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[Comparison of Memory usage between REST API in Javascript and Golang ]]>
<![CDATA[Hafizd Ardiansyah]]>;
<![CDATA[Agung Fatwanto]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 1 (2022) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i1.1325
<![CDATA[Various mobile devices have limited memory, thus it must be used as effectively as possible. As a result, apps that will operate on mobile devices must take memory usage efficiency into account. The REST API, which is typically used to connect several applications that utilize different types of technology so that the applications can be connected, is one sort of technology that is currently commonly used to construct mobile applications. Javascript and Golang are the types of technology used to create the REST API. Undoubtedly, each of these technologies offers a unique performance. Research that can give a broad overview of the variations in the impact of memory resource utilization between Javascript and Golang is therefore required. In this work, two REST APIs are created using Javascript and Golang by researchers utilizing an experimental quantitative methodology. Following that, the memory utilization of the two REST APIs was evaluated using the exact same two types of datasets obtained from console.cloud.google.com. There was a difference in memory consumption between Javascript and Golang after the Wilcoxon test, t-test for paired data, and equivalence test, but the difference was essentially inconsequential (practically insignificant).]]>
<![CDATA[GIS Flood Prone Agricultural Land East Java Using Multi-Method Attribute Utility Theory ]]>
<![CDATA[Mala Rosa Aprillya]]>;
<![CDATA[Uswatun Chasanah]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 1 (2022) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i1.1511
<![CDATA[East Java has various regional conditions. The condition of the area certainly has the potential for disasters that have a significant impact on the agricultural sector. Flood is one of the factors that damage agricultural land. Flood risk management plays an important role in guiding the government in making timely and appropriate decisions for flood rescue and relief. The purpose of this research is a study of flood risk assessment in the agricultural sector in East Java using Multi Attribute Utility Theory. The Multi Attribute Utility Theory is used to solve problems related to spatial planning and disaster management because it is systematic and suitable for solving complex problems such as the agricultural sector. The results showed that the agricultural land areas in East Java with the category of very flood-prone include Bojonegoro, Lamongan, Tuban, and Sidoarjo Regencies. Furthermore, the results of this study were visualized by mapping flood risk using a GIS. This can be used for efforts in flood disaster management. This research is expected to assist policy making at the Department of Agriculture and Food Security in monitoring flood-prone agricultural land in order to minimize the occurrence of flood disasters in the agricultural sector.]]>
<![CDATA[Combination Contrast Stretching and Adaptive Thresholding for Retinal Blood Vessel Image ]]>
<![CDATA[Anita Desiani]]>;
<![CDATA[Irmeilyana Irmeilyana]]>;
<![CDATA[Endro Setyo Cahyono]]>;
<![CDATA[Des Alwine Zayanti]]>;
<![CDATA[Sugandi Yahdin]]>;
<![CDATA[Muhammad Arhami]]>;
<![CDATA[Irvan Andrian]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 1 (2022) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i1.1654
<![CDATA[To diagnose diabetic retinopathy is to segment the blood vessels of the retinal, but the retinal images in the DRIVE and STARE datasets have varying contrast, so the enhancement is needed to obtain a stable image contrast. In this study, image enhancement was performed using the Contrast Stretching and continued with segmentation using the Adaptive Thresholding on retinal images. The image that has been extracted with green channels will be enhanced with Contras Stretching and segmented with Adaptive Thresholding to produce a binary image of retinal blood vessels. The purpose of this study was to combine image enhancement techniques and segmentation methods to obtain valid and accurate retinal blood vessels. The test results on DRIVE were 95.68 for accuracy, 65.05% for sensitivity, and 98.56% for specificity. The test results of Adam Hoover’s ground truth on STARE were 96.13% for, 65.90% for sensitivity, and 98.48% for specificity. The test results for Valentina Kouznetsova’s ground truth on the STARE were 93.89% for accuracy, 52.15% for sensitivity, and 99.02% for specificity. The conclusion obtained is that the processing results on the DRIVE and STARE datasets are very good with respect to their accuracy and specificity values. This method still needs to be developed to be able to detect thin blood vessels with the aim of being able to improve and increase the sensitivity value obtained.]]>
<![CDATA[Comparison of k-Nearest Neighbor and Naive Bayes Methods for SNP Data Classification ]]>
<![CDATA[Denny Indrajaya]]>;
<![CDATA[Adi Setiawan]]>;
<![CDATA[Bambang Susanto]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 1 (2022) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i1.1758
<![CDATA[In an accident, sometimes the identity of a person who has an accident is hard to know, so it is necessary to use biological data such as Single Nucleotide Polymorphism (SNP) data to identify the person's origin. This research aims to compare the accuracy and the F1 score of the k-Nearest Neighbor method and the Naive Bayes method in classifying SNP data from 120 people who divide into groups, namely European (CEU) and Yoruba (YRI). Determination of the best method based on the average value of accuracy and the average value of F1 score from 1000 iterations with various percentage distributions of training datasets and testing datasets. In this research, the selection of SNP locations for the classification process was carried out by correlation analysis. The average accuracy obtained for the k-Nearest Neighbor method with the value of k=31 is 98.38% where the average F1 score is 98.39% while the Naive Bayes method obtained the average accuracy of 96.74% and the average F1 score of 96.63%. In this case, the k-Nearest Neighbor method is better than the Naive Bayes method in classifying SNP data to determine the origin of a person's ancestor tends to be from CEU or YRI.]]>
<![CDATA[The Application of Repeated SMOTE for Multi Class Classification on Imbalanced Data ]]>
<![CDATA[Muhammad Ibnu Choldun Rachmatullah]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 1 (2022) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i1.1803
<![CDATA[One of the problems that are often faced by classifier algorithms is related to the problem of imbalanced data. One of the recommended improvement methods at the data level is to balance the number of data in different classes by enlarging the sample to the minority class (oversampling), one of which is called The Synthetic Minority Oversampling Technique (SMOTE). SMOTE is commonly used to balance data consisting of two classes. In this research, SMOTE was used to balance multi-class data. The purpose of this research is to balance multi-class data by applying SMOTE repeatedly. This iterative process needs to be applied if the number of unbalanced data classes is more than two classes, because the one-time SMOTE process is only suitable for binary classification or the number of unbalanced data classes is only one class. To see the performance of iterative SMOTE, the SMOTE datasets were classified using a neural network, k-NN, Nave Bayes, and Random Forest and the performance measures were measured in terms of accuracy, sensitivity, and specificity. The experiment in this research used the Glass Identification dataset which had six classes, and the SMOTE process was repeated five times. The best performance was achieved by the Random Forest classifier method with accuracy = 86.27%, sensitivity = 86.18%, and specificity = 95.82%. The result of experiment present that repeated SMOTE results can increase the performance of classification.]]>
<![CDATA[Optimization of Performance Traditional Back-propagation with Cyclical Rule for Forecasting Model ]]>
<![CDATA[Anjar Wanto]]>;
<![CDATA[Ni Luh Wiwik Sri Rahayu Ginantra]]>;
<![CDATA[Surya Hendraputra]]>;
<![CDATA[Ika Okta Kirana]]>;
<![CDATA[Abdi Rahim Damanik]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 1 (2022) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i1.1826
<![CDATA[The traditional Back-propagation algorithm has several weaknesses, including long training times and significant iterations to achieve convergence. This study aims to optimize traditional Back-propagation using the cyclical rule method to cover these weaknesses. Optimization is done by changing the training function and standard Back-propagation parameters using the training function and cyclical rule parameters. After that, a comparison of the two results will be carried out. This study uses quantitative method of time-series data on coronavirus cases sourced from the Worldometer website, then analyzed using three forecasting models with five input layers, one hidden layer (5, 10, and 15 neurons) and one output layer. The results showed that the 5-10-1 model with the training function and cyclical rule parameters and the tansig and purelin activation functions could perform well in optimization, including faster training time and smaller iterations (epochs), MSE training performance, and better tests. Low and high accuracy (92%) with an error rate of 0.01. So it was concluded that the training function and cyclical rule parameters with the tansig and purelin activation functions were able to optimize the traditional Back-propagation method, and the 5-10-1 model could be used for forecasting active cases of the coronavirus in Asia]]>
<![CDATA[Recognize The Polarity of Hotel Reviews using Support Vector Machine ]]>
<![CDATA[Ni Wayan Sumartini Saraswati]]>;
<![CDATA[I Gusti Ayu Agung Diatri Indradewi]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 1 (2022) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i1.1848
<![CDATA[A brand is very dependent on consumer perceptions of the product or services. In assessing consumer perceptions of products and services, companies are often faced with data analysis problems. One of the data that is very useful to produce a picture of consumer perceptions of the products and services is review data. So that the company's ability to process review data means that the company has a picture of the strength of the brand it has. Some of the most popular machine learning algorithms for creating text classification models include the naive Bayes family of algorithms, support vector machines (SVM) and deep learning algorithms. In this research, SVM has been proven to be a reliable method in pattern recognition. In particular, this study aims to produce a model that can be used to classify the polarity of hotel reviews automatically. The experimental data comes from review data on hotels in Europe sourced from TripAdvisor with a total of 38000 reviews. We also measure the quality of the classification engine model. The test results of the SVM model built from hotel review data are quite good. The average accuracy of the classification engine is 92.48%. Because the recall and precision values are balanced, the accuracy value is considered sufficient to describe the quality of the classification.]]>
<![CDATA[Implementation Cryptography and Access Control on IoT-Based Warehouse Inventory Management System ]]>
<![CDATA[Muhammad Yusuf]]>;
<![CDATA[Arizal Arizal]]>;
<![CDATA[Ira Rosianal Hikmah]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 1 (2022) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i1.1849
<![CDATA[Warehousing is a product storage management activity to ensure product availability, so inventory management is needed to oversee the movement of logistics and equipment. Some things need to be considered in the storage process, such as the suitability of the storage location, safe from theft, and safe from physical disturbances. Vulnerabilities can occur when unauthorized users find out information from the database regarding stored goods, so a security mechanism for the warehouse database is needed. In addition, proper identification needs to be made of someone trying to access the database. In this research, a Warehouse Inventory Management System (WIMS) was created by implementing the AES-128 cryptographic algorithm, which was built using ESP32 and Raspberry Pi 3 devices. Time Password (T-OTP). The results show that the built system can overcome inventory problems in conventional warehousing management systems and implement data security using the AES-128 algorithm. The application of two-factor authentication in the form of smartcards and T-OTP shows very good results in testing its accuracy to overcome the vulnerability of unauthorized access to the system database]]>
<![CDATA[The Improvement of Artificial Neural Network Accuracy Using Principle Component Analysis Approach ]]>
<![CDATA[Arief Hermawan]]>;
<![CDATA[Adityo Permana Wibowo]]>;
<![CDATA[Akmal Setiawan Wijaya]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 1 (2022) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i1.1880
<![CDATA[An important problem in a classification system is how to get good accuracy results. A way to increase the accuracy of a classifier system is to improve the number of input data attributes. Improving the number of input data attributes can be done using the Principal Component Analysis (PCA) method. The aim of this research is to reduce the number of input data attributes to increase the accuracy in a mushroom classification system. The research method used in this study started from collecting datasets from Kaggle.com related to mushroom-classification, then the data visualization process was carried out using pie charts then a dimension reduction process was carried out to reduce the number of variables using the PCA method. The next step is the training and testing of the artificial neural network. The architecture of artificial neural network used is backward error propagation with the number of hidden layers as much as 2 layers with the number of cells as many as 3 and 2. The training data used is 80%, while the testing data is 20%. Based on the test results, obtained an accuracy of 100% with 150,000 iterations and using 11 input variables from 22 existing input variables. By adding Principal Component Analysis part of the development that can improve the accuracy and performance of Artificial Neural Networks]]>
<![CDATA[Clustering Couples of Childbearing Age to Get Family Planning Counseling Using K-Means Method ]]>
<![CDATA[Yuniar Farida]]>;
<![CDATA[Adam Fahmi Khariri]]>;
<![CDATA[Dian Yuliati]]>;
<![CDATA[Hani Khaulasari]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 1 (2022) ]]>
Publisher : <![CDATA[LPPM Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i1.1888
<![CDATA[Couples of Childbearing Age (CCA) in the Madiun Regency have increased in the last three years. It caused the population in Madiun to overgrow with the newborn, which implies the economic, social, and environmental aspects. This study aims to cluster villages in Madiun with CCA case studies instead of birth control participants who will give birth and want children to determine the priority of getting Family Planning (in Indonesia, namely Keluarga Berencana/KB) counseling. K-Means clustering is used in this study because it has a linear space of complexity that can be executed quickly and easily. The result of this study is four (4) CCA clusters. CCA cluster 1 is a very high level of giving birth and wanting children, consisting of 7 villages. CCA cluster 2 is a high level of giving birth and wanting children with 119 villages. CCA cluster 3 is a medium level of giving birth and wanting children in 50 villages, and CCA cluster 4 is a low level of giving birth and wanting children, including 34 villages. So, cluster 1, which includes seven villages, is the most prioritized to get Family Planning counseling because it is the CCA cluster with the most birthing rate and wants children. This research obtained a silhouette coefficient of 0.42, which belongs to the medium level.]]>
