MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
MATRIK adalah salah satu Jurnal Ilmiah yang terdapat di Universitas Bumigora Mataram (eks STMIK Bumigora Mataram) yang dikelola dibawah Lembaga Penelitian dan Pengabadian kepada Masyarakat (LPPM). Jurnal ini bertujuan untuk memberikan wadah atau sarana publikasi bagi para dosen, peneliti dan praktisi baik di lingkungan internal maupun eksternal Universitas Bumigora Mataram. Jurnal MATRIK terbit 2 (dua) kali dalam 1 tahun pada periode Genap (Mei) dan Ganjil (Nopember).
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<![CDATA[Identify the Condition of Corn Plants Using Gray Level Co-occurrence Matrix and Bacpropagation ]]>
<![CDATA[Abd Mizwar A. Rahim]]>;
<![CDATA[Theopilus Bayu Sasongko]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v24i2.4035
<![CDATA[This research aims to increase the accuracy of identifying the condition of corn plants based on leaf features using the GLCM and ANN Backpropagation methods. The GLCM method is used to extract features from corn leaf images, while Backpropagation ANN is used to classify the condition of corn plants based on these features. This classification was carried out using a dataset of corn leaves from four different conditions, namely healthy, leaf-spot, leaf-blight, and leaf-rust. Next, leaf features are extracted using the GLCM method. After that, data normalization was carried out, balancing the dataset, and training was carried out on the Backpropagation ANN model to classify the condition of the corn plants. After training the model, the next model evaluation is carried out using the confusion matrix method. The research results show that the method used can produce quite high accuracy when identifying the condition of corn plants, with an accuracy of 99%. This shows that the use of GLCM and ANN Backpropagation can be a good alternative in identifying the condition of corn plants. This research provides benefits in making it easier to accurately identify the condition of corn plants.]]>
<![CDATA[Determining Toddler’s Nutritional Status with Machine Learning Classification Analysis Approach ]]>
<![CDATA[Taufik Hidayat]]>;
<![CDATA[Mohammad Ridwan]]>;
<![CDATA[Muhamad Fajrul Iqbal]]>;
<![CDATA[Sukisno Sukisno]]>;
<![CDATA[Robby Rizky]]>;
<![CDATA[William Eric Manongga]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v24i2.4092
<![CDATA[The nutritional status of toddlers is a common issue many countries face worldwide. Various facts indicate that malnutrition is a primary focus for many researchers. Several efforts have been made to address this problem, including developing analytical models for identification, classification, and prediction. This study aims to evaluate the nutritional status of children by utilizing a classification analysis approach using Machine Learning. This research aims to improve the accuracy of the classification system and facilitate better decision-making in stunted toddlers, which is a priority, especially in the health sector. The Machine Learning classification analysis process will later utilize the performance of the Naive Bayes algorithm, the Support Vector Machine algorithm, and the Multilayer Perceptron algorithm. ML performance can be optimized using gridsearchCV to produce optimal classification analysis patterns. The data set of this study uses 6812 toddler data sourced from the Health Center at the Tangerang Regency Health Office. Based on the research presented, Machine Learning performance in analyzing nutritional status classification provides maximum results. The results are reported based on a precision level with an accuracy of 88%. The results of this analysis can also present a classification of nutritional status based on knowledge. This study can contribute to and update the analysis model in determining nutritional status. The results of this study can also provide benefits in handling nutritional status problems that occur in children.]]>
<![CDATA[Integration of Image Enhancement Technique with DenseNet201 Architecture for Identifying Grapevine Leaf Disease ]]>
<![CDATA[Rudi Kurniawan]]>;
<![CDATA[Lukman Sunardi]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v24i2.4137
<![CDATA[Early detection of grapevine leaf diseases is crucial for maintaining both the quality and quantity of grape production. Manual identification methods are often ineffective and prone to errors. This research aims to develop a precise and efficient method for classifying grapevine leaf diseases using Contrast Limited Adaptive Histogram Equalization (CLAHE) and the DenseNet201 Deep Convolutional Neural Network (DCNN) architecture. The research methodology involves collecting a dataset of grapevine leaf images affected by black measles, black rot, and leaf blight alongside healthy leaves. Following this, preprocessing is conducted using the CLAHE technique to enhance image quality. Then, the processed data is trained with DenseNet201. Evaluation results indicate that the proposed model achieves an overall accuracy of 99.61%, with high precision, recall, and F1-score values across all disease classes. Receiver Operating Characteristic (ROC) curve analysis shows an Area Under the Curve (AUC) of 1.00 for each class, reflecting excellent discriminatory ability. The loss and accuracy curves illustrate consistent model performance without signs of overfitting. Additionally, the confusion matrix confirms very low classification error rates. The developed model is effective and reliable for identifying grapevine leaf diseases. Future research will focus on enhancing the dataset by incorporating more data optimizing hyperparameters, and developing field applications for real-time use.]]>
<![CDATA[A Image Classification of Poisonous Plants Using the MobileNetV2 Convolutional Neural Network Model Method ]]>
<![CDATA[Muhammad Furqan Nazuli]]>;
<![CDATA[Muhammad Fachrurrozi]]>;
<![CDATA[Muhammad Qurhanul Rizqie]]>;
<![CDATA[Abdiansah Abdiansah]]>;
<![CDATA[Muhammad Ikhsan]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v24i2.4284
<![CDATA[Poisonous plants can be dangerous for many people, but some can be used as medicines or as pest killers. Some people, especially those in environments with a wide variety of plants, can take advantage of this poisonous plant. Lack of knowledge and information causes the use of this poisonous plant to be inappropriate. This research aims to develop software to classify images of poisonous plants using the Convolutional Neural Network method with the MobileNetV2 model and to compare the accuracy of classification results with various dataset configurations and varying parameters. The research method used is a Convolutional Neural Network, which has relatively high accuracy in classifying various digital images. The data used in this research consists of eight poisonous plants and several non-poisonous plants. The research results on 153 test data show that the accuracy value was 99.34%, precision was 99%, recall was 99%, and F1-Score was 99%. This research contributes to developing software that can quickly provide information and knowledge about poisonous plants, offering a high-accuracy solution for classifying poisonous plants using image data. Furthermore, implementing MobileNetV2 provides an efficient and lightweight model suitable for deployment on mobile devices, enhancing accessibility and usability in the field. The potential applications of this software extend beyond individual use, potentially benefiting agricultural, medical, and educational sectors. Future work will expand the dataset to include more plant species and refine the model to improve its robustness against diverse environmental conditions and image qualities.]]>
<![CDATA[Analysis of Combination Machine Learning Classification with Feature Selection Technique for Lecturer Performance Analysis Model ]]>
<![CDATA[Ni Luh Putri Srinadi]]>;
<![CDATA[I Nyoman Suraja Antarajaya]]>;
<![CDATA[Luh Putu Wiwien Widhyastuti]]>;
<![CDATA[Dandy Pramana Hostiadi]]>;
<![CDATA[Erma Sulistyo Rini]]>;
<![CDATA[Pharan Chawaphan]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v24i2.4356
<![CDATA[Machine learning-based classification techniques are widely utilized for accurate analysis in various fields. This study focuses on assessing lecturer performance in higher education to enhance teaching standards and produce high-quality learning outcomes. Previous studies have employed multiparameter approaches, such as statistical correlation analysis, but these methods fail to achieve optimal accuracy and precision due to limited alignment with data characteristics. This research proposes a lecturer performance measurement model by evaluating three machine learning algorithms: k-Nearest Neighbors (k-NN), Decision Tree, and Na¨pve Bayes. The model integrates three feature selection techniques to improve classification performance: ANOVA, Information Gain, and Chi-Square. The study aims to enhance classification accuracy and assess the impact of feature selection techniques on performance metrics. A significant contribution of this research is introducing a dynamic feature selection approach tailored to data characteristics, which improves classification model performance. The methodology comprises three main stages: data loading and measurement of relevant parameters; data preprocessing, including filtering, cleaning, transformation, normalization, and feature selection; and performance evaluation using a machine learning-based classification approach. Experimental results demonstrate that the Decision Tree algorithm combined with Chi-Square feature selection achieved an accuracy of 0.887, precision of 0.903, recall of 0.887, and F1-score of 0.884. The proposed modelprovides a reliable framework for evaluating lecturer performance and can be utilized to recognize and reward high-performing lecturers effectively.]]>
<![CDATA[Analyzing the Application of Optical Character Recognition: A Case Study in International Standard Book Number Detection ]]>
<![CDATA[Imam Fahrur Rozi]]>;
<![CDATA[Ahmadi Yuli Ananta]]>;
<![CDATA[Endah Septa Sintiya]]>;
<![CDATA[Astrifidha Rahma Amalia]]>;
<![CDATA[Yuri Ariyanto]]>;
<![CDATA[Arin Kistia Nugraeni]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v24i2.4367
<![CDATA[In the era of advanced education, assessing lecturer performance is crucial to maintaining educational quality. One aspect of this assessment involves evaluating the textbooks authored by lecturers. This study addresses the problem of efficiently detecting International Standard Book Numbers (ISBNs) within these textbooks using optical character recognition (OCR) as a potential solution. The objective is to determine the effectiveness of OCR, specifically the Tesseract platform, in facilitating ISBN detection to support lecturer performance assessments. The research method involves automated data collection and ISBN detection using Tesseract OCR on various sections of textbooks, including covers, tables of contents, and identity pages, across different file formats (JPG and PDF) and orientations. The study evaluates OCR performance concerning image quality, rotation, and file type. Results of this study indicate that Tesseract performs effectively on high-quality, low-noise JPG images, achieving an F1 score of 0.97 for JPG and 0.99 for PDF files. However, its performance decreases with rotated images and certain PDF conditions, highlighting specific limitations of OCR in ISBN detection. These findings suggest that OCR can be a valuable tool in enhancing lecturer performance assessments through efficient ISBN detection in textbooks.]]>
<![CDATA[Combination Forward Chaining and Certainty Factor Methods for Selecting the Best Herbs to Support Independent Health ]]>
<![CDATA[Muhamad Azwar]]>;
<![CDATA[Sri Winarni Sofya]]>;
<![CDATA[Riwayati Malika]]>;
<![CDATA[Hairani Hairani]]>;
<![CDATA[Juvinal Ximenes Guterres]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v24i2.4485
<![CDATA[The use of herbal medicine as an alternative treatment is increasingly popular due to its natural benefits and cultural significance. However, a lack of public knowledge about the effectiveness, appropriate dosages, and processing methods of herbal remedies poses a significant barrier to their proper utilization. This knowledge gap often leads to suboptimal or even unsafe usage of herbal medicines. To address this issue, this study proposes an application-based system combining the Forward Chaining and Certainty Factor methods to provide personalized recommendations for the best herbal remedies supporting self-health management. The research aims to enhance accessibility to reliable information on herbal treatments while ensuring accurate and user-specific recommendations. By utilizing the ForwardChaining and Certainty Factor method, this system identifies suitable herbal plants based on the type of disease, processing techniques, recommended dosages, and duration of treatment. Meanwhile, the Certainty Factor method calculates the level of certainty for each recommendation provided. The study’s results showed a validation rate of 90%, indicating that the combination of these two methods effectively bridges the gap between traditional herbal knowledge and modern health needs. This study concludes that the system offers a practical tool for individuals to select and use herbal treatments safely and effectively, promoting better health outcomes.]]>
<![CDATA[Predicting Gen Z’s Sentiments on Gorontalo’s CulturalWisdom Using Sentiment Analysis Models ]]>
<![CDATA[Hermila A.]]>;
<![CDATA[Rahmat Taufik R. L Bau]]>;
<![CDATA[Sitti Suhada]]>;
<![CDATA[Abdulaziz Ahmed siyad]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v24i2.4591
<![CDATA[In the digital era, Generation Z faces both opportunities and challenges in understanding and preserving local cultural wisdom. It is essential to ensure that cultural values go beyond superficial consumption in cyberspace and remain deeply valued as integral aspects of identity and history. As technological advances and globalization continue influencing young people to preserve, local culture presents an increasing challenge. This study aims to determine Generation Z’s perceptions of Gorontalo’s local cultural wisdom, focusing on the Dikili and Meeraji traditions through a sentiment analysis approach. This research method uses the Naive Bayes algorithm to analyze positive, negative, and neutral sentiments derived from text data processed through a structured pre-processing stage. The findings reveal that Generation Z’s perceptions of the Dikili and Meeraji cultures are mostly positive, reflecting a strong appreciation and acceptance of these cultural values. However, the presence of negative sentiment highlights a critical view among some members of Generation Z, who consider certain aspects of these traditions less relevant or controversial in a modern context. In addition, neutral sentiment indicates a segment of young people who may need more exposure or information to form an informed opinion. The study concludes that while Dikili and Meeraji cultures still hold value among Generation Z, a more inclusive and adaptive approach to cultural preservation is needed. The findings offer valuable insights for strategies to preserve and develop local cultural heritage in the digital age]]>
<![CDATA[Comparison of Text Representation for Clustering Student Concept Maps ]]>
<![CDATA[Reni Fatrisna Salsabila]]>;
<![CDATA[Didik Dwi Prasetya]]>;
<![CDATA[Triyanna Widyaningtyas]]>;
<![CDATA[Tsukasa Hirashima]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v24i2.4598
<![CDATA[This research aims to address the critical challenge of selecting a text representation method that effectively captures students’ conceptual understanding for clustering purposes. Traditional methods, such as Term Frequency-Inverse Document Frequency (TF-IDF), often fail to capture semantic relationships, limiting their effectiveness in clustering complex datasets. This study compares TF-IDF with the advanced Bidirectional Encoder Representations from Transformers (BERT) to determine their suitability in clustering student concept maps for two learning topics: Databases and Cyber Security. The method used applies two clustering algorithms: K-Means and its improved variant, K-Means++, which enhances centroid initialization for better stability and clustering quality. The datasets consist of concept maps from 27 students for each topic, including 1,206 concepts and 616 propositions for Databases, as well as 2,564 concepts and 1,282 propositions for Cyber Security. Evaluation is conducted using two metrics Davies-Bouldin Index (DBI) and Silhouette Score, to assess the compactness and separability of the clusters. The result of this study is that BERT consistently outperforms TF-IDF, producing lower DBI values and higher Silhouette Scores across all clusters (k= 2 - k=10). Combining BERT with K-Means++ yields the most compact and well-separated clusters, while TF-IDF results in overlapping and less-defined clusters. The research concludes that BERT is a superior text representation method for clustering, offering significant advantages in capturing semantic context and enabling educators to identify student misconceptions and improve learning strategies.]]>
<![CDATA[Revealing Interaction Patterns in Concept Map Construction Using Deep Learning and Machine Learning Models ]]>
<![CDATA[F.ti Ayyu Sayyidul Laily]]>;
<![CDATA[Didik Dwi Prasetya]]>;
<![CDATA[Anik Nur Handayani]]>;
<![CDATA[Tsukasa Hirashima]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v24i2.4641
<![CDATA[Concept maps are educational tools for organizing and representing knowledge, enhancing comprehension, and memory retention. In concept map construction, much knowledge can be utilized. Still, concept map construction is complex, involving actions that reflect a user’s thinking and problemsolving strategies. Traditional methods struggle to analyze large datasets and capture temporal dependencies in these actions. To address this, the study applies deep learning and machine learning techniques. This research aims to evaluate and compare the performance of Long Short-Term Memory (LSTM), K-Nearest Neighbors (K-NN), and Random Forest algorithms in predicting user actions and uncovering user interaction patterns in concept map construction. This research method collects and analyzes interaction logs data from concept map activities, using these three models for evaluation and comparison. The results of this research are that LSTM achieved the highest accuracy (83.91%) due to its capacity to model temporal dependencies. Random Forest accuracy (80.53%), excelling in structured data scenarios. K-NN offered the fastest performance due to its simplicity, though its reliance on distance-based metrics limited accuracy (70.53%). In conclusion, these findings underscore the practical considerations in selecting models for concept map applications; LSTM demonstrates effectiveness in predicting user actions and excels for temporal tasks, while Random Forest and K-NN offer more efficient alternatives in computational.]]>
