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All Journal Compiler Informasi Interaktif Emerging Information Science and Technology Jurnal Mandiri IT Jurnal Informatika Teknologi dan Sains (Jinteks) AnoaTIK: Jurnal Teknologi Informasi dan Komputer Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Buletin Pos dan Telekomunikasi
Ryan Ari Setyawan
Program Studi Teknik Informatika Universitas Janabadra
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Social Sciences

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Journal : Emerging Information Science and Technology

 1 
Implementation of Finite State Automata to Optimize the Waste Collection Process in the Greenify Application Setyawan, Ryan Ari; Christianto, Devri Budi; Bening, Ridho Gilang
Emerging Information Science and Technology Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v6i1.27032

Abstract

Efficient waste management is a major challenge in urban life. One promising solution is the use of Finite State Automata (FSA) to optimize the waste containment process within the Greenify application. This study aims to explore the application of FSA in designing the logical flow for waste management, which includes identifying waste types, collection locations, and pick-up schedules. The methodology employed is a theoretical approach that implements the FSA model to regulate statuses and transitions between different steps in the waste management process. The results demonstrate that FSA can improve operational efficiency, reduce management errors, and enhance the user experience. The application of FSA in Greenify facilitates a more structured and automated waste management system, while also improving the accuracy of scheduling and waste collection. This conclusion highlights the significant potential of FSA as a technological solution for environmentally friendly waste management, with the goal of optimizing the performance of the Greenify application and advancing urban waste management practices.
Convolutional Neural Network-Based Model for Indonesian Offensive Text Classification Mayndeta, Daniel; Setyawan, Ryan Ari; Haryanto, Eri
Emerging Information Science and Technology Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v6i2.29704

Abstract

This study presents a Convolutional Neural Network (CNN)-based model for classifying offensive and non-offensive Indonesian text using a dataset of 10,054 tweets collected from Twitter/X. The dataset was manually annotated into two classes and processed through a series of text-cleaning, tokenization, and padding steps before being used to train the model. Several training durations were tested to evaluate the effect of epoch variation on model performance. The results show that the model trained for 70 epochs achieved the best overall performance, with a testing accuracy of 86.73%, precision of 0.8793, recall of 0.8834, F1-score of 0.8814, and a ROC-AUC value of 92.08%. The confusion matrix analysis indicates strong classification capability for both classes, with the model performing slightly better in identifying offensive text due to distinctive lexical patterns. These findings demonstrate that the CNN architecture, supported by trainable word embeddings, is effective for Indonesian offensive-text classification. Future improvements may include integrating pretrained language models or expanding the dataset to enhance contextual understanding and robustness.
Co-Authors Aswintama, Putranta Atapukan, Walter F Bening, Ridho Gilang Bororing, Jemmy Edwin Christianto, Devri Budi Fatsyahrina Fitriastuti Fitriastuti, Fatsyarina Haryanto, Eri Helio Rofino Correia Mayndeta, Daniel Rahmawaty, Zulekha Ridwan, Ridwan Rizqi Mirza Fadilla Safitri Sulthon Syahril Oku Taofiq Krisdiyanto Teguh Widodo Tyas Nur Taufiq Walter F Atapukan Yumarlin MZ