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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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Perbandingan Algoritma K-Nearest Neighbor dan Support Vector Machine Pada Pengenalan Pola Tulisan Tangan Widiyanti, Adella; Megawaty, Dyah Ayu
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.v8i3.7757

Abstract

Handwriting is a biometric characteristic because each person has a unique handwriting pattern. This uniqueness can be utilized as a biometric identity. Handwriting pattern recognition is one of the important fields in document analysis to biometric authentication. This research explores the implementation of K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) algorithms in the context of handwriting pattern recognition. In addition, this research incorporates digital image processing technology by utilizing feature extraction using Gray-Level Co-occurrence Matrix (GLCM). This process involves taking handwriting samples, digitizing them into digital images, and utilizing GLCM to extract texture features. These features play an important role in capturing the unique characteristics of each handwriting pattern. This research was conducted because handwriting has a wide implementation in various fields. In the field of data security, handwriting recognition can be used for data verification in financial transactions and official documents. A comparison of the K-NN and SVM algorithms was conducted to determine the most effective and efficient algorithm in handwriting pattern recognition. These two algorithms are very popular and often used in classification. By comparing these two algorithms, this research aims to evaluate and compare the performance of two classification algorithms in handwriting pattern recognition so as to provide recommendations for implementation in handwriting pattern recognition. The main focus of this research is to investigate the effectiveness and accuracy of the K-NN and SVM algorithms in recognizing and classifying handwriting. K-NN algorithm produces the highest accuracy value of 82.11%, while the SVM algorithm produces the highest accuracy value of 83.87%, so that the SVM algorithm becomes the best algorithm in the classification of handwriting pattern recognition.
DESIGNING AN AI CHATBOT (STYLESAVVY) FOR CHOOSING CLOTHES FASHION BASED ON SKIN TONE AT XI-XIU STORE Ayuningsih, Ekatri Ayuningsih; Lubis, Sevana Rizki; Tembusai, Zulkarnain
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.v8i3.8220

Abstract

Chatbot is a computer program with artificial intelligence to interact with humans through conversation, provide responses, and offer services. Its function is to provide services or information to users in a way that resembles human conversation. This study can give recommandation towaards fashion and makeover based on skin tone and enhance confidence about self  performance. This study did not only provide recommendation by picture and description, but also a link to view the video that can be seen by the users. The result of this study gives varies benefits for researcher in understanding further about AI technology users in fashion industry, then for the company and education institution in progressing service and relevant curriculum. On the other side, this study contributes on AI technology development in the future, especially in terms of fashion and aesthetic.
Analisis Sentimen Masyarakat Indonesia Terhadap Pengalaman Belanja Thrifting Pada Media Sosial Twitter Menggunakan Algoritma Naïve Bayes Wulandari, Sania; 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.v8i2.7520

Abstract

Thrifting is an increasingly popular second-hand shopping activity in Indonesia, especially among millennials and generation Z as a cost-saving shopping alternative. Thrifting activities have a clear positive impact on the Indonesian people in protecting the environment by reducing the purchase of new goods. However, thrifting is considered illegal and can harm the domestic textile industry. So sentiment analysis needs to be done to find out how people respond to thrifting activities. This study aims to calculate the number of positive and negative comments from Twitter users, and find out how accurately the Naïve Bayes algorithm is used in the classification. The data used is taken from Twitter social media as many as 900 tweets, then processed through several advanced stages such as pre-processing which consists of cleansing, tokenize, and filter stopwords. Then at the labeling stage the data is divided into training data and test data with a ratio of 60:40. After being classified using the Naïve Bayes algorithm, the results obtained tend to be positive with a total of 368 positive comments and 181 negative sentiments. After going through the evaluation stage, the accuracy value is 95.92%, the precision value is 95.76%, and the recall value is 97.41%. The evaluation results show that the Naïve Bayes algorithm is proven to have a high level of accuracy used in classification.
Prediksi Cuaca Kabupaten Sleman Menggunakan Algoritma Random Forest Taqiyuddin, Muhammad; Bayu Sasongko, Theopilus
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.v8i3.7897

Abstract

Indonesia, as a tropical country, exhibits complex and varied weather patterns influenced by high temperatures, precipitation, and humidity throughout the year. This high weather variability often leads to uncertainties in weather forecasting, affecting sectors such as agriculture, transportation, and tourism. This study aims to predict the weather in Sleman Regency using the Random Forest algorithm to address forecasting uncertainties and provide more accurate predictions. The method involves collecting daily weather data from BMKG, conducting exploratory data analysis to understand data characteristics, and processing the data, including cleaning and normalization, before applying it to the Random Forest model. The study's goal is to improve the accuracy of weather predictions to support more precise and effective decision-making. Preliminary results show that the Random Forest model performs well with a Mean Absolute Error (MAE) of 0.060, Mean Squared Error (MSE) of 0.009, Root Mean Squared Error (RMSE) of 0.094, and R-squared of 0.691. The model evaluation indicates good performance in predicting weather in the study area. With these results, the developed weather prediction model holds significant potential to enhance sustainability and operational efficiency in various sectors reliant on weather conditions.
Analisis Klasifikasi Komoditas Harga Pangan Menggunakan Artificial Neural Network pada Kualitas Beras Pulau Jawa Lifera, Fruita Cancer; Ahdika, Atina
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.v8i3.7719

Abstract

Rice is a highly important food commodity that significantly influences the welfare of the Indonesian population, with approximately 98% of the Indonesian population relying on it as their main food source. There are several qualities of rice such as low-quality rice, medium-quality rice, and super-quality rice. The differences in rice quality types are usually influenced by several factors, including the level of processing, nutrient content, and quality level. Thus, in general, many consumers are unable to differentiate rice quality based on its physical appearance when purchasing rice. Instead, they tend to assess rice quality solely based on its price aspect. Therefore, this study classifies rice quality by focusing on the price variable as the main reference. Artificial Neural Network (ANN) method is used to classify rice quality based on price. ANN is a non-linear technique commonly used for model classification. ANN method is one of the famous data mining prediction models known for its accuracy in classification. By using the ANN method, it is found that the best proportion is using an 85% training and 15% testing proportion, and obtaining an accuracy value of 70% which can be considered quite good. The ANN method shows that the ANN model can learn significant patterns from the given data.
Implementasi Algoritma Backpropagation Untuk Prediksi Jumlah Siswa SMA Salis, Rahmi; Windarto, Agus Perdana; Suhendro, Dedi
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.v8i3.7774

Abstract

Senior High School (SMA) is one form of formal education unit that organizes general education at the secondary education level as a continuation of Junior High School (SMP). The number of high school students in Pematangsiantar City has decreased and increased from year to year. The factors causing the decrease and increase in the number of students are economic factors, population growth rate, distance from home, age, low quality of schools, lack of teachers and teaching media. This is because the number of students is very influential in determining when additional teachers, classrooms, textbooks and teaching media are needed to support the learning process. This study aims to predict the number of high school students in Pematangsiantar City. The dataset used is a dataset of the number of high school students in Pematangsiantar City in 2019-2023 obtained from the Ministry of Education, Culture, Research and Technology (Dapodik) website https://dapo.kemdikbud.go.id/pd/2/076300. The dataset is then divided into 2 parts, namely training and testing datasets. The algorithm used in the research is the Backpropagation algorithm with 6 architectural models, namely 4-15-1, 4-25-1, 4-45-1, 4-55-1, 4-75-1, and 4-85-1. The results of this study obtained the best architectural model, namely 4-25-1 with an accuracy level of 87.5%, Epoch 65, MSE Training 0.000967055, and MSE Testing 0.001440343. Based on this best architecture model will be used to predict the number of high school students in Pematangsiantar City for 2024.
Pemantauan Kelembapan dan Pengendalian Suhu Serta Pemberian Nutrisi Selada di Prototype Greenhouse dengan Nutrient Film Technique Berbasis IoT Derafi, Ryan; Ullah, Aulia; Maria, Putut Son; Faizal, Ahmad
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.v8i3.7674

Abstract

Nutrient Film Technique (NFT) is a hydroponic plant cultivation method in which plant roots grow in a shallow, particulated nutrient layer that allows plants to obtain water, nutrients and oxygen. In order to maintain a stable condition, kasturi farm hydroponic farmers experience problems in controlling and monitoring the condition of their plants, without having to sacrifice other activities. This research was carried out through a series of processes, starting from analyzing the needs, designing, and testing the design in providing nutrition, temperature control and monitoring the humidity of rockwool foam based on ESP 32. The hardware used are TDS (Total Dissolved Solids) Sensor, Ultrasonic Sensor, LDR (Light Dependent Resistor) Sensor, DHT 11 Sensor, Soil Sensor and DC Pump. The control used is ESP 32, and the application used is Blynk. The optimal control system in maintaining nutrients is 560 to 660 ppm and otherwise experiences a significant change of 1185 ppm. optimal temperature control with the highest temperature of 29.20 ËšC, and not using control will be more unstable. Rockwool humidity experienced an ideal stable increase of 50% - 56%. The average height of lettuce from the test results is about 22.07 cm with an average weight of 119.805 g. Measurement of lettuce plants indicated variations in fresh weight and height, with 3 prominent plants. The system effectively monitors and controls plant growth conditions, creating an optimal hydroponic environment. Based on the test results, the use of IoT technology in hydroponic NFT systems has proven to be very helpful for farmers in monitoring humidity, temperature and nutrients in real-time. Farmers can easily access plant condition information through remote devices, allowing farmers to immediately take necessary actions to maintain the health and productivity of hydroponic plants.
K-Means Clustering Algorithm for Grouping Eligible Community Recipients of Government Assistance Helmy Muzaqqi, Farid; Rakhmawati, Desty; Wijaya, Anugerah Bagus
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.v8i3.7766

Abstract

The government assistance program is financial support provided to the community with the aim of helping to improve the quality of life and welfare of almost all provinces in Indonesia including Central Java Province, especially in Ajibarang District, Banyumas Regency. This program aims to overcome the gap between the upper middle economic community and the lower middle economic community. However, the implementation of aid so far has not been even and fair, so it has not achieved its goal of helping people who really need it. In the process of providing assistance, it is important to have a strong basic foundation in determination and decision making. The program must ensure that aid is provided to truly deserving communities. The problem is that aid programs often do not work as intended, and are not effective in addressing economic inequality. One way to resolve this issue is to review the recipient's previous data. This research uses data mining to extract data from people who deserve assistance or not by applying clustering methods. Clustering, which is the process of grouping data without a class target (unsupervised learning), was used in this study. The algorithm used in this research is K-Means clustering. The results showed that there were two clusters of K-Means algorithm application, with 6 people in cluster 1 and 4 people in cluster 2. This research was successful and had an impact on aid providers to conduct more thorough data collection not just choosing the community to be given assistance.
Analisis Perbandingan CNN dan Vision Transformer untuk Klasifikasi Biji Kopi Hasil Sangrai Leonardi, M. Anjas; Chandra, Albert Yakobus
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.v8i3.7732

Abstract

Coffee is a globally cherished beverage, popular among various segments of society, including in Indonesia. The coffee processing procedure plays a pivotal role in determining the taste and final quality of the beverage. One crucial stage in this process is selecting the maturity level of coffee beans after the roasting process. However, determining this maturity level often faces challenges, particularly in large-scale processing contexts. This research focuses on evaluating two main approaches in classifying the maturity level of roasted coffee beans: Convolutional Neural Network (CNN) and Vision Transformer (ViT). The main issue encountered is the lack of studies comparing the effectiveness of these two methods in the context of coffee bean classification. Therefore, this study aims to fill this knowledge gap and provide better insights into which method is more suitable for this purpose. In this study, two CNN models, namely Xception and InceptionV3, as well as one ViT model, ViT-B16, are utilized. The dataset includes images of roasted coffee beans, raw coffee beans, and non-coffee bean images, with image sizes of 224 x 224 pixels. A comparison analysis is conducted based on classification accuracy and the ability of each model to capture characteristic features in coffee bean images. The experimental results show that the ViT-B16 model outperforms both CNN models with an accuracy of 99.33%, while the Xception and InceptionV3 models achieve accuracies of 96.67% and 96.00%, respectively. ViT-B16 demonstrates better capability in capturing global features in images, while CNN is more effective in detecting local features. However, both approaches face some challenges, including computational requirements and training time. In conclusion, although both methods have their own advantages, ViT-B16 offers significant potential for more accurate and efficient classification systems for images of roasted coffee beans. This research provides a crucial contribution to the development of coffee bean classification technology, which can enhance efficiency and consistency in the coffee processing industry.
Implementasi Algoritma Naive Bayes Terhadap Klasifikasi Jenis Pertanyaan Pada Perancangan Chatbot Untuk Aplikasi Penjualan Songket Rosa, Adelia; Salamah, Irma; Suroso, Suroso
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.v8i3.7908

Abstract

Rapid developments in science and technology have had a significant impact on all aspects of people's lives, especially in business. E-commerce has become a popular choice among internet users in Indonesia, including MSMEs. However, conventional sales of songket products still limit product reach and competitiveness. In addition, the sales process generally provides customer service in charge of interacting and serving customer inquiries that can be contacted via telephone number. However, it is considered less effective because the seller has difficulty when responding to various questions from customers, so customers have to wait to get answers regarding the information needed. Therefore, the purpose of this research is to design a chatbot for songket sales applications using the naive bayes algorithm in classifying the types of customer questions, to improve the efficiency and effectiveness of interactions between sellers and customers, and expand the market reach of songket products. This chatbot is designed to simulate interactive conversations and provide sales information to customers quickly and efficiently. The naive bayes algorithm was chosen due to its ease of implementation and high accuracy in text classification. In this study, the chatbot was tested with various types of user questions with 5% of the total 50 questions as testing data. The test results show that the chatbot can classify the type of question with an accuracy of 90% and a precision value of 94%, recall 92%, and F1-Score 92%. In addition, testing of the application system as a whole shows that the application and chatbot are able to provide appropriate and efficient responses to various user questions. With this system, it is hoped that a technology-based solution can be realized that can improve the sales process and customer interaction with sellers, and increase the business potential of traditional songket craftsmen. 

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