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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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Enhancing Fire Detection in Images using Faster R-CNN with Gaussian Filtering and Contrast Adjustment Dwiki Lazzaro; Febryanti Sthevanie; Kurniawan Nur Ramadhani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6486

Abstract

A system is designed with an accurate and efficient model to detect fires, aiming to assist in fire prevention. Designing such a system poses a challenging task, as numerous aspects need to be considered, including model accuracy, parameter count, computational complexity, and more. Therefore, the research will incorporate techniques such as Image Smoothing Filtering and Contrast Adjustment to enhance the fire detection process. The primary objective is to develop a robust system that can effectively identify and detect fire occurrences. Accuracy is crucial to ensure reliable results, while efficiency plays a significant role in real-time fire detection. By implementing Image Smoothing Filtering, the system can reduce noise and enhance image quality, improving detection performance. Contrast Adjustment techniques will further contribute to the system's efficiency by emphasizing fire patterns and enhancing their visibility. The system's design encompasses careful consideration of various factors to strike a balance between accuracy, efficiency, and computational complexity. By utilizing Image Smoothing Filtering and Contrast Adjustment, the research aims to develop a comprehensive fire detection system that can aid in preventing fire incidents. This study endeavors to contribute to the advancement of fire detection technologies and pave the way for future innovations in this field.
Analisa Algoritma K-Means Untuk Menentukan Strategi Marketing Bias Yulisa Geni; Okto Kurnia; Nova Hayati; Muhammad Thoriq; Kiki Hariani Manurung
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7085

Abstract

This research explored the application of the K-Means algorithm in the marketing field to increase the effectiveness of ABC Cosmetic Store marketing strategies. Sales data can be processed using data mining to be used as decision making at ABC Cosmetic Stores. One of the techniques in data mining is Clustering, which is used to categorize data. The K Means algorithm is used to identify hidden patterns in the data. By using bodylotion sales data, this research aims to classify consumers into several groups. The data group in question is sales data that is of great interest to consumers and data that is of little interest to consumers. The results of clustering using k=2 show that cluster 1 consists of 5 products with product transactions sold being 1295 products. In this case, it shows that cluster 1 is a group of product data whose quantity sold has increased and can provide profits at the ABC Cosmetic Store. Meanwhile, cluster 2 has 1 data with 214 product transactions sold and is grouped as data with products that are less popular with consumers so there is no need to increase the stock available in the warehouse by ABC Cosmetic Shop. The results of this research show that K Means-based customer segmentation can increase personalization in marketing communications and increase the efficiency of marketing resource allocation. This study provides new insights into how data mining techniques can be involved in marketing strategies to determine product availability for the future.
Comparative Analysis of ARIMA and LSTM Models for Predicting Physical Fatigue in Bandung Workers Kiki Dwi Prasetyo; Rifki Wijaya; Gia Septiana Wulandari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7282

Abstract

In today's era of rapid economic growth, there is an increasing demand for workers to increase productivity by working longer and harder. However, these demands often lead to irregular and excessive working hours, which can potentially lead to negative consequences, such as physical fatigue-a state in which the body feels tired after physical activity. Factors that influence this fatigue include age, gender, health conditions, workload and work environment. Physical fatigue poses a significant challenge in ensuring workplace safety, especially in the transportation and industrial sectors, as it can reduce overall performance, productivity and quality of work. In addition, physical fatigue also increases the likelihood of decision-making errors and workplace accidents. Predicting physical fatigue is crucial to addressing these challenges. Heart rate serves as a parameter to measure fatigue, given its proven efficacy as a marker to predict physical fatigue, which is derived from the electrocardiogram and regulated by the autonomic nervous system. This research utilizes two machine learning algorithms - ARIMA and LSTM - with heart rate (bpm) and number of steps as variables. Performance evaluation, using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), showed that the LSTM model outperformed the ARIMA model. The LSTM model showed better performance, with MSE of 0.1108 and RMSE of 0.3329, compared to the ARIMA model with MSE of 0.2397 and RMSE of 0.4895.
Word2Vec Optimization on Bi-LSTM in Electric Car Sentiment Classification Siti Uswah Hasanah; Yuliant Sibaroni; Sri Suryani Prasetyowati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7200

Abstract

The Indonesian government is actively promoting electric vehicles. This policy has generated many sentiments from the public, both positive and negative. Public sentiment can have a significant impact on the success of government policies. Therefore, it is important to understand public sentiment towards these policies. This research develops a sentiment classification model to understand public sentiment towards electric vehicles in Indonesia. Sentiment classification is the process of identifying and measuring the positive or negative sentiment in a text. This research uses a Bi-LSTM model to perform classification on a dataset of tweets related to electric vehicles. To evaluate the performance, testing was conducted through two main scenarios. In Scenario I, the focus was on finding the optimal embedding size for two Word2Vec architectures, namely CBOW and Skip-gram. Model evaluation was performed using cross-validation to gain a deeper understanding of model performance. Scenario II focused on searching for the best dropout parameters for the Bi-LSTM model. This step aimed to find the optimal configuration for the model to generate more accurate and consistent predictions in classifying tweets related to electric vehicles. The results showed that in the context of sentiment classification on tweets about electric vehicles, the combination of CBOW with an embedding size of 200 and the Bi-LSTM model with a Dropout value of 0.2 is the best choice and achieves an accuracy of 96.31%, precision of 92.57%, Recall of 98.61%, and F1-Score of 95.49%.
Prediksi Kegagalan Transformator Daya dengan Metode DGA (Dissolved Gas Analysis) Menggunakan Random Forest Berbasis TDCG Sugiman, Marcelino Maxwell; Purnomo, Hindriyanto Dwi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7036

Abstract

Transformers are critical components, and early detection of potential failures plays an important role in the reliable operation of the power system. This article describes a novel approach for power transformer failure prediction based on dissolved gas analysis (DGA) by applying the TDCG method with Random Forest algorithm. DGA data from operational transformers are used to train and test the predictive model. The Random Forest method based on TDCG enables comprehensive analysis of dissolved gas changes in transformer oil, thus enabling early detection of failure conditions. Experimental results show that the predictive model using the model created by applying hyperparameter tuning for optimal parameter tuning to have high accuracy, the accuracy obtained reaches 96% in detecting potential failures, the standard used for accuracy presentation uses confusion matrix as the accuracy of the predictive model. In addition, it can optimize time efficiency in analyzing failures and prevent human error when calculating gas fault identification or potential failures.
Sentiment Analysis of the Waste Problem based on YouTube comments using VADER and Deep Translator Yuliansyah, Herman; Mulasari, Surahma Asti; Sulistyawati, Sulistyawati; Ghozali, Fanani Arief; Sudarsono, Bambang
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6918

Abstract

The waste problem is a severe problem that significantly affects the environment and public health. To effectively determine the public’s perception of the waste problem, it is necessary to examine public sentiment toward waste management. This research aims to develop a sentiment analysis model using VADER and deep-translator and analyze the Yogyakarta waste emergency problem. This research was conducted in two phases, namely, the first phase was developing a sentiment analysis model by evaluating its performance based on public data. Then, the second phase classifies public comments from YouTube regarding the waste problem to understand public perceptions and evaluations by identifying positive, negative, and neutral sentiments. The model evaluation results show that sentiment analysis using VADER and deep translator can achieve Accuracy, Precision, Recall, and F1-score values of 0.716, 0.837, 0.853, and 0.738, respectively. The sentiment results from YouTube comments obtained positive, neutral, and negative sentiments of 30.0%, 31.7%, and 37.3%, respectively. The results of the sentiment analysis are neutral sentiment discussing waste management, disappointment in negative sentiment, and hope for waste management in positive sentiment.
Klasifikasi Kondisi Kendaraan Berpotensi Kecelakaan Berbasis Android Menggunakan Long Short Term Memory Nabila, Puspita Aliya; Soim, Sopian; Handayani, Ade Silvia
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7005

Abstract

Traffic accidents are a severe problem that often results in loss of life and property damage. Efforts to overcome this situation require real-time vehicle monitoring with the capture and collection of relevant data to provide information about the driver and family at home to reduce the risk of accidents by identifying potentially dangerous vehicle conditions automatically and quickly. This research utilizes Long Short Term Memory technology to analyze sensor data installed in the vehicle to an android device to be classified according to three conditions that recognize vehicle conditions as safe, alert, or dangerous. The Long Short Term Memory model used achieved a high level of accuracy with a value of 99.96% when training on data. After testing, this model still has a good level of accuracy with a value of 93.3%. In the test, the precision value of each class is 83.33% for the safe class, 80% for the danger class, and 100% for the alert class. In indicating that Long Short Term Memory in this study is very efficient in identifying and classifying vehicle conditions to reduce potential accidents. The information processed by Long Short Term Memory will be transmitted to an Android application capable of delivering up-to-date insights into the vehicle's condition. This app incorporates cautionary alerts in the presence of potential accident indicators to aid in vigilance and accident prevention. The integration of this system aims to enhance road safety and diminish the occurrence of accidents resulting from suboptimal vehicle conditions or hazardous driver conduct. This application can provide convenience for vehicle owners to know the state of the vehicle in real-time remotely in optimal conditions.
Handling Imbalance Dataset on Hoax Indonesian Political News Classification using IndoBERT and Random Sampling Fathin, Muhammad Ammar; Sibaroni, Yuliant; Prasetyowati, Sri Suryani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7099

Abstract

The rapid adoption of the internet in Indonesia, with over 200 million active users as of January 2022, has dramatically transformed information dissemination, particularly through social media and online platforms. These platforms, while democratizing information sharing, have also become hotbeds for the spread of misinformation and hoaxes, significantly impacting the political landscape, as seen in the Jakarta gubernatorial election from late 2016 to April 2017. Research by the Indonesian Telematics Society (MASTEL) revealed a high prevalence of hoax content, predominantly socio-political, underscoring the critical need to address this misinformation and hoaxes challenge. This research delves into the challenge of detecting hoaxes in Indonesian political news, particularly focusing on the classification of news as factual or hoax in the presence of class imbalances within datasets. The dataset exhibits a significant class imbalance with 6,947 articles identified as hoaxes and 20,945 as non-hoaxes, Utilizing the IndoBERT model, a specialized variant of the BERT framework pre-trained on the Indonesian language, the study aims to assess its effectiveness in discerning between factual and hoax news. This involves fine-tuning IndoBERT for specific text classification tasks and exploring the impact of various resampling techniques, such as Random Over Sampling and Random Under Sampling, to address class imbalances since the dataset, significantly imbalanced with 6,947 articles labeled as hoaxes and 20,945 as non-hoaxes, necessitated these approaches. The study's findings demonstrate the IndoBERT model's consistent accuracy across different resampling methods like Random Over Sampling (ROS) and Random Under Sampling (RUS), highlighting its effectiveness in handling imbalanced datasets produce the accuracy of hoax detection with the 98.2% accuracy, 97.5% Recall, 97.8% F1-score, and 97.2% Precision. This is particularly relevant for tasks like misinformation detection, where data imbalance is common. The success of IndoBERT, a language-specific BERT model, in text classification for the Indonesian language contributes to the understanding of BERT-based models in diverse linguistic contexts.
Analisis Sentimen Berbasis Aspek Ulasan Aplikasi Mobile JKN dengan Lexicon Based dan Naïve Bayes Salsabila Roiqoh; Badrus Zaman; Kartono Kartono
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6194

Abstract

Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan is a legal entity that provides social health insurance programs for the public released application called Mobile JKN to support various health services activities using users devices. Mobile JKN has not fully received a positive public perception and still has many shortcomings. It is necessary to conduct a deeper evaluation and analysis of the Mobile JKN. This study focuses on aspect-based sentiment analysis of user reviews on the Google Play Store to evaluate the Mobile JKN. The review data used are the last two versions, 4.2.3 and 4.3.0. This study was carried out by modeling aspects/topics using the Latent Dirichlet Allocation method and sentiment analysis using Naïve Bayes and Lexicon-Based methods. This research resulted in 3 aspects, namely Services and Features, Register and Login, and User Satisfaction. This was obtained based on the model with the highest coherence score of 0.6392 obtained in the model looping with the number of topics from 1 to 9, random state = 42, passes =50, and iteration = 60. Meanwhile, based on the sentiment analysis results, the Naïve Bayes method is better than the Lexicon-Based (Inset Lexicon) method. This is evident from performance of the Naïve Bayes with the highest accuracy score of 94.75% and Lexicon Based with Inset Lexicon obtained an accuracy score of 59.99%.
Prediksi Harga Cryptocurrency Binance Berdasarkan Informasi Blokchain dengan Menggunakan Algoritma Random Forest Asbullah, Jumjumi; Samsudin, Samsudin
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7100

Abstract

This study proposes the use of the Random Forest algorithm to forecast the cryptocurrency Binance's daily prices. With a dataset covering 1992 observations from January 1, 2018, to June 15, 2023, the research focuses on PT. Tennet Depository Indonesia. Through Python implementation, the experimental results indicate that Random Forest is effective in providing accurate price predictions, with an average Mean Absolute Percentage Error (MAPE) of approximately 1.38% and an average Root Mean Squared Error (RMSE) of about 4.38. The uniqueness of the system lies in the algorithm's capability to handle market complexities and volatility, offering adaptive solutions to the unpredictable dynamics of the market. Nevertheless, limitations in historical data and market volatility persist as inhibiting factors, emphasizing the need for a holistic approach. The average MAPE and RMSE results provide an indication of the overall reliability of the model in facing cryptocurrency market volatility. These conclusions can contribute to the development of more robust and adaptive models to respond to the evolving market conditions.

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