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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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Pengamanan Pada Citra Digital dengan Menggunakan Modifikasi Blok Data Algoritma AES - Rijndael Muhammad Haris; Maya Silvi Lydia; Sutarman Sutarman
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5458

Abstract

Digital Image Security is one of the most important information security topics today. Along with the increasing use of digital images both for communication and documentation purposes, the security of information contained in digital images needs serious attention. Rijndael is a cryptographic method that is not only used for text encryption but also for digital image encryption. Just like block-based cryptographic methods in general, encoding data bytes only has an effect on the internal environment of the block, because the transformation process in Rijndael is done separately for each input block. In digital images, this can result in visible patterns or shapes of objects contained in the image, especially when using the Rijndael standard block size of 4x4. To improve the quality of digital image encryption on Rijndael, several studies have made modifications, especially at the transformation stage. In terms of data, the statistical values obtained from the encryption results such as the correlation coefficient do show an increase, but visually the pattern of objects is still visible and modifications tend to be high. This research proposes a modification of Rijndael which focuses on increasing the input block size from 4x4 to 8x8 with minimal changes to the transformation function. The results showed that the value of the correlation coefficient was better and the results of the encryption visually disguised the shape of the object more than the usual Rijndael, especially in text images, logos and caricatures. From the process carried out there is an increase in the quality of security for the encryption process by 13.22% to 91.48%.
Disaster Management Sentiment Analysis Using the BiLSTM Method Rachdian Habi Yahya; Warih Maharani; Rifki Wijaya
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5573

Abstract

Indonesia is a country prone to natural disasters. Natural disasters occur due to the process of adjustment to changes in natural conditions due to human behavior or biological processes. Community responses through tweets on Twitter are crucial for decision-making and action in disaster management and recovery processes. From the many public reactions via Twitter, sentiment analysis can be carried out. Classification using the BiLSTM method can be carried out to determine the categories of positive and negative responses after previously being compared using the SVM, which resulted in an accuracy of 82.73% and a BERT of 81.78%. After the classification process, the testing process is carried out with Word2Vec. From a total of 2,686 Twitter data, it was concluded that there were around 2,081 positive sentiments and 605 negative sentiments related to disaster management in Indonesia. At the same time, the test results obtained accuracy reached 84%, precision 88%, recall 92%, and f1-score reached 90%.
Reduce Inventory Cost by Implementation of Just In Time Method In Raw Materials Inventory Control Website Application Naila Hida Kholik; Endra Rahmawati; Pantjawati Sudarmaningtyas
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5459

Abstract

The production process is the main activity of a manufacturing company, and one of the success factors of production is good raw material inventory management. One of the problems is the inefficiency of using raw materials that impacted raising inventory costs caused by the large number of raw materials left in the warehouse. This study aims to reduce inventory costs by embedding the Just in Time (JIT) method in the inventory application to calculate the raw material inventory. The application of the JIT method will consider 5 things, namely optimal number of shipments, order quantity per order, delivery quantity, frequency of raw material purchases and total inventory costs with JIT.The analysis used data from 2019-2021, proving the JIT could reduce the total inventory cost by 92%. In 2019 the total inventory cost decreased from Rp. 6.773.533 to Rp. 829.976. JIT is able to lower the total inventory cost in 2020 and 2021 by 93% and 96%. The application testing results using Black Box shows that all application features can be used properly and accordingly. It’s supported by the percentage of the trial pass value reaching 100%. Based on those results, it can be concluded that the established built inventory application was appropriately used, accordingly, and effectively to reduce the inventory cost.
Klasifikasi Gerakan Yoga dengan Model Convolutional Neural Network Menggunakan Framework Streamlit Mohammad Fikri Nur Syahbani; Nur Ghaniaviyanto Ramadhan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5520

Abstract

Indonesian people are not fit and lack sports activities, therefore one of the alternative sports activities is yoga. Yoga is a type of exercise that has two important components, namely breathing and movement. Yoga movements also vary and can be distinguished from body curves, but ordinary people may not be familiar with yoga movements. With advances in technology and computer performance intelligence, it is now possible for computers to recognize an image for object recognition, namely detecting yoga movements using the digital image classification method. To make it easier to classify yoga movements, you can use the CNN model. Convolutional Neural Networks (CNN) are a combination of artificial neural networks with deep learning methods. The CNN process will carry out a training and testing process for yoga movements so that an image classification can be determined from the type of yoga movement. The image of the yoga movement is divided into 80% for training and 20% for testing. The training process is carried out using two different scenarios by differentiating the input image size, batch size, optimizer. The dataset consists of goddess, plank, tree, warrior2, downdog movements. The highest accuracy results are 94.10% using 170 x 170 image input, batch size 32, RMSprop optimizer. The results of testing a total of 40 images of yoga movements, 37 images were correctly guessed. The model that has been trained is implemented into the website using the Streamlit framework.
Prediksi Volume Sampah di TPSA Banyuurip Menggunakan Metode Backpropagation Neural Network Wahyu Santoso; Maimunah Maimunah; Pristi Sukmasetya
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5499

Abstract

The current waste problem is an important issue in many big cities, including Magelang City. The increasing rate of population growth and the decreasing land area for Banyuurip TPSA also makes it difficult for the government to handle waste, causing a negative impact on the environment around the TPSA. Therefore it is necessary to have a prediction of the volume of waste that enters TPSA every day using the Backpropagation Neural Network method so that it can assist the government in preparing budgets, preparing cleaners and estimating the capacity of TPSA in the future. The data used is time series data in the form of waste volume at Banyuurip TPSA from 2019 to 2022. From the results of the Backpropagation Neural Network method with parameters 30-7-1 and 1000 epochs, the best MSE value is 0.018870. The results of the training will then be used to predict the volume of waste the next day.
Personality Classification on Twitter Social Media using BERT Yantrisnandra Akbar Maulino; Warih Maharani; Prati Hutari Gani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5597

Abstract

In the modern era, social media is a platform often used to interact with people. Twitter is a popular social media, especially for human interaction. Using tweets on Twitter can describe how a person's personality and can also describe characteristics of a person. Humans themselves based on the Big Five Model Nursing Theory (Big Five Personality), have five general personalities, namely openness, conscientiousness, extraversion, agreeableness, and neuroticism. Personality itself influences a person's judgment of many things, knowing the personality of a person can make it easier to know the characteristics, habits, and ways of that person in their daily activities. In addition, understanding someone's personality can be a reference in seeing how someone can interact with others. It can also be used when looking for a job according to their personality. Thus, this research builds a system to classify personality using the BERT model with the dataset used in the form of tweets from Twitter users by making several changes such as parameters and using tests with several ratios in determining test data and also training data. The results acquired in this study are 50%.
Pemanfaatan Algoritma BFGS Quasi-Newton untuk Melihat Potensi Perkembangan Luas Tanaman Kopi di Pulau Sumatera Safruddin Safruddin; Elfin Efendi; Rita Mawarni; Anjar Wanto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5524

Abstract

Coffee is one of Indonesia's essential export commodities and a foreign exchange source for the country. One crucial factor in coffee production development is the planted land area. Therefore, the availability of land for coffee plants in Indonesia needs to be maintained for the continuity of coffee production today and in the future. This study aimed to see the potential for the widespread development of coffee plants on the island of Sumatra. This is because the island of Sumatra is the largest coffee producer in Indonesia, so information about the potential for the development of this plant area needs to be known as early as possible, especially for the agriculture/plantation service and for coffee farmers, so that coffee production can be maintained. The algorithm proposed in this study is the Broyden Fletcher Goldfarb Shanno (BFGS) Quasi-Newton algorithm which can be used to solve data prediction (forecasting) problems. This study uses a dataset of coffee plant areas sourced from the Directorate General of Plantations for 2012-2021. This study was analyzed using 3 (three) network architecture models (4-9-1, 4-18-1, and 4-27-1). Based on the analysis, the results obtained from model 4-18-1 as the best architecture with 100% accuracy with minor MSE testing, which is 0.00036764820. Meanwhile, based on predictions made using the best architecture (predictions for 2022 and 2023), the area of coffee plantations has decreased slightly. So this needs serious attention from the respective provincial governments.
Analisis Sentimen Kendaraan Listrik Pada Media Sosial Twitter Menggunakan Algoritma Logistic Regression dan Principal Component Analysis Youga Pratama; Danang Triantoro Murdiansyah; Kemas Muslim Lhaksmana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5575

Abstract

Twitter sentiment analysis is a method for identifying a person's opinions, reactions, judgments, evaluations, and emotions towards certain topics on Twitter social media. Opinions or can be called opinions can be classified as positive or negative. This research was conducted to find out public opinion about electric vehicles on Twitter social media, which is more positive or negative. The data obtained was 1874 tweets with data divided into training data and testing data at a ratio of 80:20. Data is classified using the Logistic Regression (LR) method, and Principal Component Analysis (PCA) as an optimization to improve accuracy. In this study it was found that around 86.9% of the opinions were positive, and 13.1% of the opinions were negative on the topic of electric vehicles. The results of research conducted using the Logistic Regression algorithm obtained the best accuracy of 87.9%, and after being optimized using Principal Component Analysis the best accuracy obtained increased to 90%.
Pengelompokkan Produksi Tanaman Jagung di Sumatera Utara Menggunakan Algoritma K-Medoids Safruddin Safruddin; Joni Wilson Sitopu; Azwar Anas Manurung; Indra Satria; Anjar Wanto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5562

Abstract

Corn is a strategic commodity with bright marketing prospects, especially in North Sumatra. Therefore efforts to increase corn production need great attention because, with sufficient availability, it is hoped that the community's need for corn can be fulfilled and the selling price remains stable. This study aims to classify corn production in North Sumatra based on districts/cities so that districts/cities can be identified and developed into corn production centers to reduce food imports, specifically corn crops. This research uses a corn production dataset based on districts/cities in North Sumatra consisting of 25 regencies and eight cities in 2019-2021 obtained from the Food Crops and Horticulture Service of North Sumatra Province. The algorithm used is the K-Medoids algorithm with Rapid Miner Studio tools. The results of this study were grouping corn production which was divided into 5 (five) groups, including Group 1 was an area with very high corn production consisting of 1 Regency, Group 2 was an area with high corn production consisting of 2 Regencies, Group 3 was an area with moderate corn production consisting of 4 regencies, Group 4 is an area with low corn production consisting of 3 regencies, and Group 5 is an area with very low corn production consisting of 15 regencies and seven cities. Based on these results, Karo, Dairi, and Simalungun districts can be used as centers for corn production in North Sumatra because these three districts alone produce corn production of 65.7% of the total corn production in North Sumatra.
Personality Detection On Twitter User With RoBERTa Rianda Khusuma; Warih Maharani; Prati Hutari Gani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5598

Abstract

Social media provides a service where users can make status updates about themselves. One of the social media that has such a facility is twitter. Twitter allows its users to express themselves easily by uploading tweets to their Twitter accounts. These activities on social media can indirectly describe the personality of the account owner. One form of personality classification that can be used is the big five personality. This theory classifies individual characters into five personality types, namely openness, conscientiousness, extraversion, agreeableness, and neuroticism. In the work environment, personality will significantly affect the work that is suitable for someone to do. To do a personality test, a test that is done manually, certainly takes longer and costs more. Therefore the use of machine learning to detect personality from social media is needed. By using the RoBERTa model to perform personality classification and dataset support from Twitter tweets, a system can be formed to detect personality. In the RoBERTa model, by determining the optimal ratio of training data and test data, as well as performing hyperparameter tuning, accuracy results can be obtained in classification activities, reaching 57.14%.

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