cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
+6282161108110
Journal Mail Official
mib.stmikbd@gmail.com
Editorial Address
Jalan sisingamangaraja No 338 Medan, Indonesia
Location
Kota medan,
Sumatera utara
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
 109   110   111   112   113 
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.
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.
Pengelompokan Siswa Layak Penerima Beasiswa dengan Menerapkan Algoritma K-Means Clustering Data Mining Harpad, Bartolomius; Fahmi, Muhammad; Pahrudin, Pajar; Andrea, Reza
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.v8i2.7394

Abstract

Scholarships are financial assistance given to individuals with the aim of assisting them in financing the education they are pursuing. To overcome the gap between upper middle economic communities and lower middle economic communities in obtaining quality education. The aim of this program is to provide opportunities for financially disadvantaged students to experience quality education. In the process that occurs in awarding scholarships, there should be a strong basic foundation in the process of determining and making decisions that occur. Where the process of providing scholarships carried out so far should not be given to students who truly deserve it. The problem or impact that occurs from this is that the scholarship program does not run in accordance with the program's objectives, namely helping in the economic gap for students. One way that can be used to resolve this problem is to review previous recipient data. Data mining is a process of re-excavating data. Excavation is carried out by reviewing all the information contained in the data. In this research, the cluster analysis method is used, which is a multivariate technique used to group objects based on their characteristics. Clustering is the process of grouping data. Where the grouping process carried out on data is a grouping that does not yet have a class target or is called unsupervised learning. The results obtained in the research show that there are 2 clusters from the application of the K-Means algorithm. In cluster 1 there are 6 students in it and in cluster 2 there are 4 students in it.
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 ˚C, 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.
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.
Klasifikasi Emosi Pada Data Text Bahasa Indonesia Menggunakan Algoritma BERT, RoBERTa, dan Distil-BERT Basbeth, Faishal; Fudholi, Dhomas Hatta
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.v8i2.7472

Abstract

Previous studies in the context of sentiment analysis have been conducted in various types of text domains, including product reviews, movies, news, and opinions. Sentiment analysis focuses on recognizing the valence of positive or negative orientations. In sentiment analysis, something that is less explored but is needed in analysis is the recognition of types of emotions / classification of emotions. This research has contributed to knowledge of the applicationof distilBERT to Indonesian Language data which is still relatively new and the success of fine-tuning and hyperparameter-tuning that has been applied to distilBERT with various comparison methods. Emotion classification can help provide the right decisions in a company, government, and marketing strategy. Emotion classification is part of sentiment analysis which has more detailed and in-depth emotion labels. From these various urgencies, this research builds and fine-tuning distilBERT model to help classify emotions from an Indonesian sentence. Emotion classification in Indonesian language data using the distilBERT model is still relatively new. This research consists of 4 steps: data collection, data preprocessing, hyperparameter-tuning and fine-tuning, and comparison of results from various models that have been applied in this research: BERT, RoBERTa, and distilBERT. DistilBERT got the highest training accuracy = 94.76, the highest testing accuracy was obtained by distilBERT-frezee = 86.67, and the highest f1-score was obtained by distilBERT Modif = 87. In distilBERT-frezee the main cause of the model getting significant results is the dropout hyperparameter which reduces the f1-score value by 3 if it is not used, and the second cause is the freeze-layer which reduces the f1-score value by 1 if it is not used.
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.
Implementasi Algoritma K-Means dan K-Medoids Dalam Klasterisasi Kasus Kekerasan Terhadap Perempuan Kamilah, Nur Azizah; Rohana, Tatang; Rahmat, Rahmat; Fauzi, Ahmad
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.v8i2.7558

Abstract

The number of women's violence in Indonesia is increasing. In West Java alone, 58,395 cases of violence against women were recorded. Violence against women that occurs in West Java is among the most common compared to other provinces. This high number shows that violence against women is still not being handled seriously. Therefore, clustering is carried out to achieve a more structured solution so that it can assist the government in providing appropriate and appropriate responses to the conditions of each region, so that case handling can be more focused. The aim of this research is to group districts or cities in West Java in cases of violence against women using the K-Means and K-Medoids algorithms into two clusters, namely, high and low. In this research, data grouping was carried out using 2 methods, namely the K-Means and K-Medoids algorithms to find out which comparison between the two algorithms is more optimal. It is hoped that this research will produce the best cluster, the results of this cluster can help the government and related agencies to determine which districts or cities should be prioritized in handling cases of violence against women in West Java. The results of this research produced 2 clusters. Cluster 0 (high) and cluster 1 (low). The number of cluster 0 (high) is 14 districts and cities, while cluster 1 (low) is 13 districts and cities. Comparing the clustering evaluation between K-Means and K-Medoids, the best cluster evaluation value was obtained using the K-Medoids Algorithm with a Silhoutte Coefficient evaluation of 0.43, while the Davies Bouldin Index evaluation results showed the best cluster results using the K-Means Algorithm with a DBI value of 0.95.
The Palmprint Recognition Using Xception, VGG16, ResNet50, MobileNet, and EfficientNetB0 Architecture Aprilla, Diah Mitha; Bimantoro, Fitri; Suta Wijaya, I Gede Pasek
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.v8i2.7577

Abstract

The palmprint is a part of the human body that has unique and detailed characteristics of the pattern of palm lines, such as the length and width of the palm (geometric features), principal lines, and wrinkle lines. It began to be developed as a tool for recognize a person. The palmprint dataset used comes from Kaggle, namely BMPD. The palmprint images in this dataset were taken in 2 sessions. In the first session, there was not much variation in rotation compared to the second session. This research uses Convolutional Neural Network (CNN) models with Xception, VGG16, ResNet50, MobileNet, and EfficientNetB0 architectures to see the best performance. The results of this research showed that the MobileNet model had the best performance with an accuracy of 96.6% and a loss of 14.3%. For Precision results of 94%, Recall 96%, and F1-Score 94%. Meanwhile, Xception obtained an accuracy of 88.3% and a loss of 52.9%, VGG16 70.8% and a loss of 109.8%, ResNet50 5.8% and a loss of 307.9%, and EfficientNetB0 3.3% and a loss of 340.1%.
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.

Page 111 of 119 | Total Record : 1182

 109   110   111   112   113 

Filter by Year

2017 2024


Reset
Filter By Issues
All Issue Vol 8, No 3 (2024): Juli 2024 Vol 8, No 2 (2024): April 2024 Vol 8, No 1 (2024): Januari 2024 Vol 7, No 4 (2023): Oktober 2023 Vol 7, No 3 (2023): Juli 2023 Vol 7, No 2 (2023): April 2023 Vol 7, No 1 (2023): Januari 2023 Vol 6, No 4 (2022): Oktober 2022 Vol 6, No 3 (2022): Juli 2022 Vol 6, No 2 (2022): April 2022 Vol 6, No 1 (2022): Januari 2022 Vol 5, No 4 (2021): Oktober 2021 Vol 5, No 3 (2021): Juli 2021 Vol 5, No 2 (2021): April 2021 Vol 5, No 1 (2021): Januari 2021 Vol 4, No 4 (2020): Oktober 2020 Vol 4, No 3 (2020): Juli 2020 Vol 4, No 2 (2020): April 2020 Vol 4, No 1 (2020): Januari 2020 Vol 3, No 4 (2019): Oktober 2019 Vol 3, No 3 (2019): Juli 2019 Vol 3, No 2 (2019): April 2019 Vol 3, No 1 (2019): Januari 2019 Vol 2, No 4 (2018): Oktober 2018 Vol 2, No 3 (2018): Juli 2018 Vol 2, No 2 (2018): Apriil 2018 Vol 2, No 1 (2018): Januari 2018 Vol 1, No 3 (2017): September 2017 Vol 1, No 2 (2017): Juni 2017 Vol 1, No 1 (2017): Maret 2017 More Issue