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INDONESIA
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
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Integrating Heart Rate, Gyro, and Accelero Data for Enhanced Emotion Detection Post-Movie, Post-Music, and While-Music Setyawan, Hendy; Wijaya, Rifki; Kosala, Gamma
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7634

Abstract

The study investigates the integration of heart rate, gyro, and accelerometer data to enhance emotion recognition across different scenarios like post-movie, post-music, and during music listening. Recognizing the limitations of solely using heart rate data, the research combines gyroscopic and accelerometer data to provide a more comprehensive understanding of emotional responses. Employing machine learning algorithms, notably support vector machines, the aim is to develop robust models for real-time emotion recognition. Conducting rigorous experimental protocols involving motion sensor data from smartwatches, a user study with 50 participants examines emotional responses during activities such as movie-watching and music listening. The dataset includes data from accelerometers, gyroscopes, and heart rate sensors, with additional evaluation metrics to assess the effectiveness of the proposed method in detecting emotional states. The findings demonstrate significant effectiveness of an innovative neural network (NN) method in determining post-activity emotional states, with accuracies ranging from 59.0% to 83.4%, depending on the activity and context. Although NN accuracy is slightly lower compared to other methods like random forest and logistic regression, the differences are not significant, especially when compared to logistic regression. Overall, the research aims to advance emotion recognition technology for applications in human-computer interaction contexts.
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.
Decision Support System for Selecting a Camera Stabilizer Using Rank Reciprocal and ARAS Approaches Erkamim, Moh.; Daniarti, Yeni; Shalahudin, Mohammad Imam; Mulyadi, Mulyadi; Soares, Teotino Gomes
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7560

Abstract

The selection of camera stabilizers is a crucial aspect in the photography and videography industry, especially with the increasing use of cameras. Typically, to select a camera stabilizer, decision-makers must be aware of all specifications of the available options. However, this necessarily results in a lengthy decision-making process, and various considerations lead to imprecise decisions. The aim of this research is to develop a Decision Support System (DSS) for choosing the appropriate and swift camera stabilizer through a combination of the Rank Reciprocal weighting approach and the Additive Ratio Assessment (ARAS) method. The Rank Reciprocal approach is utilized to obtain the criteria weight values, and the ARAS method is employed to evaluate and select the best alternative based on a number of criteria. This research produced a website-based DSS that can recommend the best alternative in the form of an alternative ranking. The results from the case study conducted obtained rankings from the highest to the lowest as follows: Zhiyun Tech Weebill S with a score of 0.9428, Beholder DS1 with a score of 0.8497, Gudsen Moza AirCross S with a score of 0.8205, and Feiyu Tech Scorp C (A3) with a score of 0.7197. The system built has been tested with a usability test scoring 88.75%, indicating that the system has met its users' needs.
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.
Analisis Sentimen Terhadap Media Sosial Twitter dengan Kasus Kampanye Anti-Korupsi di Indonesia Menggunakan Naive Bayes Aprilia, Ni Wayan Ayu Sekar; Isnain, Auliya Rahman
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7582

Abstract

In Indonesia, the issue of the Anti-Corruption Campaign is often discussed on Twitter, which has become an important platform for voicing public opinions and sentiments. Sentiment analysis of the Anti-Corruption Campaign can provide valuable insights for the government and anti-corruption institutions such as the Corruption Eradication Commission. The aim of this research is to analyze the sentiment felt by the public towards the Anti-Corruption Campaign in Indonesia using the Naïve Bayes method based on data from Twitter. Tweet data related to the Anti-Corruption Campaign in Indonesia is collected via the Twitter API, then preprocessed to clean, tokenize and remove stopwords. A dictionary of positive and negative sentiments is created based on tweet analysis, and the Naïve Bayes method is used to classify tweet sentiments into positive or negative. Method performance is evaluated using a confusion matrix. The research results show that most of the tweets related to the Anti-Corruption Campaign have positive sentiments, but there are also negative ones. This research provides an understanding of public perceptions of the Anti-Corruption Campaign in Indonesia through Twitter sentiment analysis, which can help in formulating anti-corruption policies and strategies in the future. Analysis shows that 58.64% of Twitter accounts have positive sentiment and 41.36% have negative sentiment. Evaluation of test data shows an accuracy level of 74%, a precision level of 79%, and a recall of 76%.
Analisis Sentimen Produk Skincare Somethinc Niacinamide di Female Daily dengan Naïve Bayes Classifier WP, Dwi Atmodjo; Firizqi, Januponsa Dio; Amalia, Zalfa Azzah
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7571

Abstract

One of the skincare products from Somethinc that is currently hotly discussed is Niacinamide Moisture Beet Serum. On the Female Daily site, people who have used the beauty products they use express their opinions in a review on Female Daily. However, the many thousands of reviews that exist have not been structured, so it is still difficult for the public and potential consumers to understand the essence of opinions on a product and classify them into the appropriate polarity of opinion. This research aims to analyze public sentiment regarding reviews of the Somethinc Niacinamide skincare product in Female Daily using the Naïve Bayes Classifier algorithm. This method was chosen because of its capabilities in probability-based text classification and popularity in sentiment analysis. The study began with collecting review data from the Female Daily site, followed by a text pre-processing process which included case folding, tokenizing, stopwords removal, and stemming. The dataset is then divided into training data (80%) and test data (20%). The Naïve Bayes Classifier algorithm is applied to classify reviews into positive, negative, or neutral categories. The research results show that this model is successful in classifying sentiment with 61% accuracy. The conclusions of this study indicate that although the Naïve Bayes Classifier algorithm is quite effective in classifying the sentiment of skincare product reviews, there is room for improvement, especially in handling meaningful and contextual words. This research provides new insight for brand owners and potential consumers in understanding public opinion regarding the Somethinc Niacinamide skincare product.
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.
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.
Analisis Sentimen Kegiatan Pembersihan Sampah Pada Media Sosial X Menggunakan SVM dan Naïve Bayes Nugroho, Dendy Aprilianto; Hasan, Firman Noor
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7562

Abstract

Human daily activities inevitably produce waste, which negatively impacts environmental balance due to the bad habit of indiscriminately disposing of waste. As a result of this issue, there is a youth community named Pandawara Group that wants to help clean up trash on Sukabumi Beach. However, their initiative faced rejection from the local village chief and youth organization, sparking various opinions on social media platform X. Consequently, this research seeks to analyze public sentiment towards Pandawara Group's waste cleanup efforts at Sukabumi Beach using Support Vector Machine and Naïve Bayes methods. The objective is to gauge positive and negative sentiments and compare the accuracy of Support Vector Machine and Naïve Bayes.  In this sentiment analysis using 2,339 datasets, the highest accuracy was achieved using the Support Vector Machine method at 91.67%, whereas the Naïve Bayes method only achieved 63.89%. Thus, it can be concluded that Support Vector Machine is superior in classifying sentiments regarding Pandawara Group's waste cleanup activities at Sukabumi Beach compared to Naïve Bayes.

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