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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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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. 
Komparasi Algoritma Naïve Bayes dan Logistic Regression Untuk Analisis Sentimen Metaverse Ramadhani, Bagus; 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.v8i2.7458

Abstract

Digital transformation makes the world change rapidly, especially in the development of metaverse technology. The development of metaverse technology has received positive and negative responses from the public, so it is necessary to analyze whether public opinion accepts the development of metaverse technology or vice versa. This research aims to analyze 6728 public comment data regarding the metaverse on social media X using a text mining approach. By comparing text mining algorithm models, this experiment seeks to find the best algorithm for metaverse sentiment analysis, thereby providing insight to industry players involved in metaverse development. This research experiment uses a comparison of two algorithms, namely Naïve Bayes and Logistic Regression. The comparison results for the Naïve Bayes algorithm have an accuracy value of 90% and Logistic Regression of 91%, but the precision, recall, and F1-Score results are low. This indicates that the machine predominantly learns positive sentiment because this sentiment has a majority label, namely 5799 positive sentiment data, while negative sentiment is a minority label with 795 data. To overcome the problem of unbalanced data (Imbalance) in this research, SMOTE optimization was used. The results of SMOTE optimization have a superior value in the Logistic Regression algorithm, the accuracy value of 95% has also increased in the confusion matrix, namely the precision value of 94%, recall of 93%, and F1-Score of 95%. Meanwhile, the Naïve Bayes algorithm has a smaller value, namely 91% accuracy, and the negative sentiment confusion matrix has increased to 87% precision, 97% recall, and 92% F1-Score, so the accuracy and confusion matrix values have better performance.
Analisis Peramalan Harga Telur Ayam Ras Dengan Menggunakan Metode SARIMA Hakim, Ilham Luqman; Sanglise, Marlinda; Suhendra, Christian Dwi
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.v8i2.7610

Abstract

Eggs are a widely favored source of animal protein. One commonly consumed type is chicken eggs. Besides being easy to prepare, a notable advantage of chicken eggs is their affordability compared to other sources of animal protein. However, chicken eggs are subject to price fluctuations at specific times, such as religious holidays. These fluctuations can lead to losses for both producers and consumers. One way to address this issue is by forecasting. Forecasting is crucial as it provides estimated price information for the future. With this information, producers and consumers can prepare appropriate strategies to cope with price changes. The price of chicken eggs fluctuates periodically, thus the method employed in this research is Seasonal Autoregressive Integrated Moving Average (SARIMA). SARIMA is a forecasting method specifically used for data exhibiting seasonal patterns. Based on the results of this study, the best SARIMA model obtained is , demonstrating excellent performance with an RMSE value of 1491.30 and a MAPE of 3.40%. From this model, forecasted prices of chicken eggs in the traditional market of Manokwari city for the next 12 months are obtained, spanning from March 2024 to February 2025. According to the forecast results, the price of chicken eggs is expected to rise in March to June 2024 and December 2024 to January 2025.
Sentiment Analysis of Lazada Product Reviews using Convolutional Neural Network and Naïve Bayes Models Siddiq, Ikhsan Maulana; Lhaksmana, Kemas Muslim
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.v8i3.7834

Abstract

Lazada is one of the biggest marketplaces in Southeast Asia. One of the main features of Lazada is product reviews, any customer who has purchased and used a product from Lazada can provide reviews and ratings on the ones that have been purchased. Sentiment analysis on product reviews can help improve product and service quality, increase customer satisfaction, and improve purchasing decisions. Doing sentiment analysis of product reviews aims to help customers how to feel about the product, reading and analyzing each review manually is very not efficient. Sentiment analysis can automate handling large volumes of data quickly and accurately. In this research, using Lazada product review dataset to analyze sentiment by comparing Convolutional Neural Network (CNN) and Naive Bayes. CNN and Naive Bayes are two common methods used in text analysis and a comparison of their performance can provide the effectiveness of each in analyzing product sentiment. In this study, the authors propose to analyze the sentiment of product reviews using deep learning algorithm with CNN method. The results of this study explain that the CNN method can provide satisfactory results than Naive Bayes. Based on the overall evaluation, CNN gets an accuracy value of 99.31%, precision of 99.31%, recall 99.31%, and f1-score of 99.31%, while Naive Bayes gets the highest accuracy rate of 96.16%, precision 96.34%, recall 96.16%, and f1-score 96.16%.
Prediksi Jumlah Bayi Penerima Imunisasi DPT 1 dan DPT 2 Menggunakan Support Vector Regression Idriani R, Nova; Permana, Inggih; Salisah, Febi Nur; Megawati, Megawati; Rahmawita M, Medyantiwi
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.v8i3.7694

Abstract

Vaccination against diphtheria, pertussis (whooping cough), and tetanus is known as DPT immunization, which protects a person from three serious diseases. This vaccine is given in the form of an injection where there are 5 antigens in one injection of the vaccine. DPT immunization is a complete routine immunization that will be continued in grades 1 to 6 elementary school. DPT immunization is feared by mothers because of the side effects that occur in babies after the vaccine injection, namely that the baby will have a fever and be fussy. This has resulted in delays in collecting data on babies who have received this immunization, which has an impact on estimates of babies who will receive DPT immunization in the following month. Of course, this will disrupt the stock of vaccines provided, causing the potential for them to be out of stock. To overcome this problem, it is necessary to collect data on babies who have received DPT in the previous month. This data will be used to predict babies who will receive DPT immunization in the following month using the Support Vector Regression (SVR) method. So that the community health center can provide information regarding the prediction of the number of babies who will receive DPT immunization. This method uses three kernels and a Sliding Window to divide the data into smaller segments, moving alternately across the time series data, making it suitable for predicting babies who will receive DPT immunization in the next time interval. From the three kernels used on the two data that have been separated into DPT 1 and DPT 2, windowing size 3 linear kernels were obtained which were selected as an accurate evaluation of model work on DPT 1 with MAPE values of 3.35, RMSE 0.193, and R2 0.1. And windowing size 3 RBF kernels are more optimal in DPT 2 with MAPE values of 7.86, RMSE 0.163, and R2 0.288.
Implementation of User Sentiment with Naïve Bayes Algorithm to Analyze LinkedIn Application Regarding Job Vacancies in the Play Store Al Ghozi, Dhiyauddin; Hasan, Firman Noor
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.v8i3.7879

Abstract

Mobile applications have become an important part, one of which is the LinkedIn application which is a mobile application that focuses on the recruitment process, job search and as a professional networking platform which is now increasingly relevant, especially in Indonesia. The methodology involves data collection, data preprocessing, data labeling, and application of the Naïve Bayes algorithm. Sentiment analysis can be used as a reference to improve the quality of an application and the level of user satisfaction as well as knowing the number of positive and negative sentiments in user feedback. The 999 data obtained were then divided into 60% training data and 40% test data. In this analysis, negative sentiment outweighs positive sentiment, with a total of 539 negative reviews and 460 positive reviews. Based on evaluation using the confusion matrix, accuracy results were 95.74%, precision was 100%, and recall was 91.46%. This research aims to provide insight into the communication and interaction patterns of LinkedIn users in relation to job opportunities and overall sentiment towards the platform.
Penerapan Algoritma Hash Based Terhadap Penentuan Rule Asosiasi Transaksi Penjualan Sparepart Sepeda Motor Salmon, Salmon; Lailiyah, Siti; Nursobah, Nursobah; 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.v8i2.7410

Abstract

The problem that occurs in increasing sales transactions is that the large number of sales transactions every day with many kinds of spare parts makes it difficult for sales to determine strategies for offering spare parts that are relevant and really needed by consumers. The large amount of transaction data to be analyzed is not possible to do manually. Therefore, a certain technique is needed that can carry out the association rule mining process quickly on quite large data. One technique that can be used for association rules is the Hash algorithm. Hash Based Algorithm Uses hashing techniques to filter out itemsets that are not important for generating the next itemset. Generating frequent itemsets in this research requires a minimum support value and a minimum confidence value. The minimum support value used in this research is the average frequency of spare parts sales. Development is carried out in the process of generating candidate rules using a hash table on transaction data. By using the Hash Based algorithm, the association rule mining process becomes faster and more efficient in memory usage. From the results of the association rules trial with a minimum support of 40% and a minimum confidence of 75%. 14 lists of association rules were produced that met the requirements. This list becomes a reference for sales in determining strategies for offering spare parts to consumers.
Data Mining Dalam Analisis Faktor Drop Out Mahasiswa Menerapkan Algoritma Decision Tree Azhari, Mulkan; Maulana, Halim; Riza, Ferdy
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.v8i2.7379

Abstract

Graduation accuracy is one of the indicators used in assessing the suitability of undergraduate programs as a functional unit of higher education. Knowing the factors that influence student graduation time helps study programs and faculties make decisions to increase the number of students who graduate on time. The purpose of this research is to obtain an overview of the factors that influence students in the accuracy of completing the study time by using the Machine Learning algorithm, namely Decision Tree, which is expected to have high classification efficiency and good description so that it can increase the number of students graduating on time. The methods used to determine student dropout factors are Classification and Regression Tree (CART) and LightGBM. The data used is the data of undergraduate students of Universitas Muhammadiyah Sumatera Utara in 2019. The quality of classification can be read from the accuracy, sensitivity and specification values. The result using CART is 95.1% with the most influencing factors are GPA, faculty, lecture time and predicate while Lightgbm is 83% with the most influencing factors are GPA, gender, lecture time and faculty. Decision tree can be used to determine student dropout factors because of its high accuracy with GPA being the main factor.
Penggunaan Naïve Bayes Classifier dalam Analisis Sentimen Ulasan Aplikasi McDonald's: Perspektif Pengguna di Indonesia Kurniawan, Salsha Dara Shinta; Fauzy, Akhmad
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.v8i3.7765

Abstract

The McDonald's mobile app has become popular among users, who often review their experiences through various review platforms. However, the sheer number of reviews suggests that the app's performance is still not satisfactory. This research aims to analyze public sentiment towards McDonald's app reviews using the Naïve Bayes Classifier algorithm. This algorithm was chosen because of its ability to classify text based on probability and its wide use in sentiment analysis. The research process began with the collection of review data totaling 4.996. Of these, 1.575 data showed neutral sentiment, while 2.137 data revealed positive sentiment, and 1.104 data showed negative sentiment towards the app. However, for the purposes of analysis using the Naïve Bayes algorithm, the focus is only on data that has positive and negative sentiment labels. Thus, the total amount of data used is 3.241 data, consisting of 2.137 positive data and 1.104 negative data. Followed by text pre-processing which includes cleaning, normalization, stopwords, stemming, tokenizing. The dataset is then divided into training data (80%) and testing data (20%). Naïve Bayes Classifier algorithm is used to classify the reviews into positive, negative, and neutral categories ignored. The results show that this model has an 90% accuracy rate in classifying sentiment. This analysis is necessary for the company's evaluation in order to know the public sentiment regarding the McDonald's app. The conclusion of this study shows that although the Naïve Bayes Classifier algorithm is quite effective in the sentiment classification of McDonald's app reviews, it is not enough to classify the sentiment of the McDonald's app.
Analisis Sentimen Pinjaman Online di Twitter dengan Metode Naive Bayes Classifier dan SVM Arischo, Ray Shandy; Damayanti, Damayanti
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.v8i2.7406

Abstract

Online loans are a form of financial service that occurs online or online, where online loans are available in applications or information technology. Online loans can also be a place to develop small and medium enterprises, because they provide easy access to loans and are also relatively safe. The social media platform twitter is one of the platforms that discusses illegal and legal online loans. Twitter has a trending topic feature that displays topics of conversation that are being discussed at a certain time. This research uses sentiment analysis which is useful as access to track public responses to an object of interest. In this study using a comparison of algorithms, namely naïve bayes classifier with support vector machine (SVM), where from the two methods will be sought who is better at analyzing data with which value of accuracy, precision, recall, f1-score is better. The data used in as many as 2725 tweets obtained through the crawling process with the python programming language and google collaboratory tools. Sentiment analysis is divided into 3 categories, namely positive, negative, and neutral, with data calculations divided into 70% training data and 20% test data. The naïve bayes classifier algorithm has an accuracy value of 55%, with a support of 404 data. Meanwhile, the support vector machine (SVM) accuracy is 77% with a support of 818 data. The results of the accuracy value of the SVM method are better than the naïve bayes classifier method in this study.

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