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All Journal Journal Computer Science and Informatic Systems : J-Cosys Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Sistem Informasi Komputer dan Teknologi Informasi
Agustian Prakarsya
Universitas Serelo Lahat
Computer Science & IT

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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
Prediktor NPK Berbasis AI untuk Budidaya Kopi dengan Whale Optimization Algorithm Firza Septian; Agustian Prakarsya
BETRIK Vol. 16 No. 03 (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: 10.36050/6t030w66

Abstract

Coffee is one of Indonesia’s major agricultural commodities, yet its productivity is often limited by inefficient fertilizer management, particularly in determining nitrogen (N), phosphorus (P), and potassium (K) requirements. Although conventional soil and leaf analyses are reliable, they are time-consuming and less practical for smallholder farmers. This underscores the need for an accurate, scalable, and cost-effective solution to optimize fertilizer usage. To address this issue, the study introduces an AI-based predictor for assessing NPK sufficiency in coffee plants. The research integrates computer vision and metaheuristic optimization to form a practical decision-support system. A dataset containing 12,000 images of coffee leaves was classified into three categories: Deficient, Sufficient, and Excessive. Image preprocessing involved resizing, grayscale conversion, HSV transformation, and normalization. Feature extraction utilized Histogram of Oriented Gradients (HOG) and HSV Color Histograms, followed by classification using a Support Vector Machine (SVM) optimized with the Whale Optimization Algorithm (WOA). The model achieved an accuracy exceeding 97%, effectively recognizing Deficient and Sufficient categories, with most misclassifications occurring in the Excessive class due to visual similarities. Model performance was validated using a confusion matrix, learning curve, and PCA visualization, confirming efficient convergence. The study highlights the promise of AI-driven solutions in enhancing precision agriculture and promoting sustainable coffee farming practices.
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.
ANALISIS PERFORMA AES-128, AES-192, DAN AES-256 PADA NODE.JS M. Husni Mubarok; Agustian prakarsya; Sri Rahayu
SISKOMTI: Jurnal Sistem Informasi Komputer dan Teknologi Informasi Vol. 8 No. 2 (2026): Agustus 2026
Publisher : Universitas Lembah Dempo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54342/wym48457

Abstract

Penelitian ini bertujuan untuk menganalisis perbedaan performa enkripsi AES dengan variasi panjang kunci 128, 192, dan 256bit pada lingkungan Node.js. Eksperimen dilakukan menggunakan laptop dengan processor Intel Core i5-12500H dan Node.js versi 24.15.0. Metode yang digunakan adalah benchmarking terkontrol dengan mengenkripsi file sintetis berukuran 1 KB hingga 10 MB sebanyak 10 iterasi per konfigurasi. Hasil pengujian menunjukkan bahwa AES-256 memiliki overhead waktu sekitar 25% dibanding AES-128 untuk file kecil, namun perbedaannya menjadi sangat kecil bahkan negatif untuk file berukuran besar. Hal ini diduga karena adanya optimasi hardware AES-NI pada processor modern. Penelitian ini memberikan rekomendasi praktis bagi developer dalam memilih konfigurasi AES yang sesuai dengan kebutuhan aplikasi. 
Co-Authors Firza Septian M. Husni Mubarok Nina Dwi Putriani Sri Rahayu Yusi Nurmala Sari