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All Journal JPM (Jurnal Pemberdayaan Masyarakat) Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Systems and Informatics Jurnal Sistem Komputer dan Informatika (JSON) Jifosi Jurnal Informatika: Jurnal Pengembangan IT INOVTEK Polbeng - Seri Informatika NAWASENA BHAKTI
Ifnu Wisma Dwi Prastya
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Religion Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Journal : journal of applied informatics and computing

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Attention-Enhanced Multivariate Forecasting for Intelligent Microservice Autoscaling Nur Saifuddin; Mula Agung Barata; Ifnu Wisma Dwi Prastya
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12662

Abstract

Proactive autoscaling in cloud-native microservices requires anticipatory decisions because reactive controllers often lag under abrupt workload shifts. This study aims to improve autoscaling decision quality through a two-stage machine learning pipeline. The research adopts an experimental design using production-grade microservice traces, with strict time-respecting train-validation-test splits and training-only fitting for preprocessing and oracle-threshold estimation to prevent leakage. In the first stage, multivariate forecasting models predict future CPU and memory utilization from engineered temporal features. In the second stage, the predicted signals are combined with observed features to classify three autoscaling actions: scale down, hold, and scale up. Benchmarking shows recurrent neural models are strong baselines, while an attention-enhanced encoder-decoder performs best. The best Bahdanau-attention model with residual connection reduces test CPU RMSE from 0.030977 to 0.028924 and memory RMSE from 0.010322 to 0.005452 relative to the strongest BiLSTM baseline. For decision learning, the optimized Extreme Gradient Boosting model using prediction-augmented features achieves an accuracy of 0.950602 and an F1 score of 0.951026. Supporting downstream validation also yields lower SLO violation rates than horizontal and vertical baselines while maintaining zero downtime in the evaluated scenarios. These findings indicate that improving forecasting quality and explicitly transferring predictive signals to the decision stage strengthens proactive autoscaling performance.
TikTok Sentiment Analysis on Koperasi Merah Putih Using SVM and ANN Meliysa Pasa Bagna Aprilia Said; Kholifatus Sholihah; Ifnu Wisma Dwi Prastya; Afril Efan Pajri
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13394

Abstract

TikTok has become a relevant social media source for observing public responses to public issues, including the Koperasi Merah Putih program. This study compares Support Vector Machine (SVM) and Artificial Neural Network (ANN) for classifying sentiment in TikTok comments. The dataset was obtained through TikTok comment scraping and consisted of 25,669 raw comments. After removing empty comments and applying preprocessing stages consisting of cleaning, case folding, normalization, tokenization, stopword removal, and stemming, 21,026 comments were used for sentiment analysis. Sentiment labels were generated automatically using a lexicon-based sentiment labeling approach and grouped into three classes: positive, negative, and neutral. TF-IDF was used for feature extraction with a maximum of 5,000 features and unigram-bigram representation. The dataset was split into training and testing sets with an 80:20 ratio, while Stratified K-Fold Cross Validation and SMOTE were applied to strengthen evaluation and address class imbalance. The results show that SVM achieved the best overall performance before SMOTE with an accuracy of 86.66% and an F1-score of 86.76%. ANN achieved an accuracy of 85.31% before SMOTE and improved slightly after SMOTE to 85.47%. These findings indicate that SVM is more stable for TF-IDF-based TikTok comment classification, while SMOTE can improve ANN performance slightly but does not always increase all models equally.
Co-Authors Afril Efan Pajri Basuki Rahmat Devi Nova Kharisma Dimas Saputra Februyani, Nawafilla Ita Aristia Saida Jauhar Vikri, Muhammad Kholifatus Sholihah M. Ridlwan Hambali Meliysa Pasa Bagna Aprilia Said Muh. Mashdarul Hilmi Aufa Mula Agung Barata Nur Saifuddin Rahmat Tegar Patriot Lambang Shofiatuz Zulfia Shofiatuz Zulfia Teguh Pribadi Ucta Pradema Sanjaya Yuniar, Intan