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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
 106   107   108   109   110 
Implementasi Metode Naive Bayes Classifier Terhadap Klasifikasi Topik Kemacetan Lalu Lintas Indonesia Melalui Tweet Romindo, Romindo; Barus, Okky Putra; Pangaribuan, Jefri Junifer
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.v8i2.7470

Abstract

The causes of traffic congestion in Indonesia include traffic accidents, poor road infrastructure, and the increasing number of motor vehicles. In 2023, the number of vehicles reached 152.6 million, exceeding half of Indonesia's population of 276 million, according to the Indonesian Traffic Police Corps data. Twitter has a user base of approximately 4.23% of the total global population, which amounts to 436 million user and Indonesia is one of the countries with the largest number of Twitter users. Twitter data will be used to determine the sentiment level of traffic congestion in Indonesia using the Naïve Bayes Classifier method to evaluate overall accuracy performance, precision, recall, and f1-score. The research classified two groups, negative and positive. Classification is carried out through several stages, including data pre-processing, data training, data testing, and evaluation. After evaluating the Naive Bayes algorithm, the highest results achieved an overall accuracy of 77%, precision of 86%, recall of 82%, and f1-score of 84%.
Penerapan Algoritma Fuzzy C-Means untuk Klasterisasi Customer Lifetime Value menggunakan Model LRFMD Ramadhani, Indah; Afdal, M; Mustakim, Mustakim; Zarnelly, Zarnelly
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.v8i3.7613

Abstract

PT X is a retail company engaged in printing. The company has not differentiated between information about profitable and unprofitable customers for the company. Transaction data is only used as profit and loss information so they do not know the characteristics of the customers they have. In addition, the lack of extensive services in the merchandise category is one of the reasons the company's revenue has not reached the predetermined target. Currently, the company has opened additional services in the merchandise field. This research aims to identify customer segmentation as well as analyze the characteristics and provide a strategy proposal that will be submitted to PT. X. Customer loyalty and characteristics have a significant impact on a company. To identify customers who show loyalty to the company, the Fuzzy C-Means algorithm is used to perform clustering, using the Davies Bouldin Index (DBI) to evaluate the clustering results. The model used is in accordance with the principles of Length, Recency, Frequency, Monetary and Diversity (LRFMD) to categorize purchasing patterns. By analyzing LRFMD variables, it is possible to identify customers who are loyal to the company and those who are not. This research produces 6 clusters with the best cluster or supestar customer in cluster 6, the second best value customer or golden customer is cluster 2, the average value customer or typical customer is cluster 4 and 5 and the lowest cluster or dormant customer is in cluster 3.
Optimasi Performa Random Forest dengan Random Oversampling dan SMOTE pada Dataset Diabetes Hasbi, Hasbi; 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.v8i3.7855

Abstract

Diabetes, or high blood sugar, is a chronic condition that needs careful monitoring. If left untreated, it can lead to severe complications. This research aims to accurately diagnose diabetes, addressing the issue of class imbalance in the dataset, which can affect the model's classification accuracy. The goal is to improve diabetes classification accuracy using balancing methods, specifically the Synthetic Minority Over-sampling Technique (SMOTE) and Random Oversampling. These methods are applied to data from patients diagnosed with diabetes and those who do not have the disease.The initial step in the research involved addressing class imbalance by applying SMOTE and random oversampling to generate synthetic samples for the minority class. This was followed by data normalization using the min-max normalization method. Subsequently, the Random Forest Classifier was used to train the model for classification. The results demonstrate that this approach enhances the model's ability to identify diabetes cases, achieving an accuracy of 96%. This represents a 1% improvement over the accuracy of 95% reported in previous research.
Implementasi Data Mining dengan Menerapkan Algoritma K-Means Clustering untuk Memberikan Rekomendasi Jurusan Kuliah Bagi Mahasiswa Baru Arfyanti, Ita; Bustomi, Tommy; Haristyawan, Ivan
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.v8i2.7429

Abstract

At the tertiary level, a student studies in a field of expertise or major that suits his or her area of talent and interest. Choosing an inappropriate college major will have consequences for the future of the prospective new student. In choosing a major, a prospective new student should choose a major that suits his abilities, both academically and his talents. One way to overcome prospective students who are wrong in choosing this major is to use the K-Means Clustering method. The K-Means algorithm is part of clustering data mining which has the role of forming new groups based on cluster formation. The K-Means Clustering algorithm can solve the problem of recommending majors to prospective new students based on school grades. The results of applying the K-Means algorithm show that in Cluster 1 there are 6 prospective students, in Cluster 2 there are 11 prospective students and in Cluster 3 there are 3 prospective students.
Public Sentiment Dynamics: Analysis of Twitter/X Data on the 2024 Indonesian Election with NB-SVM Satyananda, Karuna Dewa; Sibaroni, Yuliant
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.v8i3.7710

Abstract

This research analyzes the dynamics of public sentiment towards three pairs of presidential candidates in the 2024 Indonesian Election. This research was conducted using Twitter data as a source of information to gain a deeper understanding of the pattern of public sentiment during six crucial phases in the context of the election. The data is analyzed periodically during the election period. Sentiment analysis was carried out using the Naïve Bayes-Support Vector Machine classification approach to understand the sentiment patterns that emerged in each phase. NB-SVM utilizes class frequency information from NB to weight features, then trains separate SVMs for each class using these weighted features, improving classification accuracy. Models using NB-SVM classification produce better accuracy than models using NB and SVM classification, with an average accuracy of 76%. In Pair 01, a dynamic pattern was formed, namely a decrease in the level of positive sentiment during the debate and increasing again at a later time. Meanwhile, for Pair 02 and 03, a pattern was not formed for different reasons, namely sentiment that was too stable for Pair 02, and unstable sentiment for Pair 03. While Pair 01 obtained the most positive sentiment, Pair 02 received the most negative, with an average of 65.19% during the election process. This research proves that the results of sentiment analysis on Twitter/X contradict the official results by KPU of the general election in Indonesia.
Optimization of Sentiment Analysis Classification of ChatGPT on Big Data Twitter in Indonesia using BERT Sinaga, Frans Mikael; Purba, Ronsen; Pipin, Sio Jurnalis; Lestari, Wulan Sri; Winardi, Sunaryo
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.v8i3.7861

Abstract

This research is grounded in the emergence of ChatGPT technology, supported by prior and similar studies. The urgency of the issue is highlighted by previous research indicating non-convergent classification outcomes in LSTM (Long Short-Term Memory) methods due to suboptimal hyperparameter settings and limitations in understanding text data within Big Data. The presence of ChatGPT technology brings both benefits and potential misuse, such as copyright infringement, unauthorized news extraction, and violations of accountability principles. Understanding public sentiment towards the presence of ChatGPT technology is crucial. The research aims to implement the BERT (Bidirectional Encoder Representations from Transformers) method to achieve accurate and convergent sentiment analysis classification. This study involves data preprocessing stages using Natural Language Processing (NLP) techniques. Text data, already vectorized, is classified using BERT to determine public sentiment (positive, negative, neutral) towards ChatGPT technology, ensuring greater accuracy, convergence, and contextual relevance. Performance testing of the BERT model is conducted using a Confusion Matrix. With parameters set to Max Sequence Length = 128 and Batch Size = 16, the highest classification accuracy achieved is 93.4%.
Komparasi Algoritma Machine Learning dalam Klasifikasi Kanker Payudara Afiatuddin, Nurfadlan; Wicaksono, M Teguh; Akbar, Vitto Rezky; Rahmaddeni, Rahmaddeni; Wulandari, Denok
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.v8i2.7457

Abstract

Every year, millions of women are faced with a serious global health issue: breast cancer. This research aims to improve the efficiency of breast cancer classification using machine learning. One of the main challenges encountered is the imbalance between the number of malignant and benign cases in the dataset. Therefore, this study aims to compare the performance of several machine learning algorithms in classifying breast cancer, such as Decision Tree, Naive Bayes, K-Nearest Neighbors, Logistic Regression, and Random Forest. Preprocessing data, dividing data with various ratios, and testing various classification algorithms are the techniques used in this research. The dataset used originates from the Wisconsin Breast Cancer Diagnosis dataset from the Kaggle platform. The Synthetic Minority Over-Sampling Technique (SMOTE) is used to achieve balance in the proportions of imbalanced classes. After hyperparameter tuning, Logistic Regression showed the best performance with accuracy reaching 100% in several situations. This study concludes that the use of machine learning, especially with techniques for handling class imbalance, can improve the ability to detect breast cancer early. Additionally, this research also helps understand the best algorithms to improve accuracy in classifying breast cancer, providing support for healthcare professionals in early diagnosis, and enhancing the quality of patient care.
Implementasi Algoritma Fuzzy C-Means menggunakan Model LRFM untuk Mendukung Strategi Pengelolaan Pelanggan Aini, Delvi Nur; Afdal, M.; Novita, Rice; Mustakim, Mustakim
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.v8i3.7616

Abstract

The same treatment of all customers will cause customers who are not so valuable to become value destroyers in the concept of Customer Relationship Management. Providing discounts and promos to all customers without differentiating customer segments has not provided significant benefits for a company. These two things are being experienced by BC 4 HNI Pekanbaru, so changes are needed in evaluating the strategies taken to maintain relationships with customers and form segments according to customer characteristics. Customer segments can be analyzed from sales transaction data. The purpose of this study is to manage and group sales transaction data in determining customer segmentation so that the strategy is more targeted. The analysis of customer transaction data was carried out by grouping the data using the Fuzzy C-means algorithm and the length, recency, frequency, monetary (LRFM) model, and AHP weighting.  The formation of the number of validated clusters of the silhouette index and ranking is carried out by multiplying the weight of AHP to find the customer lifetime value (CLV) so that it can be known which customer groups provide high value to the company. The result of this study is that BC 4 HNI Pekanbaru customers are grouped into 2 segments, namely the potential customer group which has a fairly frequent transaction value with an average monetary value of Rp. 2,802,495.00 and a fairly high number of transactions contribute greatly to the Company and the new customer group which means a new customer segment with uncertain funds, an average monetary of Rp. 104,567.00. Based on the segment, BC 4 HNI Pekanbaru can carry out a strategy in managing its customers according to the type of segment generated from this research.
Prediksi Potensi Keterlambatan Pembayaran Biaya Kegiatan Sekolah Menggunakan Algoritma Naïve Bayes Solehan, Solehan; Sugiarto, Sugiarto; Mahdiana, Deni; Kharmytan, Yan Baktra
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.v8i3.7831

Abstract

Tuition fees are one of very important component in the implementation of education and school development, because tuition fees are the main requirement to be able to implement school programs that have been designed in the one school year activity plan, apart from that, school fee is also used for maintenance or development of school facilities and infrastructure. Cempaka Vocational School is a private school in Central Jakarta, region two which requires its students to pay school activity fees by paying for each activity (7 days before the activity takes place) or in installments every month (total activity costs for one year divided by twelve ). Meanwhile, based on data obtained from the school treasurer, there were 23.2% arrears in the 2020/2021 school year and 38.7% arrears in the 2021/2022 school year (The data used in this research was taken from student payment data for the 2020/2021 and 2021/2022 school years ) which has not been resolved by students, this will become a big problem for Cempaka Vocational School if a solution is not immediately found to overcome this problem. The aim of this research is to build a prediction system using the Naïve Bayes method which will produce an accurate or late classification in paying school activity fees to be used as a recommendation in policy making and finding solutions early on so that there are no delays in paying school fees by students. /i. The results of this research produced an accuracy of 70.83%, precision of 70.59% and recall of 85.71 so that it could predict delays in school activity costs according to the needs of Cempaka Vocational School.
Analisis Sentimen Masyarakat Terhadap Tiktok Shop di Twitter Menggunakan Metode Naive Bayes Classifier Andrian, Eka; Isnain, Auliya Rahman
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.v8i2.7530

Abstract

This research aims to analyze public sentiment towards TikTok Shop through the Twitter platform using the Naive Bayes Classifier Algorithm. This algorithm is used to evaluate public views regarding TikTok Shop and identify Positive and Negative sentiments. The data used in this research is 3,816 data. Then, there are Positive sentiment results of 53.45% and Negative of 46.55%. After analyzing the data, the accuracy result is 78.22% using the Split Data operator. After that, for the results of the Naïve Bayes Classifier implementation on the Recall value has a result of 84% and for the class precision result of 86%. The purpose of this research is to evaluate public views on TikTok Shop through the Twitter platform by utilizing the Naive Bayes Classifier Algorithm. This algorithm is used to analyze sentiments that arise regarding TikTok Shop, with a focus on identifying whether the sentiment is Positive or Negative. This analysis is also used to find out different public opinions about TikTok Shop, such as user experience, features used, and impacts experienced. Therefore, sentiment analysis and natural data processing use the Python programming language to categorize user comment data through a splitting process.

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