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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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Analysis of Telkom University News Subjects on Popular Indonesian News Portals Using a Combination of Hidden Markov Model (HMM) and Rule Based Methods Rendhy Al-Farrel; Donni Richasdy; Mahendra Dwifebri Purbolaksono
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4566

Abstract

News media are often found in everyday life as a means of information for the public about something that is happening. In news articles, it is common to see several sentences that support the object to increase its popularity by being promoted by the subject. Part of Speech Tagging can determine the class of words in the sentence according to Tagsets provided by the corpus. That way, the search for the subject in the news article can be found from the word class obtained from a corpus. This research was focused on finding the subject "who" repeatedly spreading the news about Telkom University by using Part of Speech Tagging with the Hidden Markov Model and Rule Based on a news dataset from popular news portals about Telkom University. The process is taking all news about Telkom University on popular news portals and classifying it using the Hidden Markov Model and Rule-Based. We conducted to enhance the research results by changing the probability estimator on Hidden Markov Model. After running some scenarios, the best results obtained by the Hidden Markov Model and Rule-Based are the Accuracy of 94.96%, the Precision of 94.99%, the Recall of 94.96%, and the F1-Score of 94.95%.
Data Mining Dalam Penentuan Pemesanan Buku Perpustakaan UAD dengan Menggunakan Metode Naïve Bayes Muhammad Iqbal Hadiwibowo; Faisal Fajri Rahani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4381

Abstract

Library Universitas Ahmad Dahlan (UAD) has not utilized technology in the book ordering process. The process of ordering books from distributors requires many considerations such as the number of requests, recommendations for study programs, location, year and language. This consideration made the UAD Library take more than 2 weeks in the book selection process. This study aims to apply data mining in determining book orders using the Naïve Bayes method. This study uses 1106 book procurement data for the past year with criteria, namely the number of requests, study program recommendations, location, year, and language. Implementation of data mining using the Naïve Bayes algorithm is carried out in stages including data cleaning, data selection, data transformation, sharing of training data and data testing, implementation and results of the Nave Bayes algorithm and system testing. System testing using the Confusion Matrix method. Based on the Confussion Matrix calculation on the testing data, the accuracy is 90.24%, the precision is 89.69%, the recall is 93.54%, the specificity is 91.04%, and the F1 score is 91.57%. It was concluded that the system test results were said to be good.
Analisis Sentimen Terhadap Bantuan Subsidi Upah (BSU) pada Kenaikan Harga Bahan Bakar Minyak (BBM) Ulfa Kurniasih; Akrim Teguh Suseno
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4958

Abstract

Fuel Oil (BBM) is a very important commodity for the people of Indonesia. The increase in fuel prices will have an impact on economic instability in Indonesia. Therefore, the government makes a policy by providing Wage Subsidy Assistance (BSU) to the community to ward off the impact of rising fuel prices. However, there were various responses from the public regarding the provision of BSU on the increase in fuel prices, especially on Twitter social media, some were supportive but some did not agree. This study aims to analyze the sentiments of the Indonesian people on government policies related to the provision of BSU to the increase in fuel prices. The data used are 795 tweets on each keyword BBM and BSU. The data is divided into 2, training data of 263 and 532 for testing data. The method used is classification with Naïve Bayes algorithm. The results of the analysis show that the BBM keyword positive sentiment is 28.2%, and negative sentiment is 71.8%. For BSU keywords, positive sentiment is 65.2% and negative sentiment is 34.8%. At the level of accuracy with this method, the result is 82.64% and the precision is 92.89%. Therefore, it can be concluded that the results of public sentiment towards the Wage Subsidy Assistance (BSU) received a positive response, while the increase in the price of fuel oil (BBM) received a negative response.
Food and Beverage Recommendation in EatAja Application Using the Alternating Least Square Method Recommender System Elsa Rachel Dementieva; Z K A Baizal; Donni Richasdy
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4549

Abstract

EatAja is a startup in Indonesia that provides a mobile application-based food and beverage ordering solution for restaurants. The EatAja application uses transaction data to recommend food and beverage menus to customers. Previous studies have developed recommender systems using the Apriori and Collaborative Filtering methods. However, there are shortcomings in the recommendation system using both methods, i.e., the lack of personalization factors and low scalability. The learning method with matrix factorization can overcome the problem. In this study, we improve the food and beverage product recommender system in the EatAja application using the Alternating Least Square (ALS) matrix factorization method on Apache Spark. We will compare the results of the recommender system using the ALS method with the Collaborative Filtering method. The comparison uses the Mean Absolute Error (MAE) evaluation method. The results showed that the MAE value decreased by 0.07 with the ALS Matrix factorization method.
Penerapan Metode Double Moving Average dalam Memprediksi Permintaan Kayu Denny Irawan; Raja Tama Andri Agus; Sahren Sahren
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4648

Abstract

UD. Tunas Meranti Kuala Tanjung is a business engaged in the manufacture of furniture. Problems faced by UD. Tunas Meranti is difficult to predict the demand for the type of wood that customers want for furniture making, so it is difficult to determine which type of wood should be in stock every month. If the supply of wood exceeds demand then storage costs will increase. The risk is that wood that is stored for too long will become dry, hard and difficult to process. To maintain business continuity, a wood supply strategy is needed, namely by making predictions (forecasting). The purpose of this research is to be able to predict the demand for wood types so that business owners can provide wood types according to customer needs. The method used in this study is the Double Moving Average method in order to minimize the chance of errors between predictions and actual data. To be more efficient, the author designed a web-based forecasting system and uses a MySQL database. The conclusion from the prediction results of meranti wood demand for the next period is 43 sticks with a MAPE value of 8% and mahogany wood is 19 sticks with a MAPE value of 11%, which means that the prediction using the DMA method is very effective.
Implementasi Algoritma Haar Cascade Classifier Dalam Mendeteksi Robot Sepak Bola Beroda Joshua Sitompul; M Irwan Bustami; Desi Kisbianty
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.3929

Abstract

In soccer robot contests, generally soccer robots can recognize their own team robots through color detection using the HSV model. Robots that use color detection to identify their own team robot can detect objects that have the same color value, so objects that have the same color will be considered as their own team robot. However, if you only rely on this method, it is still lacking when viewed in terms of object tracking. Haar like feature or known as Haar Cascade Classifier is a rectangular (square) feature method, which gives a specific indication of an image. This method is able to detect quickly and in real time. Therefore, the researchers tried to unite these two detections to be able to produce a better detection system than before. It is hoped that hoped that in this study, researchers can provide input for the Robotics team of Dinamika Bangsa University in improving the accuracy of robot detection against robots in the same team. From the results of the study it was found that the detection of objects with a distance from the camera as far as 100 cm to 150 cm was successfully carried out, but with a distance of more than 150 cm the object was not detected.
Penerapan Algoritma Fisher Yates Shuffle pada Game Edukasi Pembelajaran Untuk Pendidikan Anak Usia Dini (PAUD) Muhamad Ariandi; Muhammad Dwiki Ariyadi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4683

Abstract

Games can be used as educational tools, helping students learn about a particular subject or concept. Just like playing learning educational games for Early Childhood Education (PAUD), it can be a way to learn. In learning activities such as letters, numbers, colors, names of animals, plants, and so on, they still use conventional learning media, so they are less effective in implementing learning, where currently children interact more with smartphones than books, this can This causes parents whose children are still in elementary school, such as reading, counting, writing using books that are less attractive, which can cause children to be lazy to study, and less attractive to children in learning. From that problem, the author is interested in making educational game applications by utilizing smartphone media. And the application of this learning educational game was made by utilizing the Fisher Yates Shuffle Algorithm, by randomizing the questions in this application. Based on the test results, the implementation of the Fisher Yates Shuffle Algorithm for question randomization in the early childhood education learning educational game application (PAUD) went well, was in demand by PAUD children and there were no repeated questions that made PAUD children happier in study.
Perancangan Sistem Otomatis Transaksi Pembayaran Pada Marketplace UMKM Menggunakan Metode Crawling Horspool Rifki Kosasih; Eko Sri Margianti; Suryadi Harmanto; Didin Mukodim; Hendri Dwi Putra
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4803

Abstract

UMKM MAPAN is one of the UMKM communities consisting of 300 UMKM entrepreneurs who are currently developing and located in the Depok area. These UMKM provide a wide variety of products to be marketed. In marketing these products, these UMKM group still uses the conventional method, is still by face-to-face with the buyer, so they have to rent a place first and are still limited in marketing online. This method has many weaknesses, such as the high cost of renting a place and the difficulty of finding a strategic location to market the product. Therefore, in this study, an e-commerce marketplace web system was created that could accommodate 300 UMKM MAPAN entrepreneurs in marketing their products. In addition, an automatic system for payment transactions on the UMKM marketplace was also created using the Horspool crawling method so that it could make it easier for UMKM entrepreneurs to print payment transaction reports. Based on the research results, the success rate of report printing is 100%. In this study, the complexity of the Horspool algorithm is O(n) with n is length of pattern while the time complexity of the Horspool algorithm is O(m+σ) with m is length of search string.
Deteksi Konten Pornografi Menggunakan Convolutional Neural Network Untuk Melindungi Anak Dari Bahaya Pornografi Muhammad Taufik Dwi Putra; Mochamad Iqbal Ardimansyah; Devi Aprianti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4793

Abstract

Education is one thing that must be arranged as early as conceivable in arrange to realize a quality era. When talking about education today, it cannot be separated from technology. Where we can see that technology has been used in various fields. In the field of education, one of them is the use of the internet network. However, the use of this technology has quite a bad side. Especially for elementary-level students or the age of children. That is the bad impact of exposure to pornography. Exposure to pornography is very dangerous and can damage children both psychologically and mentally. Therefore, it is important to minimize the risk of exposure to pornography. To overcome this, there are many methods that can be used. Like detecting pornographic content automatically and blocking it. One technique that can be developed to detect pornographic content is Artificial Neural Networks. However, so that the image input can be handled effectively, the model of the Artificial Neural Network has been varied into a Convolutional Neural Network (CNN) technique. So it has the ability to recognize objects for image data. The model built in this study was trained using a dataset that has been adapted to the definition of pornography in Indonesia. From the tests that have been carried out on the CNN model that was built, the best accuracy rate is 94.24%. in detecting images that fall into the category of pornographic content.
Analisis Sentimen Review Produk Skincare Dengan Naïve Bayes Classifier Berbasis Particle Swarm Optimization (PSO) Tri Astuti; Yuli Astuti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4119

Abstract

Skin care products have become the main needs of all people who are the targets of various brands of skin care products. However, not all skin care products have good quality according to consumer needs. They look for products that have the best quality by looking at reviews from other people, so they have an idea that influences their interest from other people's reviews submitted through various marketplace platforms or social media regarding the results after using these skin care products. Sentiment analysis is one way to analyze and classify reviews into positive opinions and negative opinions regarding the product in question to look for product quality based on public views. The algorithm used in this research is the Naive Bayes Classifier. The Naive Bayes Classifier method was chosen for reasons of ease of implementation, fast and high accuracy. The Naïve Bayes method also has a disadvantage, namely it is sensitive to feature selection, which results in low classification accuracy. Therefore, in this study, the feature selection method, namely Particle Swarm Optimization, was used in order to increase the accuracy of the Naïve Bayes classifier. The dataset used is 800 data reviews and tested using 10-Fold Cross Validation. The results showed an increase in accuracy from 77.96% to 79.85%.

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