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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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Detecting Disaster Trending Topics on Indonesian Tweets Using BNgram Indra Indra; Nur Aliza
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.v23i1.3308

Abstract

People on social media share information about natural disasters happening around them, such as the details about the situation and where the disasters are occurring. This information is valuable for understanding real-time events, but it can be challenging to use because social media posts often have an informal style with slang words. This research aimed to detect trending topics as a way to monitor and summarize disaster-related data originating from social media, especially Twitter, into valuable information. The research method used was BNgram. The selection of BNgram for detecting trending topics was based on its proven ability to recall topics well, as shown in previous research. Some stages in detection were data preprocessing, named entity recognition, calculation using DF-IDF, andhierarchical clustering. The resulting trending topics were compared with the topics obtained using the Document pivot method as the basic method. This research showed that BNgram performs better in detecting trending natural disaster-based topics compared to the Document pivot. Overall, BNgram had a higher topic recall score, and its keyword precision and keyword recall values were slightly better. In conclusion, recognizing the significance of social media in disaster-related information can increase disaster response strategies and situational awareness. Based on the comparison, BNgram was proven to be a more effective method for extracting important information from social media during natural disasters.
Gender Classification of Twitter Users Using Convolutional Neural Network Fitra Ahya Mubarok; Mohammad Reza Faisal; Dwi Kartini; Dodon Turianto Nugrahadi; Triando Hamonangan Saragih
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.v23i1.3318

Abstract

Social media has become a place for social media analysts to obtain data to gain deeper insights and understanding of user behavior, trends, public opinion, and patterns associated with social media usage. Twitter is one of the most popular social media platforms where users can share messages or ”tweets” in a short text format. However, on Twitter, user information such as gender is not shown, but without realizing it or not, there is information about it in an unstructured manner. In social media analytics, gender is one of the important data that someone likes, so this research was conducted to determine the best accuracy for gender classification. The purpose of this study was to determine whether using combined data can improve the accuracy of gender classification using data from Twitter, tweets, and descriptions. The method used was word vector representation using word2vec and the application of a 2D Convolutional Neural Network (CNN) model. Word2vec was used to generate word vector representations that take into account the context and meaning of words in the text. The 2D CNN model extracted features from the word vector representation and performed gender classification. The research aimed to compare tweet data, descriptions, and a combination of tweets and descriptions to find the most accurate. The result of this study was that combined data between tweets and
Electronic Tourism Using Decision Support Systems to Optimize the Trips Dedi Setiadi; Yogi Isro Mukti
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.v23i1.3331

Abstract

Pagar Alam is a tourist destination city in the province of South Sumatra, which has many very diverse tourist destinations. The problem is that there is still a lack of information about tourism that tourists can access. This research aimed to build electronic tourism to make it easier for tourists to get the best information and recommendations about tourism in the city of Pagar Alam, which can be accessed anytime and anywhere, as well as improve tourist experience in planning their tourist trips because this electronic tourism platform includes decision making support system, which helps tourists manage their tours according to their needs and abilities. The research method used was analysis by collecting data by observing tourist attractions, calculating predictions using the simple additive weighting method, and from the results of testing with several alternatives, it can be concluded that electronic tourism meets the criteria chosen by tourists after being carried out. The calculation produced the highest preference value for tourist attractions, namely Tugu Rimau, with a value of 13.25. The highest preference value for hotels is Villa Gunung Gare Pagar Alam, with a score of 8.91, and the highest preference score for eating places is Warung Ridwan, with a score of 13.25. The next stage was system design using data flow diagrams, and the final stage was implementation by building electronic tourism using the CodeIgniter framework.
Analyzing Sentiment with Self-Organizing Map and Long Short-Term Memory Algorithms Frans Mikael Sinaga; Sio Jurnalis Pipin; Sunaryo Winardi; Karina Mannita Tarigan; Ananda Putra Brahmana
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.v23i1.3332

Abstract

This research delves into the impact of Chat Generative Pre-trained Transformer, one of Open Artificial Intelligence Generative Pretrained Transformer models. This model underwent extensive training on a vast corpus of internet text to gain insights into the mechanics of human language and its role in forming phrases, sentences, and paragraphs. The urgency of this inquiry arises from Chat Generative Pre-trained Transformer emergence, which has stirred significant debate and captured widespread attention in both research and educational circles. Since its debut in November 2022, Chat Generative Pre-trained Transformer has demonstrated substantial potential across numerous domains. However, concerns voiced on Twitter have centered on potential negative consequences, such as increasedforgery and misinformation. Consequently, understanding public sentiment toward Chat Generative Pre-trained Transformer technology through sentiment analysis has become crucial. The research’s primary objective is to conduct Sentiment Analysis Classification of Chat Generative Pre-trained Transformer regarding public opinions on Twitter in Indonesia. This goal involves quantifying and categorizing public sentiment from Twitter’s vast data pool into three clusters: positive, negative, or neutral. In the data clustering stage, the Self-Organizing Map technique is used. After the text data has been weighted and clustered, the next step involves using the classification technique with LongShort-Term Memory to determine the public sentiment outcomes resulting from the presence of Chat Generative Pre-trained Transformer technology. Rigorous testing has demonstrated the robust performance of the model, with optimal parameters: relu activation function, som size of 5, num epoch som and num epoch lstm both at 128, yielding an impressive 95.07% accuracy rate.
Feature Selection on Grouping Students Into Lab Specializations for the Final Project Using Fuzzy C-Means Indradi Rahmatullah; Gibran Satya Nugraha; Arik Aranta
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.v23i1.3341

Abstract

The student’s Final Project is critical as a requirement to graduate from the University. In the PSTI at Mataram University, each student is required to choose a specialization lab to focus on the final project topic that they will work on. From the questionnaire, 57.7% of students answered that it is difficult to select a lab, and others answered that they prefer to determine the labs based on the grades of the courses that represent each lab. This research aimed to group and analyze students in the final project specialization lab by using the main method, namely Fuzzy C-Means (FCM). The methods used were FCM for clustering, Silhouette Coefficient for analysis of cluster quality results, Pearson Correlation, and Principal Component Analysis for the feature selection processing. The results of this study showed that the FCM method followed by a method for feature selection has better results than previous studies that used the K-Means method without feature selection; with this research result using 131 data, the cluster validation result is 0.501, after feature selection using Pearson correlation is 0.534. Thus, Fuzzy C-Means followed by the right feature selection method can group students into specialization laboratories with good results and can be further developed.
Optimizing Inventory with Frequent Pattern Growth Algorithm for Small and Medium Enterprises Imam Riadi; Herman Herman; Fitriah Fitriah; Suprihatin Suprihatin
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.v23i1.3363

Abstract

The success of a business heavily relies on its ability to compete and adapt to the ever-changing market dynamics, especially in the fiercely competitive retail sector. Amidst intensifying competition, retail business owners must strategically manage product placement and inventory to enhance customer service and meet consumer demand, considering the challenges of finding items. Poor inventory management often results in stock shortages or excess. To address this, adopting suitable inventory management techniques is crucial, including techniques from data mining, such as association rule mining. This research employed the FP-Growth algorithm to identify patterns in product placement and purchases, utilizing a dataset from clothing store sales. Analyzing 140 transactions revealed 24 association rules, comprising rules with 2-itemsets and frequently appearing 3-itemset rules. The highest support value in the final association rules with 2-itemsets was 10% with a confidence level of 56%, and the highest support value in the 3-itemsets was 67% with the same confidence level. Additionally, three rules had a confidence level of 100%. Thus, the association rules generated by the FP-Growth frequent itemset algorithm can serve as valuable decision support for sales of goods in small and medium-sized retail businesses.
Implementation of Port Knocking with Telegram Notifications to Protect Against Scanner Vulnerabilities Husain Husain; I Putu Hariyadi; Kurniadin Abd Latif; Galih Tri Aditya
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.v23i1.3459

Abstract

The opening of the service port on the Mikrotik router provides an opening for hackers to enter the Mikrotik service to access the router illegally. This research aimed to close certain ports that are gaps for hackers and uses port knocking and telegram bots. The Telegram bot was used as a message notification to managers in real-time to provide information that occurs when the vulnerability scanning process is carried out to find and map weaknesses in the network system. Searching for weaknesses also includes looking for open router service ports such as ports 22, 23, 80, and 8291. This research used the Network Development Life Cycle method, which started from analysis design and prototype simulation to implementation. The research results after testing were able to secure local network service ports against vulnerability scanners on routers using the port knocking method, and testing attack schemes carried out from each scheme could run well on the router’s local network and obtain notifications via telegram bots in real time to administrators. This research contributes to administrators’ ability to secure networks so irresponsible people do not easily infiltrate them.
Intelligent System for Internet of Things-Based Building Fire Safety with Naive Bayes Algorithm Ni Gusti Ayu Dasriani; Sirojul Hadi; Moch Syahrir
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.v23i1.3581

Abstract

Population growth is increasing every year. Population growth causes an increase in population density in a country. The largest population density is in urban areas. Fires in a city with a high population density will potentially cause greater damage. Material and non-material losses due to fire can be caused by not functioning maximally early warning systems, especially fire detection. In addition, other factors, such as system errors in detecting fires, can potentially cause fires. This research aims to build an intelligent system that can minimize building fire detection errors to reduce user material losses. The intelligent system can classify fire potential into four classifications, namely ”very dangerous,” ”dangerous,” ”alert,” and ”safe.” The method used in this research is Research and Development (R&D) with artificial intelligence using the Na¨ıve Bayes method, which has been integrated with the Internet of Things (IoT). This research shows that the Na¨ıve Bayes algorithm can be used to classify fire potential, proven by the overall system testing accuracy of 93.33% with an error of 6.77%.

Page 42 of 42 | Total Record : 418

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