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All Journal Journal Software, Hardware and Information Technology Journal Computer Science and Informatic Systems : J-Cosys MDP Student Conference Jurnal Software Engineering and Computational Intelligence Media Journal of General Computer Science (MJGCS) Journal of Intelligent Systems and Information Technology Jurnal Sistem Informasi Komputer dan Teknologi Informasi
Firza Septian
Universitas Serelo Lahat
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Exploring the Performance of Whale Optimization Algorithm on Rosenbrock's Function Septian, Firza
Journal of Intelligent Systems and Information Technology Vol. 1 No. 2 (2024): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v1i2.35

Abstract

Optimization of complex and nonlinear functions is essential across various domains, from engineering and finance to artificial intelligence and machine learning. Rosenbrock's function stands as a fundamental benchmark for evaluating optimization algorithms due to its highly nonlinear and multimodal nature. Among the multitude of optimization algorithms, the Whale Optimization Algorithm (WOA) has garnered attention for its inspiration from the social behavior of humpback whales. However, its performance on Rosenbrock's function remains relatively unexplored. This paper aims to investigate the effectiveness of the WOA specifically on Rosenbrock's function through rigorous experimentation and analysis. By evaluating convergence speed, solution accuracy, and robustness, this study sheds light on WOA's behavior when confronted with the challenges posed by Rosenbrock's function. Comparative analysis with other optimization algorithms further elucidates WOA's adaptability and scalability. The findings contribute valuable insights for selecting suitable optimization algorithms in real-world applications and advance understanding of optimization algorithms' behavior in challenging landscapes.
A Rule-Based AI Writing Assistant for Beginner English Learners with Visual Feedback Zikry, Arief; Sari, Yusi Nurmala; Nurfatih, Muhammad Sulkhan; Septian, Firza
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 Sari, Yusi Nurmala; Putriani, Nina Dwi; Prakarsya, Agustian; Septian, Firza
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 Arief Zikry Ema Utami Kusrini , Kusrini Mubarok, M. Husni Muhammad Sulkhan Nurfatih Prakarsya, Agus Prakarsya, Agustian Putriani, Nina Dwi TONNY HIDAYAT Yusi Nurmalasari