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
 52   53   54   55   56 
Peningkatan Akurasi Metode K-Nearest Neighbor dengan Seleksi Fitur Symmetrical Uncertainty Anirma Kandida Br Ginting; Maya Silvi Lydia; Elviawaty Muisa Zamzami
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3254

Abstract

Accuracy of K-Nearest Neighbor (KNN) tends to be lower than other classification methods. The cause of this is related to the attributes used and the percentage of the influence of these attributes on the classification process in a data. And also attributes with less relevant influence can be a problem in determining the new class. One way that can be done to overcome this is by doing Feature Selection. In this research, the author selects features on K-Nearest Neighbor by using Symmetrical Uncertainty to remove attributes that have an unfavorable effect from the data set. Testing of the proposed method uses data sets obtained from the UCI Machine Learning Repository. The results obtained from testing the proposed method using feature selection with Symmetrical Uncertainty are able to increase the classification accuracy of KNN, with an increase in accuracy obtained after feature selection is 3.00 %.
Expert System Mengatasi Anxiety Disorder Pada Mahasiswa Dalam Menghadapi Tugas Akhir Metode Backward Chaining Indah Risfia; Dewi Maharani; Muthia Dewi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i2.4001

Abstract

Anxiety Disorder is a psychological disorder that can be experienced characterized by the emergence of various symptoms which cause the sufferer to experience anxiety that is so great and excessive that it is very possible to affect the sufferer psychologically or physically. The obstacles that are most often experienced by final year students in completing their final assignments are the emergence of excessive feelings of anxiety, feeling restless, easily tired, difficult to concentrate, empty thoughts. irritability, muscle tension, difficulty sleeping, cold fingers, faster heart rate, cold sweats, headache, decreased appetite, sleeplessness and chest tightness which is often referred to as Anxiety Disorder. The relevance of computerization and technology in expert systems in diagnosing anxiety disorders in final year students is needed to help students find solutions to their problems. Therefore we need a method that can assist the analysis of the expert system design that will be made in this study, namely the Backward chaining method. Backward Chaining is an inference method that works backwards towards the initial conditions. The process starts from the Goal (which is in the THEN section and the IF-THEN rule), then the search starts to run to match whether the facts match the premises in the IF section. This technological advancement can help transfer human understanding into a system form so that it can be used by many people and is not limited by time without replacing the role of humans. 
Analisis Sentimen Pembelajaran Campuran Menggunakan Twitter Data Ronal Watrianthos; Muhammad Giatman; Wakhinuddin Simatupang; Rahmi Syafriyeti; Nelly Khairani Daulay
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3383

Abstract

Mixed learning methodologies have been disputed in terms of educational quality when it comes to self-study. Educators face specific challenges when making hybrid learning work since they must adjust to teaching while also strengthening their technological abilities. The blended learning paradigm, which is commonly adopted in many educational institutions, can produce a slew of issues for students. The goal of this essay is to gather thoughts about distance learning based on social media comments. This study creates a classification model using Twitter tweet data by assessing public perceptions and acceptance of the mixed learning model. The findings of examining thoughts regarding this model include categorizing tweets as favorable or unfavorable using the Twitter sentiment analysis approach. The results revealed an almost equal polarization of positive and negative sentiments, with 44.51 percent positive and 45.80 percent negative. More research can be done to analyze attitudes not just on Twitter but also on other social media platforms to improve public opinion accuracy about mixed learning in a pandemic crisis.
Penerapan Rapid Application Development (RAD) Dalam Pengembangan Aplikasi Penjualan Ikan Cupang Hias Berbasis Web Septian Muhammad Fauzi; Mohammad Iwan Wahyuddin
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i2.3555

Abstract

Betta.dja is a small business that sells ornamental betta fish located in Perum. Depok Jaya Agung, Jl. Apple 8, Ex. Rangapan Jaya, Depok City, in this transaction, usually Betta.Dja does COD (Cash On Delivery) for customers who want to buy fish on social media, or come directly to the store, because they do not have a sales website to provide information to customers. At this time Betta.dja recapitulates the sale of ornamental betta fish using only Microsoft Office. Because it is very inefficient in managing data. If any processed data is lost or damaged, it can result in inaccurate data. The application for selling ornamental betta fish uses a web-based RAD (Rapid Application Development) method. In this study, the expected result is an ornamental betta fish website by providing fast and accurate information.
Internet of Things (IoT) Based Free Fall Motion Instructions in Physics Subjects for Class X Students Muhammad Nabil Fauzan; Novian Anggis Suwastika; Erwid Musthofa Jadied
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i2.3774

Abstract

Physics subjects are one of the most difficult materials for students to understand. According to the research that has been done, props are one of the answers to make it easier for students to understand physics lessons. Since most physical materials are directly related to everyday life, props serve as a practical medium to facilitate the learning process. Learning physics concepts is easier to understand by using props which we simulate based on real events. One of the materials in physics class is free-fall motion. In this material, when an object falls from a height and has no initial velocity, its falling velocity is calculated. In this study, we apply the Internet of Things (IoT) to the props of free fall material and by adding Ambrose's concepts namely practice and feedback, so that students can better understand the material of free-fall motion. By implementing IoT, the system can read, record, and evaluate the experimental activities performed by users, and users who already have an account can access it online through the website. The system was evaluated based on system functionality and accuracy generated by the system. Based on the test results, it was found that all functions included in the system were 100% working. Based on the three tests performed, the system achieved an average accuracy of 80%.
Klasifikasi Penyakit Diabetes Pada Imbalanced Class Dataset Menggunakan Algoritme Stacking Yoga Pristyanto; Acihmah Sidauruk; Atik Nurmasani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3442

Abstract

Diabetes is a disease that has the potential to cause death. Based on a report from the IDF (International Diabetes Federation), it was stated that in 2019 there were 463 million people in the world suffering from this disease. According to the Ministry of Health, Indonesia is a country that is included in the top 10 highest in the world by the number of people with diabetes. Machine learning models can be a solution for the early detection of diabetes based on history and available data. The majority of the research that has been done chiefly uses a single classifier. The single classifier model has a weakness when faced with class imbalance conditions in the dataset. Therefore, this study uses the Stacking Model for the classification and prediction process on the diabetes dataset. The goal is to improve the performance of a single classifier. In addition, the Stacking Model is expected to be one of the solutions for the classification of diabetes in the imbalanced class dataset. Based on two test experiments that have been carried out using two different datasets. The Stacking algorithm can produce an accuracy value of 89%, TPR value of 89%, TNR value of 85%, and G-Mean of 86.98% in the first dataset and can produce an accuracy value of 96%, TPR value of 96%, TNR value of 94%, and G-Mean of 94.99% in the second dataset. These results are better than single classifiers such as C4.5, K-NN, and SVM of the four indicators evaluated in both diabetes datasets. Thus, the proposed algorithm, namely Stacking (C4.5-SVM), can be a solution for classifying diabetes datasets with unbalanced class distribution conditions.
Kombinasi Pembobotan Symmetrical Uncertainty Pada K-Means Clustering Dalam Peningkatan Kinerja Pengelompokan Data Suranta Bill Fatric Ginting; Sawaluddin Sawaluddin; Muhammad Zarlis
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3366

Abstract

Based on several studies that examine the K-Means Clustering method, it was found that in K-Means Clustering one of the weaknesses lies in the process of determining the center point of the cluster which also has implications for distance calculations in determining the similarity between data to obtain conclusions from the data. a cluster. And this is also caused by the influence of the percentage of the attributes used. If the attributes used are less relevant to their level of influence and also have a low contribution to the data, this can have a significant impact on the results of clustering. So from these problems, in this research, the author proposes to use the method in calculating the weight of data attributes in the clustering process, namely using Symmetrical Uncertainty. To test the proposed method, this research uses a dataset from UCI Machine Learning which consists of Iris with 150 data and Wine Quality with 178 data. The evaluation of the proposed clustering performance is based on the Davies-Bouldin Index (DBI) value. The test results in this study show that the proposed method can produce a significantly smaller Davies-Bouldin Index (DBI) value.
Evaluasi Kinerja Karyawan Kontrak Menggunalan Metode Fuzzy Tsukamoto Kartika Sari; Rosma Siregar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3441

Abstract

Contract employees are employees who work in contact with a certain time agreement. However, there are times when contact employees with good performance will change their status to permanent employees. To determine whether an employee is a permanent employee, an evaluation of whether the employee's performance is worthy of being appointed as a permanent employee is required. However, to carry out this evaluation, a variable is needed to make an assessment. In the performance evaluation it is not easy to determine the value of each variable. To assist an HRD in determining the appointment of a contact employee to become a permanent employee, a decision support system is needed to facilitate HRD work. The decision support system is made using the Tsukamoto fuzzy logic method because the Tsukamoto fuzzy has a tolerance for value data. The result of the research is that the employee can be appointed as a permanent employee with a value of 93.4. The purpose of this decision support system is to determine whether or not contract employees are eligible to become permanent employees based on alternative disciplines, ways of working and behavior.
Klasifikasi Image Untuk Jenis Buku Bacaan Anak-Anak dengan Menggunakan Convolutional Neural Network Sri Winiarti; Cendani Wukir; Ulaya Ahdiani; Taufiq Ismail
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i2.3504

Abstract

This study was made to classify the types of children's reading books based image on the on the cover. The types of books used in this research are fairy tales, educational books and comics. The problem that occurs is that there are many types of reading books so that there are no errors in identifying children's literature, then classification is carried out. Uses approximately 1002 image data. This study uses the method Convolutional Neural Network (CNN). The purpose of this study was to classify the types of children's reading books to suit the age of the reader. It is hoped that the research objectives can make it easier for parents to find books that are appropriate for their child's age. Convolutional Neural Network (CNN) is a method deep learning that is usually used to process data in the form of images. The research stages start from literature study, data collection, data processing, needs analysis, design, implementation and testing. The collection uses several methods, namely: literature study, documentation method, interview and questionnaire method. The design is carried out from input image data, followed by calculating the accuracy and classification process which results in image classification. Implementation using the Python programming language. Evaluation of the performance of testing the accuracy value using the confusion matrix. The result of the research is a system that can classify the types of children's reading books using 70% training data and 30% test data. With an accuracy rate above 80%.
Penerapan Algoritma Coupled Linear Congurential Generator (CLCG) pada Algoritma Kriptografi One Time Pad (OTP) dalam Proses Mengamankan Pesan Deny Nugroho Triwibowo; Dony Ariyus
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 3 (2020): Juli 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i3.2244

Abstract

Message is an idea, feeling, or thought of someone whose contents can be in the form of science, entertainment, information, advice, or propaganda. Nowadays, with the development of very advanced technology, one can exchange messages so easily and quickly without any limitations on distance and time. However, with the ease of exchanging messages there are problems that can occur, one of which is the message you want to send opens opportunities for people who want to steal data and information from the message to use it as a crime and will certainly harm certain parties. Therefore, the technique of securing messages is used by using the OTP algorithm and the CLCG random number generator so that messages sent to the security level can be guaranteed. The results of the merging of the OTP and CLCG algorithms in the encryption and decryption process found random key generator does not occur the same key loop with the same characters adjacent to the message. The use of periodic tables in the encryption process also increases the difficulty of deciphering messages because one plaintext character is replaced by many characters in the ciphertext

Page 54 of 119 | Total Record : 1182

 52   53   54   55   56 

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