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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Marta Riri Frimadani
Universitas Andalas
Computer Science & IT Engineering

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UI/UX Analysis and Design Development of Less-ON Digital Startup Prototype by Using Lean UX Rio Andika Malik; Marta Riri Frimadani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4454

Abstract

The growth of startups in Indonesia continues to experience upward growth. Behind the growth that continues to move up, there is a success rate statistic which is a contradiction behind its development. The startup statistics show that about 90% of startups fail. As many as 75% of unicorn startups believe that a good UI/UX design can increase startup valuations and additional investors' funds. User Interface (UI) and User Experience (UX) are closely related because UX results from UI interactions. Less-On is a provider of private tutoring service providers who serve as an intermediary bridge between teachers and students. This research will be carried out by integrating the processes in the Lean UX method into every process that exists at the stages of software engineering development. The results obtained from this study are a final prototype validated in terms of criticism and suggestions through a questionnaire as a form of Less-On branding. Positive UX and better usability are significant for further development of the prototype private tutor booking application, which plays a vital role in acceptance, satisfaction and efficiency in using this Less-ON application. The UI has good usability for users, with a SUS scoring earn 85.53, which is above average and acceptable.
Deep Sentiment Analysis of Halal Tourism: An Enhanced IndoBERT with Attention-Based and Coupling Latent Sampling on User-Generated Feedback Rio Andika Malik; Marta Riri Frimadani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7378

Abstract

Public sentiment analysis regarding government policies, particularly in niche sectors like Halal tourism, often faces significant computational challenges due to data scarcity and severe class imbalance. Traditional augmentation methods, such as Synthetic Minority Over-sampling Technique (SMOTE) applied to raw text or TF-IDF vectors, often degrade semantic integrity, while standard fine-tuning of pre-trained models like IndoBERT tends to bias predictions toward majority classes. To address these limitations, this study proposes IndoBERT-LSS (Latent Space Sampling), a novel decoupled two-stage deep learning framework. The first stage employs a Representation Learning approach integrating an Attention-Pooling mechanism with Supervised Contrastive Loss (SupCon) to enforce compact intra-class clustering in the embedding space. The second stage introduces a Latent Space Sampling strategy, where SMOTE is applied to the extracted high-dimensional embeddings rather than the raw text, followed by a final classification using a Multi-Layer Perceptron. Validated on a dataset of 1,051 textual responses regarding Halal tourism in Pariaman, Indonesia, the proposed model achieved an accuracy of 93.84% and a macro F1-score of 0.94. Notably, the model demonstrated exceptional robustness in identifying minority classes, achieving a 0.99 F1-score for neutral sentiments. These results conclude that decoupling representation learning from feature balancing in the latent space significantly enhances model performance on imbalanced short-text datasets compared to standard fine-tuning baselines.
Co-Authors Rio Andika Malik