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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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Klasterisasi Jenis Tanah pada Tanaman Cabai Menggunakan Algoritma K-Means Ike Verawati; Agnes Lucky Rebecca
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.v7i3.6140

Abstract

Chili is one of the ingredients of spices that are widely used in Indonesia. Sales opportunities in the market are also very wide so that many are competing to grow chili with the best quality. There are several parameters that can improve the quality of chili, one of which is soil. Where if seen by the eye, soil that is suitable or unsuitable can be distinguished from color and texture, but not many people know the difference. Therefore, in this study, research will be conducted on the clustering of soil images based on color features and texture features using the K-Means algorithm which previously selected features using information gain feature selection. The first stage in this research is image acquisition and then the results will be processed first. From the results of pre-processing, RGB color feature extraction and first-order texture feature extraction will be carried out which is then followed by feature selection using information gain which is expected to produce the best features which will then proceed to clustering using the K-Meaning algorithm. The final step is to conduct an analysis to obtain the results of this study. The results obtained from the clustering that have been carried out can be obtained that the K-Means algorithm can cluster suitable and unsuitable soil images with information gain, resulting in 63 suitable soil images and 37 unsuitable soil images from 100
Perbandingan Efektivitas Nave Bayes dan SVM dalam Menganalisis Sentimen Kebencanaan di Youtube Azzahra, Tarissa Aura; Winarsih, Nurul Anisa Sri; Saraswati, Galuh Wilujeng; Saputra, Filmada Ocky; Rohman, Muhammad Syaifur; Ratmana, Danny Oka; Pramunendar, Ricardus Anggi; Shidik, Guruh Fajar
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.v8i1.7186

Abstract

Advancements in the field of Natural Language Processing (NLP) have opened significant opportunities in sentiment analysis, particularly in the context of disaster response. In today's digital era, YouTube has emerged as a primary source for the public to acquire information regarding critical events. This study explores and compares two dominant sentiment analysis techniques, namely Naive Bayes and Support Vector Machine (SVM). It utilizes YouTube comment data related to natural disasters to test the effectiveness of these algorithms in identifying and classifying public sentiment as neutral, positive, or negative. The process involves collecting comment data, pre-processing the data, and applying Term-Frequency-Inverse Document Frequency (TF-IDF) weighting to prepare the data for analysis. Subsequently, the performance of both models is evaluated based on metrics such as accuracy, precision, recall, and F1 score. The results indicate that while both algorithms have their strengths and weaknesses, SVM tends to show better performance in sentiment classification, especially in terms of accuracy and precision, with an accuracy result of 92% and precision of 89% for negative predictions and 94% for positive predictions. On the other hand, Naive Bayes only achieved an accuracy of 79% and a precision of 91% for negative predictions and 73% for positive predictions. This study provides significant insights into the application of machine learning algorithms in sentiment analysis.
Comparative Prediction of Physical Fatigue Patterns in Bandung, Indonesia Workers using CNN and ANN Ardiansyah, Muhammad Fikri Raihan; Wijaya, Rifki; Wulandari, Gia Septiana
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.v8i1.7280

Abstract

This research explores the impact of physical fatigue on task performance and evaluates the effectiveness of Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) in predicting fatigue levels. Physical fatigue, as a critical factor influencing performance and safety, serves as a signal for the body's need for rest. Utilizing a smartwatch with heart rate sensors, this study applies ANN for subjective fatigue assessments and CNN for time series analysis. With a structured approach encompassing data collection, preprocessing, and model training, a confusion matrix evaluates the model's performance. Results indicate an accuracy of 92.4% for the ANN model with an RMSE of 0.275, while the CNN model achieves an accuracy of 85.46% with an RMSE of 0.381. These findings affirm the effectiveness of both models in predicting fatigue, providing valuable insights for future research and emphasizing the importance of comprehensive data analysis for a nuanced understanding of individual performance (Number of data: 149,796 from 6 subjects).
Perbandingan Metode Naïve Bayes dan Support Vector Machine Pada Klasifikasi 22 Bahasa Daerah Rakajati, Bima; Hidayat, Erwin Yudi
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.v8i1.7236

Abstract

Indonesia boasts a rich cultural diversity, encompassing over 1300 ethnic groups and 2500 regional languages. The challenge arises due to the multitude of regional languages in Indonesia, making language identification in textual form difficult. This research compares Machine Learning methods for classifying 22 regional languages in Indonesia, aiming to provide a deep understanding of the relative performance of each method. The study successfully addresses the primary difficulty, which is the identification of regional languages in Indonesia. The main constraint of this research lies in the complexity of regional languages in Indonesia, with various characteristics, variations in grammar, and differing sentence structures, resulting in accuracy not yet reaching perfection. This factor opens opportunities for future research through parameter optimization or exploration of alternative methods. Evaluation results indicate that the Support Vector Machine achieves the highest accuracy, reaching 89.41%, making it the preferred choice for model implementation. Although Naïve Bayes yields good results with an accuracy of 82.08%, Support Vector Machine remains the preferred option. The application of the model using Streamlit demonstrates the effectiveness of the Support Vector Machine in accurately predicting Javanese song lyrics. This research has the potential to assist users in identifying regional languages based on text and contributes significantly to understanding Machine Learning methods for classifying regional language texts. Despite its limitations, this study can be extended to other regional languages, enhancing model accuracy through parameter improvements.
Pengembangan Aplikasi Android Menggunakan REST API dengan Metode Waterfall Untuk Peningkatan Aksesibilitas Situs Repositori Setiawan, Muhhamad Ajun; Avianto, Donny
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.v8i1.7056

Abstract

The repository system offered by the campus to students to increase students' interest in reading, in fact, is still low in use even in an academic environment. This happens because the repository system offered is less convenient for students to use as a literacy tool. The result of the research is an application that is expected to be able to improve accessibility to repositories. A data filter feature and a new display update for the repository system will be added by the author in the implementation. Users of this service can filter their search results depending on the field of study, research specialization, and research period. Usability testing on the created application has shown that many students will start to be interested in using the repository system as their literacy material. This test used the System Usability Scale methodology, where the users of the program - in this case, university students - were given a questionnaire. According to the results obtained in the questionnaire, the usability value was quite high at 75.63 percent. The conclusion is that the repository application developed with the addition of this filtering feature can increase literacy interest in students.
Performance Comparison of k-Nearest Neighbor Algorithm with Various k Values and Distance Metrics for Malware Detection Rafrastara, Fauzi Adi; Supriyanto, Catur; Amiral, Afinzaki; Amalia, Syafira Rosa; Al Fahreza, Muhammad Daffa; Ahmed, Foez
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.v8i1.6971

Abstract

Malware could evolve and spread very quickly. By these capabilities, malware becomes a threat to anyone who uses a computer, both offline and online. Therefore, research on malware detection is still a hot topic today, due to the need to protect devices or systems from the dangers posed by malware, such as loss/damage of data, data theft, account hacking, and the intrusion of hackers who can control the entire system. Malware has evolved from traditional (monomorphic) to modern forms (polymorphic, metamorphic, and oligomorphic). Conventional antivirus systems cannot detect modern types of viruses effectively, as they constantly change their fingerprints each time they replicate and propagate. With this evolution, a machine learning-based malware detection system is needed to replace the existence of signature-based. Machine learning-based antivirus or malware detection systems detect malware by performing dynamic analysis, not static analysis as used by traditional ones. This research discusses malware detection using one of the classification algorithms in machine learning, namely k-Nearest Neighbor (kNN). To improve the performance of kNN, the number of features is reduced using the Information Gain feature selection method. The performance of kNN with Information Gain will then be measured using the evaluation metrics Accuracy and F1-Score. To get the best score, some adjustments are made to the kNN algorithm, where 3 distance measurement methods will be compared to obtain the best performance along with the variations in the k values of kNN. The distance measurement methods compared are Euclidean, Manhattan, and Chebyshev, while the variations of k values compared are 3, 5, 7, and 9. The result is, kNN with the Manhattan distance measurement method, k = 3, and using information gain features selection method (reduction until 32 features remain) has the highest Accuracy and F1-Score, which is 97.0%.
Prediction of Indonesian Presidential Election Results using Sentiment Analysis with Nave Bayes Method Firdaus, Asno Azzawagama; Yudhana, Anton; Riadi, Imam; Mahsun, Mahsun
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.v8i1.7007

Abstract

Social media serves as a solution for politicians as a campaign tool because it can save costs compared to conventional campaigns. The 2024 Indonesian Presidential Election has drawn public attention, especially among social media users. Twitter, as one of the widely used social media platforms in Indonesia, functions as an effective campaign forum. However, the problem that arises is how to automatically collect social media data related to presidential discussions and provide conclusions on the analysis results. Of course, this is not easy if done manually. Sentiment analysis is one approach that can be used for this in order to draw conclusions and analysis related to the available data. Data was collected shortly after the registration of presidential and vice-presidential candidates in November 2023. This study aims to obtain sentiment results from the latest data obtained, get the best model from the Naive Bayes method, to conduct analysis in predicting presidential election results based on sentiment. However, at the time of data collection, candidate numbers had not been assigned by the Election organizers. The obtained data amounted to 11,569 records using the Valence Aware Dictionary for Sentiment Reasoning (VADER) library for labeling. After removing duplicated tweets, the data was reduced to 4,893 records, with each candidate pair having 1,631 data points. The sentiment analysis classification model was determined using the Nave Bayes method with Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction. Based on the data, the highest percentage of positive sentiment was found in Ganjar Pranowo - Mahfud MD data at 69.16%, and the highest negative sentiment was in Prabowo Subianto - Gibran Rakabuming Raka data at 52.12%. Common words in positive sentiment for Ganjar Pranowo - Mahfud MD include "strong," "corruption," "support," "reward," and others. Meanwhile, frequently appearing negative sentiment words for Prabowo Subianto - Gibran Rakabuming Raka include "child," "eldest," "mk," "young," and others. This research achieved an average accuracy of 76.67% using the Naive Bayes method on the entire dataset, indicating its reliability in similar cases.
Advanced Forecasting of Maize Production using SARIMAX Models: An Analytical Approach Airlangga, Gregorius
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.v8i1.7268

Abstract

Agricultural production forecasting is crucial for food security and economic planning. This study conducts a detailed analysis of maize production forecasting using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, emphasizing the applicability of time-series models in capturing complex agricultural dynamics. Following a comprehensive literature review, the SARIMA model was justified for its ability to integrate seasonal fluctuations inherent in agricultural time series. Optimal model parameters were meticulously determined through an iterative process, optimizing the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The best-performing SARIMA(1, 1, 2)x(2, 2, 2, 12) model achieved an AIC of 339914.85450182937 and a BIC of 339950.64499813004, indicating its strong fit to the historical data. This model was applied to a historical dataset of maize production, providing forecasts that align closely with actual production trends on a short-term basis. Notably, the model's short-term predictions for the subsequent year showed less than a 2% deviation from the actual figures, affirming its precision. However, long-term forecasts revealed greater variability, underscoring the challenge of accounting for unforeseen environmental and economic factors in agricultural production systems. This research substantiates the efficacy of SARIMA models in agricultural forecasting, delivering strategic insights for resource management. It also points towards the integration of SARIMA with other variables and advanced modeling techniques as a future avenue to enhance forecasting robustness, particularly for long-term projections. The findings serve as a valuable resource for policymakers and stakeholders in optimizing decision-making processes for agricultural production.
Decision Support System Using a Combination of COPRAS and Rank Reciprocal Approaches to Select Accounting Software Erkamim, Moh.; Handayani, Nurdiana; Heriyani, Nofitri; Soares, Teotino Gomes
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.v8i1.7111

Abstract

Accounting software plays an important role in carrying out accounting processes that are fast, efficient, accurate and in accordance with applicable standards. With the emergence of various accounting software that offers a variety of features, users, both individuals and companies, often experience difficulty in determining the software that best suits their needs. The aim of this research is to develop a decision support system that makes it easier to choose accounting software through the application of the COPRAS approach and the Rank Reciprocal weighting technique. The Rank Reciprocal approach is used to rank or weight the criteria given by the decision-maker. The COPRAS (Complex Proportional Assessment) approach focuses on cognitive aspects so that it can accommodate the preferences and subjective assessments of decision-makers. Based on the case study that has been carried out, the highest to lowest utility value results are obtained, namely Zahir Online (A2), which obtained a score of 100. Since the decision support system's output yields a result that is identical to that of computations made by hand, it is deemed legitimate. Apart from that, the usability test obtained an average score of 91%, which proves that the system is in accordance with its usability and what is needed by its users.
Comparing Haar Cascade and YOLOFACE for Region of Interest Classification in Drowsiness Detection Andrean, Muhammad Niko; Shidik, Guruh Fajar; Naufal, Muhammad; Zami, Farrikh Al; Winarno, Sri; Azies, Harun Al; Putra, Permana Langgeng Wicaksono Ellwid
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.v8i1.7167

Abstract

Driver drowsiness poses a serious threat to road safety, potentially leading to fatal accidents. Current research often relies on facial features, specific eye components, and the mouth for drowsiness classification. This causes a potential bias in the classification results. Therefore, this study shifts its focus to both eyes to mitigate potential biases in drowsiness classification.This research aims to compare the accuracy of drowsiness detection in drivers using two different image segmentation methods, namely Haar Cascade and YOLO-face, followed by classification using a decision tree algorithm. The dataset consists of 22,348 images of drowsy driver faces and 19,445 images of non-drowsy driver faces. The segmentation results with YOLO-face prove capable of producing a higher-quality Region of Interest (ROI) and training data in the form of eye images compared to segmentation results using the Haar Cascade method. After undergoing grid search and 10-fold cross-validation processes, the decision tree model achieved the highest accuracy using the entropy parameter, reaching 98.54% for YOLO-face segmentation results and 98.03% for Haar Cascade segmentation results. Despite the slightly higher accuracy of the model utilizing YOLO-face data, the YOLO-face method requires significantly more data processing time compared to the Haar Cascade method. The overall research results indicate that implementing the ROI concept in input images can enhance the focus and accuracy of the system in recognizing signs of drowsiness in drivers.

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