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Gunawan, Alexander Agung Santoso
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Classifying Viral Twitter with Transformer Models and Multi-Layer Perceptron Tedjasulaksana, Jeffrey Junior; Gunawan, Alexander Agung Santoso
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 1 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i1.11580

Abstract

The classification of virality levels in Indonesian tweets is explored in this research using advanced natural language processing techniques and machine learning algorithms. Transformer models such as RoBERTa for sentiment analysis and XLNet for text embedding, alongside Multi-Layer Perceptron (MLP) classifiers, are leveraged to address the challenge of predicting tweet virality. Emotion features are incorporated, and cost-sensitive methods for handling class imbalance are implemented, resulting in robust performance demonstrated by our model. Intriguing correlations between tweet sentiment, emotion distribution, and virality levels are uncovered through sentiment analysis and emotion detection. The efficacy of XLNet in capturing contextual nuances, outperforming BERTweet, is highlighted by our findings. Furthermore, the integration of emotion features and cost-sensitive methods enhances the model's predictive accuracy, offering valuable insights for marketers and businesses seeking to optimize their social media strategies. The proposed model achieves an accuracy of 95% and an F1-Score of 59%.
An Experiment to Prevent Malicious Actors from Compromising Private Digital Assets Over a Public Network Hartanto, Feliks; Budiman, Budiman; Gwei, Eldwin; Gunawan, Alexander Agung Santoso; Edbert, Ivan Sebastian
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 1 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i1.10389

Abstract

In the current millennium, human society has immensely improved its ability to obtain and distribute information. This change on the other hand, has caused the majority of daily routines to actively involve the usage of computers and mobile devices, which in turn has made people rely heavily on the availability of internet access. This fact was taken advantage of, causing a massive increase in public networks by people or businesses to draw in customers or just as simple public service. This increase gives both ease and risks which this paper will address, specifically on the security measures in network devices that are nearby, and the solution proposed to provide complementary insight on securing the technologies. The authors of this paper supply the main point of the research through experimental efforts i.e., by testing the solution in a real-life scenario. The solution itself involves the configuration of a Raspberry Pi into a VPN server and rerouting all traffic into the Raspberry server so that it will be encrypted and safe from the dangers that will be mentioned in later parts of this paper. The result of the experiment shows that the proposed solution can successfully encrypt the targeted packet so it can’t be read by malicious attackers. Although the solution works it can’t be simply applied to every public network due to internet connection protocols and its inconvenience. Future research will involve the improvement or rework of the solution until the issues mentioned above are solved.
The Impact of Ensemble Stacking with Machine Learning and Statistical Models on Improving Sales Forecasting Accuracy Yohana, Prima; Gunawan, Alexander Agung Santoso; Ngarianto, Heri
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 2 (2026): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i2.16162

Abstract

Sales forecasting in retail remains a challenge, as changing customer behavior and seasonal patterns undermine predictive accuracy. The Ensemble Stacking Framework for Machine Learning & Statistical Models is being suggested to improve forecasting of dynamic demands. For example, during promotional events, managers adjust their plans to account for changes in weather or unexpected changes in consumer preferences. They are unable to predict these changes. An Ensemble Stacking Framework was created using a Meta-Learner (RidgeCV) trained on historical retail sales data and several base learners (Random Forest, Extra Trees, XGBoost, and Multiple Linear Regression). Tree-Based Models help to create non-linear relationships, while Linear Regression helps us understand trend information. The Meta-Learner learns how to weigh each model based on the error each model makes over time. We assessed the quality of our model’s performance through RMSE and MAE. Special attention was given to assessing how well our models performed during periods of high volatility (i.e. holidays and post-promotional dips). Our model performed better than all other Base Models at all times. Also, stacked predictions showed less variation week-to-week and were able to return to normal quicker than other models after sudden shifts in demand. In practice, it allows for more confident safety-stock decisions and timely replenishment adjustments. Retailers can run a number of models in parallel and continuously rebalance them with new data as it comes in. Future work may include online updates of meta-learners and external covariates such as local events or competitor pricing.
Co-Authors Edbert, Ivan Sebastian Gwei, Eldwin Hartanto, Feliks Heri Ngarianto, Heri Tedjasulaksana, Jeffrey Junior Yohana, Prima