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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 418 Documents
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IoT-based Integrated System Portable Prayer Mat and DailyWorship Monitoring System Luh Kesuma Wardhani; Nenny Anggraini; Nashrul Hakiem; M. Tabah Rosyadi; Amin Rois
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.3058

Abstract

Muslims have various difficulties in praying, such as difficulty memorizing the number of rak’ah they have been doing and determining the direction of the Qibla. In this research, we proposed a technological device for monitoring daily worship in Islam. We presented the IoT-based integrated system as a portable prayer mat serving as a rak’ah counter, Qibla direction finder, and a mobile worship monitoring system. A prototyping approach was used to produce a portable smart prayer mat, and Rapid Application Development was used to develop a mobile daily worship system. The device comprises an Arduino AT Mega 2560 powered portable prayer mat through a force-sensitive resistor sensor and an HMC 5883L compass module. The device sends the prayer activity to the worship applications in detail. The daily worship monitoring application itself has numerous features that enable users to track their daily worship activities, including the Hijri calendar, the time of compulsory prayers, the fulfillment of sunnah prayers, and fasting. Evaluation results showed that the system detected the rak’ah correctly in each cycle with average pressure to the FSR sensor of 81.36. The average time required to connect with a smartphone was 0.862 seconds. It also functions well as a Qibla finder. The black box testing results showed that the device and application performed effectively. It can send the worship data recapitulation to the application using Bluetooth.
Data Mining Earthquake Prediction with Multivariate Adaptive Regression Splines and Peak Ground Acceleration Dadang Priyanto; Bambang Krismono Triwijoyo; Deny Jollyta; Hairani Hairani; Ni Gusti Ayu Dasriani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.3061

Abstract

Earthquake research has not yielded promising results because earthquakes have uncertain data parameters, and one of the methods to overcome the problem of uncertain parameters is the nonparametric method, namely Multivariate Adaptive Regression Splines (MARS). Sumbawa Island is part of the territory of Indonesia and is in the position of three active earth plates, so Sumbawa is prone to earthquake hazards. Therefore, this research is important to do. This study aimed to analyze earthquake hazard prediction on the island of Sumbawa by using the nonparametric MARS and Peak Ground Acceleration (PGA) methods to determine the risk of earthquake hazards. The method used in this study was MARS, which has two completed stages: Forward Stepwise and Backward Stepwise. The results of this study were based on testing and parameter analysis obtained a Mathematical model with 11 basis functions (BF) that contribute to the response variable, namely (BF) 1,2,3,4,5,7,9,11, and the basis functions do not contribute 6, 8, and 10. The predictor variables with the greatest influence were 100% Epicenter Distance and 73.8% Magnitude. The conclusion of this study is based on the highest PGA values in the areas most prone to earthquake hazards in Sumbawa, namely Mapin Kebak, Mapin Rea, Pulau Panjang, and Pulau Saringi.
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 : 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 : 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%.
Evading Antivirus Software Detection Using Python and PowerShell Obfuscation Framework Umar Aditiawarman; Alfian Dody; Teddy Mantoro; Haris Al Qodri Maarif; Anggy Pradiftha
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.3088

Abstract

Avoiding antivirus detection in penetration testing activities is tricky. The simplest, most effective, and most efficient way is to disguise malicious code. However, the obfuscation process will also be very complex and time-consuming if done manually. To solve this problem, many tools or frameworks on the internet can automate the obfuscation process, but how effective are obfuscation tools to avoid antivirus detection are. This study aimed to provide an overview of the effectiveness of the obfus- cation framework in avoiding antivirus detection. This study used experimental design to test and determine the effectiveness of the payload obfuscation process. The first step was generating Python and PowerShell payloads, followed by the obfuscation process. The results showed that by using the right method of obfuscation, malware could become completely undetectable. The automatic obfus- cation process also did not deteriorate the malware’s function. It was proven that the malware could run and open a connection on the server. These findings required more Python obfuscator techniques to determine the effectiveness of the obfuscated payload on the target machines using both static and dynamic analysis
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 : 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 : 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 : 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 : 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.
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 : 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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