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All Journal INTI Nusa Mandiri Jurnal Teknologi Informasi dan Komunikasi
Desvia, Yessica Fara
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Computer Science & IT Control & Systems Engineering

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Implementation Of Finite State Automata In A Laundry Perfume Vending Machine For Clothes And Carpets Desvia, Yessica Fara; Pratama, Febryawan Yuda; Suhendri, Suhendri
Jurnal Teknologi Informasi dan Komunikasi Vol 18 No 2 (2025): October
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/jtik.v18i2.296

Abstract

Perfume is popular among various groups of people, including laundry fragrances. Laundry perfumes come in a variety of scen$ts, such as fruity, floral, a combination of fruit and floral, and woody aromas. These fragrances are typically applied during the final stage of the laundry process. Currently, customers receive their laundry with a randomly selected scent based on the availability at the laundry service, which means they cannot choose the fragrance they prefer. Therefore, a Vending Machine (VM) design is needed to allow customers to select their desired laundry perfume. The VM is designed using the Finite State Automata (FSA) approach, specifically the Non-Deterministic Finite Automata (NFA) type, as it can accommodate multiple conditions for a single option. The development of the NFA method involves stages such as business process analysis, state diagram creation, VM design, and system testing. The results of this study indicate that the implementation of this VM simplifies the process for customers to choose their preferred laundry perfume, ensuring that their laundry has a scent that matches their personal preferences.
PENINGKATAN AKURASI KNN DALAM PREDIKSI KELULUSAN MAHASISWA MELALUI OPTIMASI PARAMETER PSO Desvia, Yessica Fara; Pratama, Febryawan Yuda; Wijaya, Ganda
INTI Nusa Mandiri Vol. 20 No. 1 (2025): INTI Periode Agustus 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v20i1.7076

Abstract

Predicting student graduation is a crucial aspect in supporting academic planning and ensuring timely completion of studies. However, no prior research has specifically applied the integration of K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) for graduation prediction using student data. This study aims to evaluate the effectiveness of combining KNN and PSO in improving classification accuracy. The KNN algorithm is used for classification, while PSO is implemented as a feature selection technique to identify the most relevant attributes. A dataset of 750 student records was processed through data preprocessing and attribute weighting using PSO, followed by model training and evaluation with 10-fold cross-validation. The evaluation results show that the KNN+PSO model improves accuracy from 80.91% to 84.31%, along with increases in precision and recall. These findings indicate that PSO enhances the performance of KNN, particularly in identifying students likely to graduate on time
Co-Authors Ganda Wijaya, Ganda Pratama, Febryawan Yuda Suhendri Suhendri