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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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Analisis Perbandingan Optimizer pada Pelatihan Model Convolutional Neural Network untuk Kasus Klasifikasi Hewan Primata Solihat, Sinta; Widodo, Suprih; Sari, Dian Permata
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.v8i1.7274

Abstract

Classification is a way to group certain things, for example primate animals, based on the similarities and differences that exist in animals. This classification intends to identify similarities or characteristics in these animals. Image classification is the process of grouping an object in the form of an image into certain categories using the CNN algorithm, but the resulting accuracy level is not satisfactory. Therefore, this research aims to produce the right optimizer to be used in the CNN model. In this study, the data was collected using the web scrapping method, and the data source is Google Images, so the total amount of data obtained is 1631 images. The framework for completing this research is the AI Project Cycle, which includes problem scoping, data acquisition, data exploration, modelling, and evaluation. Based on the research results, the optimizer with the highest accuracy value is Adadelta, which has an accuracy value of 75%. Therefore, Adadelta is the right optimizer for primate classification in the CNN algorithm model.
Perbandingan Resident Set Size dan Virtual Memory Size Algoritma Machine Learning dalam Analisis Sentimen Yudhanegara, Reza Ardiansyah; Hana, Nisrina Aliya; Mahfiridho, Syahrizal Yonanda; Kardian, Aqwam Rosadi
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.v8i1.7201

Abstract

In the rapidly advancing era of digital transformation, where textual data abounds from various online sources such as social media, forums, and product reviews, sentiment analysis has become a critical component in understanding public opinions and consumer behavior. Sentiment analysis employs machine learning, natural language processing, and computational linguistics to comprehend the feelings and opinions of others. The machine learning algorithms investigated in this paper include K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Naive Bayes, ID3, and C4.5. The sentiment analysis process requires significant computational resources to handle the complexity and scale of data. This research aims to examine the differences in resource usage among these algorithms and determine which algorithm is best suited for sentiment analysis in this context. The research methodology employed is quantitative, focusing on the collection and numerical analysis of datasets. Testing is conducted using the Anaconda Library in the Python programming language to measure the usage of Resident Set Size (RSS), Virtual Memory Size (VMS), execution time, and the accuracy of each algorithm. The test results indicate that the Support Vector Machine (SVM) algorithm with an accuracy rate of 96% and the Naive Bayes algorithm with an accuracy rate of 97% are the best choices for use in the context of sentiment analysis. When considering the context of Resident Set Size (RSS) and Virtual Memory Size (VMS) usage in a single execution, ID3 is the algorithm with the smallest resource usage, with an accuracy rate of 92%. The average resources used by ID3 are 8.318.566,4 bytes for Resident Set Size (RSS) and 7.965.900,8 bytes for Virtual Memory Size (VMS) with an execution time of 2,619 seconds.
Leveraging BiLSTM and LDA for Analyzing and Dashboarding User Feedback in Applications Mutmainah, Siti; Fudholi, Dhomas Hatta
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.v8i1.7022

Abstract

The idea of prioritizing customer satisfaction to uphold or improve the excellence of a product or service can incorporate the utilization of user feedback. However, to provide a comprehensive visual summary to application developers or stakeholders, it is important to provide a detailed description of user sentiment issues. In this study, the data source used is user feedback from local telemedicine applications in Indonesia. This research builds a framework of deep learning to perform user feedback analysis and applies topic modeling to sentiment clusters. then builds visual construction of research results effectively and efficiently, to facilitate stakeholders in making decisions. Build a framework to analyze user feedback utilizing deep learning BiLSTM + IndoBERT for sentiment classification and LDA to model topics in sentiment groups. The results show that most of the user reviews of the five telemedicine applications have a positive sentiment at 91%. The model used has good prediction performance with the accuracy of the BiLSTM model with IndoBERT 96.44%. The negative sentiment group comprises 12 topics (0.58446), the most significant topics being 35.4% about telephone broadcasting, 25.3% payments, and 8.5% about medicine purchase service. For the imbalanced data case, BiLSTM showed good accuracy and precision values. The classifications and topics generated by deep learning models are affected by improper data labeling, so it is necessary to explore the data labels generated.
Penerapan Monitoring dan Controlling Suhu Ruang Server Berbasis Internet of Things (IoT) Syafii, Olynda Mufariihana Nur; Astutik, Ika Ratna Indra; Hindarto, Hindarto
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.v8i1.7141

Abstract

The server room is a space owned by every company that serves as the control center for database systems in both small and large companies. Operational activities of the server are carried out by administrators (operators) who can monitor and manage the database system on the server. The temperature in the server room is crucial for maintaining the facilities and regulating the temperature when operators are inside. From the above issues, a system can be implemented that is currently widely used by everyone, namely the use of the Internet of Things (IoT). The Internet of Things (IoT) is a concept designed to enable electronic devices to communicate autonomously and exchange data through network connections. The implementation of IoT has the potential for monitoring and control at specific locations. With the presence of IoT, it can integrate IoT, temperature sensor devices, and output applications such as Blynk and WhatsApp. Blynk facilitates the NodeMCU microcontroller and DHT22 to receive and send temperature values to users of the application. In addition to Blynk, another monitoring system uses a WhatsApp Bot that sends temperature notifications in the server room. The IoT with this concept yielded research test results, where the system facilitates users or administrators in monitoring and controlling temperature using Blynk and WhatsApp applications, benefiting from a more user-friendly interface. In conclusion, this research facilitates users or owners of server rooms to manage facilities for longer durability, and users or administrators can monitor the server room temperature without having to be inside.
Sentiment Analysis of Reviews on Lazada Apps using Naïve Bayes Algorithm Nurdiyansa, Zhafran Afif; Berlilana, Berlilana
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.v8i1.7255

Abstract

Lazada app reviews on the Google Play Store become useful information if processed properly. Existing or new users can analyze app reviews to get information that can be used to evaluate the service. The activity of analyzing app reviews is not enough just to look at the number of stars, it is necessary to look at the entire content of the review comments to be able to know the purpose of the review. A sentiment analysis system is a system used to automatically analyze reviews to obtain information including sentiment information that is part of online reviews. This time the data will be classified using the Naive Bayes method. A total of 1,000 user reviews of the Lazada app were collected to form a dataset. The purpose of this study was to conduct sentiment analysis of Lazada app reviews on Google Play Store using Naive Bayes algorithm. This stage of research involves data collection, labeling, pre-processing, sentiment classification, and evaluation. In the pre-processing stage, there are 6 stages, namely Cleaning, Case Factoring, Word Normalization, Tokenization, Hyphen Removal, and Base Word Formation. The TF-IDF (Term Frequency - Inverse Document Frequency) method is used for word weighing. The data will be grouped into two categories, namely negative and positive. Next, the data will be evaluated using accuracy parameters. The test results showed an accuracy value of 84%, then for the grouping of negative and positive reviews, it was found that Lazada application reviews tended to be negative.
Control and Monitoring System of Growing Media for Cucumber Plants Based on the Internet of Things Enriko, I Ketut Agung; Dewi, Mela Kartika; Indriyanto, Slamet; Gustiyana, Fikri Nizar
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.v8i1.7072

Abstract

Cucumber is one of the horticultural commodities that has good prospects for cultivation because cucumber plants can be marketed domestically and abroad. Soil condition and fertility a very important factors in increasing agricultural growth and production. The ideal data for a good soil pH for planting cucumbers is 6-7, for a soil temperature of 18-30C and humidity of 50-60%. Ignorance of farmers about the value and condition of the land can lead to poor production of cucumber plants. Therefore the authors created a control and monitoring system to monitor soil conditions or growing media in cucumber plants. In this system, there is a soil pH sensor, temperature sensor, soil moisture sensor, and automatic sprinkler for fertilizer when the soil pH value is less than the specified limit. This system also applies the Internet of Things concept for sending data on the Telkom IoT Platform platform. Based on the test results of testing the soil temperature sensor, it gets an average error value of 0.67% and an accuracy value of 99.33%. Testing the soil moisture sensor obtains an error value and accuracy of 4.80% and 95.20%, respectively. Whereas in testing the pH sensor which was calibrated using the linear regression method, it obtained an average error value of 1.69% and an accuracy of 98.31%.
Prediksi Jumlah Sampah di TPSA Menggunakan Pendekatan Machine Learning Almira, Venia; Maimunah, Maimunah; Sukmasetya, Pristi
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.v8i1.7278

Abstract

The amount of waste at landfills is increasing along with the growing population and human activities. Predicting the amount of waste has become one of the ways to address waste management issues. The quantity of upcoming waste can be determined through waste prediction, providing essential information for solving waste-related problems. This research involves modeling daily waste predictions using three machine learning algorithms: Linear Regression, Support Vector Regression, and Random Forest Regressor. The data used in this study is the waste data at Banyuurip landfill, Magelang City, covering the period from 2019 to 2022. In the data processing stage, attributes for data usage are selected, daily waste summation is performed, missing values are handled, and normalization is carried out using min-max normalization. The three machine learning algorithms are employed in the prediction modeling stage to obtain optimal parameters. The prediction model is evaluated by calculating the MSE. The results of the three waste prediction models using Linear Regression show a model with an MSE-train of 0.0086 and an MSE-test of 0.0083, while the RMSE-train is 0.0930 and the RMSE-test is 0.0915. The optimal SVR prediction model is obtained with hyperparameter combination C = 1, gamma = 1, and epsilon = 0.05, yielding MSE-train of 0.0030 and MSE-test of 0.0089, with RMSE-train at 0.0556 and RMSE-test at 0.0943. The Random Forest Regressor model results in a model with n_estimators of 500, random_state of 1, without the hyperparameter max_depth, and has MSE-train of 0.0012 and MSE-test of 0.0081, along with RMSE-train at 0.0353 and RMSE-test at 0.0901. Based on these three models, it is concluded that the best model is the Random Forest Regressor with the smallest MSE and RMSE values.
Pengenalan Ekspresi Wajah Menggunakan Transfer Learning MobileNetV2 dan EfficientNet-B0 dalam Memprediksi Perkelahian Handayani, Ni Made Kirei Kharisma; Hidayat, Erwin Yudi; Naufal, Muhammad; 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.v8i1.7048

Abstract

Expressions play an important role in recognizing someone's emotions. Recognizing emotions can help understand someone's condition and be a sign of their possible actions. Fighting is one of the violences that occur due to someone's negative emotions that need to be prevented and treated immediately. In this study, expression recognition is used to predict the possibility of a fight based on the expression shown by a person. The dataset used is FER-2013 which has been modified into two labels, namely "Yes" and "No". The data undergoes a preprocessing step which includes resizing and normalization. Model experiments using transfer learning from the MobileNetV2 and EfficientNet-B0 architectures have been modified by performing hyperparameter and fine tuning which includes freezing the layer by 25% in the first layers of each model and adding several layers such as flatten and dense. In the training process, some parameters used are 30 epochs, batch size 32, and Adam optimization with a learning rate of 0.0001. Model performance evaluation is measured using Confusion Matrix, then the results are compared and obtained the model that produces the best accuracy value is EfficientNet-B0 which is 82%. Meanwhile, based on the training time and model weight, MobileNetV2 is 1 hour 1 minute 43 seconds faster and 21.57 MB smaller than EfficientNet-B0.
Optimasi K-means Clustering Dengan Menggunakan Particle Swarm Optimization Untuk Menentukan Jumlah Cluster Pada Kanker Serviks Indrawan Setiaji; Affandy Affandy; Ahmad Zainul Fanani
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.v7i3.6292

Abstract

Cervical cancer is one of the most common cancers among women in the world. It is most common in developing countries. Cervical cancer develops slowly in the body. Clustering is needed so that cervical cancer can be treated quickly. The K-means method was chosen because of its ability to group large amounts of data and fast computation time. The K-means method is also very easy to implement, flexible, and uses simple principles, which can be explained non-statistically. The many advantages that K-means has, also has weaknesses because it uses random clustering numbers and the results are not optimal. The difficulty in accurately determining the amount of clustering in the dataset. The K-means method cannot provide an optimal solution for determining the number of clustering, so it needs to be improved in order to obtain an optimal solution. PSO was chosen because it has several advantages, namely requiring few parameters, easy to implement, fast convergence, more efficient because it requires little computation and is simple. The results showed that the PSO - K-means method can prove to provide a significant contribution by directly obtaining optimum clustering results without having to do repeated experiments with a Silhouette Coefficient value of 0.83 and a Davies Bouldien Index value of 1.91.
Implementation of Adam Optimizer using Recurrent Neural Network (RNN) Architecture for Diabetes Classification Nugroho, Nur Cahyo Tio; 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.v8i1.7254

Abstract

Non-communicable diseases (NCDs) present a considerable worldwide health dilemma, resulting in considerable expenses for treatment and heightened rates of mortality. Conditions like diabetes mellitus, cardiovascular diseases, cancer, and chronic respiratory diseases are primary causes of global mortality, making up 71% of total global deaths in 2016, as reported by the World Health Organization (WHO). Diabetes Mellitus (DM), marked by prolonged elevated blood glucose levels, stands out as a significant metabolic disorder. This research delves into the implementation of Recurrent Neural Networks (RNNs) utilizing the Adaptive Moment Estimation (Adam) optimizer for classifying Diabetes Mellitus (DM). RNNs, a subset of artificial neural networks tailored for sequential data processing, are employed to make predictions by incorporating recurrent connections. Situated within the dynamic landscape of Artificial Intelligence and Machine Learning, the research exhibits promising outcomes via k-fold cross-validation, confusion matrix analysis, loss graph examination, and classification report. The RNN-Adam model showcases commendable overall performance, achieving an average accuracy of 80.20% through k-fold cross-validation and 81.60% accuracy as revealed by the confusion matrix. This research offers valuable insights into the effectiveness of the RNN-Adam model for diabetes classification.

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