cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
+6282161108110
Journal Mail Official
mib.stmikbd@gmail.com
Editorial Address
Jalan sisingamangaraja No 338 Medan, Indonesia
Location
Kota medan,
Sumatera utara
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
 93   94   95   96   97 
Sistem Pakar Diagnosa Kerusakan Pada Mesin ATM Menggunakan Metode Naive Bayes Mutia Dwi Pratika; Samsudin Samsudin
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.v7i3.6468

Abstract

Detection of damage contained in ATM Machines carried out by employees still has drawback, especially if there are new employees who are still confused about finding damage to the ATM machine. This study aims to build an expert system for diagnosing damage found in ATM machines at PT Advantage SCM Medan which is equipped with solutions for indicated damage. This system was built using the Naive Bayes method where this method will be able to solve this problem, this is because it is able to predict opportunities in the past and this method only requires small training data to determine the estimated parameters it need in the classification process. The design of an expert system for diagnosing damage contained in this ATM machine has 29 symptoms and 6 damages. This expert system is designed using the MySQL database and the PHP programming language. The results of this study are in the form of an accuracy of 90% which is calculated from the comparison obtained between manual data and data in the system.
Metode MICE Support Vector Machine (MICE-SVM) untuk Klasifikasi Performance Mahasiswa Merdeka Belajar Kampus Merdeka Angga Apriano Hermawan; Galuh Wilujeng Saraswati; Etika Kartikadarma
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6821

Abstract

The Ministry of Education and Culture established a Merdeka Belajar Kampus Merdeka (MBKM) program with the aim of improving the competency of student graduates, both soft skills and hard skills, so that they are better prepared and relevant to the needs of the times, preparing graduates as future leaders of the nation who are superior and have personality. However, the MBKM program is not always effective in improving the quality of a student because there are still several shortcomings. It is also felt that some students have not received maximum results when participating in the MBKM program. In fact, not all programs offered by MBKM partners receive an assessment in the form of soft skills scores. The aim of this research is to classify whether the MBKM program influences the performance of MBKM program students by applying the Multivariate Imputation by Chained Equation (MICE) method to overcome missing values in the classification of MBKM student performance at the Faculty of Computer Science, Dian Nuswantoro University. The qualification of MBKM student performance is very important because we need to know whether the program is deemed effective or not to be continued in the future. In this study, researchers used a dataset originating from the MBKM report from students at the Faculty of Computer Science, Dian Nuswantoro University. Researchers obtained data by collecting data from MBKM student certificates and reporting the results. The data taken was 277 pieces for training and 69 pieces for testing. Next, the researchers used the Support Vector Machine (SVM) algorithm for the classification process. The research results show that the performance of the Support Vector Machine (SVM) algorithm model with MICE missing value handling has better accuracy results, with an accuracy value of 98.07% compared to using the Mean Imputation method, which only obtains an accuracy of 97.34%.
Analisis Perbandingan Algoritma SVM, Naïve Bayes, dan Perceptron untuk Analisis Sentimen Ulasan Produk Tokopedia Muhammad Aulia; Arief Hermawan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6839

Abstract

The rapid growth of the online market in Indonesia has changed the business landscape. Tokopedia, one of the leading E-commerce platforms, serves millions of users with a variety of products. In the fierce E-commerce competition, understanding customer reviews is very important. However, performing review analysis manually is a complex and time-consuming task. Sentiment analysis is needed to understand customer preferences, improve service quality, and maintain Tokopedia's competitiveness in the competitive E-commerce market. This study carried out a comparison between three algorithms, Support Vector Machine, Perceptron, and Multinomial Naïve Bayes to evaluate and determine the most effective and accurate algorithm in conducting sentiment analysis of product reviews on Tokopedia. The results of research using 2000 Tokopedia product review data show that Multinomial Naïve Bayes has the highest level of accuracy, reaching 84.00% and precision of 96.00%. Support Vector Machines has an accuracy rate of 80.00% and a precision value of 95.00%. Meanwhile, Perceptron provides 81.00% accuracy and 95.00% precision. Evaluation using the confusion matrix also indicates that Multinomial Naïve Bayes provides superior results with a truth level of 1011 for positive sentiment labels and 860 for negative sentiment labels. This research provides valuable insights regarding sentiment analysis of product reviews on Tokopedia, and the results can be a reference for further research exploring more innovative sentiment analysis methods or the application of technology to increase the efficiency of sentiment analysis in the context of E-commerce.
Perbandingan Keefektifan Metode Case-Based Reasoning dan Certainty Factor dalam Sistem Pakar Diagnosis Penyakit Multiple Sclerosis Hanifah Ekawati; Ita Arfyanti; Tommy Bustomi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6574

Abstract

The management of complex neurological diseases such as Multiple Sclerosis (MS) requires accurate and efficient diagnostic approaches. To enhance diagnostic precision, a study has conducted a comparison between two approaches within the framework of an expert system, namely the Case-Based Reasoning (CBR) Method and the Certainty Factor (CF) Method. The primary objective of this study is to evaluate the effectiveness of these two methods in supporting the diagnosis process of Multiple Sclerosis. The Case-Based Reasoning Method is an approach that relies on past experiences to address new issues. Within an expert system, CBR utilizes knowledge from previous cases to identify diagnoses that align with the current situation. On the other hand, the Certainty Factor Method is an approach that measures the confidence level in a statement based on rules and associated confidence factors. This study makes use of a dataset containing information from previous cases related to the diagnosis of Multiple Sclerosis. By employing both of these methods, an expert system is developed to provide diagnostic recommendations based on inputted symptoms and data. The effectiveness of both approaches is evaluated through diagnostic accuracy, computational speed, and confidence levels in the generated results. Research findings indicate that both methods have their respective strengths and weaknesses. The CBR method tends to yield accurate results by referring to similar cases in the past, but it may encounter challenges in unique or rare cases. On the other hand, the Certainty Factor Method has the ability to handle uncertainty and can produce results with measurable confidence levels. However, dependence on predefined rules may limit adaptation to new cases. In conclusion, this study underscores that there is no singular perfect approach within expert systems for diagnosing Multiple Sclerosis. Both the CBR and Certainty Factor methods contribute in their own ways to improving accuracy and confidence in the diagnosis process. Therefore, integrating these two methods could be a promising direction for the development of expert systems in the future.
Sentiment Analysis of the Jakarta - Bandung Fast Train Project Using the SVM Method Muhammad Daffa Dhiyaulhaq; Putu Harry Gunawan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6855

Abstract

Web growth contributes greatly to user-generated content such as user feedback, opinions and reviews. The construction of the Jakarta-Bandung High Speed Train is both an icon and a momentum for Indonesia to modernize mass transportation in an era of continuous progress. Sentiment analysis is one of the text-based research field solutions suitable for addressing satisfaction issues based on user reviews. In this research, the system will be made with review sentences from users and produce output in the form of positive and negative classes. The method used by the author is classification using the Support Vector Machine (SVM) method and Word2Vec extraction features. In addition, a comparison of the accuracy value between the Support Vector Machine method, Naïve Bayes method and TF-IDF extraction features is carried out. The data studied came from several news websites containing user reviews of the Jakarta-Bandung High Speed Train. This method is used because it represents words in a vector, besides that the training process is faster when compared to other extraction features. This research resulted in the performance of accurasy, precision, recall, and f1-score, namely accurasy of 82.74%, precision of 75.68%, recall of 97.67%, and f1-score of 85.28%. These results were obtained using the best tuning hyperparameters, namely ('C': 10, 'gamma': 0.1, 'kernel': 'rbf'). Then in the second scenario a comparison is made with the Naïve Bayes method. It was found that the accuracy of the Support Vector Machine method using the TF-IDF extraction feature obtained better and stable performance results than the other three performance results, which amounted to 86.90%. So the author concludes that the Support Vector Machine method using the TF-IDF extraction feature is better when compared to the Naïve Bayes method and the Word2vec extraction feature.
Otoritas Guru Dalam Prestasi Belajar Siswa Menggunakan Fuzzy Mamdani Desyanti Desyanti; John Suarlin; Rudi Faisal
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.v7i3.6368

Abstract

Education is a systematic effort to create good learning conditions in increasing one's potential to have good knowledge, but in the teaching and learning process one often finds students who cannot achieve achievements in accordance with their intelligence and also the teacher's attitude in teaching and educating. The teaching and learning process is sometimes less effective, teachers tend to provide material using books and blackboards, thereby reducing students' interest in learning and affecting their learning achievement. Teachers have a huge influence on each student's development, but not all teachers are able to exercise their authority in the classroom. Teachers will be able to exercise their authority if they have good teaching skills. The use of fuzzy mamdani is able to calculate the extent of the teacher's authoritative relationship to student learning achievement based on the assessment data carried out. Based on the research results, the level of authority is divided into 3 membership functions (Bad, Fair, Good), from this data the highest value is 0.74 in the good membership function, 0.25 is sufficient and 0.00 is bad. So the category of the level of relationship between teacher authority and student learning achievement with a score of 84.95 is in the Good category. The value obtained from the calculation results can be a benchmark for the teacher's attitude and upbringing towards students.
BERTopic Modeling of Natural Language Processing Abstracts: Thematic Structure and Trajectory Samsir Samsir; Reagan Surbakti Saragih; Selamat Subagio; Rahmad Aditiya; Ronal Watrianthos
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.v7i3.6426

Abstract

The rapid growth in the academic literature presents challenges in identifying relevant studies. This research aimed to apply unsupervised clustering techniques to 13,027 Scopus abstracts to uncover structure and themes in natural language processing (NLP) publications. Abstracts were pre-processed with tokenization, lemmatization, and vectorization. The BERTopic algorithm was used for clustering, using the MiniLM-L6-v2 embedding model and a minimum topic size of 50. Quantitative analysis revealed eight main topics, with sizes ranging from 205 to 4089 abstracts per topic. The language models topic was most prominent with 4089 abstracts. The topics were evaluated using coherence scores between 0.42 and 0.58, indicating meaningful themes. Keywords and sample documents provided interpretable topic representations. The results showcase the ability to produce coherent topics and capture connections between NLP studies. Clustering supports focused browsing and identification of relevant literature. Unlike human-curated classifications, the unsupervised data-driven approach prevents bias. Given the need to understand research trends, clustering abstracts enables efficient knowledge discovery from scientific corpora. This methodology can be applied to various datasets and fields to uncover overlooked patterns. The ability to adjust parameters allows for customized analysis. In general, unsupervised clustering provides a versatile framework for navigating, summarizing, and analyzing academic literature as volumes expand exponentially.
Peningkatan Akurasi Pembacaan Lembar Jawaban Komputer dengan Memperbaiki Ketidaksimetrisan Citra Hasil Pemindaian Menggunakan Transformasi Homografi Prayitno Prayitno; Guruh Fajar Shidiq; Ahmad Zainal Fanani; M. Arief Soeleman
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6651

Abstract

Answer Sheet Reading Computers are one of the technologies for converting images into information which continues to develop until now. Some of the applications of this technology include correcting various school exams, psychological tests, surveys and voting. Accuracy is something that is often a problem in various studies on reading computer answer sheets. Accuracy is greatly influenced by the scanned image. In the process of scanning computer answer sheets, images often produce asymmetry, such as tilting, shifting and dilatation.The process of scanning computer answer sheets often produces asymmetrical images, such as tilted, shifted and dilated.  This incident will affect the accuracy of the results of reading the computer answer sheet, due to deformation of the shape between the reference image and the scanned image. This study aims to improve asymmetric images to become symmetrical with the Homografi transformation in order to get better reading accuracy. The results showed that the improvement of image symmetry with Homografi transformation was better than the skew correction method. This is shown from the respective RMSE values, the Homografi transformation method produces an RMSE value of 51.54 and the skew correction method produces a value of 67.04. The results of the study also stated that the accuracy of reading computer answer sheets with the Homografi transformation method was better than skew correction. The skew correction accuracy is 95.8%, while the Homografi transformation is 99.3%.
Implementasi Aplikasi Berbasis Mobile menggunakan Framework React Native dan Algoritma C4.5 untuk Rekomendasi Kelayakan Penerima Bantuan Yatim Impian Indonesia Mochammad Alvian Kosim; Setiawan Restu Aji; Muhammad Darwis
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6783

Abstract

One of the programs of Yatim Impian Indonesia, a foundation that focuses on social and education, is sharing carts for MSMEs (Gedor UMKM). Cart beneficiaries are selected based on certain qualifications so that they are considered eligible. However, in its implementation, there are problems in determining the eligibility of beneficiaries quickly. One of them is that the selection criteria have not been structured. To overcome the difficulties in determining the eligibility of beneficiaries quickly, a classification model is needed. This research aims to create a cross-platform mobile application that has the function of classifying the eligibility of beneficiaries by utilizing the C4.5 decision tree algorithm. This algorithm can provide recommendations to the foundation regarding whether the beneficiary is eligible to get a cart, based on the performance of the decision tree in the C4.5 algorithm. Furthermore, the classification model obtained from the decision tree is implemented in a mobile application to meet the needs of foundation administrators who are on duty in the field in conducting surveys. Using K-Fold Cross Validation Testing, the results of the C4.5 algorithm classification model show 81% accuracy in determining the eligibility of beneficiaries, and the results of the mobile application built with the React Native Framework which have been tested with Black Box testing show that all functional parts of the mobile application have run well.
Perbandingan Prediksi Obat Berdasarkan Pemakaian Menggunakan Algoritma Single Moving Average dan Support Vector Regression Said Nurfan Hidayad Tillah; Alwis Nazir; Iwan Iskandar; Elvia Budianita; Iis Afrianty
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6859

Abstract

To ensure the availability and quality of drugs, Public Health Centers (PHC) must pay attention to the planning and procurement process. The problem that often arises is the increase in drug stock due to the stable use of drugs each month, resulting in excess and expired drugs that are not used. In addition, it is necessary to avoid inappropriate drug demand, which affects stock availability. Drug usage prediction is done with several methods such as the Single Moving Average (SMA) algorithm in the data mining method and the Support Vector Regression (SVR) algorithm in the machine learning method. This algorithm was chosen because the drug data of Diazepam 5 mg and Mefenamic Acid 500 mg is sustainable from January 2020 to June 2023 (42 months). Implementation using the Phyton programming language. Testing using the Mean Absolute Percentage Error (MAPE) method, this study aims to measure the accuracy of predictions in each algorithm. In research with Diazepam 5 mg and Mefenamic Acid 500 mg drugs, with a division of 80% in training data and 20% in test data. With a calculation of 3 periods, the SMA algorithm produces MAPE values of 4.10% and 4.29%, in the "very good" range. The SVR algorithm, which uses an RBF kernel with a complexity parameter of 1.0 and an epsilon parameter of 0.1, produces MAPE results of 7.35% and 9.52%, in the "Very Good" range. Thus, the SMA algorithm predicts better than the SVR algorithm.

Page 95 of 119 | Total Record : 1182

 93   94   95   96   97 

Filter by Year

2017 2024


Reset
Filter By Issues
All Issue Vol 8, No 3 (2024): Juli 2024 Vol 8, No 2 (2024): April 2024 Vol 8, No 1 (2024): Januari 2024 Vol 7, No 4 (2023): Oktober 2023 Vol 7, No 3 (2023): Juli 2023 Vol 7, No 2 (2023): April 2023 Vol 7, No 1 (2023): Januari 2023 Vol 6, No 4 (2022): Oktober 2022 Vol 6, No 3 (2022): Juli 2022 Vol 6, No 2 (2022): April 2022 Vol 6, No 1 (2022): Januari 2022 Vol 5, No 4 (2021): Oktober 2021 Vol 5, No 3 (2021): Juli 2021 Vol 5, No 2 (2021): April 2021 Vol 5, No 1 (2021): Januari 2021 Vol 4, No 4 (2020): Oktober 2020 Vol 4, No 3 (2020): Juli 2020 Vol 4, No 2 (2020): April 2020 Vol 4, No 1 (2020): Januari 2020 Vol 3, No 4 (2019): Oktober 2019 Vol 3, No 3 (2019): Juli 2019 Vol 3, No 2 (2019): April 2019 Vol 3, No 1 (2019): Januari 2019 Vol 2, No 4 (2018): Oktober 2018 Vol 2, No 3 (2018): Juli 2018 Vol 2, No 2 (2018): Apriil 2018 Vol 2, No 1 (2018): Januari 2018 Vol 1, No 3 (2017): September 2017 Vol 1, No 2 (2017): Juni 2017 Vol 1, No 1 (2017): Maret 2017 More Issue