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INDONESIA
MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Published by Universitas Bumigora
ISSN : 18584144     EISSN : 24769843     DOI : 10.30812/matrik
Core Subject : Science,
Computer Science & IT
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).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 18 Documents
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Search results for , issue "Vol 23 No 1 (2023)" : 18 Documents clear
Implementing K-Nearest Neighbor to Classify Wild Plant Leaf as a Medicinal Plants Zilvanhisna Emka Fitri; Lalitya Nindita Sahenda; Sulton Mubarok; Abdul Madjid; Arizal Mujibtamala Nanda Imron
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 1 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.2220

Abstract

in leaf shape. Therefore, this study aimed to create a system to help increase public knowledge about wild plant leaves that also function as medicinal plants by the KNN method. Leaves of wild plants, namely Rumput Minjangan, Sambung Rambat, Rambusa, Brotowali, and Zehneria japonica, are also medicinal plants in comparison. Image processing techniques used were preprocessing, image segmentation, and morphological feature extraction. Preprocessing consists of scaling and splitting the RGB components and using an RGB component decomposition process to find the color component that best describes the leaf shape and generate the blue component image. The segmentation process used a thresholding technique with a gray threshold value (T) of less than 150, which best separates objects and backgrounds. Some morphological feature extraction used are area, perimeter, metric, eccentricity, and aspect ratio. Based on the results of this research, the KNN method with variations in K values, namely 13, 15, and 17, obtained a system accuracy of 94.44% with a total of 90% training data and 10% test data. This comparison also affected the increase in system accuracy.
Comparison of the Karney Polygon Method and the Shoelace Method for Calculating Area Vikky Aprelia Windarni; Adi Setiawan; Atina Rahmatalia
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 1 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.2929

Abstract

In calculating the area of an area, latitude and longitude coordinates are based on data from Global Administrative Region Database and Google Earth can be used. The aim of this research is to calculate the area. This research uses the Karney and Shoelace method to determine its accuracy based on Median Absolute Percentage Error in calculating the area of an area. Median Absolute Percentage Error results use data based on Global Administration The Regional Database by applying the polygon method proposed by Karney is 18.73%, and the percentage is 18.19% by applying the Shoelace method. Based on Google Earth data, implementation the method proposed by Karney obtained a percentage of 19.14%, and the application of shoelaces method obtained a percentage of 19.72%. In this case, Karney polygons and the Shoelace method has good accuracy because the value is below 20%. The proposed Shoelace method is easier to perform understand compared to the Karney method for calculating land area because it uses the Universal Transverse Mercator coordinate system, which projects points on the Earth's surface onto a flat plane.
Comparison of Distance Measurements Based on k-Numbers and Its Influence to Clustering Deny Jollyta; Prihandoko Prihandoko; Dadang Priyanto; Alyauma Hajjah; Yulvia Nora Marlim
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 1 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3078

Abstract

Heuristic data requires appropriate clustering methods to avoid casting doubt on the information generated by the grouping process. Determining an optimal cluster choice from the results of grouping is still challenging. This study aimed to analyze the four numerical measurement formulas in light of the data patterns from categorical that are now accessible to give users of heuristic data recommendations for how to derive knowledge or information from the best clusters. The method used was clustering with four measurements: Euclidean, Canberra, Manhattan, and Dynamic Time Warping and Elbow approach for optimizing. The Elbow with Sum Square Error (SSE) is employed to calculate the optimal cluster. The number of test clusters ranges from k = 2 to k = 10. Student data from social media was used in testing to help students achieve higher GPAs. 300 completed questionnaires that were circulated and used to collect the data. The result of this study showed that the Manhattan Distance is the best numerical measurement with the largest SSE of 45.359 and optimal clustering at k = 5. The optimal cluster Manhattan generated was made up of students with GPAs above 3.00 and websites/ vlogs used as learning tools by the mathematics and computer department. Each cluster’s ability to create information can be impacted by the proximity of qualities caused by variations in the number of clusters.
Hyperparamaters Fine Tuning for Bidirectional Long Short Term Memory on Food Delivery Rahman Rahman; Teguh Iman Hermanto; Meriska Defriani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 1 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3084

Abstract

Food delivery is growing rapidly in Indonesia. Every food delivery order holds big promotions to attract users’ attention, so it has advantages and disadvantages. However, users only focus on evaluating drivers and restaurants, so the company does not get feedback on its services. This research aimed to understand user sentiment and maximize model accuracy with hyperparameters and fine-tuning. Sentiment analysis can be used to determine user sentiment based on reviews, and the results of this analysis can provide suggestions for companies. The bidirectional long short-term memory method was used for sentiment analysis to understand a word’s meaning better. The Bidirectional Short-Term Memory model andWord2Vec extraction features were proven to be better than several other extraction modelsand features. The dataset was balanced, and the hyperparameters in the model and optimization could also improve accuracy. So, the Gofood and Shopeefood research results had an accuracy of 98.1%, and Grabfood’s was 97.4%.
Employee Presence and Payroll Information System Using Quick Response Code and Geolocation Ahmad Homaidi; Lukman Fakih Lidimilah; Jarot Dwi Prasetyo; Nur Azizah
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 1 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3093

Abstract

The presence in the institution still uses the conventional method of affixing a signature on the attendance sheet. Presences that have been carried out so far are felt to be less effective and efficient because sometimes attendance is filled towards the end of the month, which causes the validity of attendance data to be questioned. Even errors are often found in the recording, which causes the nominal to be inappropriate and must be revised. This research aimed to design an information system using the Quick Response Code to increase the effectiveness of employee attendance and payroll, supported by geolocation, to make it more efficient. The method used in this research was the waterfall method, using the stages of communication, planning, modeling, construction, and deployment. This research produced an information system that could make it easier for employees to attend, speed up determining employee salaries and filing financial disbursements, and increase employee presence and salary validity. The test results showed that 90% said they were satisfied with the performance of the system being built.
Lungs X-Ray Image Segmentation and Classification of Lung Disease using Convolutional Neural Network Architectures Bambang Suprihatin; Yuli Andriani; Fauziah Nuraini Kurdi; Anita Desiani; Ibra Giovani Dwi Putra; Muhammad Akmal Shidqi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 1 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3133

Abstract

Lung disease is one of the biggest causes of death in the world. The SARS-CoV-2 virus causes diseases like COVID-19, and the bacteria Streptococcus sp., which causes pneumonia, are two sample causes of lung disease. X-ray images are used to detect the lung disease. This study aimed to combine the stages of segmentation and classification of lung disease. This study in segmentation aims to separate the features contained in the lung images. The classification aimed to provide holistic information on lung disease. This research method used the Deep Residual U-Net (DrU-Net) segmentation architecture and the Deep Residual Neural Network (DResNet) classification architecture. DrU-Net is a modified U-Net architecture with dropout added in its convolutional layers. DResNet is a modified Residual Network (ResNet) architecture with dropout added in its convolutional block layers. The result of this study was segmentation using the DrU-Net architecture obtained 99% for accuracy, 98% for precision, 98% for recalls, 98% for F1-Score, and 96.1% for IoU. The classification results of the segmented images using the DResNet architecture obtained 91% for accuracy, 86% for precision, 85% for recalls, and 84% for F1-Score. The performance results of DrU-Net architecture were excellent and robust in image segmentation. Unfortunately, the average performance of DResNet in classification was still below 90%. These results indicate that Dres-Net performs well in classifying lung disorders in 3 labels, namely Covid, Normal, and Pneumonia, but still needs improvement.
Investigating The Effectiveness of Various Convolutional Neural Network Model Architectures for Skin Cancer Melanoma Classification Rizky Hafizh Jatmiko; Yoga Pristyanto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 1 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3185

Abstract

Melanoma is one of the most dangerous types of skin cancer. Since 2018, the number of skin cancer cases in the US has increased and exceeded 100,000. Melanoma is the third most common cancer in Indonesia, following womb cancer and breast cancer. Standard detection of melanoma skin cancer biopsy is costly and time-consuming. The purpose of this research is to build and compare melanoma skin cancer detection using various Convolutional Neural Network method. This research used four CNN model architectures methods, VGG-16, LeNet, Xception, and MobileNet. The dataset for this research is image data that consists of 9605 data divided into benign and malignant classes. The data will be augmented to increase its quantity. After that, the data will be trained using four CNN architecture models and evaluated using the confusion matrix. The result of this study is that Xception model has the best accuracy and the lowest loss, with 93% accuracy and 19% loss, with precision 93%, recall 93,5%, and f1-score 93%. Whereas the other model, VGG-16 gives 90 % accuracy, 27% loss, LeNet 89,7% accuracy, 28% loss, and mobileNet 90,8% accuracy and 22,5% loss.
Recognizing Pneumonia Infection in Chest X-Ray Using Deep Learning Ni Wayan Sumartini Saraswati; I Wayan Dharma Suryawan; Ni Komang Tri Juniartini; I Dewa Made Krishna Muku; Poria Pirozmand; Weizhi Song
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 1 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3197

Abstract

One of the diseases that attacks the lungs is pneumonia. Pneumonia is inflammation and fluid in the lungs making it difficult to breathe. This disease is diagnosed using X-Ray. Against the darker background of the lungs, infected tissue shows denser areas, which causes them to appear as white spots called infiltrates. In the image processing approach, pneumonia-infected X-rays can be detected using machine learning as well as deep learning. The convolutional neural network model is able to recognize images well and focus on points that are invisible to the human eye. Previous research using a convolutional neural network model with 10 convolution layers and 6 convolution layers has not achieved optimal accuracy. The aim of this research is to develop a convolutional neural network with a simpler architecture, namely two convolution layers and three convolution layers to solve the same problem, as well as examining the combination of various hyperparameter sizes and regularization techniques. We need to know which convolutional neural network architecture is better. As a result, the convolutional neural network classification model can recognize chest x-rays infected with pneumonia very well. The best classification model obtained an average accuracy of 89.743% with a three-layer convolution architecture, batch size 32, L2 regularization 0.0001, and dropout 0.2. The precision reached 94.091%, recall 86.456%, f1-score 89.601%, specificity 85.491, and error rate 10.257%. Based on the results obtained, convolutional neural network models have the potential to diagnose pneumonia and other diseases.
Application of Numerical Measure Variations in K-Means Clustering for Grouping Data Relita Buaton; Solikhun Solikhun
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 1 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3269

Abstract

The K-Means Clustering algorithm is commonly used by researchers in grouping data. The main problem in this study was that it has yet to be discovered how optimal the grouping with variations in distance calculations is in K-Means Clustering. The purpose of this research was to compare distance calculation methods with K-Means such as Euclidean Distance, Canberra Distance, Chebychev Distance, Cosine Similarity, Dynamic TimeWarping Distance, Jaccard Similarity, and Manhattan Distance to find out how optimal the distance calculation is in the K-Means method. The best distancecalculation was determined from the smallest Davies Bouldin Index value. This research aimed to find optimal clusters using the K-Means Clustering algorithm with seven distance calculations based on types of numerical measures. This research method compared distance calculation methods in the K-Means algorithm, such as Euclidean Distance, Canberra Distance, Chebychev Distance, Cosine Smilirity, Dynamic Time Warping Distance, Jaccard Smilirity and Manhattan Distance to find out how optimal the distance calculation is in the K-Means method. Determining the best distance calculation can be seen from the smallest Davies Bouldin Index value. The data used in this study was on cosmetic sales at Devi Cosmetics, consisting of cosmetics sales from January to April 2022 with 56 product items. The result of this study was a comparison of numerical measures in the K-Means Clustering algorithm. The optimal cluster was calculating the Euclidean distance with a total of 9 clusters with a DBI value of 0.224. In comparison, the best average DBI value was the calculation of the Euclidean Distance with an average DBI value of 0.265.
Single elimination tournament design using dynamic programming algorithm yusri ikhwani; As`ary Ramadhan; Muhammad Bahit; Taufik Hidayat Faesal
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 1 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3290

Abstract

Finding the best single-elimination tournament design is important in scientific inquiry because it can have major financial implications for event organizers and participants. This research aims to create an optimal single-elimination tournament design using binary tree modeling with dummy techniques. Dynamic programming algorithms have been used to compute optimal single-elimination designs to overcome this effectively. This research method uses various implementations of sub-optimal algorithms and then compares their performance in terms of runtime and optimality as a solution to measure the comparison of sub-algorithms. This research shows that the difference in relative costs produced by various sub-algorithms with the same input is quite low. This is expected because quotes are generated as integer values from a small interval 1, ≤ 9, whereas costs tend to reach much higher values. From the comparison of these sub-algorithms, the best results among the sub-optimal algorithms were obtained in the Sub Optimal algorithm 3. We present the experimental findings achieved using the Python implementation of the suggested algorithm, with a focus on the best single-elimination tournament design solution.

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