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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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Tagging Efficiency Analysis of Part of Speech Taggers on Indonesian News Djatnika Widia Nugraha; 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.v7i1.5384

Abstract

Part of speech tagging (POS tagging) is a part of Natural Process Language (NLP). POS tagging is the process of automatic labeling of a word in a sentence according to the word class. There are various tagger methods in POS tagging, each tagger method has its own characteristics in its application. The research method used is Conditional Random Fields and Hidden Markov Model. The training of the two method models uses the Indonesian language corpus and Indonesian news texts as test data to determine which method is the most efficient based on the results of the accuracy and training time of each model. The method that has the best value is the CRF method with an accuracy value of 97.68 on the evaluation of the corpus test data and 90.02% for the sample Indonesian news dataset with a training time of 146.90 seconds, then there is the HMM method which has the highest accuracy value with a value of 94.25 % and shorter training time relatively shorter at 32.45 seconds and for the sample sentences containing 116 tokens, CRF method produces 90.05% accuracy which is higher than the HMM method which produces 79.31% accuracy.
Penerapan Metode Technique For Order Preference by Similarity To Ideal Solution (TOPSIS) Dalam Pengembangan Desa Terbaik Menggunakan Pembobotan Rank Order Centroid (ROC) Lakry Maltaf Putra; Warkianto Widjaja
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.v7i1.5530

Abstract

The best village development is a village development where the village can be used as an example for other villages. However, there are many villages that appear to be the best village developers, but they are not at all. In this day and age there are many people who are reluctant to develop a village, there are also many people who are narrow-minded that there is no need for village development because whether there is or not will definitely remain the same. Therefore the author wants to conduct a study in choosing the best village development by having 5 criteria, namely the number of productive age, the number of umkm, the area of the village, the number of schools, and the number of tourist destinations. So in this research a helper system is really needed to solve the problems that occur in the research. The system referred to as an auxiliary tool in solving problems is (SPK). Based on these problems, a decision support system is needed as a problem solving technique and is assisted by a method that can produce an accurate final value. The method is the TOPSIS method and ROC weighting where the method is very helpful in generating weight values from alternative data and criteria so as to get the final results obtained in Alternative Q6 with a value of 1.00000.
Penerapan Algoritma C4.5 Untuk Klasifikasi Tren Pelanggaran Kendaraan Angkutan Barang dengan Metode CRISP-DM Novie Hari Purnomo; Bayu Pamungkas; Christina Juliane
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.v7i1.5247

Abstract

Road damage due to ODOL (Over Dimension Over Loading) increases the road maintenance budget significantly, namely an average of IDR 43.45 trillion per year. In addition, many accidents involving ODOL trucks or overloading and dimensions have occurred. The level of violations caused by ODOL vehicles is still high, so technology is needed that is able to manage data and serve as a reference to find out the hidden approaches in the data set, as well as analyze the grouping between data and attributes to facilitate decision making and policy making. This study applies the CRISP-DM methodology using a decision tree model with the C4.5 algorithm. The purpose of this research is to classify trends in freight transport violations based on violation data in the UPPKB. The research data is primary data obtained from the Directorate of Road Transportation Infrastructure of the Ministry of Transportation through the online weighbridge system (JTO). The expected result of this research is to be able to find out the pattern of classification trends for freight vehicle disturbances based on the results of the C.45 algorithm decision tree, so that the research results can be used as a reference in making decisions and making policies. The results of this study indicate that the accuracy performance in data mining tests for the classification of trends in freight vehicle disturbances with 10 fold cross validation linear sampling produces an accuracy of 86.31% +/- 1.23% (micro average: 86.31%), shuffled sampling produces an accuracy of 86.34% +/ - 0.67% (micro average: 86.34%) and stratified sampling produces an accuracy of 86.34% +/- 0.67% (micro average: 86.34%).
AI Explanation related Covid Hoax Detection Using Support Vector Machine and Logistics Regression Methods Naufal Haritsah Luthfi; Agus Hartoyo
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.v7i1.5386

Abstract

Hoax news about Covid is still circulating in society. Especially on social media, this phenomenon still occurs. The existence of this disinformation can cause divisions between communities. Currently, technology can classify hoax news and non-hoax news. But no system can see the reasons for a model to classify hoax news and non-hoax news. Therefore, in this study, a system was developed that can see words on a system that detects hoax and non-hoax news using the Support Vector Machine and Logistic Regression methods. Meanwhile, the Explainable AI method is Local Interpretable Model-agnostic Explanations (LIME). The test results show that the SVM and Logistic Regression methods have the highest accuracy of 91% and 95%. The words collected in the dataset are sufficient to differentiate between a hoax and non-hoax news. It was found that hoax news about Covid-19 has many words related to Covid-19, religion, politics, medical, and words that are not related to Covid-19. Among them are "lockdown", "masjid", "rezim", "ventilator", and "kiamat". Meanwhile, non-hoax news about Covid-19 has many words related to Covid-19, government, and medical. Among them are "protokol", "isolasi", "infeksi", "menteri", and "nakes".
Analisis Sentimen dan Pemodelan Topik Aplikasi Telemedicine Pada Google Play Menggunakan BiLSTM dan LDA Siti Mutmainah; Dhomas Hatta Fudholi; Syarif Hidayat
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.v7i1.5486

Abstract

The pandemic caused by the 2019 coronavirus has revitalized telemedicine as information and communication technology-based health services and as a medium for doctors' services in diagnosing, treating, preventing and evaluating health conditions. One of the telemedicine service applications in Indonesia is Alodokter, Halodoc, KlikDokter, SehatQ and YesDok. Previous research on the same domain, namely applications telemedicine uses machine learning to perform sentiment modeling. This research performs sentiment analysis using the BiLSTM method (Bidirectional Long Short-Term Memory) which can better represent contextual information and can read user feedback information in both directions. Then sentiment analysis is described explicitly to identify topics from user sentiment using LDA (Latent Dirichlet Allocation). User feedback was collected on August 14, 2022 which was obtained in the five applications totaling 244,098. The results of the analysis on feedback obtained were 112,013 positive sentiments, 34,853 neutral sentiments and 97,228 negative sentiments. The BiLSTM and Word2Vec models used have a good performance in classifying sentiments, namely 95%, while the topic modeling for each sentiment has a coherence value of 0.6437 on positive topics, 0.6296 neutral sentiments and 0.6132 negative sentiments.
Deteksi Penyakit Epilepsi Melalui Sinyal EEG Menggunakan Metode DWT dan Extreme Gradient Boosting Erlina Agustin; Ade Eviyanti; Nuril Lutvi Azizah
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.v7i1.5412

Abstract

Epilepsy is a disorder of the central nervous system due to excessive patterns of electrical activity in the brain. This disease causes patients to experience repeated seizures in one or all parts of the body. Therefore, epilepsy must be detected early so that the patient immediately gets the right treatment so that the condition does not get worse. This study proposes the detection of epilepsy using the Discrete Wavelet Transform method for feature extraction and Extreme Gradient Boosting for classification. Detection results are classified into two classes, namely seizures and non-seizures. The EEG recording data used came from CHIB MIT Hospital Boston which was obtained online. In the classification process, this study uses four comparisons of the percentage of training data and test data as well as tuning parameters which are processed by Randomized Search Cross Validation. The combination of these methods produces the highest accuracy, namely 85.15% which is produced by the percentage of 80% training data and 20% test data. However, these results experienced a high overfitting of 13.54%. As for the most fit results produced by the research, namely an accuracy value of 81% with a training score of 88.65% and a test score of 81.20% resulting from a percentage of 80% training data and 20% test data.
Big Five Personality Detection Based on Social Media Using Pre-Trained IndoBERT Model and Gaussian Naive Bayes Ni Made Dwipadini Puspitarini; Yuliant Sibaroni; Sri Suryani Prasetiyowati
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.v7i1.5439

Abstract

A person's personality offers a thorough understanding of them and has a significant role in how well they perform at work in the future. No wonder it attracted the interest of the researcher to develop a personality detection system. Although much research about personality detection through social media was conducted, this task has been challenging to implement, especially using conventional machine learning. The issue is conventional machine learning still insufficient to make the personality detection system perform better. The purpose of this research is to detect Big Five personalities based on Indonesian tweets and increase its performance by combining machine learning with deep learning, which is Gaussian Naive Bayes and IndoBERT model. The proposed combined model in this research is summing the log probability vector on each model. Gathered 3.342 tweets from 111 Twitter accounts that were used as a dataset. This research also implemented min-max normalization to rescale the data. The result showed that for the entire dataset, the combined model has more accuracy score than Gaussian Naive Bayes by 5.42% and IndoBERT by almost 2%, which indicates the combined model is better than the Gaussian Naive Bayes and IndoBERT models.
Healthy Menu Recommendation for Malnutrition Patients Based on Ontology Igga Febrian Virgiani; Z K A Baizal; Ramanti Dharayani
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.v7i1.5543

Abstract

A healthy diet is one of the keys to creating a healthy lifestyle, but at this time the selection of a healthy and nutritious meal menu in the society is difficult to do because of the limited nutritional information contained in a food. A healthy diet can help a person to get balanced nutrition, good nutritional intake can increase the body's immunity, and make a normal or healthy body weight so that it can increase work productivity and prevention of chronic diseases. To overcome this problem, we propose the use of ontology and Semantic Web Rule Language (SWRL) to build a healthy menu recommendation system in the form of a chatbot to make it easier for users to determine the daily meal menu. These recommendations are personalized by considering the user's needs. Ontology is used to represent the required knowledge and the reasoning process uses SWRL. From the results of system testing, the recommendations get the accuracy of the F-Score value of 0.951
Analisis Penerapan Metode WASPAS dan MOORA dalam Kelayakan Pengangkatan Karyawan Tetap Kraugusteeliana Kraugusteeliana; Sitti Nur Alam; Bambang Triwahyono; Muhammad Bayu Wibisono; Fryda Fatmayati
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.v7i1.5343

Abstract

In determining the considerations for determining employees, there are many criteria that must be owned by an employee. Each company has different criteria for determining considerations and considerations, to make employees more active and get a clear position. To determine the decisions of employees who deserve to be permanent employees, a decision support system is needed. This study aims to determine the best alternative based on predetermined criteria by applying the Multi Objective Optimization On The Basis Of Ratio Analysis (MOORA) method and the Weight Aggregated Sum Product Assessment (WASPAS) method. The research was carried out by finding the values for each attribute, then a ranking process was carried out which would determine the optimal alternative, namely determining the considerations to be carried out. As for those who are eligible to become permanent employees, alternative A6 with the acquisition value of the WASPAS method is 7.175147 and the acquisition value of the MOORA method is 0.37077. So it can be said that the methods that are suitable for application in WASPAS and MOORA are methods that are one of the appropriate methods WASPAS and MOORA are suitable methods for calculating an event.
Penerapan Data Mining untuk Klasifikasi Hasil Panen Jamur Tiram Menggunakan Algoritma K-Nearest Neighbor Eka Praja Wiyata Mandala; Dewi Eka Putri; Randy Permana
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.v7i1.5252

Abstract

Oyster mushroom is a type of mushroom that can be consumed by humans. Lots of food products are made from processed oyster mushrooms. This makes mushroom farmers intensively cultivate oyster mushrooms because they see good economic prospects. However, not all mushroom cultivation processes can be successful so it will have an impact on the yield of the oyster mushrooms. So it is necessary to classify so that it is easier for mushroom farmers to determine the amount of yield from the oyster mushroom. The classification was carried out because of the difficulty of mushroom farmers in determining the amount of harvest by looking at the width of the mushroom caps, the number of mushroom caps to the mushroom harvest time. This study proposes a data mining technique to classify oyster mushroom yields using the K-Nearest Neighbors algorithm so that it can help mushroom farmers in determining the yield of oyster mushrooms being cultivated. This study used a dataset of 42 mushrooms as training data and 1 mushroom data to determine the classification of the crops. From the results of testing on 1 mushroom with a cap width of 8 cm, the number of caps is 14 pieces and the harvest time is 49 days, the results of classification results obtained from this mushroom are Less with a Mean absolute error of 0.1419, Root mean squared error of 0.2111, Relative absolute error of 36.2177% and Root relative squared error of 48.002%. The results of this research can help mushroom farmers in classifying oyster mushroom yields.

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