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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 20 Documents
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Search results for , issue "Vol. 23 No. 2 (2024)" : 20 Documents clear
Educational Data Mining: Multiple Choice Question Classification in Vocational School Sucipto Sucipto; Didik Dwi Prasetya; Triyanna Widiyaningtyas
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3499

Abstract

Data mining on student learning outcomes in the education sector can overcome this problem. This research aimed to provide a solution for selecting quality multiple choice questions (MCQ) using the results of students’ mid-semester exams in vocational high schools using a Data Mining approach. The research method used was the Cross-Industry Standard Process for Machine Learning (CRISP-ML) model. Steps to assess the accuracy of analyzing the difficulty level of questions based on student profile data and midterm test results. The data used in this research were the findings of basic computer tests on mid-term exams in mathematics disciplines at vocational high schools. This research used several classification algorithms, including SVM, Naive Bayes, Random Forest, Decision Three, Linear Regression, and KNN. The results of evaluating the classification
Stroke Prediction with Enhanced Gradient Boosting Classifier and Strategic Hyperparameter Dela Ananda Setyarini; Agnes Ayu Maharani Dyah Gayatri; Christian Sri Kusuma Aditya; Didih Rizki Chandranegara
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3555

Abstract

A stroke is a medical condition that occurs when the blood supply to the brain is interrupted. Stroke can cause damage to the brain that can potentially affect a person's function and ability to move, speak, think, and feel normally. The effect of stroke on health emphasizes the importance of stroke detection, so an effective model is needed in predicting stroke. This research aimed to find a new approach that can improve the performance of stroke prediction by comparing four derivative algorithms from Gradient Boosting by adding hyperparameters tuning. The addition of hyperparameters was used to find the best combination of parameter values that can improve the model accuracy. The methods used in this research were Categorical Boosting, Histogram Gradient Boosting, Light Gradient Boosting, and Extreme Gradient Boosting. The research involved retrieving, cleaning, and analyzing data and then the model performance was evaluated with a confusion matrix and execution time. The results obtained were Light Gradient Boosting with Hyperparameter RandomSearchCV achieved the highest accuracy at 95% among the algorithms tested, while also being the fastest in execution. The contribution of this research to the medical field can help doctors and patients predict the occurrence of stroke early and reduce serious consequences.
Optimization of SVM and Gradient Boosting Models Using GridSearchCV in Detecting Fake Job Postings Rofik Rofik; Roshan Aland Hakim; Jumanto Unjung; Budi Prasetiyo; Much Aziz Muslim
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3566

Abstract

Online job searching is one of the most efficient ways to do this, and it is widely used by people worldwide because of the automated process of transferring job recruitment information. The easy and fast process of transferring information in job recruitment has led to the rise of fake job vacancy fraud. Several studies have been conducted to predict fake job vacancies, focusing on improving accuracy. However, the main problem in prediction is choosing the wrong parameters so that the classification algorithm does not work optimally. This research aimed to increase the accuracy of fake job vacancy predictions by tuning parameters using GridSearchCV. The research method used was SVM and Gradient Boosting with parameter adjustments to improve the parameter combination and align it with the predicted model characteristics. The research process was divided into preprocessing, feature extraction, data separation, and modeling stages. The model was tested using the EMSCAD dataset. This research showed that the SVM algorithm can achieve the highest accuracy of 98.88%, while gradient enhancement produces an accuracy of 98.08%. This research showed that optimizing the SVM model with GridSearchCV can increase accuracy in predicting fake job recruitment.
Comparison of DenseNet-121 and MobileNet for Coral Reef Classification Heru Pramono Hadi; Eko Hari Rachmawanto; Rabei Raad Ali
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3683

Abstract

Coral reefs are a type of marine organism that has beauty and benefits for other sea creatures’ ecosystems. However, despite its beauty and usefulness, coral reefs are vulnerable to damage such as coral bleaching, which can impact other coral reef ecosystems. This research aims to classify digital images of healthy, bleached, and dead coral reefs. This research method is DenseNet-121 and MobileNet is based on Convolutional Neural Networks. This research uses a dataset from 1582 coral reef image data with three main classes: 720 were bleached, 150 were dead, and 712 were healthy. The testing process is carried out using several forms of split datasets, namely 60:10:30, 50:10:40, and 70:10:20. The test results obtained with a data sharing percentage of 60:10:30 show that MobileNet architecture achieved 88.00% accuracy, and DenseNet-121 achieved 91.57% accuracy. Using a data split percentage of 50:10:40, MobileNet achieved 84.51% accuracy, and DenseNet- 121 achieved 90.52% accuracy. Meanwhile, with a data separation percentage of 70:10:20, MobileNet achieved 85.48% accuracy, and DenseNet-121 achieved 92.74% accuracy.
Improving Performance Convolutional Neural Networks Using Modified Pooling Function Achmad Lukman; Wahju Tjahjo Saputro; Erni Seniwati
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3763

Abstract

The Visual Geometry Group-16 (VGG16) network architecture, as part of the development of convolutional neural networks, has been popular among researchers in solving classification tasks, so in this paper, we investigated the number of layers to find better performance. In addition, we also proposed two pooling function techniques inspired by existing research on mixed pooling functions, namely Qmax and Qavg. The purpose of the research was to see the advantages of our method; we conducted several test scenarios, including comparing several modified network configurations based on VGG16 as a baseline and involving our pooling technique and existing pooling functions. Then, the results of the first scenario, we selected a network that can adapt well to our pooling technique, whichwas then carried out several tests involving the Cifar10, Cifar100, TinyImageNet, and Street View House Numbers (SVHN) datasets as benchmarks. In addition, we were also involved in several existing methods. The experiment results showed that Net-E has the highest performance, with 93.90% accuracy for Cifar10, 71.17% for Cifar100, and 52.84% for TinyImageNet. Still, the accuracy was low when the SVHN dataset was used. In addition, in comparison tests with several optimization algorithms using the Qavg pooling function, it can be seen that the best accuracy results lie in the SGD optimization algorithm, with 89.76% for Cifar10 and 89.06% for Cifar100.
Enhancing Accuracy in Stock Price Prediction: The Power of Optimization Algorithms Vivi Aida Fitria; Lilis Widayanti
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3785

Abstract

The purpose of this research was to improve the accuracy of stock price prediction by implementing optimization algorithms on forecasting methods, in this case, the exponential smoothing method. This research implemented the Particle Swarm Optimization (PSO) and Bat Algorithm metaheuristic optimization algorithms to determine the single-exponential smoothing method’s smoothing parameters. Before implementing the optimization algorithm, the way to determine the smoothing parameters was by trial-and-error method, which is considered less effective. Therefore, the novelty of this research is tuning the parameters of the exponential smoothing method using a comparison of two metaheuristic algorithms, namely the particle swarm optimization algorithm compared to the bat algorithm. The Single Exponential Smoothing method with PSO and Bat algorithms was proven to improve accuracy. The alpha parameter found by the PSO algorithm is 0.9346, and the bat algorithm is 0.936465. With a MAPE of 1.0311%, it was better than the MAPE generated in the Single Exponential smoothing method by trial and error of 1.0316%. This research contributes to providing insight that in a highly sensitive stock prediction situation, metaheuristic algorithms can be used to create more accurate and efficient prediction results.
Enhancing Predictive Models: An In-depth Analysis of Feature Selection Techniques Coupled with Boosting Algorithms Neny Sulistianingsih; Galih Hendro Martono
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3788

Abstract

This research addresses the critical need to enhance predictive models for fetal health classification using Cardiotocography (CTG) data. The literature review underscores challenges in imbalanced labels, feature selection, and efficient data handling. This paper aims to enhance predictive models for fetal health classification using Cardiotocography (CTG) data by addressing challenges related to imbalanced labels, feature selection, and efficient data handling. The study uses Recursive Feature Elimination (RFE) and boosting algorithms (XGBoost, AdaBoost, LightGBM, CATBoost, and Histogram-Based Boosting) to refine model performance. The results reveal notable variations in precision, Recall, F1-Score, accuracy, and AUC across different algorithms and RFE applications. Notably, Random Forest with XGBoost exhibits superior performance in precision (0.940), Recall (0.890), F1-Score (0.920), accuracy (0.950), and AUC (0.960). Conversely, Logistic Regression with AdaBoost demonstrates lower performance. The absence of RFE also impacts model effectiveness. In conclusion, the study successfully employs RFE and boosting algorithms to enhance fetal health classification models, contributing valuable insights for improved prenatal diagnosis.
Detecting Hidden Illegal Online Gambling on .go.id Domains Using Web Scraping Algorithms Muchlis Nurseno; Umar Aditiawarman; Haris Al Qodri Maarif; Teddy Mantoro
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3824

Abstract

The profitable gambling business has encouraged operators to promote online gambling using black hat SEO by targeting official sites such as government sites. Operators have used various techniques to prevent search engines from distinguishing between genuine and illegal content. This research aims to determine whether websites with the go.id domain have been compromised with hidden URLs affiliated with online gambling sites. The method used in this research is an experiment using a FOFA.info dataset containing a complete list of 450,000 .go.id domains. A web scraping algorithm developed in Python was used to identify potentially compromised websites from the targeted listby analyzing gambling-related keywords in local languages, such as ’slot,’ ’judi,’ ’gacor,’ and ’togel'. The results showed that 958 of the 1,482 suspected.go.id sites had been compromised with an accuracy rate of 99.1%. This implies that security gaps have been exploited by illegal online gambling sites, posing a reputational risk to the government. Lastly, the scrapping algorithm tool developed in this research can detect illegal online gambling hidden in domains such as .ac.id, .or.id, .sch.id, and help authorities take necessary action.
DenseNet Architecture for Efficient and Accurate Recognition of Javanese Script Hanacaraka Character Egi Dio Bagus Sudewo; Muhammad Kunta Biddinika; Abdul Fadlil
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3855

Abstract

This study introduced a specifically optimized DenseNet architecture for recognizing Javanese Hanacaraka characters, focusing on enhancing efficiency and accuracy. The research aimed to preserve and celebrate Java’s rich cultural heritage and historical significance through the development of precise character recognition technology. The method used advanced techniques within convolutional neural networks (CNN) to integrate feature extraction across densely connected layers efficiently. The result of this study was that the developed model achieved a training accuracy of 100% and a validation accuracy of approximately 99.50% after 30 training epochs. Furthermore, when tested on previously unseen datasets, the model exhibited exceptional accuracy, precision, recall, and F1-score, reaching 100%. These findings underscored the remarkable capability of DenseNet architecture in character recognition, even across novel datasets, suggesting significant potential for automating Javanese Hanacaraka text processing across various applications, ranging from text recognition to digital archiving. The conclusion drawn from this study suggests that optimizing DenseNet architecture can be a significant step in preserving and developing character recognition technology for Javanese
Unsafe Conditions Identification Using Social Networks in Power Plant Safety Reports Annisa’ul Mubarokah; Rita Ambarwati; Dedy Dedy; Mashhura Toirхonovna Alimova
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3883

Abstract

Power plants in Indonesia grapple with significant challenges in managing occupational health and safety. Power generation companies urgently need to reduce workplace accidents every year and need an application for reporting every potential workplace hazard. The huge reporting data in applications such as IZAT requires thorough analysis to find out the pattern and distribution. This research aims to facilitate the company in hazard mitigation by identifying reported unsafe conditions and building a semantic association network to understand the nature of unsafe conditions between Paiton and Indramayu generating units. The research method uses social network analysis, which is carried out by preprocessing the data using programming to remove noise and then converting the data into a readable format. Then, semantic relationships between words were analyzed, and the data was visualized using the ForceAtlas2 algorithm. The findings revealed a different focus between the two units, where 6.597 reports from the Paiton generating unit mainly highlighted team response and accident-prone workplace conditions, while 5.840 reports from the Indramayu unit emphasized specific conditions, locations, and equipment that pose accident risks

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