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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
 115   116   117   118   119 
Implementasi Data Mining Metode K-Means Menggunakan Framework Python Dalam Mengelompokkan Pegawai Berdasarkan Data Presensi Mira, Mira; Nurcahyo, Azriel Christian; Gudiato, Candra; Kusnanto, Kusnanto
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.v8i3.7716

Abstract

Computerized employee attendance data collection provides convenience in terms of real-time monitoring. The computerized attendance data collection process has been widely implemented by government and private agencies. With a computerized system, the data collection process is easier to carry out. The data that has been collected increases as time passes. To improve discipline in employee attendance patterns. Perumdam Tirta Bengkayang implements an attendance application with features consisting of working hours, shifts, attendance location and is equipped with documentation of incoming and outgoing absences in the form of selfies attached to the application. The data that has been collected is then analyzed to assess employee discipline levels using data mining techniques, namely the K-Means method. Data mining methods are used to group employee attendance data patterns. Data mining is the process of collecting data, and finding patterns or relationships between data. The K-Means method works by dividing data into k closest clusters. The calculation begins by determining the value of k, centroid, and closest point. Meanwhile, the analysis uses the Python library by importing the necessary libraries such as numpy, pandas, matplotlib, sklearn. Based on the results of the analysis and grouping of employees, 26.76% of employees fall into cluster 0, namely the low level of discipline, 71.83% of employees fall into cluster 1, namely the medium level of discipline, and 1.41% of employees fall into cluster 2, namely the high level of discipline.
Penerapan Model Hybrid Convolutional Neural Network dan Long Short-Term Memory untuk Pengenalan Real-Time Sistem Isyarat Bahasa Indonesia (SIBI) Hidayat, Syahrul; Via, Yisti Vita; Mandyartha, Eka Prakarsa
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.v8i3.7837

Abstract

The Indonesian Sign Language System (SIBI) is an essential means of communication for the deaf and speech-impaired community in Indonesia. However, the limited public understanding of SIBI often hinders effective communication. This study develops a real-time SIBI sign recognition model to facilitate effective communication for the deaf and speech-impaired in Indonesia. The proposed method integrates a hybrid CNN-LSTM model to process the spatial and temporal information from the data. The study evaluates the model's performance on 25 types of SIBI signs. The dataset used consists of image sequences captured in real-time. Training is conducted with various parameters, including batch size, learning rate, and epochs. Model evaluation is carried out using accuracy, precision, recall, and f1-score metrics. The training and validation results show an increase in accuracy with the number of epochs: 87% at 10 epochs, 93% at 25 epochs, and 100% at 50 epochs. In real-time detection tests, the model with the image sequence dataset accurately detected SIBI signs in environments and with objects consistent with the dataset. The real-time detection program generates SIBI sign predictions in text form and sentences. The output of this research is efficient and accurate SIBI sign recognition technology. This research is expected to facilitate more effective communication for the deaf and speech-impaired community in Indonesia.
Analisis Performa Akurasi Klasifikasi Citra Jenis Sayur Salada Menggunakan Arsitektur VGG16, Xception dan NasNetMobile Nurafiya, Nurafiya; 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.v8i3.7661

Abstract

Salad is a type of leafy vegetable belonging to the Compositae family, genus Lactuca. It is rich in nutrients, including fiber, vitamin A, and minerals. Salad greens can cleanse the blood and fat, help people with coughs, and prevent high cholesterol, constipation, and insomnia. With the increasing population and awareness of the benefits of a balanced diet, consumer demand for lettuce has significantly increased. Consequently, farmers have expanded lettuce cultivation to meet consumer demand, which has the potential to cause errors in sorting different types of lettuce. Therefore, research focusing on the classification and detection of lettuce varieties is crucial to help farmers efficiently harvest lettuce based on its type using Convolutional Neural Network (CNN) methods, comparing three models: VGG16, Xception, and NasNetMobile. Data were directly obtained from lettuce farms and Kaggle. After pre-processing steps such as resizing and augmentation, the data were trained with various amounts, 200 epochs, and 64 batches during the architectural modeling stage. Based on the research results, the accuracy analysis with image classification of various types of lettuce concluded that using the Convolutional Neural Network (CNN) method by comparing three models VGG16, Xception, and NasNetMobile can classify each type of lettuce based on its class with high accuracy. In the tests conducted on the trained model, using an input size of 120 x 120, 200 epochs, and a batch size of 64, the NasNetMobile architecture model achieved the highest scores with an accuracy of 98.33%, precision of 97.8%, recall of 97.9%, and an F1-score of 97.8%. With these excellent accuracy values, the researchers hope that this analysis will make a significant contribution to the development of a superior and more efficient image classification system for agriculture, especially in selecting the appropriate CNN architecture.
Sistem Deteksi Kosakata Bahasa Isyarat Secara Real Time dengan Tensorflow Menggunakan Metode Convolutional Neural Network Widjaya, Vincent Surya; Wasito, Ito
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.v8i3.7714

Abstract

Most people are not used to communicating via sign language because sign language is not a mandatory language to learn so very few people understand how to communicate using sign language. This problem for people with special needs to interact and communicate with other people, especially those who do not understand how to communicate using sign language. Convolutional Neural Network is the method that will be used in this research because this method is one of the deep learning methods that currently provides the best results in object detection. The Convolutional Neural Network method is able to imitate human abilities in the image recognition system of the human visual cortex, so that it is able to process visual information. This research used Tensorflow to develop various models as well as work related to other statistical analysis. The evaluation metrics results obtained from this research show precision of 82.99%, recall of 84.42% and f1-score of 83.68%. The value of average precision is 0.830% and average recall is 0.844%. There is also a loss value produced by this model of 0.039065. For accuracy results from real time sign language detection, the accuracy value was 94.64%.
Faktor Terkait Kinerja Perawat di Rumah Sakit Datu Beru Takengon Tahun 2023 Rana, Revaranda Genta; Nababan, Donal; Tarigan, Frida Lina; Sitorus, Mido Ester J; Wandra, Toni
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.v8i3.8267

Abstract

This research focuses on finding out factors related to the performance of nurses at Datu Beru Takengon Regional Hospital. The aim of this research is to determine the factors related to the performance of nurses at Datu Beru Regional Hospital. This research is a type of quantitative research, with a research design using a cross-sectional approach. The sample in this study consisted of 72 nurses at Datu Beru Takengon Regional Hospital using a simple random sampling technique, processing bivariate data using the chi-square test. The data collection method uses a questionnaire. The results of statistical tests show that there is no relationship between income and nurse performance (p value 0.399), there is no relationship between motivation and nurse performance (p value 0.605), there is a relationship between work stress and nurse performance (p value 0.000), and there is a relationship between workload and work behavior can be seen that (p value 0.000). Based on the assessment of the workload of room nurses, the results showed that the workload level of nurses at Datu Beru Regional Hospital was mostly moderate. The results obtained from the assessment of nurses' work stress were that the level of work stress for nurses at Datu Beru Regional Hospital was classified as moderate. There needs to be an evaluation of load, work stress and other possible factors so that the work tasks assigned to nurses are appropriate
Sentimen Analisa Ulasan Aplikasi Access by KAI pada Google Play Store menggunakan Algoritma K-NN Jiana, Nur Afina; Hartono, Budi
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.v8i3.7730

Abstract

Access by KAI is an application developed by PT. Kereta Api Indonesia (KAI) that plays an important role in simplifying the train ticket booking process for its users. The application has been downloaded more than 10 million times through the Google Play Store, with a review rating of 2.2 out of a scale of 1 to 5. This relatively low rating indicates a level of dissatisfaction among users. Moreover, the high number of negative reviews compared to positive reviews could also cause bias in the analysis results. Reviews from other users greatly influence the perception of the app's performance and quality among potential users. Therefore, this study aims to conduct a sentiment analysis of the application reviews published on the Google Play Store. The review data analyzed includes 1300 negative reviews and 457 positive reviews considered most relevant to provide a comprehensive overview of user perception. The method used in this analysis is the K-Nearest Neighbor (K-NN) Algorithm. The results of the study show an accuracy rate of 80%, a precision of 73%, a recall of 63%, and an f1-score of 65%. These findings are expected to provide insights for the app developers to improve the quality and performance of the Access by KAI application.
Analisis Sentimen Cyberbullying Pada Media Sosial X Menggunakan Metode Support Vector Machine Syahira, Melani Alka; Kurniawan, Rakhmat
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.v8i3.7926

Abstract

Twitter or the platform now known as social media X is now one of the social networks most popular. Its popularity not only does it have a positive impact but it also has a negative impact on both users and non-users of the X platform. The negative impact is a lot many social media users use it to insult or defame, known as Cyberbullying. Cyberbullying is a deliberate act and occurs virtually through verbal intimidation or ongoing harassment on the internet or social media. Cyberbullying can cause serious emotional impacts for the victim, such as depression, behavior changes, mood swings, and sleep and appetite disorders. To overcome this problem, sentiment analysis using data from X to determine the level of accuracy with the Support Vector Machine algorithm. Data was collected through Crawling as many as 1000 data, then Preprocessing was carried out. After preprocessing, data labeling was carried out manually, there were 157 positive data and 843 negative data. Then, the data was separated into two parts, namely 80% training data and 20% testing data. The results of data processing showed 87% accuracy, 88% precision, 99% recall, and 93% f1-score.
Analisis Tingkat Kemiskinan di Indonesia Menggunakan Model Vanilla Long Short-Term Memory dan Stacked Long Short-Term Memory Fajar, Rizqi Al; Sasongko, Theopilus Bayu
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

Abstract

Indonesia, as the fourth most populous country in the world and a developing nation, faces significant challenges in addressing widespread poverty. Poverty is a condition where individuals or groups have limited access to adequate economic resources, quality food, healthcare services, and education. Despite government efforts to implement programs aimed at reducing poverty levels in Indonesia, these programs have often been ineffective and poorly targeted. The objective of this research is to compare the performance of two Long Short-Term Memory (LSTM) models, Vanilla LSTM and Stacked LSTM, in analyzing poverty levels in Indonesia. The data used for this study is from the year 2021 and encompasses 514 cities across Indonesia. This data includes variables such as the percentage of the impoverished population, regional gross domestic product, life expectancy, average years of schooling, and per capita expenditure, all of which are relevant to Indonesia's economic and social conditions.The research employs Vanilla LSTM and Stacked LSTM models. Evaluation is conducted using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), and Mean Absolute Error (MAE) as the main metrics to measure the accuracy of the model predictions. The results indicate that Vanilla LSTM consistently outperforms Stacked LSTM, achieving an MSE of 0.0109, RMSE of 0.1046, NRMSE of 0.1334, and MAE of 0.0795. In contrast, Stacked LSTM shows an MSE of 0.0119, RMSE of 0.1091, NRMSE of 0.1391, and MAE of 0.0833. These findings suggest that Vanilla LSTM has lower and more stable prediction errors and is more accurate in estimating poverty levels. Vanilla LSTM is therefore a better choice for analyzing and reducing poverty levels in Indonesia. This model can serve as an effective tool for policymakers to design more efficient and targeted strategies to reduce poverty rates.
Sentiment Analysis on TikTok App using Long Short-Term Memory (LSTM) with Stochastic Gradient Descent (SGD) Optimization Rizky, Muhammad Zacky Faqia; Sibaroni, Yuliant; Prasetiyowati, Sri Suryani
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.v8i3.7699

Abstract

TikTok is currently one of the most popular social media apps. The site contains content that is creative, educational, innovative, as well as content that features lifestyle, cyberbullying, and inappropriate behavior. These diverse contents can trigger both positive and negative sentiments. This research aims to analyze the sentiment of the TikTok application by integrating feature extraction techniques, feature expansion, and optimization algorithms to improve the performance of the Long Short-Term Memory (LSTM) model. This research uses a dataset of 15,049 TikTok app reviews from the Google Play Store. Sentiment analysis is performed through four scenarios: the first scenario uses the LSTM model as the basis for classification, the second scenario combines LSTM with Word2Vec as feature extraction to convert initially unstructured text data into a structured format, the third scenario integrates LSTM and Word2Vec with FastText as feature expansion to improve the quality of representation and the model's ability to understand complex contexts, and the fourth scenario adds the Stochastic Gradient Descent (SGD) optimization algorithm to help improve the performance of the LSTM model. The results obtained showed that through the integration of feature extraction techniques, feature expansion, and optimization algorithms, the performance of LSTM increased by 7.44%. This research successfully developed an effective method that proved positive outcomes and will contribute to the development of a sentiment analysis system designed to help policymakers and application developers solve negative issues.
Perbandingan Algoritma Naïve Bayes dengan K-Nearest Neighbor Untuk Analisis Sentimen Aplikasi InDrive di Playstore Irfan, Muhammad; Erizal, Erizal
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.v8i3.7780

Abstract

Transportation is already part of the main needs in moving from one place to another. One of them is land transportation which is the most widely used in daily needs. There are various technology companies that are competing to create online-based transportation, one of which is inDrive. Of the several online transportation services, inDrive has a different system that can negotiate or bargain for transportation rates directly. it is interesting to do an analysis to find out whether the system is worth maintaining or it will get constructive criticism so that in the future it can improve the quality of the inDrive application. Review or comment data taken from google play through google colab as much as 1200 data and processed using RapidMiner. Testing is carried out in two stages, namely training data and testing data, training data that is greater than testing data will affect the accuracy of a method. The purpose of this research is to see various positive and negative reviews in the inDrive application and make a comparison between the Naïve Bayes method and K-Nearest Neighbor with the results of 97.50% accuracy, 92.71% precision and 100% recall for Naïve Bayes. While accuracy 83.21%, precision 85% and recall 57.30% for KNN, from these results it can be concluded that the Naïve Bayes method has superior accuracy in making classifications.

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