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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
Forecasting the Poverty Rates using Holt’s Exponential Smoothing Riza Prapascatama Agusdin; Sylvert Prian Tahalea; Vynska Amalia Permadi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.2672

Abstract

As a developing country with many provinces, Indonesia has a poverty problem that needs to be overcome. This research aimed to predict the poverty level in the Special Region of Yogyakarta using poverty data provided by the Central Statistics Agency for the Special Region of Yogyakarta. The method used in this research was Holt exponential smoothing to predict poverty levels in Yogyakarta City and four districts (Sleman, Bantul, Kulon Progo, and Gunungkidul) in this province. Three performances were measured to evaluate forecast results: sum squared error, mean squared error, and root mean squared error. The research results showed that the best configuration for the cities of Yogyakarta and Bantul is , = 0.9, 0.4; Kulon Progo and Gunungkidul are , = 0.9, 0.9; and Sleman are , = 0.9, 0.6. The forecasting results for 2022 to 2024, using a 95% confidence interval, showed that the poverty rate will increase in every city and district in the Special Region of Yogyakarta.
Learning Accuracy with Particle Swarm Optimization for Music Genre Classification Using Recurrent Neural Networks Muhammad Rizki; Arief Hermawan; Donny Avianto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3037

Abstract

Deep learning has revolutionized many fields, but its success often depends on optimal selection hyperparameters, this research aims to compare two sets of learning rates, namely the learning set rates from previous research and rates optimized for Particle Swarm Optimization. Particle Swarm Optimization is learned by mimicking the collective foraging behavior of a swarm of particles, and repeatedly adjusting to improve performance. The results show that the level of Particle Swarm Optimization is better previous level, achieving the highest accuracy of 0.955 compared to the previous best accuracy level of 0.933. In particular, specific levels generated by Particle Swarm Optimization, for example, 0.00163064, achieving competitive accuracy of 0.942-0.945 with shorter computing time compared to the previous rate. These findings underscore the importance of choosing the right learning rate for optimizing the accuracy of Recurrent Neural Networks and demonstrating the potential of Particle Swarm Optimization to exceed existing research benchmarks. Future work will explore comparative analysis different optimization algorithms to obtain the learning rate and assess their computational efficiency. These further investigations promise to improve the performance optimization of Recurrent Neural Networks goes beyond the limitations of previous research.
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 : LPPM 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.
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 : LPPM 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%.
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 : LPPM 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.
Optimizing Treatment of Herbal Plant Using SOPHERBAL Android Application Fordward Chaining Method Muhamad Azwar; Eka Nurul Qomaliyah; Nurul Indriani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3371

Abstract

The utilization of traditional herbal medicine among the inhabitants of Lombok is notably prevalent yet frequently hindered by a lack of comprehension regarding the efficacy of herbal remedies for specific ailments. Addressing this challenge, this study proposes the development of an Android application called ”sopherbal,” aimed at delivering personalized herbal plant recommendations via easily accessible mobile devices. Employing forward chaining methodology, the application identifies optimal herbal remedies based on ailment type, processing techniques, usage instructions, and recommended dosage and treatment duration. Notably, while effective in this context, the forward chaining approach entails certain trade-offs and hurdles. Previous research indicates that forward chaining facilitatesaccurate recommendation generation, and it may be constrained by its reliance on predefined rules and limited adaptability to complex, evolving scenarios. Despite these challenges, the ”sopherbal” application, featuring 50 Sasak medicinal plants curated for 15 common ailments, achieved an 86% validation rate, affirming its efficacy in bridging the gap between traditional herbal knowledge and modern healthcare needs.
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 : LPPM 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 : LPPM 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.
Hostage Liberation Operations using Wheeled Robots Based on LIDAR (Light Detection and Ranging) Sensors Kasiyanto Kasiyanto; Aripriharta Aripriharta; Dekki Widiatmoko; Dodo Irmanto; Muhammad Cahyo Bagaskoro
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3493

Abstract

Hostage release operations require a high level of precision, alertness, and skill, which are carried out manually by soldiers of the Indonesian National Army. This medium presents a significant risk to soldiers. This research aims to improve the effectiveness of hostage release operations by integrating wheeled robot technology based on Light Detection and Ranging (LIDAR) sensors. The research method used is experiment-based in developing and testing a prototype of a mobile robot equipped with LIDAR technology and a web camera capable of mapping the location of hostages in three dimensions. The research showed that this robot has high accuracy, reaching 97.87%, and can createthree-dimensional route maps and display real-time video on a computer. The use of this technology has the potential to reduce risks to soldiers and improve the accuracy of mapping hostage locations, which can ultimately improve the safety and effectiveness of hostage release operations in the context of special operations tasks by soldiers of the Army.
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 : LPPM 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.

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