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p-Index From 2021 - 2026
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All Journal Journal of Intelligent Systems Technology and Informatics
Indri Tri Julianto
Universitas Siliwangi
Computer Science & IT Control & Systems Engineering Engineering

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Sentiment Analysis of the Residency Policy Launch in the New Student Admission System Using Automatic Labeling with Meta AI Indri Tri Julianto; Lindawati
Journal of Intelligent Systems Technology and Informatics Vol 2 No 1 (2026): JISTICS, March 2026
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v2i1.44

Abstract

The launch of a domicile-based policy in Indonesia's New Student Admission System (SPMB) has triggered various public responses, especially on social media platforms. Understanding these sentiments is essential for evaluating policy acceptance and guiding future improvements in educational governance. This study aims to analyze public sentiment toward the policy using automatic labeling techniques and machine learning classification, with a focus on identifying dominant public perceptions. The research applies the CRISP-DM methodology, consisting of six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. A total of 1,105 comments were collected from Instagram and YouTube via web scraping and then preprocessed using text cleaning, stemming, and tokenization. Sentiment labels were generated using three automatic methods: Meta AI, RoBERTa, and TextBlob. Classification was performed using the Support Vector Machine (SVM) algorithm with four kernel variations. The results indicate that the combination of TextBlob labeling and an SVM with the Sigmoid kernel achieved the highest accuracy (0.99), along with strong precision, recall, and F1 Scores. Word cloud visualizations revealed that positive sentiment was related to educational access and teacher appreciation, while negative sentiment focused on dissatisfaction with fairness and system transparency. In conclusion, this study demonstrates that automated sentiment analysis, when supported by proper preprocessing and class balancing, is a powerful approach to extracting meaningful insights from public discourse. The findings are expected to support policymakers in developing data-driven strategies for improving future education policies.
Load Testing-Based Performance Evaluation of the SiUKT API System Andi Nur Rachman; Rahmi Nur Shofa; Irfan Nafis Sjamsuddin; Genta Najwar Tarempa; Indri Tri Julianto; Bifahmi Ahmad Athoillah
Journal of Intelligent Systems Technology and Informatics Vol 2 No 1 (2026): JISTICS, March 2026
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v2i1.89

Abstract

Software testing is a crucial stage in the system development lifecycle. Previous studies on SiUKT have used only the black-box method, focusing on functionality without providing insights into performance optimization. This study aims to analyze and improve the performance of the SiUKT API using an intelligent load-testing approach with Apache JMeter. The testing measures three key indicators—response time, throughput, and error rate—across 10 API endpoints with concurrent user simulations of 10, 100, 250, and 500 users. The results show that the SiUKT website performs effectively under moderate load conditions, with an average response time of 338 ms and a throughput of 8.2 requests per second for 10 users. Under high load (500 users), performance declines, with response times ranging from 6 to 8 seconds, while throughput remains stable and the error rate stays at 0.00%. Only the register endpoint experienced a 100% error rate due to validation conflicts. These findings demonstrate the system's ability to maintain stability under varying loads and highlight performance degradation patterns as user traffic increases. The research contributes to the optimization of intelligent system performance by establishing quantitative benchmarks for API scalability and providing recommendations for adaptive infrastructure improvements to support automated intelligent load management.
Co-Authors Andi Nur Rachman Bifahmi Ahmad Athoillah Genta Najwar Tarempa Irfan Nafis Sjamsuddin Lindawati Rahmi Nur Shofa