cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Hairani
Contact Email
matrik@universitasbumigora.ac.id
Phone
+6285933083240
Journal Mail Official
matrik@universitasbumigora.ac.id
Editorial Address
Jl. Ismail Marzuki-Cilinaya-Cakranegara-Mataram 83127
Location
Kota mataram,
Nusa tenggara barat
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 420 Documents
 5   6   7   8   9 
Cluster Validity for Optimizing Classification Model: Davies Bouldin Index – Random Forest Algorithm Prihandoko Prihandoko; Deny Jollyta; Gusrianty Gusrianty; Muhammad Siddik; Johan Johan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4043

Abstract

Several factors impact pregnant women’s health and mortality rates. The symptoms of disease in pregnant women are often similar. This makes it difficult to evaluate which factors contribute to a low, medium, or high risk of mortality among pregnant women. The purpose of this research is to generate classification rules for maternal health risk using optimal clusters. The optimal cluster is obtained from the process carried out by the validity cluster. The methods used are K-Means clustering, Davies Bouldin Index (DBI), and the Random Forest algorithm. These methods build optimum clusters from a set of k-tests to produce the best classification. Optimal clusters comprising cluster members withstrong similarities are high-dimensional data. Therefore, the Principal Component Analysis (PCA) technique is required to evaluate attribute value. The result of the research is that the best classification rule was obtained from k-tests = 22 on the 20th cluster, which has an accuracy of 97% to low, mid, and high risk. The novelty lies in using DBI for data that the Random Forest will classify. According to the research findings, the classification rules created through optimal clusters are 9.7% better than without the clustering process. This demonstrates that optimizing the data group has implications for enhancing the classification algorithm’s performance.
Optimizing Currency Circulation Forecasts in Indonesia: A Hybrid Prophet- Long Short Term Memory Model with Hyperparameter Tuning Vivin Nur Aziza; Utami Dyah Syafitri; Anwar Fitrianto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4052

Abstract

The core problem for decision-makers lies in selecting an effective forecasting method, particularly when faced with the challenges of nonlinearity and nonstationarity in time series data. To address this, hybrid models are increasingly employed to enhance forecasting accuracy. In Indonesia and other Muslim countries, monthly economic and business time series data often include trends, seasonality, and calendar variations. This study compares the performance of the hybrid Prophet-Long Short-Term Memory (LSTM) model with their individual counterparts to forecast such patterned time series. The aim is to identify the best model through a hybrid approach for forecasting time series data exhibitingtrend, seasonality, and calendar variations, using the real-life case of currency circulation in South Sulawesi. The goodness of the models is evaluated using the smallest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. The results indicate that the hybrid Prophet- LSTM model demonstrates superior accuracy, especially for predicting currency outflow, with lower MAPE and RMSE values than standalone models. The LSTM model shows excellent performance for currency inflow, while the Prophet model lags in inflow and outflow accuracy. This insight is valuable for Bank Indonesia’s strategic planning, aiding in better cash flow prediction and currency stock management.
Determining Toddler's Nutritional Status with Machine Learning Classification Analysis Approach Taufik Hidayat; Mohammad Ridwan; Muhamad Fajrul Iqbal; Sukisno Sukisno; Robby Rizky; William Eric Manongga
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4092

Abstract

The nutritional status of toddlers is a common issue many countries face worldwide. Various facts indicate that malnutrition is a primary focus for many researchers. Several efforts have been made to address this problem, including developing analytical models for identification, classification, and prediction. This study aims to evaluate the nutritional status of children by utilizing a classification analysis approach using Machine Learning. This research aims to improve the accuracy of the classification system and facilitate better decision-making in stunted toddlers, which is a priority, especially in the health sector. The Machine Learning classification analysis process will later utilize the performance of the Naive Bayes algorithm, the Support Vector Machine algorithm, and the Multilayer Perceptron algorithm. ML performance can be optimized using gridsearchCV to produce optimal classification analysis patterns. The data set of this study uses 6812 toddler data sourced from the Health Center at the Tangerang Regency Health Office. Based on the research presented, Machine Learning performance in analyzing nutritional status classification provides maximum results. The results are reported based on a precision level with an accuracy of 88%. The results of this analysis can also present a classification of nutritional status based on knowledge. This study can contribute to and update the analysis model in determining nutritional status. The results of this study can also provide benefits in handling nutritional status problems that occur in children.
Population Prediction Using Multiple Regression and Geometry Models Based on Demographic Data M Safii; Rika Setiana
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4121

Abstract

Population growth is an important issue because it significantly impacts a country’s growth and development. Large population growth can impact potential resources that drive the pace of the economy and national development. On the other hand, it can also be a problem of poverty, hunger, unemployment, education, health, and others. The government needs to control population growth to balance it with good population quality. Data sourced from the Population and Civil Registration Office of Simalungun Regency, Tanah Java sub-district has a high population and continues to increase every year. The impact of the population increase is that it affects the population’s welfare, most of whom work as laborers and farmers. To overcome this problem, it is necessary to predict the number of people in the future so that the government can make the right decisions and policies in controlling the population. This study aims to make predictions using two models, namely Multiple Linear Regression, to find linear equations and Geometry Models for population growth projections. This study utilizes multiple regression analysis and geometric models using three independent variables, namely birth rate (X1), migration rate (X2), and death rate (X3), as well as one bound variable, population number (Y). This study’s results show that the Tanah Java sub-district population is expected to increase in the next five years (2024-2028). Predictions show that by 2024, the population is expected to reach 61178 people from 59589 in 2023. Based on the results of the study, the conclusion of this study it can be used as a guide for the authorities in planning strategies and resource allocation and making a significant contribution in estimating population development in the Java region so that there will be no population explosion in the future so that it does not have a negative impact.
Integration of Image Enhancement Technique with DenseNet201 Architecture for Identifying Grapevine Leaf Disease Rudi Kurniawan; Lukman Sunardi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4137

Abstract

Early detection of grapevine leaf diseases is crucial for maintaining both the quality and quantity of grape production. Manual identification methods are often ineffective and prone to errors. This research aims to develop a precise and efficient method for classifying grapevine leaf diseases using Contrast Limited Adaptive Histogram Equalization (CLAHE) and the DenseNet201 Deep Convolutional Neural Network (DCNN) architecture. The research methodology involves collecting a dataset of grapevine leaf images affected by black measles, black rot, and leaf blight alongside healthy leaves. Following this, preprocessing is conducted using the CLAHE technique to enhance image quality. Then, the processed data is trained with DenseNet201. Evaluation results indicate that the proposed model achieves an overall accuracy of 99.61%, with high precision, recall, and F1-score values across all disease classes. Receiver Operating Characteristic (ROC) curve analysis shows an Area Under the Curve (AUC) of 1.00 for each class, reflecting excellent discriminatory ability. The loss and accuracy curves illustrate consistent model performance without signs of overfitting. Additionally, the confusion matrix confirms very low classification error rates. The developed model is effective and reliable for identifying grapevine leaf diseases. Future research will focus on enhancing the dataset by incorporating more data optimizing hyperparameters, and developing field applications for real-time use.
Segmentation and Classification of Breast Cancer Histopathological Image Utilizing U-Net and Transfer Learning ResNet50 Nella Rosa Sudianjaya; Chastine Fatichah
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4186

Abstract

Breast cancer is the most common type of cancer among various types of cancer. Approximately 1 in 8 women in the United States die from breast cancer. Early screening and accurate diagnosis are essential for prevention and accelerated treatment intervention. Several artificial intelligence methods have emerged to develop effective segmentation, detection, and classification to determine cancer types. Although there has been progress in automated algorithms for breast cancer histopathology image analysis, many of these approaches still face several challenges. This study aims to address the challenges in breast cancer image analysis. This research method uses the development of the U-Net architecture combined with Transfer Learning using ResNet50. The encoder path aims to improve the model’s sensitivity in the segmentation and classification of cancer areas by utilizing deep hierarchical features extracted by ResNet50. In addition, data augmentation techniques are used to create a diverse and comprehensive training dataset, which improves the model’s ability to distinguish between different tissue types and cancer areas. The results of this study are U-Net and ResNet50, which show an average IoU of 0.482 and a Dice coefficient of 0.916. This study concludes that integrating UNet with Transfer Learning ResNet50 improves the segmentation and classification accuracy in breast cancer histopathology images and overcomes the problem of high computational requirements. This approach shows significant potential for improvement in early breast cancer detection and diagnosis.
Development of Smart Charity Box Monitoring Robot in Mosque with Internet of Things and Firebase using Raspberry Pi Nenny Anggraini; Zulkifli Zulkifli; Nashrul Hakiem
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4209

Abstract

Mosques are the center of Muslim communities’ spiritual and communal life, thus requiring effective financial management. The purpose of this study was to develop a smart donation box robot that utilizes Internet of Things technology to address efficiency and increase transparency in managing donations. The methodology in this study used a prototyping method consisting of Rapid Planning, Rapid Modeling, Construction, and Evaluation stages, which aimed to develop a functional prototype quickly. The results showed that the smart donation box robot detected and counted banknote denominations with varying degrees of success, achieving a detection success rate of 100% for all tested denominations at an optimal sensor distance of 1 cm. However, the detection rate dropped to 42.86% at 0.5 cm and 28.57% at 1.5 cm, highlighting the significant impact of sensor placement on performance. Coin detection was performed accurately, correctly identifying and sorting denominations without error. This enabled real-time financial monitoring via the Telegram application, significantly increasing transparency for mosque administrators and congregants. The conclusion of this study confirms that IoT technology can substantially improve mosque donation management by automating the collection process and providing real-time
Higher Education Institution Clustering Based on Key Performance Indicators using Quartile Binning Method Virdiana Sriviana Fatmawaty; Imam Riadi; Herman Herman
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4244

Abstract

The Key Performance Indicators of Higher Education Institutions (KPI-HEIs) are a crucial component of the internal quality assurance system that supports the achievement of excellence status for higher education institutions. Many private higher education institutions face challenges in independently analyzing the key performance assessment indicators of Private Higher Education Institutions (PHEIs), which often require complex methodological approaches and specialized expertise. The research aims to cluster PHEIs based on achieving key performance indicators (KPIs). Research the method used descriptive statistical methods and quartile binning techniques to analyze and cluster data based on the achievement of KPI-HEIs. The research results, based on descriptive statistical analysis, identified outliers in eight KPI-HEIs, along with a dominance of zero values in KPI 1, KPI 2, KPI 6, KPI 7, and KPI 8, with the highest proportion reaching 90.91% for KPI 8. Based on these findings, clustering using the quartile binning method resulted in four clusters of PHEIs based on KPIs: Cluster 1 consists of 19 institutions with poor, Cluster 2 consists of 14 institutions with fair achievement, Cluster 3 consists of 16 institutions with good achievement, and Cluster 4 consists of 17 institutions with very good achievement, which can serve as examples for other institutions. This research concludes that the quartile binning method successfully categorized private higher education institutions based on their achievement of KPIs into four clusters: poor, fair, good, and very good. This outcome demonstrates the effectiveness of the method in understanding the performance distribution of these institutions. It provides valuable insights for stakeholders to develop data-driven strategies aimed at enhancing educational quality.
Optimizing Hotel Room Occupancy Prediction Using an Enhanced Linear Regression Algorithms Dewa Ayu Kadek Pramita; Ni Wayan Sumartini Saraswati; I Putu Dedy Sandana; Poria Pirozmand; I Kadek Agus Bisena
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4254

Abstract

Predicting the correct hotel occupancy rate is important in the tourism industry because it has a major impact on the level of revenue and maintenance of a hotel’s reputation. With accurate predictions, hotel performance can be optimized regarding resources, staff, and hotel facilities. The linear regression method has been proven to perform causal predictions well. However, this method has several weaknesses, such as the function of the relationship between dependent variables and independent variables that are not linear, overfitting, or underfitting in building the prediction model. The purpose of this study was to optimize the linear regression model in predicting hotel occupancy rates. The method used in this study was a Linear Regression method optimized with Polynomial Regression and regularization techniques to reduce overfitting using Ridge Regression and Lasso Regression. The results of the model evaluation showed that linear regression, which was optimized with Polynomial Regression and Ridge Regression in the model with the historical data of the Adiwana Unagi occupancy rate, historical data of the hotel occupancy rate in Bali, and the number of tourist visits in Bali, gave the best performance, with a mean absolute error score of 1.0648, root mean square error of 2.1036, and R-squared of 0.9953. The conclusion of this research was optimization using polynomial regression, achieving the best evaluation scores, where the prediction model performance indicates that variable X7 (tourist visit numbers) strongly influences the prediction of the occupancy rate.
Multi-Algorithm Approach to Enhancing Social Assistance Efficiency Through Accurate Poverty Classification Christofer Satria; Peter Wijaya Sugijanto; Anthony Anggrawan; I Nyoman Yoga Sumadewa; Aprilia Dwi Dayani; Rini Anggriani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4275

Abstract

The determination of poverty status in Lombok Utara district depends on criteria such as income, access to health and education services, and housing conditions. These factors are crucial for assessing the level of community welfare and guiding the allocation of social assistance by the district government. The purpose of this study is to address the gap by utilizing advanced data mining techniques to improve the accuracy of poverty status classification in North Lombok, thereby informing more effective social assistance policies. The method used in this research is the Random Forest (RF), K-Nearest Neighbor (KNN) and Naïve Bayes with split data 80% data training and 20% data testing. The finding indicated that the machine learning model the RF algorithm, which achieved an accuracy rate of 82.56%, proved to play an important role in this process by effectively distinguishing between different categories of poverty based on these criteria. In comparison, the KNN algorithm achieved an accuracy of 70.94% and the Naïve Bayes model achieved an accuracy of 53.47%. It means that the machine learning model using the RF algorithm has more accurate accuracy than the KNN and Naïve Bayes algorithm in predicting or recommending Recipients of Social Assistance from the District Government. The implication is that RF machine learning can help the role of social service officers in predicting the economic status of the community. The high accuracy of the RF algorithm enhances its role in informing targeted policy decisions and optimizing the effectiveness of social assistance programs. Nonetheless, continuous improvement is essential to refine the model's predictive capabilities and ensure the accuracy and reliability of poverty assessments. These continuous improvements are essential to effectively alleviate poverty and break the cycle of socio-economic disparities in the region.

Page 7 of 42 | Total Record : 420

 5   6   7   8   9 

Filter by Year

2015 2026


Reset
Filter By Issues
All Issue Vol. 25 No. 2 (2026) Vol. 25 No. 1 (2025) Vol. 24 No. 3 (2025) Vol. 24 No. 2 (2025) Vol. 24 No. 1 (2024) Vol. 23 No. 3 (2024) Vol. 23 No. 2 (2024) Vol. 23 No. 1 (2023) Vol. 22 No. 3 (2023) Vol. 22 No. 2 (2023) Vol. 22 No. 1 (2022) Vol. 21 No. 3 (2022) Vol. 21 No. 2 (2022) Vol. 21 No. 1 (2021) Vol. 20 No. 2 (2021) Vol. 20 No. 1 (2020) Vol. 19 No. 2 (2020) Vol. 19 No. 1 (2019) Vol. 18 No. 2 (2019) Vol. 18 No. 1 (2018) Vol. 17 No. 2 (2018) Vol. 17 No. 1 (2017) Vol. 16 No. 2 (2017) Vol. 16 No. 1 (2016) Vol. 15 No. 2 (2016) Vol. 15 No. 1 (2015) More Issue