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
 85   86   87   88   89 
Tourism Recommendation System using Weighted Hybrid Method in Bali Island Diffo Elza Pratama; Dade Nurjanah; Hani Nurrahmi
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.v7i3.6409

Abstract

Tourism is a promising sector for global economic growth, as it has shown resilience during the global crisis. In Bali, tourism is a leading sector alongside agriculture and industry, making a significant contribution to regional and community development. However, Bali's popularity as a sought-after tourist destination also raises the need for an information system that can provide destination recommendations. To overcome the problem of information overload, a recommendation system is needed. This study tested the tourism recommendation system in Bali using the Weighted Hybrid technique which combines two methods, namely Collaborative Filtering and Content-Based using the weighted value technique. Collaborative Filtering, Content-Based, and Weighted Hybrid approaches will be compared in this study to improve the performance and accuracy of current recommendation systems. Utilizing the MAE, MSE, and RMSE values, the evaluation is carried out by comparing the evaluation matrices of the three Collaborative Filtering, Content-Based, and Weighted Hybrid methods. With MAE, MSE, and RMSE values of 0.4854, 0.4034, and 0.6351 respectively, the evaluation findings show that the Weighted Hybrid technique beats Collaborative Filtering and Content-Based with a weight value of 0.4.
Optimasi Perawatan Pasien Rawat Inap Berbasis Android Untuk Pemantauan Cairan Infus Sumarno Sumarno; Arief Wisaksono; Mochammad Luthfan Hakim; Riski Yulianto; Hindarto Hindarto
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.v7i3.6060

Abstract

Good and thorough patient care at the hospital is very necessary, so that someone who is treated at the hospital can be served properly, one of which is giving infusions to patients. So far, many hospitals or puskesmas provide infusions manually, relying on nurses or those who look after the patient. This study aims to provide Android-based infusion fluids, so that nurses or caregivers can find out the condition of the patient's infusion. The research used is quantitative research with experimental methods. The location of the research was carried out at the Siti Suaibah clinic located in Tempel Village, Kec.Krian, Kab.Sidoarjo. Data processing methods are used to sort data according to the topic where the data produces something from a research and also Coding data. The research carried out consisted of two stages, the first was making software and the second was making hardware. The results of the research are sending infusion fluid data every 10-12 seconds, and auto refreshing on the blynk application every 5 seconds with a measurement error percentage of 2.15 from the results of manual measurements as well as sending notifications on smartphones if the volume of intravenous fluids is below 6 mm by 70 time. The data sharing process was successful because the data held by the ultrasonic and Mlx90614 temperature of the infusion fluid were successfully stored in the Blynk database via the Arduino Wemos D1R2.
Perbandingan Metode Penyesuaian Kontras Citra Pada Pengenalan Ekspresi Wajah Menggunakan Fine-Tuning AlexNet Akhmad Sarif; Dadang Gunawan
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.v7i3.6382

Abstract

Research related to facial expression recognition (FER) has become a significant topic of interest in the field of computer vision due to its broad applications. Artificial intelligence technologies, such as deep learning, have been applied in FER research. The use of deep learning models in FER requires a dataset for training, which plays a crucial role in determining the performance of deep learning. However, the available FER datasets often require preprocessing before being processed using deep learning. In this study, a comparison of contrast adjustment preprocessing methods was conducted using Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). Subsequently, the dataset images were used with a fine-tuned deep learning model, specifically AlexNet, to classify them according to the categories of human facial expressions. The objective of this research is to determine the superior contrast adjustment method for FER dataset images in improving the performance of the deep learning model employed. The CK+ dataset (The Extended Cohn-Kanade) and KDEF dataset (The Karolinska Directed Emotional Faces) were used in this study. The results indicate that the CLAHE method outperforms HE in both the CK+ and KDEF datasets. In the CK+ dataset, the CLAHE method achieved an average accuracy of 93.21%, while the average accuracy of the HE method was 91.50%. For the KDEF dataset, the average accuracy of the CLAHE method was 88.35%, compared to 84.70% for the HE method.
Misogyny Text Detection on Tiktok Social Media in Indonesian Using the Pre-trained Language Model IndoBERTweet Perwira Hanif Zakaria; Dade Nurjannah; Hani Nurrahmi
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.v7i3.6438

Abstract

Social media is a popular communication and information platform due to its ease and speed of access. By using social media, one can express himself freely. This triggers irresponsible individuals to utter hate speech with the aim of bringing down a person or group of people. Misogyny is a form of hate speech directed at women. The problem of misogyny should not be underestimated because misogyny can be one of the main reasons women feel miserable. In this study, a model will be built to detect misogyny text on the Indonesian language TikTok social media using the IndoBERTweet pre-trained model. IndoBERTweet is a pre-trained model based on the BERT model, which has been trained using Indonesian language datasets taken from the previous Twitter social media, resulting in a good performance for detecting misogynous texts on social media by classifying them. The dataset used is in the form of text data taken from misogyny comments by focusing on forms of misogyny in the form of stereotypes, dominance, sexual harassment, and discredit in short video content on women's TikTok social media accounts. The performance of built model performs hyperparameter settings which include batch size 16, epochs 10, and learning rate 7e-5 and is evaluated using a confusion matrix with the best accuracy results of 76.89%.
Sentiment Analysis on Tweets of Kanjuruhan Tragedy Using Deep Learning IndoBERTweet Adhyaksa Diffa Maulana; Kemas Muslim Lhaksmana
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.v7i3.6115

Abstract

The incident that occurred in Indonesian football at the Kanjuruhan Stadium was caused by unscrupulous supporters who entered the field and unscrupulous officers who fired tear gas into the stands. With this incident, many responses and opinions were given by the Indonesian people through social media Twitter in the form of positive, negative, and neutral opinions. This difference in opinion occurred because of the many victims who died or were injured, with many supporters who did not like the actions taken by the authorities during the riots. With this incident, the government must make decisions to ease the concerns of the community. Therefore, research will be conducted to analyze the sentiment of public opinion regarding the Kanjuruhan tragedy using the IndoBERTweet method with a comparison using naive Bayes. The results of this study using the IndoBERTweet method get better results than naive Bayes method. With the results of the IndoBERTweet method 88% accuracy, 82% precision value, 85% recall value, and 84% f1-score value, naive the Naive Bayes results are 62% accuracy, 59% Precision Value, 61% Recall Value, and f1-Score of 59%.
Analisis Sentimen Publik Terhadap Elektabilitas Ganjar Pranowo di Tahun Politik 2024 di Twitter dengan Algoritma KNN dan Naïve Bayes Dede Sandi; Ema Utami; Kusnawi Kusnawi
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.v7i3.6298

Abstract

The political year for 2024 has now increasingly entertained all Indonesian people to hold a democratic party. Various political parties have become quite dramatic in expressing their coalitions and declaring their alignment with several presidential candidates that are known to the whole community. The electability of each presidential candidate that is determined is increasingly interesting and hotly discussed, which often makes anyone take action to voice their partisanship between the pros and cons. One of them is Ganjar Pranowo, who is a political figure for the governor of Central Java. Recently, in the middle of 2023, a political party has proposed him to advance to the seat of head of state as a presidential candidate for the upcoming 2024 election. With the existence of various polemics of opinion from various layers of society, this is the right moment to carry out an analysis as a form of polarization unanimity which is presented from various public opinions as a general description and an outline in sentiment in the form of information on the conclusions of public opinion. The stages in this research began with conducting a literature study and exploring studies related to opinions and alignments with public sentiment regarding the electability of Ganjar Pranowo as a presidential candidate, and then collecting opinion data from Twitter on the electability of Ganjar Pranowo. At the experimental stage, the authors divided the data with a percentage of 80% training data and 20% testing data. The modeling used is K-Nearest Neighbor (KNN) and Naïve Bayes to classify text data as well as make comparisons of the two. In the implementation process, the author uses python as a programming language in building the model. Confusion Matrix is used for every performance evaluation related to model accuracy in each algorithm. The results showed that the division of training data and testing data and the value of k in the K-Nearest Neighbor (KNN) model greatly affect the accuracy of the model. From the test results on the comparison of the two models, the K-Nearest Neighbor model has the best accuracy with an accuracy value of 99% of the K-Nearest Neighbor with an accuracy value of 96%. The percentage of sentiment with a comparison of 96.6% positive sentiment and 3.4% negative sentiment concluded that most people still dominate positive sentiment.
Group Recommender System using Matrix Factorization Technique for Book Domain Moh Naufal Mizan Saputro; Z. K. A. Baizal
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.v7i3.6435

Abstract

A recommender system helps users to select the desired items by analyzing the user's habit of interacting with the system. Recommender system also help the group of users for selecting items due to information overloads. Group Recommender System (GRS) is designed to identify all preferences within a group. An aggregation strategy is needed to accommodate all user preferences in a group. GRS is required in many cases, for example in the book domain, a bookstore recommends a list of books through a display for a group of visitors. We design a GRS for the book domain using Matrix Factorization technique. We utilize three methods to design GRS, such as After Factorization (AF), Before Factorization (BF), and Weighted Before Factorization (WBF). These three approaches were applied to three different group categories, i.e., small groups, medium groups, and large groups. We aim to find the best approach for each group category in this research. The evaluation metrics used are precision and recall in building this GRS. The results of this research indicate that a small group is suitable for using all three approaches, AF methods is the best approach methods for medium groups, and the best approach method for large groups is WBF.
Penerapan Metode TOPSIS Dalam Sistem Pendukung Keputusan Seleksi Penerimaan Asisten Dosen Berbasis Web Richard William Kho; Hindriyanto Dwi Purnomo
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.v7i3.6268

Abstract

Technological developments in the field of education in tertiary institutions help and facilitate students in carrying out lecture processes such as conducting online classes, making it easy to obtain material provided by lecturers and various administrative processes that can be taken care of online. In the classroom, lecturers in teaching need teaching assistants as companions so that one of the success factors in the lecture process is based on the interaction between teaching assistants and students where the teaching assistant acts as a liaison between students and lecturers. At the Faculty of Information Technology, Satya Wacana Christian University, Informatics Engineering Study Program, to register as a teaching assistant, various processes are needed to meet the criteria that help in the selection process for teaching assistants, but in the selection process for accepting teaching assistants, it is still influenced by personal preferences which causes injustice in assessment and decision making. , various selection processes are complicated and time-consuming due to the many criteria with different levels of importance, the selection process is still done manually so that the selection process for hiring assistant lecturers is not effective and efficient. And in the process of storing data that is also ineffective, it is possible that it can be damaged or deleted. By using a decision support system and the TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution) it can assist in calculating all criteria simultaneously and giving weight to each criterion so that criteria can be identified that have more influence on the selection process and produce mathematical calculations so that the results which can be determined at the best value. The purpose of this study is to speed up the process of selecting teaching assistants by implementing the TOPSIS method to assist in selecting admission assistant lecturers. The results obtained are websites using the PHP programming language, Laravel framework, Bootstrap framework and MySQL database which are running well and have an attractive appearance. black box testing, validation of manual calculations where the results of the calculations are in accordance with the results on the website made with brandon which gets the highest prevalence value of 0.58.
Analisis Sentimen Terhadap Kenaikan Harga Bahan Pokok Menggunakan Metode Naive Bayes Classifier Muhammad Muslimin; Veronica Lusiana
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.v7i3.6418

Abstract

Basic necessities are the main needs that are important for people’s lives. The increase in the price of basic necessities certainly has a huge impact on the operational costs of the community and has become a very crucial issue. This event gave rise to pro and con responses from the public expressed through social media Twitter. From this event, sentiment analysis research was conducted related to the increase in the price of basic commodities. The amount of data used for this research is 2070 tweet data. The analysis results show that negative sentiment appears more than positive sentiment, with a percentage of 2,8% positive sentiment and 97,2% for negative sentiment. Retrival of tweet data is done through the netlytic.org website with the keyword staples. The classification method uses the naïve Bayes Classifier method. Furthermore, data division is carried out on the dataset with a ratio of 6:4. The data is divided into 60% training data and 40% test data. The size of the test data as much as 40% of the overall data produces the best accuracy rate. From the results of testing the model with the Naïve Bayes Classifier method the evaluation value results are the highest accuracy score of 94,38%, precision of 59,67%, recall of 67,93%, and F-measure of 62,32%. It can be concluded that the results of sentiment analysis on the increase in the proce of basic commodities get a negative response from the public. This research proposes a method of sentiment analysis of rising prices of basic commodities by considering the level opinion sentiment on Twitter.
Analisis Sentimen Pembatalan Indonesia Sebagai Tuan Rumah Piala Dunia FIFA U-20 Menggunakan Naïve Bayes Harry Setiawan; Ilka Zufria
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.v7i3.6144

Abstract

The FIFA U-20 World Cup is a prestigious event for young footballers around the world. Indonesia was originally to host the event in 2023, but FIFA eventually had to cancel the World Cup in Indonesia because some Indonesian public figures did not accept the presence of the Israeli national team in Indonesia at the football match. The refusal was made on security grounds, because the participation of the Israeli national team was considered a potential threat to Indonesia's security, especially considering the Palestinian conflict. Another reason, Indonesia does not have diplomatic relations with Israel, FIFA's decision is enough to cause a variety of opinions both positive and negative. The purpose of this research is to function so that people can be tabayyun by providing solutions by conducting sentiment analysis, namely collecting Twitter user opinions automatically so that they can be useful for the community. The data obtained is 946 tweets after going through the preprocessing stage then the data can be used in the labelling stage using the Lexicon Based method. Grouping is divided into positive sentiment and negative sentiment which is classified by the Naïve Bayes algorithm reinforced by Lexicon Based weighting resulting in positive sentiment as many as 150 tweets with a percentage of 15.86% and negative sentiment as many as 796 tweets with a percentage of 84.14%. From the confusion matrix results, the classification performance with the Naïve Bayes algorithm reinforced with Lexicon Based weighting then produces an accuracy percentage of 84%, with a precision of 86%, a recall of 95% and an f-measure value of 90%.

Page 87 of 119 | Total Record : 1182

 85   86   87   88   89 

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