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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 78 Documents
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Search results for , issue "Vol 6, No 4 (2022): Oktober 2022" : 78 Documents clear
Pemetaan Karakteristik Sekolah Sasaran Promosi pada UNKRISWINA SUMBA menggunakan K-Means Murry Albert Agustin Lobo; Sri Yulianto J Prasetyo; Kristoko D Hartomo
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.v6i4.4464

Abstract

The rapid development of technology has an impact on how data is collected. A high level of data productivity will be in vain if it is not followed by the ability to process data that can produce information that helps the development of the organization. This study aims to help the Promotion Section of UNKRISWINA SUMBA in mapping the characteristics of the target schools and then provide alternative promotion strategies as input in formulating forms of institutional promotion. The data used is in the form of student data who have registered at UNKRISWINA SUMBA since 2016 – 2020. Data processing uses the concept of data mining by applying the K-Means algorithm. K-Means algorithm is used for clustering promotion target schools as many as 4 clusters. Cluster determination is carried out using the elbow method to determine the optimal value of k to perform calculations. Based on the results of processing based on the K-Means algorithm, it is known that as many as 8 schools in cluster 0 are the schools with the most students enrolling in UNKRISWINA SUMBA, 76 schools in cluster 1 are schools with the fewest students enrolling in UNKRISWINA SUMBA, 21 schools those in cluster 2 are schools with quite a lot of students enrolling in UNKRISWINA SUMBA, and 1 school in cluster 3 is a school with quite a number of students enrolling in UNKRISWINA SUMBA but focusing on the Economic Development and Management study program.
Penerapan Metode K-Medoids Clustering Untuk Mengelompokkan Ketahanan Pangan N P Dharshinni; Ciok Fandi
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.v6i4.4939

Abstract

Food is a basic need that must be fulfilled and easily accessible to the entire community. After the end of the pandemic period, it still caused several sectors to decline, including the agricultural sector, which resulted in crop yields also declining. The problem faced by several regions in Indonesia, one of which is the North Sumatra region, is that the availability of food products has decreased and increased unstably due to the lack of information about the grouping food security every year. This results in the food needs of the people in each region being unfulfilled. The purpose of this study is to group areas with the number of increases and decreases in food crop yields in North Sumatra using the K-Medoids algorithm. The K-Medoids algorithm includes a deflection algorithm that is quite efficient in carrying out the shaking of small datasets and the search for the most representative points and can overcome outliers. So that it can be used in the floundering of the influence of productivity and the level of food security. The results showed that the application of the K-Medoids algorithm resulted in a DBI (Davies Bouldin Index) value of 0.062 and a Silhouette Coefficient value of 0.8980, with the number of clusters as many as 3 clusters where Cluster_0 dominated by corn food crops experienced an increase in production by 5% and peanuts by 5%, Cluster _1 was dominated by a decrease in the number of soybean production yields by 38%, and Cluster_2 dominated by a decrease in green bean yield by 33%.
Deteksi Tingkat Kemiripan Judul Menggunakan Algoritma Oliver Pada Sistem Informasi Pengajuan Skripsi Nopi Fitrianingsih; Marsani Asfi; Dwi Prasetyo; Ricky Perdana Kusuma; Muhammad Afif Sulhan
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.v6i4.4409

Abstract

Thesis is the final requirement of academic education. The procedural system for the title checking process is based on the memory of the huma, thus allowing for the similarity of the thesis title. Therefore, a system can detect the degree of similarity of  titles automatically in order to prevent redundancy of thesis titles. This study applies the Oliver algorithm which is a function in the PHP programming language that produces a similarity value of two strings. Oliver's algorithm has not been widely applied in plagiarism detection techniques. Oliver algorithm testing using test data as much as 10 test title data and 217 titles for training data. The test results show that Oliver's algorithm produces a percentage of the title similarity level in accordance with the accepted provisions, namely 60%. It was found that 2 out of 10 thesis titles were rejected with a similarity percentage of 70.06% and 62.89% and 8 thesis titles were accepted with a similarity percentage of 31.94%, 30.48%, 26.11%, 31.86%, 29.47%, 31.13%, 35.01% and 37.08%.
Klasifikasi Emosi Pada Lirik Lagu Menggunakan Algoritma Multiclass SVM dengan Tuning Hyperparameter PSO Helen Sastypratiwi; Hafiz Muhardi; Mega Noveanto
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.v6i4.4609

Abstract

Currently, it is increasingly difficult to determine the emotion in a song because the numbers of the songs continue to increase, based on this problem, the researcher makes a classification model using text classification. Based on these problems, this study uses the Multi Class Support Vector Machine (SVM) method with Particle Swarm Optimization (PSO) as a tuning hyperparameter and comparing the effect of 3 datasets (lines, verses, and whole songs) in the case of classifying the emotions of song lyrics. In this case, there are five basic human emotions, in-between love, happiness, anger, fear, and sad. Based on the test results on each model, scenario 2 (SVM-PSO Perbaris) does provide the best model performance with an accuracy value of 92.13%. However, if we look at the performance value, it changes from the evaluation of the training data to the testing data presented in table 4.3, the most significant changes occur in the verses dataset and the whole song dataset. This can happen because the content or value of the per-bait dataset and the whole song has more sentences than the per-line dataset. So that the quality will be better if you use the verses dataset or the whole song. This research has also succeeded in make the classification of emotions so that it can classify the class of emotions from the text of Indonesian song lyrics.
Hoax Detection Tweets of the COVID-19 on Twitter Using LSTM-CNN with Word2Vec Prisla Novia Anggreyani; Warih Maharani
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.v6i4.4564

Abstract

The growth of Twitter users is increasing every year, impacting activities in social media such as hoaxes that are increasingly widespread on various platforms. During this pandemic, the rate of hoaxes is growing because nowadays, it is very easy for humans to interact with each other, have opinions, and exchange information. One of the hoaxes that often appears is the hoax about the Covid-19 virus. Therefore, a method for detecting hoaxes is needed, especially for the topic of the Covid-19 virus in Indonesia. The method used in hoax detection is LSTM-CNN with Word2Vec. More than 1000 tweets data are used in this study, divided into hoax and non-hoax categories. Detection is carried out to analyze the hoax results obtained by using Word2Vec as a method to convert data as a classification vector and LSTM-CNN to classify the data. This work's result showed that the LSTM-CNN model with Word2Vec achieves 79.71% accuracy, surpassing the LSTM model and CNN model.
Design and Build Smart Street Lights by Utilizing Sensor and Tranduser Technology Based on Arduino Uno Marthin Martunas Sihotang; Iqbal Kamil Siregar; Rika Nofitri
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.v6i4.4520

Abstract

Street lights or also known as Public Street Lighting (PJU) are lights that are used for street lighting at night making it easier for road users to see more clearly the road to be traversed, so as to improve traffic safety and security for road users and prevent the occurrence of accidents. crime, and gives an aesthetic value to a place. Public street lighting is an electronic item that is vulnerable or can be said to have a short service life, so repair and maintenance activities are absolutely necessary. Repairs can include network repairs, replacement of dead lights, checking cables and checking the condition of Public Street Lighting (PJU). The working system of street lights in Sei Kamah 1, Pasiran Village, Sei Dadap District, Asahan Regency, currently still uses the switch button on every electricity pole that is turned on by PLN officers or the surrounding community at night, and street lights still use essential light bulbs. so that the power released is too large and is detrimental to the electricity supplyer, although there are some street lights whose system turns on automatically when it is night and turns off automatically during the day but the installation is only in certain places plus sometimes street lights like this become the target of theft by irresponsible people. This study aims to create an automatic system street light design system in which this automatic system uses a motion sensor and a light sensor so that it can save more electric current. The device used is an arduino uno controller and uses the arduino ide programming language. The results of this study are expected to be a recommendation in the design of Public Street Lighting (PJU).
Analisis Perbandingan Metode AHP dan Metode MFEP Pada Sistem Pendukung Keputusan Pemilihan Vendor Yuda Aji Pramukti; Septi Andryana
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.v6i4.4634

Abstract

Vendor is a party in the form of a company that provides goods and services to consumers or other business actors. Pasar Minggu Regional General Hospital is one of the Class B Non-Educational General Hospitals located in the heart of South Jakarta. The selection of vendors at Pasar Minggu Hospital was previously still using the traditional system and had not used it. The traditional method used previously often had several problems, starting from the administration of vendor data that was not accurate, as well as writing vendor data that was always wrong, then there was negligence in vendor assessment, and up to vendor selection that was not in accordance with the right mechanism. From these problems, Pasar Minggu Hospital requires a Decision Support System application in searching for vendors to be selected for a project. Because of this, Pasar Minggu Hospital plans to make a Decision Support System application to carry out the process of selecting the best vendor and in accordance with the criteria determined by the management of Pasar Minggu Hospital. The criteria between vendors include C1 (Finance), C2 (Performance), C3 (Safety) and C4 (Quality). On the Decision Support System website there are 5 menus, namely Company Data, Data Criteria, Criteria Comparison, Assessment Data and Analysis Results. In the calculation using the appropriate AHP consistency ratio of 0.10, it means that the data is declared consistent. And in the MFEP method the total Factor Weight is 9.00 with details of Finance 2.00, work safety 2.00, quality 3.00 and performance 2.00. From the calculation results of the two methods above that the first order of comparison of vendor results is occupied by PT. Bangun Rezeki with a percentage using AHP of 87%, and using MFEP of 71%, in second position by PT. Building Award with a percentage of 81% and 73% with MFEP. And the last position by PT. Ocean figures with a percentage of 72% using AHP and 56% using MFEP.
Sistem Pencacah Sampah Berbasis Computer Vision Menggunakan Metode Eigenface Aditya Wijayanto; Alon Jala Tirta; Afifah Dwi Ramadhani
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.v6i4.4636

Abstract

Indonesia is one of the largest countries in Asia, with a very dense population. According to data from The World Bank, the human population indicator in Indonesia in 2019 increased by 270,625.57 people. It shows that population density in Indonesia is related to world problems related to household waste. The household sector contributes as the top waste producer in Indonesia. Adipura data in 2019 stated that at least household waste contributed 36%. This figure is higher than the heap of waste generated from the traditional market, which reaches 24%. The accumulation of waste that occurs without any sorting of waste makes the waste more difficult to decompose and difficult to recycle. Apart from that, the current landfill causes air pollution. Therefore, it is necessary to increase public awareness regarding waste sorting and processing to overcome these problems. We propose to make a device that can help sort organic and inorganic waste with Artificial Intelligence technology based on Computer Vision using the Eigenface method and the Internet of Things. Eigenface is a method that has the working principle of using an XML file to perform face recognition. 1. Implementation of a Computer Vision-Based Garbage Counting System Using the Eigenface Method can run well, where when the system detects an organic object, the door of the counting machine can be opened, and vice versa if it detects inorganic, the machine door is closed. Accuracy results for organic are 70% and for inorganic 75%. It is due to the lack of variation in the dataset and changes in the object's physical condition.
Penerapan Algoritma Asosiasi dengan Metode Apriori Untuk Strategi Meningkatkan Hasil Penjualan Spare Part Motor Entin Sutinah; Nani Agustina
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.v6i4.4778

Abstract

Sales transactions in everyday life are very common, but in the sales business you must have a strategy to increase sales results optimally. This research was conducted on PD. Tri Jaya Motor, where this trading company is engaged in trading motorcycle spare parts for all types of motorcycles, both Honda, Yamaha, Kawasaki and so on. Currently PD. Tri Jaya Motor wants to make a strategy to increase sales results, but the problem is that PD. Tri Jaya Motor still doesn't get the right and effective way, one of which is in the preparation of goods that still don't have the right placement pattern, so the items that should be close together, but this stored far apart, to solve these problems a method is needed, therefore the method used in this study is the association algorithm with the a priori method to analyze motorcycle spare part sales transaction data, which in this study takes motorcycle spare part sales transaction data in one year. The purpose of this study is to find out which items are purchased by consumers simultaneously so that from this information it can be used to make sales strategies, one of which is by arranging items that are close to the items purchased by consumers simultaneously. The results of this study show that when buying a kilometer cable, 86% will buy a brake cable with a support value of 30% and 70% confidence. By knowing the products that are purchased a lot and the interrelationships between goods with one another, so the company can develop a strategy by increasing the inventory of these goods for PD. Tri Jaya Motor.
Implementasi Face Recognition Pada Absensi Karyawan Menggunakan Local Binary Pattern Histogram dan SHA 256 bit Ellanda Purwawijaya; Roy Nuary Singarimbun; Hendra Pasaribu
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.v6i4.4923

Abstract

Attendance is an activity that states the presence or absence of an employee. One of the technology models used to express the presence of an employee is a fingerprint machine. PT Global Andalas Cargotama has implemented an employee attendance system using a fingerprint. However, in the process of implementation, the fingerprint machine has weaknesses that are vulnerable to manipulation, fingerprint identification which is often hampered due to the need for routine machine maintenance and attendance recapitulation which is still done manually and not in real time. The face recognition system is an application to recognize faces in real time and accurately and can be applied to overcome the weaknesses of the fingerprint machine. This study uses the LBPH (Local Binary Pattern Histogram) method in recognizing employees' faces when doing attendance, this is based on several studies that have been done previously, this method has a high level of accuracy in recognizing faces. Meanwhile, SHA256 bit cryptography is used to solve the problem of attendance manipulation. The results of facial recognition both when attendance comes in and attendance goes home will be stored on the server computer locally to state employee attendance. The test results from this study showed that the attendance application was able to recognize the employee's face accurately with an average accuracy rate of 0.9511 by paying attention to the room lighting parameters, accessories on the face and the distance from the camera to the face. Through this research, it is expected to be able to overcome the problem of the weakness of the fingerprint machine and the attendance process at PT Global Andalas Cargotama to be more effective.

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