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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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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 : 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.
Optimizing the Amount of Production Using Hybrid Fuzzy Logic and Census II Susana Limanto; vincentius Riandaru Prasetyo; Mirella Mercifia
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.v22i3.2938

Abstract

Companies should do planning before the production process. Production planning is expected to avoid excessive or insufficient product stocks that harm the company. This study aims to help a plastic spoon company in Gresik, East Java to determine the optimal amount of production using the Fuzzy method. The input variables used are the amount of demand and supply. However, the amount of demand that fluctuated, especially during the Covid-19 pandemic, made it difficult for the company to estimate the amount of demand in the upcoming production period. Therefore, in this study, the amount of demand is calculated from the results of forecasting with the Cencus II method. The results of the study provide an accuracy of the recommendations for the amount of production of 77% and an accuracy of forecasting results of 82%.
Hate Speech Detection for Banjarese Languages on Instagram Using Machine Learning Methods Muhammad Alkaff; Muhammad Afrizal Miqdad; Muhammad Fachrurrazi; Muhammad Nur Abdi; Ahmad Zainul Abidin; Raisa Amalia
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.v22i3.2939

Abstract

Hate speech refers to verbal expression or communication that aims to provoke or discriminate against individuals. The Ministry of Communication and Information of Indonesia has encountered and dealt with 3,640 cases of hate speech transmitted through digital channels between 2018 and 2021. Particularly in South Kalimantan, hate speech in the local language, Banjarese has become increasingly prevalent in recent years. Surprisingly, there is a lack of research on using machine learning to detect hate speech in the Banjarese language, specifically on Instagram. Therefore, this study aimed to address this gap by constructing a dataset of Banjarese language hate speech and comparing various feature extraction and machine learning models to detect Banjarese language hate speech effectively. Thisresearch used several feature extraction techniques and machine learning methods to detect Banjareselanguage hate speech. The feature extraction methods used were Word N-Gram, Term Frequency- Inverse Document Frequency (TF-IDF), a combination of Word N-Gram and TF-IDF, Word2Vec, and Glove, while the machine learning methods used were Support Vector Machine (SVM), Na¨ıve Bayes, and Decision Tree. The results of this study revealed that the combination of TF-IDF for feature extraction and SVM as the model achieves exceptional performance. The average Recall, Precision, Accuracy, and F1-Score score exceeded 90%, demonstrating the model’s ability to identify Banjarese hate speech accurately.
Comparison of Support Vector Machine Performance with Oversampling and Outlier Handling in Diabetic Disease Detection Classification Firda Yunita Sari; Maharani sukma Kuntari; Hani Khaulasari; Winda Ari Yati
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.v22i3.2979

Abstract

Diabetes mellitus is a disease that attacks chronic metabolism, characterized by the body’s inability to process carbohydrates, fats so that glucose levels are high. Diabetes mellitus is the sixth cause of death in the world. Classifying data about diabetes mellitus makes it easier to predict the disease. As technology develops, diabetes mellitus can be detected using machine learning methods. The method that can be done is the support vector machine. The advantage of SVM is that it is very effective in completing classification, so it can quickly separate each positive and negative point. This study aimed to obtain the best SVM classification model based on accuracy, sensitivity, and precision values in detecting diabetes by adding Synthetic Minority Over-Sampling Technique (SMOTE) and handling outliers. The SMOTE method was applied to handle class imbalance. The Support Vector Machine (SVM) method aimed to produce a function as a dividing line or what can be called a hyperplane that matches all input data with the smallest possible error. The data studied were indications of diabetes, consisting of 8-factor variables and 1 class variable. The test results show that the SVM-SMOTE scenario produces the best accuracy. The SVM SMOTE scenario produced an accuracy value of the RBF kernel of 88% with an error of 12%, and this is obtained from the division of test data and training data of 90:10. This SVM-SMOTE scenario produced a precision value of 0.880 and a sensitivity value of 0.880. The research results showed that factor classification was more accurate if it is carried out using the support vector machine (SVM) method with imbalance data handling (SMOTE), and it can be concluded that the distribution of test data and training data influences a test scenario.
K Value Effect on Accuracy Using the K-NN for Heart Failure Dataset Alya Masitha; Muhammad Kunta Biddinika; Herman Herman
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.v22i3.2984

Abstract

Heart failure is included in the category of cardiovascular disease. Heart disease is not easy to detect, and its detection needs to be done by experienced and skilled medical professionals. Most patients with heart failure require hospitalization. Common symptoms of heart disease, such as chest pain and high or low blood pressure, vary from person to person. This study aims to find the most optimal k value based on the accuracy obtained based on calculations by testing different k values, namely 1, 3, 5, 7, and 9. After getting the results of the accuracy of the five k values, compare which accuracy has the highest value, best for K-Nearest Neighbor (K-NN) models. The classification process uses the K-NN algorithm. This algorithm is quite easy to use because some parameters work using distance metrics and k values. Therefore, the value of k in the K-NN algorithm greatly affects the accuracy that will be produced. In the results of this study, the accuracy obtained was k = 7 and k = 9, which are the most optimal results because they have the highest accuracy compared to other k values, with an accuracy of 88%. The expected benefit of this research is that it can make a scientific contribution to research in the field of machine learning classification, especially in predicting heart failure
Mobile Forensic for Body Shaming Investigation Using Association of Chief Police Officers Framework Yana Safitri; Imam Riadi; Sunardi Sunardi
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.v22i3.2987

Abstract

Body shaming is the act of making fun of or embarrassing someone because of their appearance, including the shape or form of their body. Body shaming can occur directly or indirectly. MOBILEdit Forensic Express and Forensic ToolKit (FTK) Imager are used to perform testing of evidence gathered through Chat, User ID, Data Deletion, and Groups based on digital data obtained on IMO Messenger tokens on Android smartphones. This study aimed to collect evidence of conversations in body shaming cases using the Association of Chiefs of Police (ACPO) framework with MOBILedit Forensic Express and FTK Imager as a tool for testing. Based on the research findings, MOBILedit Forensic Express got an extraction yield of 0.75%. In contrast, using the FTK Imager got an extraction yield of 0.25%. The ACPO framework can be used to investigate cases of body shaming using mobile forensics tools so that the extraction results can be found. The results of this study contributed to forensic mobile knowledge in cases of body shaming or cyberbullying ACPO framework as well as for the investigators.
OWASP Framework-based Network Forensics to Analyze the SQLi Attacks on Web Servers Imam Riadi; Abdul Fadlil; Muhammad Amirul Mu'min
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.v22i3.3018

Abstract

One of dangerous vulnerabilities that attack the web is SQLi. With this vulnerability, someone can obtain user data information, then change and delete that data. The solution to this attack problem is that the design website must improve security by paying attention to input validation and installing a firewall. This study's objective is to use network forensic tools to examine the designlink website's security against SQLi attacks, namely Whois, SSL Scan, Nmap, OWASP Zap, and SQL Map. OWASP is the framework that is employed; it is utilized for web security testing. According to the research findings, there are 14 vulnerabilities in the design website, with five medium level, seven low level, and two informational level. When using SQL commands with the SQL Map tool to get username and password information on its web server design. The OWASP framework may be used to verify the security of websites against SQLi attacks using network forensic tools, according to the study's findings. So that information about the vulnerabilities found on the website can be provided. The results of this study contribute to forensic network knowledge against SQLi attacks using the OWASP framework as well as for parties involved in website security.
Social Media Engagement and Student’s Intention in Indonesian Higher Education Using Unified Theory of Acceptance and Use of Technology Matrissya Hermita; Budi Hermana; Suryadi Harmanto; Adang Suhendra; Munawir Pasaribu
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.v22i3.3019

Abstract

Understanding the motivations behind the use of social media in higher education is crucial for assessing its potential benefits and challenges. However, examining the contribution of social media on collaborative learning within the context of Indonesian universities holds significance due to the country’s growing digital landscape and increasing adoption of social media platforms. This research aimed to analyze proposed collaborative learning models involving social media use among Indonesian University students. The proposed models are constructed based on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The quantitative method used was analyzing primary data from 143 private university students in Indonesia. Data were collected using a 5-point Likert scale self-reported questionnaire of Internet Anxiety, Habit, and Performance Expectancy and Behavior Intention as well as Social Media Engagement to mediate collaborative learning. The result of this study was that Social Media Engagement and Behavior Intention significantly influence Performance Expectancy and Habit. There was also a significant influence of Internet Anxiety on behavioral intention. Thus, Collaborative Learning is significantly influenced by Social Media Engagement. These results provided insight into developing effective strategies for integrating social media into higher education.
Performance Prediction of Airport Traffic Using LSTM and CNN-LSTM Models Willy Riyadi; Jasmir Jasmir
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.v22i3.3032

Abstract

During the COVID-19 pandemic, airports faced a significant drop in passenger numbers, impacting the vital hub of the aircraft transportation industry. This study aimed to evaluate whether Long Short-Term Memory Network (LSTM) and Convolutional Neural Network - Long Short-Term Memory Network (CNN-LSTM) offer more accurate predictions for airport traffic during the COVID-19 pandemic from March to December 2020. The studies involved data filtering, applying min-max scaling, and dividing the dataset into 80% training and 20% testing sets. Parameter adjustment was performed with different optimizers such as RMSProp, Stochastic Gradient Descent (SGD), Adam, Nadam, and Adamax. Performance evaluation uses metrics that include Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-squared (R2). The best LSTM model achieved an impressive MAPE score of 0.0932, while the CNN-LSTM model had a slightly higher score of 0.0960. In particular, the inclusion of a balanced data set representing a percentage of the base period for each airport had a significant impact on improving prediction accuracy. This research contributes to providing stakeholders with valuable insights into the effectiveness of predicting airport traffic patterns during these unprecedented times.
Digital Forensic Analysis of WhatsApp Business Applications on Android-Based Smartphones Using NIST William Barkem; Jeckson Sidabutar
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.v22i3.3033

Abstract

WhatsApp Business is an Android application that can be downloaded on Playstore to serve small business owners. This provides an opportunity for criminals to take advantage of the app’s features. These crimes can take the form of fraud, misdirection, and misuse of applications, so digital forensics is necessary because there has never been any research that has done this. This study aims to obtain digital evidence and is carried out on Android smartphones with the WhatsApp Business application installed with four scenarios tested. This study uses the NIST SP 800-101 Rev 1 guidelines with four stages: preservation, acquisition, inspection & analysis, and reporting. The forensic method used is static forensics using the MOBILedit forensic express forensic tools and SysTools SQLite Viewer. The results of this study in scenario 1, by not deleting, get a 100% percentage. Then, scenario 2, namely direct write-off, gets a percentage of 71%. Furthermore, scenario 3, namely uninstalling the application, does not get digital evidence, and scenario 4, namely deleting data through the application manager, also does not get any evidence. The contribution of this research is expected to be a reference in uncovering cases in the WhatsApp Business application with digital forensics.

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