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All Journal International Journal of Advances in Applied Sciences Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Widya Cipta : Jurnal Sekretari dan Manajemen Applied Information System and Management JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) International Journal of Advances in Data and Information Systems Journal of Applied Data Sciences Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Jurnal Janitra Informatika dan Sistem Informasi LEBAH Journal of Information System and Education Development JURNAL DATA SAINS DAN TEKNOLOGI INFORMASI (DASTIS) Edumaspul: Jurnal Pendidikan Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Widya Cipta : Jurnal Sekretari dan Manajemen
Rio Andika Malik
Universitas Perintis Indonesia
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IndoBERT-SupCon: A Supervised Contrastive Learning Model for Analyzing Public Perception on Halal Tourism Sri Mona Octafia; Rio Andika Malik; Annisa Weriframayeni; Delpa Delpa
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1045

Abstract

The primary objective of this research is to develop and evaluate a robust deep learning model for accurately analyzing stakeholder perceptions of halal tourism development in Pariaman, West Sumatra, based on qualitative textual data. The main contribution is the introduction of IndoBERT-SupCon, a novel architecture that enhances the Indonesian BERT model with a Supervised Contrastive Learning (SupCon) mechanism. A novel method for producing more discriminative feature representations for complex viewpoints is presented in this paper, which is one of the first to use this sophisticated fine-tuning technique to Indonesian socio-political sentiment analysis. Conceptually, the model is trained to simultaneously minimize classification error while optimizing the feature space, pulling representations of similar sentiments closer together and pushing dissimilar ones further apart. To achieve this, we collected 1,022 primary textual responses through online surveys with tourists and in-depth interviews with key stakeholders, including SME owners and government officials. The SMOTE oversampling technique was employed on the training data to mitigate class imbalance. Experimental results on the test data demonstrate that the IndoBERT-SupCon model achieved outstanding performance, with a final accuracy of 96.59% and a macro F1-score of 0.97. These results significantly surpass the performance of a standard fine-tuned IndoBERT baseline, confirming the effectiveness of the SupCon approach. The findings provide the Pariaman local government with a highly valid, data-driven tool for more responsive and effective policy formulation. This research offers a robust framework that can be applied to other public policy domains, showcasing the value of advanced deep learning in transforming qualitative stakeholder feedback into actionable insights.
Easily Determining Post-Study System Usability for Anime Community E-Commerce Analysis Rio Andika Malik; Sri Mona Octafia; Vicky Setia Gunawan
Applied Information System and Management (AISM) Vol. 7 No. 2 (2024): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v7i2.39352

Abstract

The rapid growth of e-commerce as a form of electronic commerce has transformed the global shopping landscape. To maintain user satisfaction and competitiveness, e-commerce sites must effectively understand and improve the usability of their systems after launch. The aims of the study are to improve e-commerce post-launch usability so that anime fans' enthusiasm can be capitalized upon for financial gain and market expansion. In this study, we adopted a combined approach that included user observations, interviews, surveys, and performance measurement for anime community e-commerce analysis Weeboo web commerce. Through this method, we analyze the behavior and views of users towards e-commerce systems. The results show that most users experience a positive experience in shopping online by appreciating the usability of the layout, search process, and fast checkout process. The results indicated that most users have a positive online shopping experience, appreciating the layout, search process, and fast checkout process. The SUS score of 75.375 (grade B) and the overall PSSUQ satisfaction score of 2.0296 indicate that the system is acceptably well-received. The proposed recommendations can help e-commerce companies quickly identify usability issues and implement relevant fixes.
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 Anam, M Khairul Ayu Mahessya, Raja Azhara Rezki Ananda Carelsa, Hasnah Vithon Delpa, Delpa Dina Hadia Dina Hadia Dwipa Junika Putra Elsa Widia Elsa Widia Faizal Riza Faizal Riza Firdaus, Muhammad Bambang Frimadani, Marta Riri Gunawan, Chichi Rizka Ikhsan Ikhsan Karfindo, Karfindo Marta Riri Frimadani Marta Riri Frimadani Marta Riri Frimadani Muzaki, M.Nabil Najwa Ramanda Natrio, Yeyep Pandu Pratama Putra, Pandu Pratama Putra, Dwipa Junika Rinawati Kasrin, Rinawati Sarjon Defit Sri Mona Octafia Sri Mona Octafia Teri Ade Putra Vicky Setia Gunawan Vicky Setia Gunawan Vicky Setia Gunawan Vicky Setia Gunawan Werifmayeni, Annisa Weriframayeni, Annisa Wilis Firmansyah Wilis Firmansyah Yesri Elva Yuhandri Yuhandri, Yuhandri