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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
 108   109   110   111   112 
Enhancing Sentiment Analysis of Garden by the Bay Reviews on TripAdvisor Platform Using CRISP-DM through DT and SVM with SMOTE Singgalen, Yerik Afrianto
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.v8i2.7485

Abstract

This research aims to improve sentiment analysis of reviews related to Garden by the Bay, a prominent tourist destination in Singapore, by leveraging the CRISP-DM methodology and Synthetic Minority Over-sampling Technique (SMOTE). The study employs a comprehensive approach, integrating CRISP-DM phases to systematically collect, clean, and analyze data from online reviews. The dataset comprises a substantial number of reviews, reflecting diverse visitor experiences. Using SMOTE, class imbalance issues within the dataset are addressed, leading to enhanced performance of sentiment analysis algorithms. The evaluation of Decision Tree (DT) and Support Vector Machine (SVM) algorithms, both with and without SMOTE, reveals significant improvements in accuracy, precision, recall, and F-measure metrics when SMOTE is applied. These findings underscore the efficacy of SMOTE in optimizing sentiment analysis algorithms for the Garden by the Bay dataset, thereby facilitating a deeper understanding of visitor sentiments and experiences, which inform strategies for enhancing the tourism experience at Garden by the Bay.
Alat Pendeteksi Dini Titik Api Kebakaran Hutan Menggunakan Komunikasi LoRa (Long Range) Rizky, Muhammad; Zarory, Hilman; Ullah, Aulia; 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.v8i3.7768

Abstract

Forests play an important role in maintaining environmental balance. Forests provide oxygen, host a variety of animals, protect soil, regulate water, and provide cultural, recreational and livelihood resources for humans. Despite their important role, forests are often threatened by fires caused by natural and human factors. Forest fires can cause economic losses, environmental damage, and adverse effects on human health. This research aims to create a tool that can detect forest fires early, monitor the condition of the forest environment in real time, stores data in the databas, and send early warnings via WhatsApp application. In this research, the tool uses sensors such as MQ-2, DHT22, and Soil Humidity Moisture which are connected to the ESP32 microcontroller, as well as LoRa communication technology to send data. This system can work well even in remote areas without a network. The test results show that this tool can provide accurate information about environmental conditions such as air temperature, humidity, and CO2 levels. This research makes an important contribution by providing an effective solution to monitor and prevent forest fires early, especially in remote areas that are difficult to reach by networks. With this tool, it is expected to reduce the negative impact of forest fires on the environment, economy, and human health. This system is expected to be an effective tool in monitoring and preventing forest fires early
Analisis Sentimen Publik Terhadap Pengungsi Rohingya di Indonesia dengan Metode Support Vector Machine dan Naïve Bayes Ananda, Dhea; Suryono, Ryan Randy
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.v8i2.7517

Abstract

The arrival of Rohingya refugees in Indonesia has become a highly controversial topic, eliciting various responses from the public. In this context, public sentiment analysis regarding the arrival of Rohingya refugees is crucial for understanding the dynamics of feelings, opinions, and attitudes of the Indonesian society towards this issue. In conducting public sentiment analysis, the selection of methods is crucial to ensure accurate results. The aim of this research is to conduct sentiment analysis regarding the arrival of Rohingya refugees using the Support Vector Machine (SVM) and Naive Bayes methods. The main focus is to evaluate public sentiment and compare the performance of both methods. Two common methods used in sentiment analysis are Support Vector Machine (SVM) and Naïve Bayes. This research utilized a dataset of 3350 tweets to conduct public sentiment analysis on the arrival of Rohingya refugees in Indonesia. In this study, data was divided using the 70:30 split method, where 70% of the data was used for model training and 30% for model testing. The research findings indicate that the SVM model has an accuracy of 76%, while the Naïve Bayes model has an accuracy of 70%. This suggests that the SVM model is better at predicting sentiments and has lower error rates compared to the Naïve Bayes model.
Kolaborasi Metode Naive Bayes dan MPE dalam Pengambilan Keputusan Pemilihan Supplier Ban Motor Agustina, Nani; Sutinah, Entin; Martini, Martini
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.v8i2.7538

Abstract

Almayra Jaya Ban is a company operating in the trading business sector which faces complexity in managing various products originating from several different suppliers. The main problem faced by this company is the difficulty in processing different supplier data for optimal evaluation in terms of supplier potential, quality of goods, and availability of goods from various suppliers. Supplier selection is an important decision that can affect operational performance and profits for the company. To increase accuracy and objectivity in decision making, this research uses collaboration between the Naive Bayes Method and the Exponential Comparison Method (MPE). Through experiments and sensitivity analysis, the results show that the integration of these two methods produces more stable and reliable decisions. Validation is carried out using historical data and trials to ensure accuracy. So by collaborating between the Naive Bayes Method and MPE it can be an effective approach in the decision making system for selecting motorbike tire suppliers, by providing more accurate, objective and accountable decisions for this company. The results of this research obtained a decision in selecting suppliers by selecting 7 alternative suppliers to be retained in the motorbike tire procurement cooperation, namely: Dermaga Jaya Motor with a score of 218.90, Ardendi Jaya Sentosa with a score of 217.90, Kumala Motor with a score of 213.08, Sinar Maju Motor got a score of 209.90, Raissa Jaya Motor got a score of 200.90, Nias Jaya Motor got a score of 198.18, Ciruas Jaya Motor got a score of 195.00.
Analisis Sentimen Pengguna Twiter terhadap Perubahan Kebijakan Skripsi sebagai Syarat Wajib Kelulusan menggunakan Metode Naïve Bayes Classifier Hablinawati, Laela; Dzikrullah, Abdullah 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.v8i3.7746

Abstract

The Minister of Education, Culture, Research, and Technology, Nadiem Makarim, has issued a policy to abolish theses, dissertations, or final papers as mandatory graduation requirements for undergraduate and postgraduate students in universities. The requirement to write a thesis is still enforced in most universities in Indonesia to obtain a bachelor's degree. The advancement of information system technology and the ease of accessing social media have caused news to spread rapidly. This policy has sparked pros and cons among the public, including on the social media platform X (formerly Twitter). Some people agree with it, considering that it can reduce the burden on students and increase the relevance of higher education to the needs of the job market. However, others argue that abolishing theses could lower the quality of university graduates and that the replacement could be even more burdensome. The purpose of this research is to understand Twitter users' sentiments towards the policy of abolishing theses as a graduation requirement and to determine the accuracy of the Naïve Bayes Classifier in classifying these sentiments. The data used consists of 656 tweets, which were processed through several stages, including cleaning, case folding, normalization, stopword removal, tokenizing, and stemming. The data was then labeled using a lexicon-based approach, resulting in 353 negative labels and 273 positive labels. The data was subsequently weighted using TF-IDF for the classification process. The dataset was split into training and testing data with a ratio of 90:10. After classification, the study found that the Naïve Bayes Classifier successfully categorized sentiments with an accuracy of 76%.
Implementasi Algoritma Transformers BART dan Penggunaan Metode Optimasi Adam Untuk Klasifikasi Judul Berita Palsu Subagyo, Ageng Ramdhan; 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 | DOI: 10.30865/mib.v8i3.7852

Abstract

Classification is a process of identifying new data provided based on validation of previous data. One classification process that can be used is fake news classification. The classification process requires as little time as possible to get maximum results, so a faster method is needed to classify news. The BART algorithm can be a method that can be used to carry out classification and use Adam optimization to improve the performance of the algorithm. The aim of this research is to classify fake news, whether the BART algorithm and Adam optimization are able to provide good results and to label whether the news is fake or not. The results of this process are based on the use of a dataset of 65% for training, 30% for validation, and 5% to produce 2 BART models. With the additional use of Adam optimization and several other parameters for the training process, the first model was able to provide accuracy performance of 92.88%, training loss reached 12.2%, and validation loss reached 28.4% and the second model produced an accuracy of 92.63 %, training loss 15% and validation loss reaching 20.2%. In the first model, it can predict 105 data labeled negative and 1306 positive data. Meanwhile, the second model was able to predict 128 data labeled negative and 1283 positive data.
Penerapan Data Mining Untuk Pengelompokan Terhadap Kualitas Kinerja Karyawan Dengan Menggunakan Algoritma K-Medoids Clustering Karim, Abdul; Esabella, Shinta; Kusmanto, Kusmanto; Hidayatullah, Muhammad; Suryadi, Sudi
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.v8i2.7445

Abstract

HR management is recognized as a global issue and an integral part of competitiveness in the arena of globalization. The organizational structure is the placement of tasks from the very top to the placement of very basic tasks. Assessing the quality of employee performance is one of the work evaluation sessions that can provide the best for industry and citizens. In position placement, if someone does not suit the position they have, it will cause problems such as the company's operational processes not running well. Performance appraisal of employees aims to see the performance results that have been carried out or given by employees when occupying a position. Problems related to performance appraisal are important problems that must be resolved immediately. Data mining is a data processing process in the past, where data in data mining is a collection of data that has been collected over a certain period of time. Information data mining is a series of processes for exploring added value in the form of data produced by extracting and identifying patterns in an information base. Clustering is a part of data mining that aims to group based on the formation of new clusters. The K-Medoids algorithm is a partitional clustering procedure that minimizes the distance between labeled points. The K-Medoids algorithm is a classic Clustering partition technique that groups data sets of ni objects into k groups known a priori. From the results of research conducted using the K-Medoids method, 3 clusters were obtained. Where in cluster 1 there are 4 employees, in cluster 2 there are 3 employees and in cluster 3 there are 3 employees.
Optimasi Random Forest dengan Genetic Algorithm dan Recursive Feature Elimination pada High Dimensional Data Stunting Samarinda Satria, Bima; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
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.v8i3.7883

Abstract

Stunting is a chronic malnutrition problem that disrupts children's growth, with long-term impacts on physical growth, cognitive development, and productivity in adulthood. In Indonesia, the prevalence of stunting is still above the WHO threshold, reaching 24.4% according to the 2021 Indonesian Nutritional Status Study (SSGI), and in Samarinda City, the prevalence reached 24.7% in 2021 with 1,402 toddlers identified as stunted. Addressing this problem requires a more structured data-driven approach to provide targeted interventions. This study uses data from the Samarinda City Health Office, encompassing 150,474 stunting data points, and involves data collection, data cleaning, feature selection, and classification model application. This study aims to improve the accuracy of stunting data classification in Samarinda City in 2023 using the Random Forest algorithm enhanced with Recursive Feature Elimination (RFE) feature selection techniques and Genetic Algorithm (GA) optimization. The feature selection results using RFE show that the most influential features are Weight, ZS TB/U, ZS BB/U, and BB/U. The application of RFE increased the model's average accuracy from 91.91% to 93.64%, while GA optimization further increased the average accuracy to 98.39%. The definite accuracy increased from 94.23% (baseline model) to 97.10% (with RFE) and reached 99.70% (with RFE and GA). The combination of RFE and GA has proven effective in tackling data complexity and improving the reliability of stunting predictions. This study significantly contributes to the development of machine learning techniques for high-dimensional data analysis in health and is expected to be the foundation for more effective intervention programs in addressing stunting issues in Indonesia.
Penerapan Machine Learning Pada Analisis Sentimen Twitter Sebelum dan Sesudah Debat Calon Presiden dan Wakil Presiden Tahun 2024 Dwinnie, Zairy Cindy; Novita, Rice
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.v8i2.7504

Abstract

The 2024 Presidential Election has become the hottest topic in the past two years. The KPU has confirmed that there are 3 candidates for President and Vice President. For this reason, as a momentum for voters to assess the 2024 Presidential and Vice Presidential candidates, the KPU is holding the 2024 Presidential Choice Debate which is based on Law Number 7 of 2017 concerning General Elections. Based on the information presented on the kpu.go.id page, the debate will be held 5 times with 3 presidential candidate debates and 2 vice presidential candidate debates. For this reason, it is necessary to carry out an analysis to find out how public sentiment is positive, negative, and neutral on Twitter towards the three candidates for President and Vice President in 2024 before and after the debate was held. The aim is to estimate public support or disapproval of the three candidate pairs. This research uses three algorithms as a comparison of classification accuracy, namely the Support Vector Machine algorithm, Random Forest, and Logistic Regression. Where the data used is tweet data on Twitter related to before and after the debate as many as 30 datasets with a total of 9000 data. From the classification results, the average accuracy obtained for the three algorithms, namely SVM and Random Forest, was 78%, and Logistic Regression was 79%. The highest polarity obtained from the classification of the three algorithms is in the positive class. This indicates that the Logistic Regression algorithm provides better performance in classifying Twitter sentiment regarding the 2024 presidential and vice presidential candidate pairs.
Penerapan Algoritma K-Means Menggunakan Model LRFM Dalam Klasterisasi Nilai Hidup Pelanggan Afifah, Tiara Afrah; Novita, Rice; Ahsyar, Tengku Khairil; Zarnelly, Zarnelly
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.v8i2.7605

Abstract

In implementing customer relationship management, there are still many companies that have not utilized CRM optimally as part of their business strategy. As is the case with UD Sandeni. UD Sandeni still has problems in managing its relationships with customers because UD Sandeni does not fully understand the difference between customer information that is profitable and unprofitable for the company's sustainability. UD Sandeni has used a system to manage customer transaction data. However, this system is only used to calculate profits and create bookkeeping for registered agents so that UD Sandeni does not have an in-depth understanding of the characteristics of its customers. To overcome this problem, the solution that can be applied is to use customer grouping techniques, such as clustering. Customer transaction data is processed using a clustering process with K-Means and LRFM. Test the validity of cluster results using DBI and calculate CLV values using AHP weights to produce cluster rankings. The results of this research obtained customer clustering which consists of 2 segments, namely cluster 1 which has the highest CLV value of 0.3171156 with a total of 298 customers and includes the High Value Loyal Customers segmentation, and cluster 2 with a CLV value of 0.1434054 with a total of 72 customers. which is included in the segmentation of uncertain new customers (uncertain lost customers).

Page 110 of 119 | Total Record : 1182

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