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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
 102   103   104   105   106 
Analisis Sentimen Warganet Terhadap Keberadaan Juru Parkir Liar Menggunakan Metode Naive Bayes Classifier Mukti, Avis Tantra; Hasan, Firman Noor
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.v8i1.6982

Abstract

The still high rate of poverty in Indonesia causes many impacts, one of which is the emergence of illegal parking attendants. This condition continues to be supported by the creation of a parking area for business actors for their visitors. The parking areas provided are often free, and even have signs saying so. However, there are individuals who use the free parking space to earn income. There are many netizens' sentiments regarding the phenomenon of lying parking attendants on social media. Therefore, in this research, an analysis was used in the form of netizen sentiment towards illegal parking attendants on social media X using Naïve Bayes. The main objective of this research is to understand the public's feelings towards the existence of illegal parking attendants operating in the parking area. The dataset used in this analysis was 905 taken from social media The results of this research netizens felt very annoyed, angry and disturbed by the presence of illegal parking attendants operating. This is proven by the results of negative sentiment which dominates 93% of the total data or as many as 841 negative sentiments regarding this phenomenon.
Medical Image Classification of Brain Tumors using Convolutional Neural Network Algorithm Muis, Alwas; Sunardi, Sunardi; Yudhana, Anton
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.v8i1.6939

Abstract

Brain tumor is a highly dangerous and deadly disease. It can occur due to the abnormal growth of cells or tissues in the head. Treatment for brain tumor is done with surgery and chemotherapy aimed at killing or destroying the cells that affect the growth process of brain tumor. Diagnosis of brain tumor is done using medical scans such as MRI, CT Scan, and PET Scan by analyzing the resulting images. Another method used to detect brain tumors is through biopsy, which is a process of taking cells or tissue from the body for examination in the laboratory. However, this method takes a long time because the cells taken from the patient will be examined in the laboratory. Therefore, a technique is needed to speed up accurate brain tumor diagnosis in order to obtain quick treatment. Machine learning can solve this problem with the classification of images produced by MRI. The classification technique that can be used is the GoogLeNet architecture in CNN. Because GoogLeNet is the algorithm that won the ImageNet Large Scale Visual Recognition Challenge (ILSVC) in 2014 The purpose of this study is to classify brain images using the GoogLeNet architecture. The dataset used in this study consists of 7023 images, consisting of 6320 images for training the model and 703 for testing the model. The results of this study obtained an accuracy percentage of 96%. This result is higher than previous studies that obtained an accuracy value of 94%.
Analisis Topic-Modelling Menggunakan Latent Dirichlet Allocation (LDA) Pada Ulasan Sosial Media Youtube Alpiana, Vika; Salam, Abu; Alzami, Farrikh; Rizqa, Ifan; Aqmala, Diana
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.v8i1.7127

Abstract

This research explores the role of Micro, Small, and Medium Enterprises (MSMEs) in the Indonesian economy, focusing on sales and marketing challenges in the era of social media, especially YouTube. With millions of individuals using this platform to share product insights, reviews, and experiences, MSMEs need to receive relevant feedback. This study applies text mining, particularly the topic modeling analysis method with Latent Dirichlet Allocation (LDA), to analyze user comments on MSME videos, with an emphasis on Lumpia Gang Lombok Semarang on YouTube. Through the application of LDA, the identification of ten main topics is conducted, with the highest coherence value reaching 0.414027. The visualization of the intertopic distance map provides an understanding of the relationships between topics and dominant words. Comment analysis provides valuable insights into user preferences and perceptions of products, supporting MSMEs in understanding customer satisfaction and enhancing value for those enterprises. These findings also affirm the effectiveness of YouTube as a relevant data source for understanding public preferences for MSME products. This research details text processing methods, including extraction, cleaning, tokenization, normalization, removal of stopwords, and stemming. With this approach, the research not only provides insights into topic analysis in the context of social media but also makes a valuable contribution to the development and marketing of MSMEs through a better understanding of social media data, especially on the YouTube platform.
Deteksi Jamur Beracun dengan Algoritma Convolutional Neural Network dan Arsitektur EfficientNet-B0 Mauludy, Muhammad Wildan; Rulyana, Devita; Hardjianto, Mardi
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.v8i1.7276

Abstract

Indonesia is a tropical country that has abundant natural resources and biodiversity, one of which is mushrooms. Mushrooms have various shapes and types. Some of them contain mushrooms that cannot be consumed because they contain toxins that will have an impact on human health. Mushroom species that can be consumed sometimes have a similar shape to mushrooms that cannot be consumed, causing cases of poisoning due to consuming the wrong mushrooms. This research focuses on detecting poisonous mushrooms using a Convolutional Neural Network (CNN) with the EfficientNet-B0 architecture. Mushroom data was obtained from Kaggle, and after praprocessing, the model was trained by varying the number of epochs and batch size. Based on the results of research and discussion on the detection of poisonous and non-toxic mushrooms, it is concluded that the CNN algorithm with the EfficientNet-B0 architecture can differentiate between poisonous and non-toxic mushrooms with a high level of accuracy. In scenario testing, the model trained using batch size 32 had an accuracy of 84.2% and loss of 0.39, precision of 0.855, recall of 0.805, and f1 score of 0.815. This shows that the CNN architecture EfficientNet-B0 is an efficient and accurate approach in classifying poisonous and non-poisonous mushrooms. Apart from that, this research also found that parameters such as the number of epochs and the number of batch sizes influence the model training process.
Evaluasi Performa Oversampling dan Augmentasi pada Klasifikasi Penyakit Kulit Menerapkan Convolutional Neural Network Iskandar, Deo Andrianto; Salam, Abu
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.v8i1.7119

Abstract

The skin is the largest outer part of the human body. Maintaining skin is very important. The appearance of unusual things on the skin will raise concerns because it is possible that the skin could be affected by fatal diseases. Limited specialist doctor examinations in Indonesia add to the difficulty in preventing skin diseases. Therefore, this research was conducted to facilitate the classification of skin diseases. Skin disease classification must have good accuracy or precision in classifying each type. This study classifies skin diseases accurately and precisely by evaluating the performance of Oversampling and Augmentation techniques. This research uses the Convolutional Neural Network (CNN) approach. Using the HAM10000 dataset which contains dermoscopic images with a total of 10015 images. This study applies Oversampling to overcome data imbalance and applies image augmentation to improve model training performance. The performance of the model is evaluated using accuracy, recall, precision, f1-score, specificity, sensitivity, gmean. Comparisons are obtained from testing the original dataset, the dataset with oversampling and various augmentation techniques. The evaluation results show that the third test, namely classification using the CNN approach with oversampling and augmentation rotation, zoom, width, height, vertical_flip, gets the best results, namely accuracy 0.98, recall 0.98, precision 0.98, f1-score 0.98, specificity 0.99, sensitivity 0.98, gmean 0.98.
Comparison of Support Vector Machine and Random Forest Method on Static Analysis Windows Portable Executable (PE) Malware Detection Ismail, Hazim; Utomo, Rio Guntur; Bawono, Marastika Wicaksono Aji
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.v8i1.7110

Abstract

Malware has emerged as a significant concern for computer system security, as it spreads rapidly and adversely affects system performance. Detecting malware has become crucial, and one of the methods utilized is Machine Learning classification, which learns the characteristics of an application without executing it. In this study, the author evaluates the efficacy of malware detection in the static analysis of Windows Portable Executable (PE) files using the Support Vector Machine (SVM) and Random Forest algorithms. The author employs a dataset containing both malware-related PE files and safe applications to train the SVM and Random Forest models to classify PE files as either malware or safe. The objective is to determine the most effective machine learning algorithm for malware detection in PE files. The research compares the performance of both algorithms to identify the superior one for malware detection. The results indicate that the Random Forest algorithm achieves an impressive accuracy of 98.53%, while the SVM algorithm performs slightly lower with an accuracy of 97.14%.
Sentimen Analisis Masyarakat Terhadap Pembangunan IKN Menggunakan Algoritma Lexicon Based Approach dan Naïve Bayes Setiawan, Samuel Budi; 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.v8i2.7506

Abstract

The relocation and construction of IKN (Capital City of the Archipelago) as a center for state administration activities has many benefits and shortcomings, starting from the selection of locations, the ratification of laws that are considered too hasty then raises pros and cons by the Indonesian people. President Joko Widodo decided to move the country's capital outside Java in a meeting on April 29, 2019. The location of the IKN development was determined in East Kalimantan. This research was conducted by retrieving data via Twitter with the keyword "IKN Development". The data that has been collected totals 3,680 tweets. Data analysis was carried out with two methods, namely Naïve Bayes Classifier and Lexicon Based, and the best accuracy value was found between the two methods in analyzing data on public responses to IKN Development. The initial step of the data analysis process is the preprocessing process which contains stages such as labelling, case folding, cleaning, tokenizing, stopword removal, stemming. It is known that the results obtained from the analysis of the Naïve Bayes Classifier method have an accuracy value of 79%, and Lexicon Based has an accuracy value of 76%. Sentiment analysis of the two methods has Positive, Negative, and Neutral sentiments. With the stages of the analysis process using the Naïve Bayes Classifier and lexicon based methods, it can be seen that the Naïve Bayes Classifier method shows a Positive sentiment of 47.18%, Negative of 6.33%, and Neutral of 46.49%, while for Lexicon Based, Positive sentiment reaches 54.15%, Negative 29.36%, and Neutral 16.49%. It should be noted that the highest positive polarity result is found in the Lexicon Based algorithm at 54.15%, while in the Naïve Bayes Classifier 47.18%. It can be concluded from the results of both methods that Naïve Bayes Classifier has a better analysis compared to Lexicon-Based analysis.
Rancangan Alat Absensi Berbasis Internet of Things dan Notifikasi Smartphone Menggunakan App Inventor Warman, Aziz; Jufrizel, Jufrizel; Ullah, Aulia; Zarory, Hilman
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.v8i3.7720

Abstract

Technological developments have become a major focus when it comes to improving everyday productivity and innovation. Intelligent tools are created to facilitate and assist human work in life. Smart attendance machines are designed to replace the role of manual paper attendance and switch to an automated system. The problem with manual attendance is that there is potential for fraud in attendance and attendance data is vulnerable to damage and loss of data because it only uses paper media for storage and the use of manual attendance also has disadvantages related to attendance data that can be known by student guardians every day. This paper discusses the design of an attendance system using a Radio Frequency Identification (RFID) card as an identity used for the attendance process. The use of Liquid Crystal Display (LCD) to display information from card readings. ESP8266 microcontroller is used to run commands according to the program and access to Wifi that can connect to the internet. To store attendance data using application software that can be accessed by the admin. To monitor attendance, App Inventor is used which can be connected to a smartphone. The results of testing the tool show that the attendance machine can operate in accordance with the design and provide satisfactory results, the tool works by attaching the identity card to the card reader and the data will appear on the LCD in the form of card ID and attendance information, the time it takes to read the card until it appears on the LCD is 2-3 seconds.   Attendance data in the form of names, hours in, hours out and attendance information is successfully sent to the website application via an internet connection with the average time required of 4.43 seconds. Data in the form of sample photos was successfully sent by ESPcam to the website application via an internet connection with an average delivery time of 9.36 seconds. The attendance message was successfully sent to the Smartphone using App Inventor. 
Analisis Perbandingan Prediksi Tingkat Kemiskinan Menggunakan Metode XGBoost dan Random Forest Regression Prastiyo, Isnan Wisnu; Febriandirza, Arafat
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.v8i3.7892

Abstract

This research aims to compare the performance of two prediction algorithms, XGBoost Regression and Random Forest Regression, in predicting poverty levels in the DKI Jakarta area. For this research, researchers obtained data from the DKI Jakarta Central Statistics Agency (BPS) covering the period 2010 to 2023. The testing method used involved measuring Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) to assess the accuracy of predictions from the two algorithms. . The findings show that the Random Forest Regression algorithm generally produces more accurate predictions compared to the XGBoost Regression algorithm as seen from the test results on (MSE) and (MAPE) for most of the areas analyzed. As with MAPE for the West Jakarta area, the test results for XGBoost Regression were 1.43, while Random Forest Regression produced 1.42, so Random Forest Regression is better than XGBosst Regression. However, in the Seribu Islands, the MAPE for XGBoost is better with a value of 4.49 than for Random Forest Regression which has a value of 4.56. Then MSE Random Forest is better than XGBoost in this prediction test. For example, in the Central Jakarta area with a value of 0.02 for XGBoost Regression, while Random Forest Regression has a smaller test result with a value of 0.01.
Decision Support System for Selecting Peer-to-Peer Lending Applications using ARAS and Rank Sum Approaches Diana, Elviza
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.v8i1.7179

Abstract

The development of information technology has brought major changes to the world of finance, especially with the emergence of new business models such as Peer-to-Peer (P2P) lending. Even though the potential for high profits can be obtained through P2P lending platforms, the challenges faced by investors in choosing the right investment application are increasingly complex. With so many choices in P2P lending applications, decision-makers must be careful in making their choice. This creates problems if the decisions made are incorrect, resulting in financial losses. So, the aim of this research is to build a decision support system for choosing a Peer-to-Peer lending application by applying a combination of the Additive Ratio Assessment (ARAS) method and Rank Sum weighting to make it easier for users to determine their choice. Based on the case study, the utility value obtained is highest to lowest, namely: Amartha Microfinance (A3) got a score of 0.9034, Asetku (A4) got a score of 0.8954, KoinWorks (A5) got a score of 0.8640, Investree (A2) got a score of 0.8484, and Danamas Lender (A1) got a score of 0.8080. Besides that, the usability test got an average score of 88.75%, which means the system is appropriate for its use and function. The resulting decision support system has features that make it easier for decision makers to determine P2P lending applications because the system developed can display alternative rankings.

Page 104 of 119 | Total Record : 1182

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