10.4018/979-8-3693-6366-9.ch001

Tuo, Y., Wu, J., Zhao, J., & Si, X. (2025). Artificial intelligence in tourism: Insights and future research agenda. Tourism Review, 80(4), 793–812. https://doi.org/10.1108/TR-03-2024-0180

Wang, R., Guo, C., Shabaz, M., Rida, I., Cambria, E., & Zhu, X. (2025). CIME: Contextual Interaction-Based Multimodal Emotion Analysis With Enhanced Semantic Information. IEEE Transactions on Computational Social Systems, 1–11. https://doi.org/10.1109/TCSS.2025.3572495

Yang, R., Yang, X., Qi, L., Meng, X., Dai, L., Jin, X., Zhou, J., Lu, H., Xia, C., & Li, J. (2025). Adhesive conductive wood-based hydrogel with high tensile strength as a flexible sensor. Carbohydrate Polymers, 351, 122954. https://doi.org/10.1016/j.carbpol.2024.122954

Yang, X., Wang, Y., Lin, Y., Zhang, M., Liu, O., Shuai, J., & Zhao, Q. (2025). A Multi?Task Self?Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors. Advanced Science, 12(13), 2412987. https://doi.org/10.1002/advs.202412987

Zhang, T., Manshaii, F., Bowen, C. R., Zhang, M., Qian, W., Hu, C., Bai, Y., Huang, Z., Yang, Y., & Chen, J. (2025). A flexible pressure sensor array for self-powered identity authentication during typing. Science Advances, 11(11), eads2297. https://doi.org/10.1126/sciadv.ads2297

Zhou, X., Chen, X., Yang, B., Luo, S., Guo, M., An, N., Tian, H., Li, X., & Shao, J. (2025). Advancements in Functionalizable Metal?Organic Frameworks for Flexible Sensing Electronics. Advanced Functional Materials, 35(32), 2501683. https://doi.org/10.1002/adfm.202501683

Zhu, X., Liu, Z., Cambria, E., Yu, X., Fan, X., Chen, H., & Wang, R. (2025). A client–server based recognition system: Non-contact single/multiple emotional and behavioral state assessment methods. Computer Methods and Programs in Biomedicine, 260, 108564. https://doi.org/10.1016/j.cmpb.2024.108564

Authors

Afen Prana Utama Sembiring
afen@mikroskil.ac.id (Primary Contact)
Filbert Ivander
Erlanie Sufarnap
Prana Utama Sembiring, A., Ivander, F., & Sufarnap, E. . (2026). HUMAN COMPUTER INTERACTION DESIGN ENHANCING USABILITY IN MOBILE COMPUTING APPLICATIONS. Journal of Computer Science Advancements, 4(1), 1–12. https://doi.org/10.70177/jsca.v4i1.3390

Article Details