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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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Part-of-Speech Tagging Implementation on Telkom University News using Bidirectional LSTM Method Rheza Ramadhan Putra; Donni Richasdy; Aditya Firman Ihsan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5506

Abstract

News is a tool used to disseminate information through various media, one of which is the internet. Various kinds of news articles have words that are not recognized in the dictionary such as slang words and have foreign words that do not exist in the corpus. How can a POS tagging model built on the corpus be able to handle word class labeling in Indonesian news. The research was conducted to check the results of POS tagging on a collection of news about Telkom University which was selected manually. By using the bidirectional LSTM model, three test scenarios were attempted to improve the performance of the built model, the test scenarios were applying the best padding for the corpus, comparing the performance results of the modified corpus model with the original corpus model, and determining the dimensions of the Word2vec vector. Then the selected model from each corpus is implemented on the news that has been labeled manually. One of the best scenario tests is obtained by modifying the corpus by removing double words in the word class "X" and changing some of the word classes "X" which are more likely to be foreign words so that they are changed to the word class "FW". The best performance results in the implementation of news about Telkom University using the bidirectional LSTM model which was built based on the modified corpus get accuracy values of 92.74%, precision of 92.85%, recall of 92.74%, and F1-score 92.48%.
Implementasi Metode Convolutional Neural Network untuk Klasifikasi Breast Cancer pada Citra Histopatologi Muhammad Afrizal Amrustian; Merlinda Wibowo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5194

Abstract

Breast cancer is a tumor that manifests as an abnormal lump in the breast of a woman. The occurrence of breast cancer in women can be triggered by genetic and lifestyle factors. According to Global Cancer Statistics, 600,000 out of 2.3 million occurrences of breast cancer result in death. The death rate from breast cancer in Indonesia is likewise relatively high, reaching 17% for every 100,000 female inhabitants. Using histological pictures to diagnose breast cancer is one technique. The patient will capture an image of her breast cells, which will be examined and diagnosed by medical personnel. Even if histopathological scans are utilized as a baseline for the detection of breast cancer, the death rate associated with this disease remains rather high. One of the causes for the high mortality rate associated with breast cancer is the late detection of the disease, which results in patients being treated when the disease is in a severe state, and sometimes a misdiagnosis. The authors propose creating a breast cancer classification model utilizing the convolutional neural network (CNN) method in order to address the described issues. The study's findings indicate that CNN can classify breast cancer patients with an accuracy of 85 percent. Moreover, by calculating the loss function, the constructed model prevents overfitting.
Penerapan Metode SMART Pada Sistem Pendukung Keputusan Rekrutmen Karyawan Baru Humisar Hasugian; Agus Umar Hamdani; Wulandari Wulandari; Nofiyani Nofiyani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5195

Abstract

At non-bank institutions, the recruitment process of new employees starts with the selection stage of the curriculum vitae, if they meet the requirements then process followed by psychological tests, interviews and programming tests. The determination of employees who are accepted to be hired by the companies is still subject to interviews results without considering other tests, therefor the hired employees are not ready for the work due to lack of technical ability. To overcome this problem, it is proposed to use a simple multi-attribute rating technique method for the processing of employee selection data because this method can process the data quantitatively and qualitatively with various assessments for each criteria and making the decision-making process easier and faster. The research objective is to assist the employee recruitment process using a web-based decision support system application by simple multi attribute rating technique method. The result of this study is in the form of a decision support system application website for recruiting new employees, using the criteria for Psycho-test Results (C1) weighting 20%, Age (C2) weighting 10%, Work Experience (C3) weighting 20%, Interview (C4) weighting 20%, and Mastery of Technical Aspects (C5) weighting of 30%. As for the alternative results of data processing using the above method are Febila Manda Dewi worth 0.666, Dini Apiani worth 0.447, Miqhiyal Noer Sopyani worth 0.890, and Irvan Egiawan worth 0.042, then based on the result it can be concluded that the prospective employee is Miqhiyal Noer Sopyani who got the highest score so he is recommended as the alternative new employee. The results of this study are objective due to the data processing is based on all criteria, so that the selected employees are certain to be ready to work.
Penerapan Metode WASPAS Pada Sistem Pendukung Keputusan Penentuan Tempat Wisata Kuliner Elok Nur Hamdana; Dina Risky Alin Saputri; Deasy Sandhya Elya Ikawati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5330

Abstract

Culinary tourism is one of the tourist attractions when visiting an area with the aim of enjoying food with a certain taste. Culinary tourism in Batu City and Malang City offers a variety of culinary products, ranging from filling food to snacks such as typical and legendary dishes. So this creates its own problems for the community and tourists because they feel confused about making choices. Therefore, there is a need for a solution to this problem, namely by creating a decision support system that can help the community and tourists in determining culinary tourism spots appropriately according to their needs. This study uses the WASPAS (Weighted Aggregated Sum Product Assessment) method to calculate which culinary tourism spots are suitable based on the needs of the user or users. Based on this research, the system calculation using the WASPAS (Weighted Aggregated Sum Product Assessment) method on a culinary tourism decision support system can provide the right recommendation for users and users by using a Likert scale calculation obtained from the questionnaire results getting a final score of 88.95 %. From the test results using Spearman Correlation, correlation values are obtained of 0.6 and 0.8 which indicate that there is a relationship between the ratings generated by the system and the calculated rankings of people who like to eat.
Efek Transformasi Wavelet Diskrit Pada Klasifikasi Aritmia Dari Data Elektrokardiogram Menggunakan Machine Learning Dodon Turianto Nugrahadi; Tri Mulyani; Dwi Kartini; Rudy Herteno; Mohammad Reza Faisal; Irwan Budiman; Friska Abadi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.4859

Abstract

Arrhythmia is one of the abnormalities of the heart rhythm, and some patients who suffer from arrhythmia do not feel any symptoms. Automating the early detection of arrhythmia is necessary by using an electrocardiogram. Previous research that had been done conducted classifications using several methods of data mining. In this research, the transformation for processing signals used is Discrete Wavelet Transformation, where a filtering process occurs that separates signals into high and low-frequency signals without losing the information from signals and is carried out with a two-level decomposition. After that, data normalization was performed using min-max normalization and was put into the model classification using the Support Vector Machine method with a Gaussian Radial Basis Function kernel of Naïve Bayes and K-Nearest Neighbor. Each data that was being used consisted of 140 data with a total of 35 data for each label. This research shows that at level 1 decomposition, the highest accuracy was obtained at db7 for the classification using Support Vector Machine with an accuracy of 73,57%, 68,57% for Naïve Bayes, K-Nearest Neighbor with k=3 resulting in an accuracy of 59,64%, and K-Nearest Neighbor with k=5 resulting in an accuracy of 63,57% while at level 2 decomposition the highest accuracy was obtained at db6 dan db8 for the classification using Support Vector Machine with an accuracy of 70,71%, 67,50% for Naïve Bayes, K-Nearest Neighbor with k=3 resulting in an accuracy of 66,07%, and K-Nearest Neighbor with k=5 resulting in an accuracy of 65%. From this research, it can be concluded that the highest accuracy is produced by decomposition level 1 using Support Vector Machine classification and that the Daubechies wavelet type has better results than the Haar wavelet.
Analisis Metode WASPAS Dalam Pemilihan Pimpinan Perusahaan Badrul Anwar; M. Giatman; Hasan Maksum; Asyahri Hadi Nasyuha
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5170

Abstract

The head of the company is an important figure in a business. In selecting a leader, criteria are needed that reflect a good leader. Some companies still do not use the system in selecting leaders, and even this is still nepotism, in this case the selected leaders are related to the owner of the company. A decision support system is a system that can assist in making decisions. Decision support systems are very effective in providing decisions on a problem, because this system has alternative problems and criteria according to the problems that occur. The WASPAS method in a decision support system provides the right solution for selecting prospective leaders with appropriate criteria because the WASPAS method is a unique combination of the known MCDM (Multi Criteria Decision Making) approach, namely the WSM (Weighted sum model) and the WPM weighted product model. (Weighted Product Method) initially requires linear normalization of the elements of the decision matrix by using two equations. From the results of Qi calculations using the WASPAS method, of the 6 alternatives calculated in the selection of company leaders, the best results are obtained with the highest score, namely 2.3732.
Penerapan Convolutional Neural Network pada Timbangan Pintar Menggunakan ESP32-CAM Hanung Pangestu Rahman; Jamaludin Indra; Rahmat Rahmat
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5469

Abstract

Scales are needed by traders, including vegetable traders, but the scales created in the market can only determine the weight. That way traders need time to calculate the price based on the weight and type of vegetables. In previous research there has been research on smart scales that can calculate the total price based on the weight and type of vegetables being weighed, this study used the Raspberry Pi 3 Model B and the Convolutional Neural Network (CNN) as a method for the scales to be able to identify the types of vegetables that are on it. Along with the rapid development of technology, the price of the Raspberry Pi for all variants has increased in price. Therefore the need for research on smart scales with components that have relatively cheaper prices. In this study, researchers used the ESP32-CAM microcontroller, which is priced relatively cheaper than the Raspberry Pi 3 Model B. This research still uses the Convolutional Neural Network (CNN) method and a load cell equipped with the HX711 module as a sensor to obtain the weight value of an object. The dataset collected totaled 600 image data with 150 image data for each type of vegetable, classes in the training data consisted of tomatoes, cabbage, carrots, and potatoes. Smart scales using the ESP32-CAM get results of a classification accuracy of 90% and the average difference of the tools built is 0.8 grams compared to the SF-400 brand digital scales.
Studi Bibliometrik Jurnal Media Informatika 2018-2022 Ronal Watrianthos; Ahmad Habin Sagala; Rahmi Syafriyeti; Yuhefizar Yuhefizar; Mesran Mesran
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5559

Abstract

This study aims to conduct a bibliometric analysis of the Budidarma Informatics Media Journal (MIB) for the 2018-2022 period using the Dimensions database. The period produced 683 publications which then became the dataset in this study. The dataset was analyzed using Bibliometrix R-package Biblioshiny, and research trends were mapped using VOSviewer visualization. The analysis results show that the collaboration index of MIB journals has a moderate value and tends to be low, although the annual growth rate of 79.08% looks very significant. The number of citations over five years only amounted to 157, so the average citation per article was 0.22. The average FCR is 0.16, which indicates a citation that is well below average. The results of testing Lotka's postulates for author productivity in this period showed a significant difference between the analysis results and the theory in Lotka's postulates. As for trends, research related to "algorithms" became the most dominant word and the center of the cluster connected to other clusters, becoming the most widely used research topic.
Aspect-Based Sentiment Analysis on iPhone Users on Twitter Using the SVM Method and Optimization of Hyperparameter Tuning I Gusti Ayu Putu Sintha Deviya Yuliani; Yuliant Sibaroni; Erwin Budi Setiawan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5430

Abstract

One form of information and communication technology development is a smartphone. Today's popular smartphone products are the iPhone and the social media used to share opinions is Twitter. One of the topics that is often discussed on Twitter is related to iPhone reviews which can refer to different aspects. Therefore, aspect-based sentiment analysis can be applied to iPhone reviews to get more detailed results. This study applies TF-IDF feature extraction as a weighting vocabulary and the Support Vector Machine classification method. This study also uses hyperparameter tuning to optimize parameters to get the best performance. The results of this study obtained the highest accuracy performance results by using the Support Vector Machine classification on the linear kernel and TF-IDF feature extraction on the camera aspect with accuracy 98.07%, battery aspect with accuracy 97.52%, design aspect with accuracy 96.82%, price aspect with accuracy 98.62%, and specification aspect with accuracy 97.07%. As well as getting an increase in the results of the highest accuracy performance by using hyperparameter tuning on the linear kernel for the camera aspect with accuracy 98.07%, battery aspect with accuracy 97.52%, design aspect with accuracy 97.02%, price aspect with accuracy 98.82%, and specification aspect with accuracy 97.22%.
Klasifikasi Tingkat Kesegaran Ikan Nila Menggunakan K-Nearest Neighbor Berdasarkan Fitur Statistis Piksel Citra Mata Ikan Rahmat Widadi; Bongga Arifwidodo; Kholidiyah Masykuroh; Ariyatno Saputra
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5196

Abstract

Healthy tilapia will experience a decrease in quality when stored out of water even with the refrigerator. The decline in fish quality can be seen from the fish's eye. In this study the aim was to develop a tilapia freshness classification system based on fisheye images utilizing image processing and machine learning. Fisheye images were taken at intervals of 0 to 16 hours after being removed from the water. Ten tilapias were used in this study. The distance between the camera and the fish has also been changed with a variation of 4 distances. The total data obtained is 640 fisheye images. The method used in fisheye image classification consists of two stages, namely feature extraction and classification. The four types of statistical features used from image pixel values are the mean, standard deviation, skewness, and kurtosis. While at the classification stage using K-Nearest Neighbor. The scenario that has been determined is then used at the system implementation stage using the Python Programming Language. Testing and analysis using k-fold cross validation and confusion matrix. In the results of the study, the accuracy rate was obtained with an average of 100% using 2 classes, namely 0-2 hours and 2-4 hours. The accuracy rate using 4 classes was obtained with an average of 75%, namely classes 0-2 hours, 2-4 hours, 10-12 hours, and 14-16 hours, and when using all classes, an average accuracy rate of 45% is obtained.

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