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All Journal Journal of Information Systems and Informatics Proceeding of International Conference Health, Science And Technology (ICOHETECH) INOVTEK Polbeng - Seri Informatika
Ermatita
Sriwijaya University
Computer Science & IT Decision Sciences, Operations Research & Management Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology

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Application of the Apriori Algorithm and FP-Growth to find out the Association Rule between Gender, Education level on wages of SMEs workers in Palembang City Antonius Wahyu Sudrajat; Idham Cholid; Ermatita
Proceeding of International Conference on Science, Health, And Technology 2021: Proceeding of the 2nd International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1108.204 KB) | DOI: 10.47701/icohetech.v1i1.1115

Abstract

Small, Medium Enterprises (SMEs) are one of the supporting parts of the Indonesian economy by absorbing high labor and production value. One of the factors that greatly influences the development of SMEs in Indonesia is the workforce involved in SMEs business activities. Several factors that influence the workforces are gender, education level, marital status and wages level. The data mining used can help processing data into new knowledge that can be used in decision making. The purpose of this study was to analyze the data of SMEs workers in the Palembang city by comparing the Apriori algorithm and the FP-Growth algorithm to see the association between gender, education level, marital status and wages earned by SMEs’s workers in Palembang city. The sample used in this research are 400 SMEs’s workers who were randomly selected from 5 sub-districts in Palembang city. The results show that with a confidence level of 0.8, the Apriori Algorithm produces 25 association patterns while the FP-Growth Algorithm has 11 association patterns. In the Apriori Algorithm, it was found that 72% of associations with 1 level confidence while the rest is less than 1. As for the FP-Growth Algorithm, 9% of the association patterns have 1 confidence level and the rest is less than 1. These results indicate that the Apriori algorithm is able to explain more association between gender, education level, marital status and wages of SMEs’s workers in Palembang City. The results of this study can be used as a consideration for increasing the capacity of SMEs’s workers in Palembang City.
Class-Level Behavior Analysis under Metric Disagreement in Imbalanced Multi-Label Indonesian Emotion Classification Jahda Rusti Putri; Ermatita; Abdiansah
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1664

Abstract

This study aims to analyze class-level model behavior under metric disagreement in imbalanced multi-label Indonesian emotion classification, using the divergence between Macro F1 and Micro F1 as a diagnostic signal rather than a mere performance indicator. A machine-translated Indonesian version of the GoEmotions dataset, comprising approximately 58,000 samples across 28 fine-grained emotion categories, is used as the experimental setting. The translated dataset was not manually revalidated, and findings are scoped to this translated GoEmotions setting. Two transformer-based models are evaluated: IndoBERT, a monolingual Indonesian model, and DistilBERT, a multilingual model, both fine-tuned with class-specific threshold optimization. The results reveal opposing divergence patterns: IndoBERT achieves higher Micro F1 than Macro F1, indicating performance concentrated on high-frequency classes, while DistilBERT exhibits the reverse pattern, suggesting broader but less precise label activation. Per-class analysis further shows that most minority classes consistently fall into unstable or non-functional performance regimes across both models. This study concludes that aggregate metrics alone are insufficient for evaluating model behavior in imbalanced multi-label settings. A behavior-oriented interpretation framework for Macro–Micro F1 divergence and a regime-based class reliability categorization are proposed to support more structured and informative evaluation practices.
Sentiment Analysis of Indonesia's Economic Acceleration Program 2025 Using Support Vector Machine Kamelia Lestari; Ermatita
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/r2tz5h34

Abstract

Indonesia's Economic Acceleration Program 2025 is a government policy to accelerate national economic growth in response to the global economic slowdown. The implementation of this program has generated a variety of public responses on social media. This study aims to analyze public sentiment towards the Indonesian Economic Acceleration Program in the launch and implementation phases and identify differences in sentiment distribution in both phases. Data in the form of TikTok comments was collected through web scraping and then processed through preprocessing, lexicon-based sentiment labeling validated using manually labeled samples, TF-IDF feature representation, and classification using the Support Vector Machine. Model evaluation was carried out using 10-fold cross-validation. The results of the study showed that SVM provided superior performance to the comparison model. In the launch phase, SVM achieved an accuracy of 81%, while in the implementation phase it achieved an accuracy of 79.5% with superior performance in all evaluation metrics. The distribution of sentiment in the launch phase was dominated by neutral sentiment by 67.1%, while in the implementation phase the proportion of negative sentiment increased to 46.38%. These results show that there is a difference in the distribution of sentiment between the launch and implementation phases, so they can be an input in understanding the public's response to the Indonesian Economic Acceleration Program.
Co-Authors Abdiansah Idham Cholid Jahda Rusti Putri Kamelia Lestari Wahyu Sudrajat