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All Journal JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI
Andi Arniaty Arsyad
Universitas Al-Azhar Indonesia
Biochemistry, Genetics & Molecular Biology Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Public Health

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Pemodelan Emosi Pengguna Berbasis Ulasan Digital Melalui Integrasi Natural Language Processing dan Ilmu Sosial Komputasional Adinda Riska Safitri; Andi Arniaty Arsyad
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 11, No 2 (2026): Mei 2026
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v11i2.5425

Abstract

Internal company apps such as PT Astra Honda Motor’s AHM Mobile receive a wide range of reviews on the Google Play Store that contain users’ emotional expressions. However, the emotional dimension of these reviews has rarely been systematically analyzed, as most previous studies have focused only on positive, negative, and neutral sentiments. This study aims to analyze the emotions of AHM Mobile users through the integration of Natural Language Processing (NLP) and Computational Social Science perspectives. The dataset consists of 2.117 reviews obtained via web scraping and classified into six emotional categories: angry, sad, afraid, neutral, surprised, and happy. The annotation process was conducted by two annotators in the fields of clinical psychology and linguistics using a Seniority-Based Tie-Breaking mechanism with a Cohen’s Kappa value of 0.636. Emotion classification was performed using a combination of TF-IDF and Logistic Regression as classical models, as well as IndoBERT as the main model. Evaluation results show that the classical model achieved an accuracy of 0.43 and a macro-F1 score of 0.183, while IndoBERT reached an accuracy of 0.7831 and a macro-F1 score of 0.5582. Collective emotion analysis indicates that anger dominates user reviews and is strongly correlated with user ratings, as evidenced by a Spearman correlation coefficient of 0.7141. These results indicate that sentiment analysis using the IndoBERT model can provide more effective insights for evaluating the quality of usage of internal corporate applications in Indonesia.Keywords - Computational Social Science (CSS), Emotion Classification, IndoBERT, Natural Language Processing (NLP), User Reviews of the AHM Mobile Application.
EduCrypt: A Secure File Data Sharing Platform using Hybrid Encryption for Cloud-Based E-Learning Andi Arniaty Arsyad; Riri Safitri; Lambda Sangkala Murbawisesa
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 11, No 1 (2026): Januari 2026
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v11i1.5086

Abstract

The rise of cloud-based learning environments has transformed education but has also exposed vulnerabilities in data security. Learning Management Systems (LMSs) often handle sensitive academic data without adequate protection, leaving it vulnerable to unauthorized access and data breaches. This study introduces EduCrypt, a secure file-sharing platform that uses hybrid encryption, combining AES for fast data encryption and RSA for secure key exchange. In this research, a Hybrid Waterfall–Iterative method was used as the overarching research methodology to guide requirements analysis, architectural design, system implementation, and evaluation. EduCrypt integrates seamlessly with Moodle LMS via token- and credential-based authentication. Its architecture includes asynchronous RabbitMQ workers for efficient synchronization and a user-friendly web interface for secure file operations. Benchmark tests demonstrate stable encryption and decryption times under 300 ms for files up to 30 MB, showcasing both scalability and efficiency. Security evaluations confirm that EduCrypt effectively mitigates brute-force attacks, SQL injection, and man-in-the-middle attacks. This research resulted in a validated enhancement in file security practices for LMS through the implementation of a hybrid cryptographic model. Furthermore, the outcome includes a fully functional EduCrypt prototype integrated with Moodle, along with performance and security evaluation results.Keywords - Data Security, Hybrid encryption, File sharing, E-learning, Cloud computing.
Co-Authors Adinda Riska Safitri Lambda Sangkala Murbawisesa Riri Safitri