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JURNAL MEDIA INFORMATIKA BUDIDARMA
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 26145278     EISSN : 25488368     DOI : http://dx.doi.org/10.30865/mib.v3i1.1060
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Decission Support System, Expert System, Informatics tecnique, Information System, Cryptography, Networking, Security, Computer Science, Image Processing, Artificial Inteligence, Steganography etc (related to informatics and computer science)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,182 Documents
 115   116   117   118   119 
Optimizing Emotion Recognition with Wearable Sensor Data: Unveiling Patterns in Body Movements and Heart Rate through Random Forest Hyperparameter Tuning Nur, Zikri Kholifah; Wijaya, Rifki; Wulandari, Gia Septiana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7761

Abstract

This research delves into the utilization of smartwatch sensor data and heart rate monitoring to discern individual emotions based on body movement and heart rate. Emotions play a pivotal role in human life, influencing mental well-being, quality of life, and even physical and physiological responses. The data were sourced from prior research by Juan C. Quiroz, PhD. The study enlisted 50 participants who donned smartwatches and heart rate monitors while completing a 250-meter walk. Emotions were induced through both audio-visual and audio stimuli, with participants' emotional states evaluated using the PANAS questionnaire. The study scrutinized three scenarios: viewing a movie before walking, listening to music before walking, and listening to music while walking. Personal baselines were established using DummyClassifier with the 'most_frequent' strategy from the sklearn library, and various models, including Logistic Regression and Random Forest, were employed to gauge the impacts of these activities. Notably, a novel approach was undertaken by incorporating hyperparameter tuning to the Random Forest model using RandomizedSearchCV. The outcomes showcased substantial enhancements with hyperparameter tuning in the Random Forest model, yielding mean accuracies of 86.63% for happy vs. sad and 76.33% for happy vs. neutral vs. sad.
FAKTOR RISIKO HIPERTENSI PADA KELOMPOK UMUR ≤45 DI PUSKESMAS PB SELAYANG II TAHUN 2023 Cristiana, Eva; Nababan, Donal; Sitorus, Mido Ester J; Ginting, Daniel; Manurung, Jasmen
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.8266

Abstract

Hypertension, a silent killer disease, stands as a leading cause of mortality worldwide and a significant health concern in Indonesia. Individuals with hypertension exhibit systolic and diastolic blood pressure levels surpassing 140 mmHg and/or 90 mmHg, contributing to its high prevalence—a major global public health challenge today. This study aimed to establish correlations between age, family history, smoking habits, physical activity, and obesity concerning hypertension incidence at the PB Selayang II Community Health Center in Medan City, North Sumatra. Employing an analytical survey method with a case-control design, the research focused on a population of 70 individuals under 45 years old. Random sampling yielded 35 cases and 35 controls. Primary data collection involved interviews, blood pressure measurements, and height and weight assessments. Secondary data were sourced from Medical Records (RM) at the PB Selayang II Community Health Center. Data analysis employed the chi-square test with a significance level of p0.05 for identifying significant variables, and Odds Ratio (OR) values for quantifying the strength of associations. The results underscored age (p=0.008), family medical history (p=0.004), smoking habits (p=0.002), physical activity (p=0.000), and obesity (p=0.000) as factors significantly linked to hypertension incidence. Consequently, promoting lifestyle changes—such as maintaining a healthy diet to prevent obesity, avoiding smoking, and increasing physical activity—emerges as crucial advice for the public. Furthermore, the implementation of a weight control program at the health center is recommended, given obesity's predominant association with hypertension occurrence
Implementasi Metode AHP dan Multi-Objective Optimization by Ratio (MOORA) dalam Pemilihan Motor Listrik Sembiring, Kristian Eykman; Purnomo, A Sidiq
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7723

Abstract

The mode of transportation is very widely used because it is an access to take one place to another and is also used as a tool to transport an object. There are many types of transportation in Indonesia, one of which is a motorcycle. The increasing number of motorcycle users who consume fuel oil (BBM), raises concerns about climate change and air pollution. With these concerns, it can increase public awareness of the importance of protecting the environment. Efforts can be made to protect the environment in using transportation equipment, namely by using fuel from electrical energy. The number of electric motorbikes on the market with various types, makes it difficult for ordinary people to determine the choice of electric motorbikes that match their wishes and on average ordinary people determine the choice of motorbikes only from brands, selling prices, and do not understand the specifications of each of the criteria on electric motorbikes. From this problem, the researcher presents a decision support system to support lay people in determining the desired electric motorbike, using the AHP method to determine the weight and continued with the MOORA method to get the recommended electric motorbike. Electric motors that have affordable prices with good specifications get the highest rank in ranking with a value of 0.0718. The purpose of this research is to increase understanding of the factors that are important in the selection of electric motors, this method has never been used before for the selection of electric motors so it is hoped that the results of this research can develop new methods for selecting electric motors, and this research can provide recommendations for the best electric motors for prospective buyers so as to facilitate prospective buyers in making decisions in selecting electric motors.
Optimization of Perfume Sales through Data Mining with K-Means Algorithm Rahayu, Mia Setya; Yunita, Ika Romadoni; Widiawati, Chyntia Raras Ajeng
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7922

Abstract

This time the research used the abc Parfume shop as the research site. This store offers various types of perfumes with different variants because, there are many variants so that not all perfumes sell quickly and some even do not sell at all. To recap sales and expenses in abc stores is still done manually so that it often causes mistakes in increasing stock and hinders the development of marketing strategies. The data that has been collected should be used as a decision-making system to solve business problems. For this reason, the author conducts data mining calculations that are carried out automatically in the hope of providing effective and maximum results in analyzing perfume sales at abc perfume stores. The application of Data Mining in collaboration with the K-Means Algorithm has proven to provide the best analysis and be a solution in developing the perfume business. The results of this study divided the clustering into three clusters for the final result there were nine cluster projects with nine products, cluster two with three products, and cluster three or the last cluster with thirteen products from a total of twenty-five data collected. The results of each cluster are grouped such as Cluster One which is the best seller, Cluster two is grouped to the middle position because sales are stable, while products in Cluster Cluster three are less in demand. This research was successfully conducted and contributed to a deeper understanding of the K-Means algorithm.
Optimization of the Activation Function for Predicting Inflation Levels to Increase Accuracy Values Windarto, Agus Perdana; Rahadjeng, Indra Riyana; Siregar, Muhammad Noor Hasan; Yuhandri, Muhammad Habib
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7776

Abstract

This study aims to optimize the backpropagation algorithm by evaluating various activation functions to improve the accuracy of inflation rate predictions. Utilizing historical inflation data, neural network models were constructed and trained with Sigmoid, ReLU, and TanH activation functions. Evaluation using the Mean Squared Error (MSE) metric revealed that the ReLU function provided the most significant performance improvement. The findings indicate that the choice of activation function and neural network architecture significantly influences the model's ability to predict inflation rates. In the 5-7-1 architecture, the Logsig and ReLU activation functions demonstrated the best performance, with Logsig achieving the lowest MSE (0.00923089) and the highest accuracy (75%) on the test data. These results underscore the importance of selecting appropriate activation functions to enhance prediction accuracy, with ReLU outperforming the other functions in the context of the dataset used. This research concludes that optimizing activation functions in backpropagation is a crucial step in developing more accurate inflation prediction models, contributing significantly to neural network literature and practical economic applications.
Prototype Resusitasi Jantung Paru (RJP) menggunakan Motor Nema 23 dan Sensor Detak Jantung untuk Memudahkan Media Interface Maili, Ramadoni; Jufrizel, Jufrizel; Ullah, Aulia; Maria, Putut Son
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7690

Abstract

The growth of technology has an important role in supporting medical equipment facilities, especially for tools for providing compression or cardiopulmonary resuscitation (CPR). CPR is a first aid measure for victims of sudden cardiac arrest in which cardiac compression is carried out for 4 cycles, each cycle consisting of 30 compressions. CPR given manually can endanger the victim due to certain factors such as insufficient or excessive pressure applied. So that the above can be realized, a tool is needed that can make it easier for someone to check their heart rate on their skin quickly and effectively. The MAX30102 sensor is a sensor that can calculate heart rate. This research aims to create an optimal automatic cardiopulmonary resuscitation device to help victims continue to breathe. This RJP tool uses a Nema 23 stepper motor as the driving motor, the motor driver uses a TB6600 driver as the motor controller. During testing, 1 minute produced 103 compressions and 30 compressions in 18 seconds. The torque strength of the stepper motor is only capable of lifting a load of 7 kg, the torque produced is 2.06 Nm. This research also uses sensors to detect heartbeats. This allows the device user to monitor the patient's heartbeat while undergoing RPJ.
Alat Pendeteksi Dini Titik Api Kebakaran Hutan Menggunakan Komunikasi LoRa (Long Range) Rizky, Muhammad; Zarory, Hilman; Ullah, Aulia; Faizal, Ahmad
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7768

Abstract

Forests play an important role in maintaining environmental balance. Forests provide oxygen, host a variety of animals, protect soil, regulate water, and provide cultural, recreational and livelihood resources for humans. Despite their important role, forests are often threatened by fires caused by natural and human factors. Forest fires can cause economic losses, environmental damage, and adverse effects on human health. This research aims to create a tool that can detect forest fires early, monitor the condition of the forest environment in real time, stores data in the databas, and send early warnings via WhatsApp application. In this research, the tool uses sensors such as MQ-2, DHT22, and Soil Humidity Moisture which are connected to the ESP32 microcontroller, as well as LoRa communication technology to send data. This system can work well even in remote areas without a network. The test results show that this tool can provide accurate information about environmental conditions such as air temperature, humidity, and CO2 levels. This research makes an important contribution by providing an effective solution to monitor and prevent forest fires early, especially in remote areas that are difficult to reach by networks. With this tool, it is expected to reduce the negative impact of forest fires on the environment, economy, and human health. This system is expected to be an effective tool in monitoring and preventing forest fires early
Analisis Sentimen Pengguna Twiter terhadap Perubahan Kebijakan Skripsi sebagai Syarat Wajib Kelulusan menggunakan Metode Naïve Bayes Classifier Hablinawati, Laela; Dzikrullah, Abdullah Ahmad
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7746

Abstract

The Minister of Education, Culture, Research, and Technology, Nadiem Makarim, has issued a policy to abolish theses, dissertations, or final papers as mandatory graduation requirements for undergraduate and postgraduate students in universities. The requirement to write a thesis is still enforced in most universities in Indonesia to obtain a bachelor's degree. The advancement of information system technology and the ease of accessing social media have caused news to spread rapidly. This policy has sparked pros and cons among the public, including on the social media platform X (formerly Twitter). Some people agree with it, considering that it can reduce the burden on students and increase the relevance of higher education to the needs of the job market. However, others argue that abolishing theses could lower the quality of university graduates and that the replacement could be even more burdensome. The purpose of this research is to understand Twitter users' sentiments towards the policy of abolishing theses as a graduation requirement and to determine the accuracy of the Naïve Bayes Classifier in classifying these sentiments. The data used consists of 656 tweets, which were processed through several stages, including cleaning, case folding, normalization, stopword removal, tokenizing, and stemming. The data was then labeled using a lexicon-based approach, resulting in 353 negative labels and 273 positive labels. The data was subsequently weighted using TF-IDF for the classification process. The dataset was split into training and testing data with a ratio of 90:10. After classification, the study found that the Naïve Bayes Classifier successfully categorized sentiments with an accuracy of 76%.
Implementasi Algoritma Transformers BART dan Penggunaan Metode Optimasi Adam Untuk Klasifikasi Judul Berita Palsu Subagyo, Ageng Ramdhan; Sasongko, Theopilus Bayu
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7852

Abstract

Classification is a process of identifying new data provided based on validation of previous data. One classification process that can be used is fake news classification. The classification process requires as little time as possible to get maximum results, so a faster method is needed to classify news. The BART algorithm can be a method that can be used to carry out classification and use Adam optimization to improve the performance of the algorithm. The aim of this research is to classify fake news, whether the BART algorithm and Adam optimization are able to provide good results and to label whether the news is fake or not. The results of this process are based on the use of a dataset of 65% for training, 30% for validation, and 5% to produce 2 BART models. With the additional use of Adam optimization and several other parameters for the training process, the first model was able to provide accuracy performance of 92.88%, training loss reached 12.2%, and validation loss reached 28.4% and the second model produced an accuracy of 92.63 %, training loss 15% and validation loss reaching 20.2%. In the first model, it can predict 105 data labeled negative and 1306 positive data. Meanwhile, the second model was able to predict 128 data labeled negative and 1283 positive data.
Analisis Sentimen Masyarakat Terhadap Presiden dan Calon Presiden Terpilih 2024 Menggunakan Naïve Bayes Yusrizal, Muhammad; Sasongko, Theopilus Bayu
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7882

Abstract

It has been more than 4 months since the KPU announced the results of the general election on Wednesday, 20 May 2024. With the election of pair number 2 as President and Vice President of the Republic of Indonesia for the 2024-2029 period, various public opinions, both positive and negative, emerged on Twitter. The main problem faced in this research is knowing the dominant sentiment of society towards Prabowo Subianto and Gibran Rakabuming after their election, whether negative or positive sentiment dominates. If you look at it with the naked eye, there are indeed many negative sentiments written by netizens through their tweets on Twitter. This research aims to analyze the sentiment of the Twitter community towards these two figures. Data collected through data crawling using tweet-harvest was 1520 tweets with several keywords such as Prabowo, Wowo, Minister of Defense, Gibran, and Samsul. The stages carried out on the data include preprocessing, translating, labeling, splitting the data, and applying the Naive Bayes algorithm. The analysis results show that positive sentiment is 39.02% and negative sentiment is 60.98%, with an accuracy value of 75%. With detailed values of 79% precision, 80% recall and 80% f1-score. It is hoped that this research will provide an overview of public opinion towards the elected President and Vice President as well as provide evaluation material for them and their supporting political parties to increase positive sentiment in the future.

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