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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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Data Exfiltration Anomaly Detection on Enterprise Networks using Deep Packet Inspection Jelita Asian; Dimas Erlangga; Media Ayu
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.v22i3.3089

Abstract

Advanced persistent threats (APT) are threat actors with the advanced Technique, Tactic and Procedure (TTP) to gain covert control of the computer network for a long period of time. These threat actors are the highest cyber attack risk factor for enterprise companies and governments. A successful attack by the APT threat Actors has the capabilities to do physical damage. APT groups are typically state-sponsored and are considered the most effective and skilled cyber attackers. The final goal for the APT Attack is to exfiltrate victims data or sabotage system. This aim of this research is to exercise multiple Machine Learning Approach such as k-Nearest Neighbors and H20 Deep Learning Model and also employ Deep Packet Inspection on enterprise network traffic dataset in order to identify suitable approaches to detect data exfiltration by APT threat Actors. This study shows that combining machine learning techniques with Deep Packet Inspection significantly improves the detection of data exfiltration attempts by Advanced Persistent Threat (APT) actors. The findings suggest that this approach can enhance anomaly detection systems, bolstering the cybersecurity defenses of enterprises. Consequently, the research implications could lead to developing more robust strategies against sophisticated and covert cyber threats posed by APTs.
Comparing Long Short-Term Memory and Random Forest Accuracy for Bitcoin Price Forecasting Munirul Ula; Veri Ilhadi; Zailani Mohamed Sidek
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.v23i2.3267

Abstract

Bitcoin’s daily value fluctuations are very dynamic. Understanding its rapid and intricate price movements demands advanced techniques for processing complex data. This research aims to compare the accuracy of two machine learning methods, Random Forest (RF) and Long Short-Term Memory (LSTM), in predicting Bitcoin price. This research employs RF and LSTM algorithms to forecast Bitcoin prices using a two-year Yahoo Finance dataset. The evaluation metrics used were accuracy based on Mean Absolute Percentage Error (MAPE) and computational power (CPU-Z). As a result of this research, the LSTM model demonstrates higher accuracy compared to the RF model. MAPE reveals LSTM’s precision of 99.8% and RF’s accuracy of 90.1%. Regarding computational time and resources, RF shows slightly better performance than LSTM. The visual comparison further emphasizes LSTM’s better performance in predicting Bitcoin prices, highlighting its potential for informed decision-making in cryptocurrency trading. This research contributes valuable insights into the effectiveness, strengths, and weaknesses of LSTM and RF models in predicting cryptocurrency trends.
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 : 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.
Sentiment Analysis of e-Government Service Using the Naive Bayes Algorithm Winny purbaratri; Hindriyanto Dwi Purnomo; Danny Manongga; Iwan Setyawan; Hendry Hendry
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.v23i2.3272

Abstract

E-Government which involves the use of communication and information technology to provide Public services have three obstacles. One of these obstacles is the implementation of e-Government by autonomous regional governments is still carried out individually. Apart from that, implementing the website regions are also not supported by efficient management systems and work processes, this is partly the case This is largely due to the lack of preparation of regulations, procedures and limited resources man. Apart from that, many local governments consider implementing e-Government only involves developing local government websites. More precisely, the implementation of e-Government It is only limited to the maturity stage and ignores the three other important stages that need to be completed. The aim of this research is to determine the level of public approval for government application services. This research uses the Naive Bayes Classifier approach as the methodology. The data sources used in this research consist of user reviews and comments obtained from Google Play Store. The results of this investigation produce a level of precision The highest is achieving a score of 83%. Additionally it shows an accuracy rate of 83%,levelcompleteness is 100%, and F-measure is 90.7%.
Development of the Multi-Channel Clustering Hierarchy Method for Increasing Performance in Wireless Sensor Network Robby Rizky; Zaenal Hakim; Sri Setiyowati; Susilawati susilawati; Ayu Mira Yunita
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3348

Abstract

Wireless Sensor Networks are technologies that make it possible to observe phenomena. The problem is data delays in covering the distance from the origin to the destination. Packet Loss is a condition that shows the number of lost packets and the total queue length caused by data processing time. This research aims to develop a cluster-based protocol. This research uses a multichannel hierarchical clustering method and adds odd-even by dividing the network into several channels and forming a cluster head for each channel. The results of this research are Channel 1 with a throughput value of 1.88, channel 2 with a throughput value of 21.68, channel 3 with a throughput value of 1.62, and channel 4 with a throughput value of 42.44. The conclusion of this study is that the throughput results are smaller compared to the Multi-Channel Clustering H ierarchy method, because not all nodes are active
Regional Clustering Based on Types of Non-Communicable Diseases Using k-Means Algorithm Tb Ai Munandar; Ajif Yunizar Yusuf Pratama
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.v23i2.3352

Abstract

Noncommunicable diseases (NCDs) have become a global threat to public health, necessitating a comprehensive understanding of their geographic and epidemiological distribution in order to devise appropriate interventions. The objective of this study is to clustering areas of Banten Province based on NCDS profiles using the unsupervised learning technique. The method used in this study is the k-means algorithm for grouping types of non-communicable diseases based on region. The processing and normalisation of NCDS prevalence data from various health sources preceded cluster analysis using the k-means clustering algorithm. This research is categorised into two scenarios: the first involves the clustering of data obtained from outlier analysis, while the second scenario excludes any outliers. The objective is to observe disparities in regional clustering outcomes by categorising non-communicable diseases according to these two scenarios. The silhouette index is used to determine the validity of cluster results. These findings are analysed in depth to determine the geographic and socioeconomic patterns associated with each cluster's NCDS profile. Based on the mean silhouette index value of 0.812, the results indicate that the sum of k = 2 in the k-means algorithm is the optimal cluster result in this case. Five non-communicable diseases, namely diabetes, hypertension, obesity, stroke, and cataracts, necessitate significant focus in the first cluster (C1), where 202 regions were grouped. Six regions belong to the second cluster (C2), which includes areas that are not only susceptible to the five non-communicable diseases in cluster C1 but also to breast cancer, cervical cancer, heart disease, chronic obstructive pulmonary disease (COPD), and congenital deafness.
Modeling the Farmer Exchange Rate in Indonesia Using the Vector Error Correction Model Method Yuniar Farida; Afanin Hamidah; Silvia Kartika Sari; Lutfi Hakim
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.v23i2.3407

Abstract

The agricultural sector plays a crucial role in the Indonesian economy. However, the farm sector still has serious problems, including agricultural product prices, which often fall when the harvest supply is abundant. So often, the income obtained is not proportional to the price spent by farmers, which has an impact on decreasing the welfare of farmers. An indicator to observe changes in the interest of Indonesian farmers is the Farmer Exchange Rate Index (NTP). This study aims to form a model and project the welfare level of farmers in Indonesia, focusing on NTP indicators, which are caused by the influence of variables such as inflation, Gross Domestic Product (GDP), interest rates, and the rupiah exchange rate. The method used is the Vector Error Correction Model (VECM), used when there are indications that the research variables do not show stability at the initial level and there is a cointegration relationship. The results of this study show that in the long run, significant factors affecting NTP are inflation, interest rates, and the rupiah exchange rate. Meanwhile, in the short term, the variables that have an impact are GDP and the rupiah exchange rate. The resulting VECM model shows a MAPE error rate of 1.79%, indicating excellent performance, as the MAPE error rate is below 10%. The implication of this research is provides information related to NTP projection that can be used to formulate strategies to strengthen Indonesia's agricultural sector.
Implementation of Neural Machine Translation in Translating from Indonesian to Sasak Language Helna Wardhana; I Made Yadi Dharma; Khairan Marzuki; Ibjan Syarif Hidayatullah
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.v23i2.3465

Abstract

Language translation is part of Natural Language Processing, also known as Machine Translation, which helps the process of learning foreign and regional languages using translation technology in sentence form. In Lombok, there are still people who are not very fluent in Indonesian because Indonesian is generally only used at formal events. This research aimed to develop a translation model from Indonesian to Sasak. The method used was the Neural Machine Translation with the Recurrent Neural Network - Long Short Term Memory architecture and the Word2Vec Embedding with a sentence translation system. The dataset used was a parallel corpus from the Tatoeba Project and other open sources, divided into 80% training and 20% validation data. The result of this research was the application of Neural Machine Translation with the Recurrent Neural Network - Long Short Term Memory algorithm, which could produce a model with an accuracy of 99.6% in training data and 71.9% in test data. The highest ROUGE evaluation metric result obtained on the model was 88%. This research contributed to providing a translation model from Indonesian to Sasak for the local community to facilitate communication and preserve regional language culture.
Power Efficiency using Bank Capacitor Regulator on Field Service Shoes with Fast Charge Method Dekki Widiatmoko; Aripriharta Aripriharta; Kasiyanto Kasiyanto; Dodo Irmanto; Muchamad Wahyu Prasetyo
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.v23i2.3494

Abstract

Power efficiency is a key factor in military equipment, including field service boots used by personnel in various field situations that often demand durability and reliable electricity availability. This research focused on improving the power efficiency of field service shoes by using capacitor bank regulators and fast charging methods. By designing and implementing this system, this research aims to optimize the use of power sources, extend battery life, and improve personnel comfort in the field. The method used in this research is the fast charge method. The fast charge method enables faster battery charging, which is important in field situations with limited time availability. The findings of this research show that the capacitor bank regulator can keep the DC output stable despite instability in the input. The total power usage in the circuit is 0.20 W, and the power efficiency is about 60.61%. The research shows the potential of this voltage conversion circuit for efficient applications. Although it has not achieved maximum efficiency, the capacitor bank regulator can maintain output stability even in input voltage instability. This circuit can effectively cope with voltage conversion in various applications with further optimization.
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

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