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[Multi-Level Pooling Model for Fingerprint-Based Gender Classification ]]>
<![CDATA[Sri Suwarno]]>;
<![CDATA[Erick Kurniawan]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i2.2551
<![CDATA[It has been widely reported that CNN (Convolutional Neural Network) has shown satisfactory results in classifying images. The strength of CNN lies in the type and the number of layers that construct it. However, the most apparent drawbacks of CNN are the requirement for a large labeled dataset and its lengthy training time. Although datasets are available, labeling that data is a significant problem. This work mimics the CNN model but only utilizes its pooling layers. The novelty of this model is removing convolution layers and directly processing fingerprint images using pooling layers. Three pooling layer models, namely maximum pooling, average pooling, and minimum pooling, are used to generate fingerprint features to classify their owner gender. These pooling layers are arranged consecutively up to eight levels. Removing convolution layers makes the process straightforward, and the computation is much faster. This study utilized 200 fingerprint datasets from the NIST (National Institute of Standards and Technology), with male and female fingerprints of 100 samples each. The extracted features were then classified using K-NN (K-Nearest Neighbors) algorithm. The proposed method resulted in an accuracy of 61% to 71.5% or an average of 66.25%.]]>
<![CDATA[Hybrid Model for Sentiment Analysis of Bitcoin Prices using Deep Learning Algorithm ]]>
<![CDATA[Rizky Afrinanda]]>;
<![CDATA[Lusiana Efrizoni]]>;
<![CDATA[Wirta Agustin]]>;
<![CDATA[Rahmiati Rahmiati]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i2.2640
<![CDATA[Bitcoin is a decentralized digital currency, which is not controlled by a single authority or government. Bitcoin uses blockchain technology to verify transactions and guarantee user security and privacy. The fluctuating value of bitcoin is influenced by opinions that develop because many people use these opinions as a basis for buying or selling bitcoins. Knowledge to find out the market conditions of bitcoin based on public opinion is very necessary. This study aims to develop a hybrid model for bitcoin sentiment analysis. The dataset used came from comments on the Indodax website chat room, as many as 2890 data were successfully collected, then do data preprocessing, translate to english, text labeling and used hybrid parallel CNN and LSTM using word embedding glove 100 dimensions. Results of the experiments conducted, at 90:10 data splitting and 100 epochs is the best model with 88% accuracy, 86% precision, 78% recall and 81% f1-score, while the classification of opinion text comments on indodax chat results in 64.22% neutral comments, 21.14% positive comments and 14.63% negative comments. Based on research results, use of a parallel hybrid model provides a high accuracy value in classifying text, from these results positive comments are more than negative so that investors are advised to buy bitcoins.]]>
<![CDATA[Convolutional Neural Network for Colorization of Black and White Photos ]]>
<![CDATA[Siti Ummi Masruroh]]>;
<![CDATA[Andrew Fiade]]>;
<![CDATA[Muhammad Ikhsan Tanggok]]>;
<![CDATA[Rizka Amalia Putri]]>;
<![CDATA[Luigi Ajeng Pratiwi]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i2.2652
<![CDATA[People today are very fond of capturing moments by taking pictures. Various photo functions are used to document all forms of information that you want to store. In photos with digital images that have black and white, the information obtained is less than optimal, so an image processing process is needed to get color photos. Based on this, the author wants to change photos from black and white to color photos. The method used in this research is Convolutional Neural Network (CNN). This study uses Atlas 200 DK hardware and Ascend 310 processor. The data used in this study are 32 black and white photos in .jpg format as training data and perform 6 experimental scenarios with different numbers of black and white photos in each experiment. The total black and white photos used to experiment were 81 photos. The results obtained are models that successfully perform processing in the form of color photos with the appropriate color results in predicting the possible color of the object in each pixel in the photo. Based on this research, the trend of artificial intelligence can be implemented in changing the color of photos according to color predictions.]]>
<![CDATA[Support Vector Machine for Predicting Candlestick Chart Movement on Foreign Exchange ]]>
<![CDATA[Annisa Nurul Puteri]]>;
<![CDATA[Suryadi Syamsu]]>;
<![CDATA[Topan Leoni Putra]]>;
<![CDATA[Andita Dani Achmad]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i2.2676
<![CDATA[Foreign Exchange, commonly called Forex, is a form of investment in the non-real sector in great demand. Forex is a marketplace that specializes in foreign exchange trading. Technology advancements have made it easy to monitor investment conditions in real time and present them in an easyto - understand graphical form. As a result, predictions are closely related to investment, starting from market sentiment and economic conditions to technical matters. One of the Artificial Intelligence methods that can be used in classifying is the Support Vector Machine (SVM). SVM is a machine learning classification method based on the Structural Risk Minimization (SRM) principle to find the best hyperplane that separates two classes in the input space that determines the classification decision function by minimizing empirical risk. This study used candlestick patterns to predict foreign exchange chart movements using the Support Vector Machine (SVM) classification method. The purpose of this study was to measure the accuracy of the Support Vector Machine method in making predictions using candlestick patterns so that it can assist traders in making decisions in forex trading. The accuracy level obtained from the data classification results reached 90.72% with a precision of 87.69%. With a relatively good level of accuracy, the Support Vector Machine (SVM) method can be used to predict chart movements in foreign exchange using candlesticks to indicate the current trend’s direction.]]>
<![CDATA[Tsunami Vulnerability and Risk Assessment in Banyuwangi District using machine learning and Landsat 8 image data ]]>
<![CDATA[Gallen cakra adhi wibowo]]>;
<![CDATA[Sri Yulianto Joko Prasetyo]]>;
<![CDATA[Irwan Sembiring]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i2.2677
<![CDATA[The tsunami is a disaster that often occurs in Indonesia, there are no valid indicators to assess and monitor coastal areas based on functional land use and based on land cover which refers to the biophysical characteristics of the earth's surface. One of the recommended methods is the vegetation index. Vegetation index is a method from LULC that can be used to provide information on how severe the impact of the tsunami was on the area.In this study, an increase in the vegetation index was carried out using machine learning. The purpose of this study was to develop a tsunami vulnerability assessment model using the Vegetation Index extracted from Landsat 8 satellite imagery optimized with KNN, Random Forest and SVM. The stages of study, are: 1)extraction Landsat 8 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction of vegetation indices using KNN, Random Forest, and SVM algorithms. 3) accuracy testing using the MSE, RMSE, and MAE,4) spatial prediction using the Kriging function and 5) tsunami modelling vulnerability indicators. The results of this study indicate that the NDVI interpolation value is 0 - 0.1 which is defined as vegetation density, biomass growth, and moderate to low vegetation health. the NDWI value is 0.02 - 0.08 and the MNDWI value is 0.02 - 0.09 which is interpreted as the presence of surface water along the coast. MSAVI is a value of 0.1 – 0 which is defined as the absence of vegetation. The NDBI interpolation value is -0.05 - (-0.08) which is interpreted as the existence of built-up land with social and economic activities. From the results of research on the 10 areas studied, there are 3 areas with conditions that have a high level of tsunami vulnerability. 2 areas with medium vulnerability and 5 areas with low vulnerability to tsunami.]]>
<![CDATA[Seamless Security on Mobile Devices Textual Password Quantification Model Based Usability Evaluation of Secure Rotary Entry Pad Authentication ]]>
<![CDATA[Herman Kabetta]]>;
<![CDATA[Hermawan Setiawan]]>;
<![CDATA[Fetty Amelia]]>;
<![CDATA[Muhammad Qolby Fawzan]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i2.2700
<![CDATA[Mobile devices are vulnerable to shoulder surfing and smudge attacks, which should occur when a user enters a PIN for authentication purposes. This attack can be avoided by implementing a rotary entry pad mechanism. Despite this, several studies have found that using a rotary entry pad reduces user usability. This study uses a Design Research Methodology approach. It will implement a rotary entry pad authentication in the Android operating system as an authentication method to protect the device against Shoulder Surfing Attacks and Smudge Attacks. Furthermore, it combined JSON Web Token (JWT) to secure the authentication process from the client to the server. At the end of implementation, it compared with other studies in terms of usability and evaluated it using the TQ-Model, which showed that the usability aspect has improved. Regarding security, we conducted a shoulder surfing attack simulation to assess the efficacy of guessing PINs. The results showed that only a limited number of attempts were successful, with two out of five samples failing to guess any numbers and only one sample successfully guessing six 10-digit PIN combinations out of 10 to the power of 10. The security test results show that shoulder surfing attacks are more difficult to perform after implementing the rotary entry pad. The evaluation showed that the JSpinpad performed better, with seven parameters showing improvement, one parameter showing a decline, and ten parameters remaining unchanged.]]>
<![CDATA[A Novel Algorithm of Distance Calculation Based-on Grid-Edge-Depth-Map and Gyroscope for Visually-Impaired ]]>
<![CDATA[budi rahmani]]>;
<![CDATA[Ruliah Ruliah]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i2.2757
<![CDATA[This paper presented a new algorithm for determining the distance of an object in front of a stereo camera placed on a helmet. By using a stereo camera with a Sum of Absolute Difference with a Sobel edge detector, our previous Grid-Edge-Depth map algorithm could calculate the objects’ distance up to 500 cm. The problem started when a vision disability person used the device with an unfixed stereo camera angle. The unspecified angle caused by the helmet’s movement influenced the distance calculation result. This novel process started with calculating the distance from a Grid-Edge-Depth map considering unfixed angle data of the x-axis from a gyroscope sensor placed on the stereo camera using the trigonometry formula. The angle data used was the x-axis data. The distance measurement results by the system were then computed based on the unfixed angle compared to the actual distance. The test was carried out with three scenarios which required the user to stand at a distance of 100 cm, 125 cm, and 150 cm from a table, chair, or wall, with 30 tests for each scenario. The test results showed an average accuracy of 96.05% with three experimental scenarios, which meant that this machine was feasible to implement.]]>
<![CDATA[Application of KNN Machine Learning and Fuzzy C-Means to Diagnose Diabetes ]]>
<![CDATA[Anthony Anggrawan]]>;
<![CDATA[Mayadi Mayadi]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i2.2777
<![CDATA[The disease is a common thing in humans. Diseases that attack humans do not know anyone and do not know age. The disease experienced by a person starts from an ordinary level until it can be declared severe to the point of being at risk of death. In this study, the early diagnosis was carried out related to diabetes, where diabetes is a condition in which the sufferer’s body has low sugar levels above normal. Symptoms experienced by sufferers include frequent thirst, frequent urination, frequent hunger, and weight loss. Based on these problems, a system is needed that can quickly find out the diagnosis experienced by a patient. This research aimed to diagnose diabetes early on based on early symptoms. The methods used are KNN and web-based fuzzy C-means. Creating a web-based system can represent medical personnel experts in a fast-diagnosing approach to diabetes. This system was a computer program embedded with the knowledge of the characteristics of diabetes. The results of testing the KNN and Fuzzy C-means applications and methods get an accuracy of 96% for the KNearest Neighbor method, while for the Fuzzy C-Means method with Confusion Matrix calculations, an accuracy of 96% is obtained, so it can be concluded that the Fuzzy C-means method Means better than the K-Nearest Neighbor method.]]>
<![CDATA[Blockchain-based Trust, Transparent, Traceable Modeling on Learning Recognition System Kampus Merdeka ]]>
<![CDATA[Irawan Afrianto]]>;
<![CDATA[Andri Heryandi]]>;
<![CDATA[Sufa Atin]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i2.2780
<![CDATA[Kampus Merdeka is an evolution of education in Indonesia that accommodates various changes. The existence of a mechanism that includes various actors in it makes Kampus Merdeka have many new outcomes which must be recognized by all stakeholders who need it. Blockchain technology and smart contract offer the ability to build trust between all actors in the Kampus Merdeka activities with their transparent nature and reliable, immutable data storage capabilities. Every stage that occurs in it can be traced from upstream to downstream. This study aims to design an architectural model of a blockchain system for the learning recognition system Kampus Merdeka. It uses the analyticalstudy to identify the possible problems and the stakeholders involved and design the model solution proposed. As a result, it proposed the type of blockchain and the most suitable architecture for use in the learning recognition system Kampus Merdeka. In this study, the blockchain model is proposed as a mechanism for identifying and recognizing learning outcomes in the Kampus Merdeka environment more securely, challenging to modify, and traceable by all parties to ensure the authenticity of the learning outcomes. Furthermore, it can be recognized by all parties in it.]]>
<![CDATA[The UTAUT Model for Measuring Acceptance of the Application of the Patient Registration System ]]>
<![CDATA[Tugiman Tugiman]]>;
<![CDATA[Herman Herman]]>;
<![CDATA[Anton Yudhana]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v22i2.2844
<![CDATA[The Covid-19 pandemic forced hospitals to innovate so that services comply with health protocols in the new adaptation period. Electronic Health (E-Health) such as online patient registration is expected to be a solution for hospitals with high patient visit rates. The purpose of this study was to analyze the level of user acceptance and the factors that influence the implementation of the online patient registration system for hospital patients. This research was conducted at PKU Muhammadiyah Gombong Hospital, which has implemented an online patient registration system based on Android since May 2020. The evaluation model uses Unified Theory of Acceptance and Use of Technology(UTAUT) and the analysis uses the Structural Equation Model (SEM) method using smart PLS. The results of the research show that all the hypotheses formed show valid values. So it can be said that the application of SIPENDOL in hospitals has been well received by users.]]>
