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[Development of the Multi-Channel Clustering Hierarchy Method for Increasing Performance in Wireless Sensor Network ]]>
<![CDATA[Robby Rizky]]>;
<![CDATA[Zaenal Hakim]]>;
<![CDATA[Sri Setiyowati]]>;
<![CDATA[Susilawati susilawati]]>;
<![CDATA[Ayu Mira Yunita]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 3 (2024) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v23i3.3348
<![CDATA[Wireless Sensor Networks are technologies that make it possible to observe phenomena. The problem is data delays in covering the distance from the origin to the destination. Packet Loss is a condition that shows the number of lost packets and the total queue length caused by data processing time. This research aims to develop a cluster-based protocol. This research uses a multichannel hierarchical clustering method and adds odd-even by dividing the network into several channels and forming a cluster head for each channel. The results of this research are Channel 1 with a throughput value of 1.88, channel 2 with a throughput value of 21.68, channel 3 with a throughput value of 1.62, and channel 4 with a throughput value of 42.44. The conclusion of this study is that the throughput results are smaller compared to the Multi-Channel Clustering H ierarchy method, because not all nodes are active]]>
<![CDATA[Classification of Cash Direct Recipients Using the Naive Bayes with Smoothing ]]>
<![CDATA[Eko Prasetyo]]>;
<![CDATA[Muhammad Faris Al-Adni]]>;
<![CDATA[Rahmawati Febrifyaning Tias]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 3 (2024) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v23i3.3584
<![CDATA[Direct Cash Assistance is a social program distributed to residents meeting specific requirements. The village government determines the recipients using a conventional system through village meetings. This approach is greatly influenced by the decision-holders’ subjectivity with non-transparent thinking. This research aims to solve the problem of classifying Direct Cash Assistance recipients by applying probability-based classification. The research method used is smoothed Nave Bayes, which improves Nave Bayes by adding a constant to avoid zero classification. The datasets use variables such as age, type of work, and criteria for receiving assistance. The last variable includes five nominal data, which debilitates Nave Bayes by not obtaining a posterior probability as a prediction class result. We used Direct Cash Assistance data from the Sedati sub-district, Sidoarjo district, East Java. The results of research with original Nave Bayes and smoothed Nave Bayes classification show that smoothed Nave Bayes has good prediction performance with an accuracy of 95.9% with a data split of 60:40. Smoothed- Nave Bayes also solves the problem of 8 data without predictive classes. The prediction results show that Smoothed Nave Bayes performs better than standard Nave Bayes. This research contributes to refining Nave Bayes to complement probability-based classification by adding refinement constants to avoid zero classification.]]>
<![CDATA[Improved Image Segmentation using Adaptive Threshold Morphology on CT-Scan Images for Brain Tumor Detection ]]>
<![CDATA[Syafri Arlis]]>;
<![CDATA[Muhammad Reza Putra]]>;
<![CDATA[Musli Yanto]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 3 (2024) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v23i3.3619
<![CDATA[Diagnosing disease by playing the role of image processing is one form of current medical technology development. The results of image processing performance have been able to provide accurate diagnoses to be used as material for decision-making. This research aims to carry out the process of detecting brain tumor objects in Computed Tomography (CT-Scan) images by developing a segmentation technique using the Adaptive Threshold Morphology (ATM) algorithm. The performance of the ATM algorithm in the segmentation process involves the Extended Adaptive Global Treshold (eAGT) function to produce an optimal threshold value. This research method involves several stages of the process in detecting tumor objects. The preprocessing stage is carried out using the cropping and filtering process which is optimized using the eAGT function. The next stage is the morphological segmentation process involving erosion and dilation operations. The final stage of the segmentation process using the ATM algorithm is labeling objects that have been detected. The research dataset used 187 Computed Tomography-Scan images from 10 brain tumor patients. The results of this study show that the accuracy rate for detecting brain tumor objects in Computed Tomography-Scan images is 93.47%. These results can provide an automatic and effective detection process based on the optimal threshold value that has been generated. Overall, this research contributes to the development of segmentation algorithms in image processing and can be used as an alternative solution in the treatment of brain tumor patients.]]>
<![CDATA[Quality Improvement for Invisible Watermarking using Singular Value Decomposition and Discrete Cosine Transform ]]>
<![CDATA[Danang Wahyu Utomo]]>;
<![CDATA[Christy Atika Sari]]>;
<![CDATA[Folasade Olubusola Isinkaye]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 3 (2024) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v23i3.3744
<![CDATA[Image watermarking is a sophisticated method often used to assert ownership and ensure the integrity of digital images. This research aimed to propose and evaluate an advanced watermarking technique that utilizes a combination of singular value decomposition methodology and discrete cosine transformation to embed the Dian Nuswantoro University symbol as proof of ownership into digital images. Specific goals included optimizing the embedding process to ensure high fidelity of the embedded watermark and evaluating the fuzziness of the watermark to maintain the visual quality of the watermarked image. The methods used in this research were singular value decomposition and discrete cosine transformation, which are implemented because of their complementary strengths. Singular value decomposition offers robustness and stability, while discrete cosine transformation provides efficient frequency domain transformation, thereby increasing the overall effectiveness of the watermarking process. The results of this study showed the efficacy of the Lena image technique in gray scale having a mean square error of 0.0001, a high peak signal-to-noise ratio of 89.13 decibels (dB), a universal quality index of 0.9945, and a similarity index structural of 0.999. These findings confirmed that the proposed approach maintains image quality while providing watermarking resistance. In conclusion, this research contributed a new watermarking technique designed to verify institutional ownership in digital images, specifically benefiting Dian Nuswantoro University. It showed significant potential for wider application in digital rights management.]]>
<![CDATA[Color Feature Extraction for Grape Variety Identification: Naïve Bayes Approach ]]>
<![CDATA[Putri Jafar]]>;
<![CDATA[Dolly Indra]]>;
<![CDATA[Fitriyani Umar]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 3 (2024) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v23i3.3823
<![CDATA[The problem addressed in this research is the lack of an efficient and accurate method for automatically identifying grape varieties. Accurate identification is crucial for quality control in the agricultural and food industries, impacting product labeling, pricing, and consumer trust. The aim of this research is to develop an automated system to classify green, black, and red grapes using digital image processing technology. This research method employs Naïve Bayes classification combined with color feature extraction. Testing was conducted under two scenarios: a database scenario with predefined grape image datasets and an out-of-database scenario with images resembling grape colors. Image processing includes resizing images to 200x200 pixels, Gamma Correction, Gaussian filtering, conversion to Lab* color space, K-Means Clustering for segmentation, followed by feature extraction and Naïve Bayes classification. The results of this research are that in the database scenario, the system achieved accuracies of 98.33% with an 80:20 data split and 98.89% with a 70:30 split. In the out-of-database scenario, accuracies were 96.67% with an 80:20 split and 97.78% with a 70:30 split. The conclusion of this research is the proposed method provides a reliable and efficient solution for automatic grape variety identification, benefiting quality control in agriculture and food industries.]]>
<![CDATA[Multiclass Text Classification of Indonesian Short Message Service (SMS) Spam using Deep Learning Method and Easy Data Augmentation ]]>
<![CDATA[Nurun Latifah]]>;
<![CDATA[Ramaditia Dwiyansaputra]]>;
<![CDATA[Gibran Satya Nugraha]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 3 (2024) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v23i3.3835
<![CDATA[The ease of using Short Message Service (SMS) has brought the issue of SMS spam, characterized by unsolicited and unwanted. Many studies have been conducted utilizing machine learning methods to build models capable of classifying SMS Spam to overcome this problem. However, most of these studies still rely on traditional methods, with limited exploration of deep learning-based approaches. Whereas traditional methods have a limitation compared to deep learning, which performs manual feature extraction. Moreover, many of these studies only focus on binary classification rather than multiclass SMS classification which can provide more detailed classification results. The aim of this research is to analyze deep learning model for multiclass Indonesian SMS spam classification with six categories and to assess the effectiveness of the text augmentation method in addressing data imbalace issues arising from the increased number of SMS categories. The research method used were Indonesian version of Bidirectional Encoder Representations from Transformers (IndoBERT) model and exploratory data analysis (EDA) augmentation technique to address imbalance dataset issue. The evaluation is conducted by comparing the performance of the IndoBERT model on the dataset and applying EDA techniques to enhance the representation of minority classes. The result of this research shows that IndoBERT achieves 91% accuracy rate in classifying SMS spam. Furthermore, the use of EDA technique results in significant improvement in f1-score, with an average 12% increase in minority classes. Overall model accuracy also improves to 93% after EDA implementation. This research concludes that IndoBERT is effective for multiclass SMS spam classification, and the EDA is beneficial in handling imbalanced data, contributing to the enhancement of model performances.]]>
<![CDATA[Image Data Acquisition and Classification of Vannamei Shrimp Cultivation Results Based on Deep Learning ]]>
<![CDATA[Melinda Melinda]]>;
<![CDATA[Zharifah Muthiah]]>;
<![CDATA[Fitri Arnia]]>;
<![CDATA[Elizar Elizar]]>;
<![CDATA[Muhammad Irhmasyah]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 3 (2024) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v23i3.3850
<![CDATA[This research aimed to employ deep learning techniques to address the classification of Litopenaeus vannamei cultivation results in land ponds and tarpaulin ponds. Despite their similar appearance, distinguishable differences exist in various aspects such as color, shape, size, and market price between the two cultivation methods, often leading to consumer confusion and potential exploitation by irresponsible sellers. To mitigate this challenge, the research proposed a classification method utilizing two Convolutional Neural Network (CNN) architectures: Visual Geometry Group-16 (VGG-16) and Residual Network-50 (ResNet-50), renowned for their success in various image recognition applications. The dataset comprised 2,080 images per class of vannamei shrimp from both types of ponds. Augmentation techniques enhanced the dataset’s diversity and sample size, reinforcing the model’s ability to discern shrimp morphology variations. Experiments were conducted with learning rates of 0.001 and 0.0001 on the Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (ADAM) optimizers to evaluate their effectiveness in model training. The VGG-16 and ResNet-50 models were trained with a learning rate parameter of 0.0001, leveraging the flexibility and reasonable control provided by the SGD optimizer. Lower learning rate values were chosen to prevent overfitting and enhance training stability. The model evaluation demonstrated promising results, with both architectures achieving 100% accuracy in classifying vannamei shrimp from soil ponds and tarpaulin ponds. Furthermore, experimental findings highlight the superiority of using SGD with a learning rate of 0.0001 over 0.001 on both architectures, underscoring the significant impact of optimizer and learning rate selection on model training effectiveness in image classification tasks.]]>
<![CDATA[Normalization Layer Enhancement in Convolutional Neural Network for Parking Space Classification ]]>
<![CDATA[sayuti rahman]]>;
<![CDATA[Marwan Ramli]]>;
<![CDATA[Arnes Sembiring]]>;
<![CDATA[Muhammad Zen]]>;
<![CDATA[Rahmad B.Y Syah]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 3 (2024) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v23i3.3871
<![CDATA[The research problem of this study is the urgent need for real-time parking availability information to assist drivers in quickly and accurately locating available parking spaces, aiming to improve upon the accuracy not achieved by previous studies. The objective of this research is to enhance the classification accuracy of parking spaces using a Convolutional Neural Network (CNN) model, specifically by integrating an effective normalizing function into the CNN architecture. The research method employed involves the application of four distinct normalizing functions to the EfficientParkingNet, a tailored CNN architecture designed for the precise classification of parking spaces. The results indicate that the EfficientParkingNet model, when equipped with the Group Normalization function, outperforms other models using Batch Normalization, Inter-Channel Local Response Normalization, and Intra-Channel Local Response Normalization in terms of classification accuracy. Furthermore, it surpasses other similar CNN models such as mAlexnet, you only look once (Yolo)+mobilenet, and CarNet in the same classification task. This demonstrates that EfficientParkingNet with Group Normalization significantly enhances parking space classification, thus providing drivers with more reliable and accurate parking availability information.]]>
<![CDATA[Gender Classification Using Viola Jones, Orthogonal Difference Local Binary Pattern and Principal Component Analysis ]]>
<![CDATA[Muhammad Amirul Mukminin]]>;
<![CDATA[Tio Dharmawan]]>;
<![CDATA[Muhamad Arief Hidayat]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 3 (2024) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v23i3.3879
<![CDATA[Facial recognition is currently a widely discussed topic, particularly in the context of gender classification. Facial recognition by computers is more complex and time-consuming compared to humans. There is ongoing research on facial feature extraction for gender classification. Geometry and texture features are effective for gender classification. This study aimed to combine these two features to improve the accuracy of gender classification. This research used the Viola-Jones and Orthogonal Difference Local Binary Pattern (OD-LBP) methods for feature extraction. The Viola-Jones algorithm faces issues in facial detection, leading to outliers in geometry features. At the same time, OD-LBP is a new descriptor capable of addressing pose, lighting, and expression variations. Therefore, this research attempts to utilize OD-LBP for gender classification. The dataset used was FERET, which contained various lighting variations, making OD-LBP suitable for addressing this challenge. Random Forest and Backpropagation were employed for classification. This research demonstrates that combining these two features is effective for gender classification using Backpropagation, achieving an accuracy of 93%. This confirms the superiority of the proposed method over single-feature extraction methods.]]>
<![CDATA[A Comparison of Enhanced Ensemble Learning Techniques for Internet of Things Network Attack Detection ]]>
<![CDATA[Edi Ismanto]]>;
<![CDATA[Januar Al Amien]]>;
<![CDATA[Vitriani Vitriani]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 3 (2024) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v23i3.3885
<![CDATA[Over the past few decades, the Internet of Things (IoT) has become increasingly significant due to its capacity to enable low-cost device and sensor communication. Implementation has opened up many new opportunities in terms of efficiency, productivity, convenience, and security. However, it has also brought about new privacy and data security challenges, interoperability, and network reliability. The research issue is that IoT devices are frequently open to attacks. Certain machine learning (ML) algorithms still struggle to handle imbalanced data and have weak generalization skills when compared to ensemble learning. The research aims to develop security for IoT networks based on enhanced ensemble learning by using Grid Search and Random Search techniques. The method used is the ensemble learning approach, which consists of Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). This study uses the UNSW-NB15 IoT dataset. The study's findings demonstrate that XGBoost performs better than other methods at identifying IoT network attacks. By employing Grid Search and Random Search optimization, XGBoost achieves an accuracy rate of 98.56% in binary model measurements and 97.47% on multi-class data. The findings underscore the efficacy of XGBoost in bolstering security within IoT networks.]]>
