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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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Optimasi Biaya Distribusi Kusen Pintu Menggunakan Model Transportasi Northwest Corner Method, Russel Approximation Method, dan Stepping Stone Poppy Andriani; Hendra Cipta
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.v7i1.5224

Abstract

The transportation method is a method used to calculate distribution costs based on the origin that provides the same products to the destination location optimally using minimum costs. Previously, Cv Prima had to pay Rp 72,356,720 for transportation costs. So that the CV Prima company experienced swelling in distribution costs. Expenses for excess distribution costs result in the quality of door frame delivery not being optimal. In this study, the Northwest Corner Method and the Russell Approximation Method were used as the initial solution and continued with the Stepping Stone method as the optimal solution. So that this research produces the lowest costs after applying the Northwest Corner method revealing transportation costs of IDR 69,256,630 and using the Russel Approximation Method, transportation costs are IDR 67,304,680. For Stepping Stone optimization, the transportation cost is IDR 66,641,020. Therefore, the Northwest Corner and Russell Approximation Methods and continued with the Stepping Stone method for this problem are more efficient, because the company found a reduction in transportation costs of 7.899% or IDR 5,715,700.
Penerapan K-Means Untuk Clustering Kondisi Gizi Balita Pada Posyandu Candra Adi Rahmat; Hilda Permatasari; Errissya Rasywir; Yovi Pratama
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.v7i1.5142

Abstract

Malnutrition in children is a major public health problem in developing countries, including Indonesia. National data show that 36.8% of children under five years of age (toddlers) are stunted (short and very short, measured by height for age). To be able to know the nutritional condition of the toddler, can use analysis and a calculation method. In this study, the authors utilize an analysis and calculation of data, namely data mining. One of the techniques in data mining is clustering. K-Means Clustering is one of the algorithms in the Clustering technique in data mining. In this study the authors used as many as 20 data on toddlers. From the 20 data on toddlers, the authors determined the cluster center randomly as much as 3 data and resulted that, 4 toddlers were malnourished, 7 toddlers were well nourished, and 9 toddlers were obese.
Smart Packgaes Box Berbasis Internet Of Things Menggunakan Telegram Bot Sri Ayu Nur Hidayati Putri; Oktaf Brillian Kharisma; Harris Simaremare; Abdillah Abdillah
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.v7i1.5517

Abstract

The trend of online shopping increases the use of delivery services. However, packages ordered online are often delivered when the customer is away from home, so the package is usually placed outside and unattended, which is not secure. Therefore, a safe place is needed to secure the package before received by the owner. Based on the problem, this study designed a Smart Packages Box based on the Internet of Things using the Telegram Bot, Wemos D1 R32 that connects the system with Telegram, Barcode Scanner GM66 to scan the barcode on the shipping label, Solenoid Door Lock, and ESP 32 Cam to monitor the condition of the package. Based on the Smart Packages Box test, the door lock can be opened if the barcode on the shipping label matches the receipt number that the owner has registered. The owner will get a notification status if the package has arrived through Telegram Bot. The barcode scan's response time test by courier was 3.20 to 4.50 seconds with a distance of 5 cm to 40 cm until the door locks open and locks again in 5 seconds. Based on the tests carried out there is a graph that illustrates a decrease at a distance of 50 cm which is caused by the distance being too far.The receipt number can be registered by writing "Daftar 1" on the Telegram bot with a limit of 10 receipts, and the owner can check the information status by writing "Cek" on the Telegram Bot. Hopefully, Smart Packages Box can improve the safety of packages even though it has not been received directly by the owner.
Explainable AI: Identification of Writing from Famous Figures in Indonesia Using BERT and Naive Bayes Methods Firdaus Putra Kurniyanto; 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.v7i1.5392

Abstract

Identifying the writings of well-known figures in Indonesia is a form of appreciation for the writing itself. By knowing the language style used by every famous figure in Indonesia, we can know the uniqueness of each writer, and it can help us understand the thoughts, ideas, and ideas they convey. This research has yet to be done, so it is still interesting to do further research. In this study, only a few writers were used, so it is still impossible to know the overall language style used by every famous figure in Indonesia. In this study, a system was built to determine the language style used by well-known figures in Indonesia based on their writing using the BERT, Naïve Bayes, and LIME algorithms for explainable AI processes. The results are that the BERT algorithm is better at classifying text with an accuracy of 92% compared to Naïve Bayes, which has an accuracy of 90%. From this study, it was also found that KH. Abdurrahman Wahid and Emha Ainun Nadjib have almost the same style of language in which their writings contain many words with political and religious elements. Dahlan Iskan, his writing contains many words with political and socio-cultural elements, while Pramoedya Ananta Toer's writing uses many pronouns.
Perbandingan Metode Klasifikasi Untuk Deteksi Stress Pada Mahasiswa di Perguruan Tinggi Merlinda Wibowo; Muh. Rizieq Fazlulrahman Djafar
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.v7i1.5182

Abstract

The outbreak of the COVID-19 pandemic is increasingly affecting the high level of stress in humans. Stress due to this pandemic has also occurred, especially for students. This stress is caused by students spending too much time studying online. Using student data can act as a tool to identify student stress by processing it through various machine-learning methods. This method can extract information and find patterns and information from the data. Classification techniques are used as data groupings based on mapping data into sample data. This study used several classification methods: Naïve Bayes, Decision Tree, Support Vector Machine (SVM), Neural Network, Random Tree, Random Forest, and K Nearest Neighbor (KNN). These methods were successfully compared to determine which is the best for detecting stress precisely and accurately based on the classification performance results of each method. Random Tree and Decision tree were chosen as the best methods for the results of this performance comparison with an 80:20 split reaching up to 100%.
Analisis Terhadap Tagar #LGBT di Twitter Menggunakan Analisis Jaringan Sosial (SNA) Erwianta Gustial Radjah; Ade Iriani; Daniel H.F. Manongga
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.v7i1.5476

Abstract

Twitter is utilized to express opinions and respond to social phenomena in real life. Responses to events through Twitter conversations are relatively fast and create popular topics. Popular topics using hashtags are often used to attract, invite and influence other users to respond as well. In May 2022, the hashtag "#LGBT" was widely discussed, which was due to the invitation of a gay couple to attend a podcast by Youtuber Deddy Corbuzier. This resulted in a decrease in Deddy Corbuzier's subscribers and the blocking of one of the TikTok accounts of a gay couple. This study aims to analyze opinions on social media that influence the decisions of other social media users. Negative impacts on content creators and affects the real-life community of religious Indonesians. An analysis of network structure, group, and actors involved was conducted using Social Network Analysis to detect and study the opinions that occurred. Network structure mapping of Density, Diameter, and Reciprocity. Group detection using Modularity and Centrality to identify influential actors. Crawling Dataset of 10,000 tweets with 7,761 nodes (actors) and 8,371 edges. The results of the network structure research showed the farthest distance to reach other accounts is 18 (Diameter), the low-Density value is 0.000164 and the low Reciprocity value between accounts is 0.049480. The results of the research show that the value of the group formed is relatively high, 0.868000. Centrality identification shows @brn as the account with the most connections of 287 and @sop as the account with the highest intermediary among other accounts at 0.000303, and @aiy as the account with the closest distance to other nodes at 1.0. Based on the quality of the node to other nodes, @brn is the highest at 1.0
Implementasi Deep Learning Menggunakan Metode You Only Look Once untuk Mendeteksi Rokok Ahmad Harun; Mustakim Mustakim; Oktaf Brilian Kharisma
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.v7i1.5409

Abstract

Cigarettes are processed products from tobacco products which are used by burning and then smoked. Smoking activities are often found in everyday life, including in public infrastructure. The approach taken to prevent this activity generally uses manual information or human intervention. In terms of this approach, there are often many problems and failures due to the lack of manpower and supporting rules. Therefore, this study was structured with the aim of being able to detect smoking objects in real time using the You Only Look Once (YOLO) method. YOLO which is based on deep learning is very good at detecting objects, this model provides a single convolution neural network in assigning location and classification. So that in its application, YOLO is very fast in detecting and recognizing objects. This study conducted experiments on the training dataset in testing the YOLOv3, YOLOv3-Tiny and YOLOv4 models. The best training results were obtained in the YOLOv4 model with a composition of 80% training and 20% validation data sharing with a Mean Average Precision (mAP) of 92.54% and an F1-Score of 0.89. This study also conducted experiments on testing to detect cigarettes in real time, where the system can detect cigarettes up to a distance of 4.5 meters, and the highest detection accuracy is obtained at a distance of 1 meter, namely 99.03%.
Klasifikasi Sentimen Publik Terhadap Jenis Vaksin Covid-19 yang Tersertifikasi WHO Berbasis NLP dan KNN Primandani Arsi; Iphang Prayoga; Muhammad Hasyim Asyari
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.v7i1.5418

Abstract

The corona virus epidemic became an epidemic at the end of 2019 in the world. Some people are going through a pandemic with fear or even just being normal. The expression of fear, they discussed on social media. Now, social media is a means for some people to express their emotions namelly twitter. In order to end this pandemic the company is trying to develop a covid-19 vaccine, such as Pfizer, AstraZeneca, and Moderna which have obtained licenses from the World Health Organization (WHO). However, the discovery of the vaccine was not welcomed by some people. This is because of the post-vaccine impact and the vaccine development period which is considered too short. In this study, sentiment analysis was carried out based on public sentiment on Twitter social media about the covid-19 vaccine that has obtained a license from WHO uses NLP (Natural Language Processing) and machine learning algorithms. The purpose of this research is to find out the sentiment circulating on Twitter towards WHO-certified vaccines such as Pfizer, Moderna and AstraZeneca based on NLP as decision makers and sources of reference for the general public. Based on the research results, the highest positive sentiment was the Pfizer vaccine then Moderna, namely 47.30% and 46.20%. Meanwhile, the AstraZeneca vaccine received the lowest sentiment rating of the three, namely 40.09%.
Elliott Wave Prediction Using a Neural Network and Its Application to The Formation of Investment Portfolios on The Indonesian Stock Exchange Muhammad Rifqi Arrahim Natadikarta; Deni Saepudin
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.v7i1.5525

Abstract

Predicting stock trends is a complex problem because their movements constantly fluctuate and are affected by many factors such as political elements, world economic situation, investor expectations, and psychological factors. One way to analyze stock prices is Technical Analysis. This method focuses on stock indicators and patterns formed. Elliott Wave is one of the Technical Analysis methods. This study suggests an approach based on the combination of Neural Networks and Elliott Wave theory which will predict the possible direction of future trends. This model uses Fast Fourier Transform (FFT) coefficients to look for similarities with Elliott Wave patterns. When the dataset is similar to Elliott Wave patterns, the Neural Network will predict the direction of the stock trend. This study was conducted to confirm that the Elliott Wave-based Neural Network method helps predict stock trends. Experimentation is being carried out in some stocks on the Indonesian Stock Exchange, with profits exceeding 90%.
Analysis of Distributed Denial of Service Attacks Using Support Vector Machine and Fuzzy Tsukamoto Paradise Paradise; Wahyu Adi Prabowo; Teguh Rijanandi
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.v7i1.5199

Abstract

Advances in technology in the field of information technology services allow hackers to attack internet systems, one of which is the DDOS attack, more specifically, the smurf attack, which involves multiple computers attacking database server systems and File Transfer Protocol (FTP). The DDOS smurf attack significantly affects computer network traffic. This research will analyze the classification of machine learning Support Vector Machine (SVM) and Fuzzy Tsukamoto in detecting DDOS attacks using intensive simulations in analyzing computer networks. Classification techniques in machine learning, such as SVM and fuzzy Tsukamoto, can make it easier to distinguish computer network traffic when detecting DDOS attacks on servers. Three variables are used in this classification: the length of the packet, the number of packets, and the number of packet senders. By testing 51 times, 50 times is the DDOS attack trial dataset performed in a computer laboratory, and one dataset derived from DDOS attack data is CAIDA 2007 data. From this study, we obtained an analysis of the accuracy level of the classification of machine learning SVM and fuzzy Tsukamoto, each at 100%.

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