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All Journal Journal Computer Science and Informatic Systems : J-Cosys Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Media Journal of General Computer Science (MJGCS)
Yusi Nurmala Sari
Universitas Serelo Lahat
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Optimasi Hyperparameter WOA-SVM pada Citra Daun Kopi Terpupuk NPK Agustian Prakarsya; Nina Dwi Putriani; Yusi Nurmala Sari; Firza Septian
BETRIK Vol. 16 No. 02 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/zrj1e094

Abstract

This study aims to analyze the impact of Whale Optimization Algorithm (WOA) optimization on the performance of Support Vector Machine (SVM) in classifying images of coffee leaves treated with NPK fertilizer. WOA is employed to find the optimal combination of SVM parameters to improve classification accuracy. The dataset consists of coffee leaf images that have undergone feature extraction based on color and texture. Performance evaluation was conducted using a confusion matrix, classification report, and heatmap visualization. The results show that the SVM model optimized with WOA performs better than the non-optimized SVM. Specifically, the non-optimized SVM achieved a precision of 0.82, recall of 0.81, and F1-score of 0.81. After optimization with WOA, the model’s precision increased to 0.90, recall to 0.88, and F1-score to 0.87. This study demonstrates that metaheuristic approaches like WOA can significantly enhance the performance of classification algorithms in the context of digital image processing. The findings have practical implications for early detection of plant quality through image-based analysis in technology-driven agriculture
A Rule-Based AI Writing Assistant for Beginner English Learners with Visual Feedback Arief Zikry; Yusi Nurmala Sari; Muhammad Sulkhan Nurfatih; Firza Septian
Media Journal of General Computer Science Vol. 3 No. 1 (2026): MJGCS
Publisher : MASE - Media Applied and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62205/mjgcs.v3i1.149

Abstract

The increasing adoption of artificial intelligence (AI) in educational technology has created new opportunities to support second language (L2) writing development. Beginner English learners often struggle with grammatical accuracy, limited vocabulary, and unclear sentence construction, while immediate and individualized feedback remains difficult to provide in traditional learning settings. This study proposes a rule-based AI writing assistant designed to deliver automated, transparent, and interpretable feedback for beginner-level English writing without relying on data-intensive machine learning models. The system employs symbolic AI principles through predefined grammatical rules and heuristic textual metrics to evaluate writing quality across three dimensions: grammar accuracy, vocabulary richness, and text clarity. Grammar errors are detected using regular expression-based rules, vocabulary quality is measured via lexical diversity ratios, and clarity is estimated using a length-based heuristic. These metrics are normalized and combined to produce an overall writing quality score. To enhance usability and learner engagement, the system integrates visual feedback elements, including progress bars, graphical score representations, and animated character responses. Functional testing using sample beginner texts demonstrates that the proposed system effectively identifies common writing issues, provides consistent scoring, and delivers immediate, explainable feedback. The results indicate that rule-based AI, when combined with visual feedback mechanisms, can offer a lightweight, efficient, and pedagogically meaningful solution for beginner English writing support. This approach is particularly suitable for educational contexts that prioritize explainability, accessibility, and low computational requirements.
Predicting Purchase Decision Using a Hybrid KNN-WOA Model Based on Social Media Marketing and Word of Mouth Quality Yusi Nurmala Sari; Nina Dwi Putriani; Agustian Prakarsya; Firza Septian
Journal Computer Science and Information Systems : J-Cosys Vol 5, No 2 (2025): September
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53514/jco.v5i2.701

Abstract

Penelitian ini bertujuan untuk mengembangkan dan menguji model hybrid K-Nearest Neighbor (KNN) yang dioptimasi dengan Whale Optimization Algorithm (WOA) dalam memprediksi keputusan pembelian konsumen berdasarkan variabel Social Media Marketing (SMM) dan Word of Mouth Quality (WQ). Data penelitian diperoleh dari 100 responden dengan 22 indikator yang diukur menggunakan skala Likert 1–7. Variabel dependen berupa Purchase Decision dibentuk dari lima indikator dan dikonversi menjadi kelas biner untuk keperluan klasifikasi. Hasil analisis deskriptif menunjukkan bahwa indikator SMM dan WQ memiliki distribusi yang stabil dengan kecenderungan nilai tinggi, serta korelasi positif terhadap keputusan pembelian. Model hybrid KNN–WOA menghasilkan akurasi sebesar 95% dengan precision 0.95, recall 1.00, dan f1-score 0.97 pada kelas positif. Temuan ini menegaskan bahwa kualitas konten media sosial dan kredibilitas informasi Word of Mouth berperan signifikan dalam memengaruhi keputusan pembelian konsumen. Penelitian ini memberikan kontribusi teoritis dalam pengembangan model prediktif berbasis optimasi metaheuristik serta kontribusi praktis bagi perusahaan dalam merancang strategi pemasaran digital yang lebih efektif dan berbasis data.
Co-Authors Agustian Prakarsya Firza Septian Muhammad Sulkhan Nurfatih Nina Dwi Putriani