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This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science Advance Sustainable Science, Engineering and Technology (ASSET)
Mukti Ali
Universitas Islam Negeri Salatiga
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Cloud computing: google firebase firestore optimization analysis Andi Bahtiar Semma; Mukti Ali; Muh Saerozi; Mansur Mansur; Kusrini Kusrini
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1719-1728

Abstract

Cloud computing is a new paradigm that provides end users with a secure, personalized, dynamic computing environment with guaranteed service quality. One popular solution is Google cloud firestore, a global-scale not only structured query language (NoSQL) document database for mobile and web apps. Recent research on cloud-based NoSQL databases often discusses the difference between them and SQL databases and their performance. However, using cloud-based NoSQL databases such as firestore is tricky without any scientific comparison methodology, and it needs analysis of how its particular systems work. This study aims to discover what is the best design that could be implemented to optimize data read cost, response size, and time regarding the cloud firestore database. In this study, we develop a grade point average (GPA)-report mocking application to assess data read based on our institution’s needs. This application consists of three functions. Add the graduated GPA and students’ names, and view the ten highest GPAs, GPA average, and total graduated students. The finding indicates that aggregating data on the client side or utilizing the Google cloud function trigger, then updating aggregation data in one transaction significantly reduces document read count (cost), response size, and time.
Enhanced Self-Esteem Classification: Leveraging Data Augmentation and Transformer-Based Sentence Embeddings Reza Ahmadiansah; Mukti Ali; Rasimin; Achmad Maimun; Kastolani; Imam Subqi; Embun Bening Di Moravia; Andi Bahtiar Semma
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i4.2741

Abstract

This study investigates automated self-esteem assessment from self-descriptive text using transformer-based sentence embeddings. Although prior research has explored text, behavioral, and multimodal signals, the combined effects of data augmentation, embedding choice, and classifier complexity in text-only self-esteem classification remain insufficiently understood.Accordingly, this study aims to systematically evaluate embedding–classifier combinations under both low-resource and augmented data conditions. Textual self-descriptions were collected from 298 undergraduate students at UIN Salatiga and labeled using the Indonesian version of the Rosenberg Self-Esteem Scale, yielding three self-esteem categories. To address data scarcity, a controlled translation-based augmentation pipeline with expert psychological validation was applied exclusively to the training set. Seven multilingual sentence embedding models were paired with eight classification algorithms, and performance was evaluated using macro-averaged metrics, along with training and inference time. Results reveal a two-regime pattern: (1) in limited-data settings, strong embeddings with simple classifiers perform best, (2) whereas in augmented settings, representation quality dominates and classifier choice has a marginal effect. The findings suggest that prioritizing high-quality embeddings and carefully validated data augmentation enables accurate, scalable, and cost-effective text-based self-esteem assessment for real-world psychological applications.
Co-Authors Achmad Maimun Andi Bahtiar Semma Embun Bening Di Moravia Imam Subqi Kastolani Kusrini Mansur Mansur Muh Saerozi Rasimin Reza Ahmadiansah