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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika INTI Nusa Mandiri Jurnal Sistem Komputer dan Informatika (JSON) 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
Model Prediktif Keterlambatan Pembayaran Mahasiswa Berbasis Seleksi Fitur dengan Particle Swarm Optimization Desvia, Yessica Fara; Suharjanti; Suhardjono; Irmawati Carolina; Resti Lia Andharsaputri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.8973

Abstract

Keterlambatan pembayaran biaya kuliah menjadi salah satu permasalahan krusial di perguruan tinggi swasta yang dapat berdampak pada risiko akademik, seperti cuti atau putus studi. Penelitian ini diarahkan untuk mengembangkan model prediktif dalam mengidentifikasi keterlambatan pembayaran oleh mahasiswa, dengan memanfaatkan algoritma klasifikasi Decision Tree dan Random Tree, serta menerapkan metode Particle Swarm Optimization (PSO) untuk proses seleksi fitur. Data yang digunakan dalam penelitian ini mencakup 15.697 mahasiswa, masing-masing memiliki enam atribut sebagai variabel prediktor serta satu atribut target yang menunjukkan status mahasiswa, yaitu aktif atau cuti. Tahapan penelitian mencakup pengumpulan data, pra-pemrosesan, klasifikasi, seleksi fitur, dan evaluasi model dilakukan dengan menggunakan metrik akurasi, serta kurva ROC dan nilai AUC. Hasil penelitian menunjukkan akurasi model mencapai 98,83%, dengan peningkatan signifikan AUC pada Random Tree dari 0,632 menjadi 0,825 setelah seleksi fitur menggunakan PSO. Temuan ini menunjukkan bahwa PSO efektif dalam meningkatkan performa model klasifikasi dan mengurangi kompleksitas fitur yang tidak relevan. Sistem prediktif yang dihasilkan dapat membantu institusi pendidikan dalam melakukan deteksi dini mahasiswa berisiko menunggak, sehingga memungkinkan pengambilan tindakan preventif dan intervensi lebih tepat sasaran untuk mendukung keberlangsungan akademik mahasiswa.
A Statistical Benchmarking of Imbalance-Aware Ensemble Models for Cervical Cancer Prediction Sumarna, Sumarna; Astrilyana, Astrilyana; Sugiono, Sugiono; Wijaya, Ganda; Desvia, Yessica Fara
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15995

Abstract

Cervical cancer remains one of the leading causes of cancer-related mortality among women worldwide, particularly in developing countries. Early prediction through machine learning has the potential to support clinical decision-making; however, cervical cancer datasets often suffer from severe class imbalance, which reduces the ability of conventional models to correctly detect minority cases. This study aims to improve minority class detection in cervical cancer prediction by evaluating several imbalance-aware ensemble learning approaches. The proposed study compares five models, namely Random Forest (RF), SMOTE combined with Random Forest (SMOTE+RF), Balanced Random Forest (BRF), EasyEnsemble, and RUSBoost. The models were evaluated using 5-fold cross-validation with performance metrics including accuracy, recall, F1-score, and Area Under the Curve (AUC). Statistical validation was conducted using the Friedman test, followed by the Wilcoxon signed-rank test and Kendall’s W effect size analysis to assess the significance and magnitude of performance differences. Unlike prior studies that primarily focus on performance improvement, this study introduces a statistically rigorous comparative evaluation to assess both significance and practical effect of imbalance-aware ensemble methods. Experimental results show that imbalance-aware ensemble methods significantly improve minority detection compared to the baseline RF model. In particular, BRF achieved the highest AUC of 0.9469 with improved recall stability, while RUSBoost produced the highest F1-score of 0.7451. Although the Friedman test indicated no statistically significant difference among models (p = 0.2037), the Kendall’s W value of 0.297 suggests a small-to-moderate practical effect. These findings indicate that imbalance-aware ensemble learning can enhance the robustness of cervical cancer prediction models, particularly for minority class detection. The results highlight the importance of incorporating imbalance-handling strategies in medical prediction systems and suggest potential directions for future research in improving diagnostic decision-support models.
Co-Authors Astrilyana Astrilyana, Astrilyana Ganda Wijaya, Ganda Irmawati Carolina Pratama, Febryawan Yuda Resti Lia Andharsaputri Sugiono, Sugiono Suhardjono S suharjanti Suhendri Suhendri Sumarna Sumarna