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All Journal Jurnal Buana Informatika Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Sains Jurnal TAM (Technology Acceptance Model) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JurTI (JURNAL TEKNOLOGI INFORMASI) JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi ABSYARA: Jurnal Pengabdian Pada Masyarakat IJAIRTec Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Klik - Jurnal Ilmu Komputer MDP Student Conference Jurnal Ilmiah Sistem Informasi INOVTEK Polbeng - Seri Informatika International Journal of Artificial Intelligence and Robotic Technology (IJAIRTec) Jurnal TAM (Technology Acceptance Model)
Derry Alamsyah
Universitas Multi Data Palembang
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Mathematics Physics Other

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Journal : INOVTEK Polbeng - Seri Informatika

 1 
Plagiarism Detection in English Academic Documents using a Lexical Semantic Hybrid and Support Vector Machine Virginia, Callista; Alamsyah, Derry
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/2zz12581

Abstract

Detecting plagiarism in academic writing has become increasingly challenging due to advanced text modification strategies that reduce surface-level similarity while preserving the original meaning. This study proposes a hybrid plagiarism detection system that integrates lexical and semantic similarity features to distinguish between plagiarism and altered documents in academic texts. As a key contribution, this study provides a systematic evaluation of a lexical–semantic hybrid plagiarism detection approach using Support Vector Machine (SVM) on English-language academic documents, where all plagiarism cases across different obfuscation levels are consolidated into a single plagiarism class. Lexical similarity is modeled using Term Frequency–Inverse Document Frequency (TF–IDF), while semantic similarity is captured through Sentence-BERT embeddings. These features are combined into a two-dimensional hybrid similarity representation and classified using SVM. The proposed approach is evaluated on the PAN 2025 dataset using stratified 5-fold cross-validation. Experimental results show that the hybrid SVM-based model achieves an average accuracy of 92.5% with the optimal kernel, along with competitive precision, recall, F1-score, and AUC values. Kernel-based evaluation and cross-validation analyses further demonstrate the robustness and generalization capability of the proposed framework, indicating that the hybrid lexical–semantic representation is effective for distinguishing plagiarism and altered content in English academic writing.
Detection of Coffee Bean Defects in Speciality Coffee Association Standards using YOLOv12 Hocwin Hebert; Alamsyah, Derry
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/47yqwd13

Abstract

Coffee is a high-value plantation commodity with a significant role in the global economy. Coffee consumption, reaching more than two billion cups per day, continues to increase global demand for coffee beans. To ensure quality and consumer acceptance, green coffee bean quality evaluation must follow consistent international standards. However, inspection is still carried out manually, making it time-consuming and subjective. This study proposes coffee bean defect detection based on the Specialty Coffee Association (SCA) standard using YOLOv12. YOLOv12 addresses limitations of previous YOLO versions by integrating R-ELAN to improve training efficiency and reduce gradient loss, as well as Flash Attention to enhance focus on important regions in complex images. A total of 225 images were obtained through augmentation from 45 original samples captured using a smartphone camera under controlled indoor conditions, with each image representing 300 grams of Mandheling coffee beans. The dataset was divided into training (80%), validation (10%), and testing (10%). Eight experimental configurations were evaluated using variations in initial learning rate (0.001 and 0.0005), batch size (8 and 16), and epochs (100 and 150). The optimal configuration, an initial learning rate of 0.0005, a batch size of 16, and 150 epochs, achieved a precision of 87%, a recall of 85%, and an F1 score of 84%. These results indicate that the effectiveness of YOLOv12 in detecting coffee bean defects depends on proper hyperparameter tuning. The model performs well on visually prominent defects such as cherry pods but shows reduced performance on subtle defects including floaters, fungus damage, and slight insect damage.
Color and Texture Feature Extraction for Disease Identification in Chili Leaves Using K-Nearest Neighbors Andreyas; Alamsyah, Derry
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/s9v7mn76

Abstract

Manual identification of chili leaf diseases has the weakness of subjectivity, which impacts the decline in harvest productivity. This study aims to build an accurate automatic classification system using a machine learning approach. The research methodology integrates the extraction of Hue, Saturation, Value (HSV) color features and Gray Level Co-occurrence Matrix (GLCM) texture on a dataset of 1,856 images divided with a ratio of 80:20. Hyperparameter optimization was performed using Grid Search on the K-Nearest Neighbors (K-NN) algorithm to find the best performance. The test results show that the optimal configuration is achieved at a value of K = 3 with the Manhattan distance metric, which produces a test accuracy of 92%. It is concluded that the integration of color and texture features with appropriate parameter optimization is proven to be effective as a reliable and efficient diagnostic solution.
Banana Leaf Disease Identification Using SqueezeNet Architecture with Convolutional Block Attention Module Wijaya, Daniel; Alamsyah, Derry
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ktx6vp08

Abstract

Banana leaf diseases significantly reduce crop productivity and quality, while conventional visual inspection methods are often subjective, time-consuming, and inefficient for large-scale plantations. This study proposes an automated banana leaf disease identification approach using a lightweight Convolutional Neural Network (CNN) based on the SqueezeNet architecture integrated with the Convolutional Block Attention Module (CBAM). The dataset consists of four classes Cordana, Healthy, Pestalotiopsis, and Sigatoka with image augmentation applied to increase data variability. Several experimental scenarios were conducted to evaluate the impact of data augmentation and CBAM integration on model performance. The models were evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that SqueezeNet combined with CBAM achieved superior performance compared to the baseline SqueezeNet model, particularly in non-augmented conditions, with an accuracy of 93.75% while maintaining a relatively small number of parameters. Although data augmentation alone led to performance degradation, the inclusion of CBAM mitigated this effect by enhancing spatial and channel-wise feature representation. These findings indicate that the proposed SqueezeNet–CBAM model offers an effective and computationally efficient solution for banana leaf disease identification, with strong potential for real-world agricultural applications.
Co-Authors Alwanda, Muhammad Rafly andreyas Daniel Wijaya Devella, Siska Dicky Pratama Dicky Pratama Dicky Pratama Dicky Pratama Dicky Pratama Dicky Pratama Farisi, Ahmad Ferdyansyah Wijaya gasim, Gasim Hafiz Irsyad Hartati, Ery Hocwin Hebert Idham Cholid Ignatius Rivaldo Lie Inayatullah Inayatullah Irmeilyana Karolina, umi Khan, Zicola Vladimir VIky Leonardo Leonardo Maryati Gultom Muhammad Bemby Putra Mansyah Muhammad Rachmadi, Muhammad Mukhlisa Mukhlisa Nataliatara Nataliatara Novan Wijaya Paniza, Mehri Pranata, Steven Putra BJ Bangun Putra, Ivan Pratama Ramadhan, Raden Putra Kurniawan RENDY PRATAMA Reynard Saputra Ricky Julianto rusbandi rusbandi, rusbandi Sanders, Carmel Edra Tinaliah, Tinaliah Tommy Oktavianus Saputra Triana Elizabeth, Triana Valentino Ruben Ho Virginia, Callista Wijang Widhiarso Willyanto, Aldo Yoannita Yoannita, Yoannita