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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 Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer 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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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.
Penerapan KNN, DT, dan NB untuk Memprediksi Task Success Developer Berbasis AI-Metrics Iski Mediansyah; Muhammad Bitrayoga; Arief Zikry; 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/rsvfdr22

Abstract

This study is motivated by the limited utilization of AI-based metrics to predict task success among developers in software development projects. The main issue addressed is the absence of a systematic comparative approach to classification algorithms in identifying the most effective model in this context. Therefore, this research compares the performance of three classification algorithms—K-Nearest Neighbors (KNN), Decision Tree (DT), and Naïve Bayes (NB)—in predicting task success using AI-metrics data. The evaluation metrics include precision, recall, F1-score, and accuracy, presented through classification reports and confusion matrices. The results show that DT achieved an accuracy of 91%, KNN 92%, and NB 86%. The confusion matrix analysis indicates that DT demonstrates high precision, KNN shows minor imbalance, and NB struggles to identify minority classes. Additionally, clustering was performed using the K-Means algorithm and visualized in two dimensions through Principal Component Analysis (PCA),  revealing clear segmentation among developer groups. The ultimate benefit of this study is to provide a foundation for decision-making in selecting the most appropriate algorithm to enhance developer team effectiveness and personalize managerial strategies. The novelty of this research lies in the combined application of classification and clustering approaches using AI-metrics to more accurately and datadrivenly identify developer task success. 
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.
Identification of Determinants of Inclusive Economic Growth Using the Metaheuristic Whale Optimization Algorithm Approach Firza Septian; Nina Dwi Putriani
Jurnal Software Engineering and Computational Intelligence Vol 3 No 01 (2025)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v3i01.5396

Abstract

Inclusive economic growth demands the identification of key factors that drive equitable improvements in regional welfare. However, the complex interrelationships among social, economic, and demographic variables make traditional approaches insufficient for handling high-dimensional data. This study introduces an innovative approach by combining the Whale Optimization Algorithm (WOA) for feature selection with a Random Forest Regressor model to predict Gross Regional Domestic Product (GRDP) per capita as the main indicator of regional prosperity. The dataset consists of 210 regional observations and 18 independent variables. Feature selection using WOA was guided by minimizing the mean squared error (MSE), resulting in the identification of the 8 most relevant features. The retrained Random Forest model on the selected features achieved a high prediction performance, with an R² value of 0.9938 and a low RMSE. Furthermore, GRDP values were categorized into three regional welfare classes (Low, Medium, High), and the classification yielded 97.92% accuracy with high precision, recall, and F1-scores across all classes. These findings demonstrate that combining metaheuristic optimization and machine learning enables efficient and accurate identification of the key determinants of inclusive economic growth. The results provide valuable insights for formulating more targeted regional development policies.
Co-Authors Agustian Prakarsya Arief Zikry Ema Utami Iski Mediansyah Kusrini , Kusrini Mubarok, M. Husni Muhammad Bitrayoga Muhammad Sulkhan Nurfatih Nina Dwi Putriani Prakarsya, Agus Prakarsya, Agustian Putriani, Nina Dwi TONNY HIDAYAT Yusi Nurmala Sari Yusi Nurmalasari