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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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Penerapan Business Intelligence Terhadap Data Penjualan UMKM (Foodendez) Menggunakan Metode Algoritma Apriori Dalam Menentukan Segmentasi Pasar Akhmad Rafi Oktavian; Fitrah Rumaisa
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4338

Abstract

MSMEs in Indonesia are MSMEs with a business scale level of 98.7% are micro-enterprises and this MSME-scale assessment category was assessed or researched 10 years ago, the results are still the same as the previous assessment, due to the average MSMEs in Indonesia not having a way or the right innovation in developing its business, especially in terms of the products produced and other things, an example of MSMEs experiencing this is MSME Foodendez, which causes these MSMEs to not have significant sales progress. Therefore, to improve the quality and progress of the Foodendez MSME business, the current sales data is used to recapitulate and evaluate the Foodendez MSME market segmentation. By using the a priori algorithm, the sales data can be used to find out predictive information on consumer interest based on age, gender, and sales location criteria. The application of business intelligence uses an a priori algorithm so that it can help provide predictive information on consumer interest in a product and can clearly know its market segmentation by collecting data through product sales in the marketplace it can be seen which products are most interested in by consumers, then data on the amount followers, comments, and likes in every post on social media in order to determine engagement (promotional strategies through social media). In this research, testing is carried out based on the location of sales at Foodendez SMEs so as to produce market segmentation data. The conclusion from the temporary test results, the frequency of sales in the marketplace is the highest at 52%, then the lowest frequency of sales is 12% in sales through exhibition bazaars.
Pengujian ISO 25010 Pada Smart Chair Akupresure Berbasis Internet Of Things (IoT) Diki Daryanto; M. Khairul Anam; Yoyon Efendi; Rahmaddeni Rahmaddeni
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4134

Abstract

Smart chair acupressure is a technology that has been built using the application of the internet of things concept. The purpose of this study was to determine the quality level of the Acupressure Smart Chair, so that there are no errors in some features and the occurrence of errors in its functionality, which must be in accordance with the specified usability, the researchers conducted a test by utilizing the characteristics of ISO 25010. Related to the researcher's reasons taking ISO 25010 as one of the tests on the acupressure smart chair is because the smart chair acupressure technology cannot be said to be feasible because there has been no testing carried out in the measurement, the researchers used ISO 25010 which utilizes several characteristics that can be used such as functional suitability, usability and performance efficiency which focuses on measuring the electronic equipment used. Basically the concept used in this research is the internet of things which is a system that functions to control and simplify the workings of acupressure massage techniques for users. Whereas other problems that occur and arise often depend on customer satisfaction related to the quality provided on software and devices. This study uses the ISO 25010 method as the standard for the feasibility test on the device, where ISO 25010 basically also has 8 characteristics. In this study, to test the smart acupressure hair, the researchers used 7 characteristics that exist in ISO 25010, namely functional suitability, compatibility, usability, performance efficiency, reliability, maintability and portability. The instrument used is looking for an approach using the formula for reliability testing and processing on SPSS software. The results obtained from this study are that the acupressure smart chair has not been able to meet the ISO 25010 standard because the results obtained are 34.9% with an alpha cronbch value of 0.46 (unacceptable)
Rancang Bangun Perangkat Wearable Pemantau Kondisi Kesehatan di Masa Pandemi Covid-19 Endang Sri Rahayu; Listanto Listanto; Reza Diharja
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4195

Abstract

Wearable device is a device that is worn on the body to measure the clinical parameters of the body. The purpose of using this tool is to determine the value of each parameter being measured. In this research on the design of wearable to monitor health conditions during the Covid-19 pandemic, wearable made to measure body temperature, oxygen saturation (SpO2) which is a clinical parameter of the body during the Covid-19 pandemic. In addition to helping prevent the transmission of the Covid-19 virus, this tool also realizes physical distancing automatically. This study describes the manufacture of technological tools that are appropriate and useful for the community, as a solution to the problem pandemic Covid-19This tool can perform (1) physical distancing automatically, (2) monitor oxygen saturation (SpO2) in real time. (3) monitored body temperature in real time, and equipped with an Android application for monitoring. The variables studied in this study were the manufacture of tools equipped with Bluetooth Low Energy (BLE) technology, besides the microcontroller and an android application based on the Dart programming language with the Software Development Kit Flutter from the results of observations and analysis, it is known that BLE technology is efficient for use for Internet of Things, and short-range communication. BLE can realize distance measurements to provide physical distancing automatically by using Received Signal Strength Indication (RSSI), ranging from 0cm to100 cm. The results of measuring SpO2 and body temperature on wearable monitor health conditions during the Covid-19 pandemic using the MAX30102 sensor can work well. The results of the SpO2 measurement get a standard deviation value of 0.96%, while the results of body temperature measurements get a standard deviation value of 1.64%.
Alat Pendeteksi Kebakaran Dini Berbasis Internet Of Things (IoT) Menggunakan NodeMCU Dan Telegram Yonatan Surya Kristama; Indrastanti Ratna Widiasari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4445

Abstract

One of the unpredictable disasters is fire. Fires often occur in densely populated areas and are triggered by a variety of factors, for example an electric short circuit. Usually a fire disaster is only realized when the fire has grown and has spread to all places. Based on these problems, a solution is needed to prevent the occurrence of fire disasters, namely by designing an Early Fire Detection Tool Based On Internet of Things (IoT) Using NodeMCU And Telegram. The microcontroller used in this study is NodeMCU which is equipped with WiFi ESP8266. This tool uses two fire sensors, namely the LM393 type fire sensor and the KY-026 type fire sensor, besides that this tool also uses buzzer which is used to provide warnings in the form of sound and Light Emitting Diode which are used as a sign that the microcontroller is connected to Telegram. This study produces a device that can send notifications in the form of messages to the user's telegram if the two fire sensors detect the presence of fire in which the two fire sensors can detect the source of the fire up to a distance of 50 cm. This tool can make it easier for users to monitor the condition of the room in real - time by sending messages through the telegram application and this tool will reply to the message automatically according to the conditions of the two fire sensors.
Klasifikasi Data Review IMDb Berdasarkan Analisis Sentimen Menggunakan Algoritma Support Vector Machine Gita Cahyani; Wiwi Widayani; Sharazita Dyah Anggita; Yoga Pristyanto; Ikmah Ikmah; Acihmah Sidauruk
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4023

Abstract

Advances in Web 2.0 technology encourage the creation of personal website content involving sentiments such as blogs, tweets, web forums, and various types of social media. The Internet Movie Database (IMDb) is a website that provides information about films from around the world, including the people involved, nominations received, and reviews from visitors. The number of movies and reviews on IMDb causes users or visitors to check the reviews to find out the film rating, so it takes time for users who have no experience using IMDb. Sentiment analysis can be a solution to label positive and negative reviews. One of the algorithms used in sentiment analysis is the Support Vector Machine (SVM) algorithm. This study aimed to test the accuracy of the SVM algorithm in the classification of sentiment review films on IMDb. The tests carried out using the Support Vector Machine algorithm resulted in an accuracy value of 86.5%. The SVM algorithm can also produce a precision value of 90.67% and a recall value of 91.62%.
Market Basket Analysis Menggunakan Association Rule dan Algoritma Apriori Pada Produk Penjualan Mitra Swalayan Salatiga Elfira Umar; Danny Manongga; Ade Iriani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4217

Abstract

Market Basket analysis is learning to manage associations in data processing in various fields. The main purpose of Market Basket analysis in the field of sales is to convey an important message to the company so that it can find out the behavior patterns of entering goods into the shopping basket by consumers so that partners can make a decision. In this study, the Apriori Algorithm is used to take into account changes that occur in the data. This study discusses data mining techniques in analyzing what items are most often purchased at the same time by consumers so that they can change the placement of items that are close together to increase the impulse buying effect. The results obtained are 5 rules where one of the rules obtains the highest confidence value when buying cigarettes, the dominant item is taken simultaneously, namely eggs by obtaining a confidence value that can meet the highest confidence requirements, namely 67%.
Sentiment Analysis of Hate Speech on Twitter Public Figures with AdaBoost and XGBoost Methods Daffa Ulayya Suhendra; Jondri Jondri; Indwiarti Indwiarti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4394

Abstract

Public figures are often scrutinized by social media users, either because of what they say or even because of their role in a television series. Generally, public figures upload something on their social media accounts to help shape their image. But not everyone who sees it is happy. Some even dislike the upload. This study aims to determine public sentiment towards public figure Anya Geraldine conveyed on Twitter in Indonesian. The classification process in this study uses the Adaptive Boosting (AdaBoost) and Extreme Gradient Boosting (XGBoost) classification methods with text preprocessing using cleaning, case folding, tokenization, and filtering. The data used are tweets in Indonesian with the keyword ”@anyaselalubenar”, with a total dataset of 7,475 tweets divided into 6,887 positive and 588 negative tweets. From the label results using oversampling to avoid excessive overfitting problems. The feature used is TF-IDF weighting. Four experimental scenarios were carried out to validate the effectiveness of the model used: first model performance without oversampling, second model performance with oversampling, third model performance with undersampling, and fourth model performance with Hyperparameter tune. The experimental results show that XGBoost+SMOTE+Hyperparameter achieved 95% compared to AdaBoost+SMOTE+Hyperparameter of 87%. The application of SMOTE and Hyperparameter tune is proven to overcome the problem of data imbalance and get better classification results.
Clustering Pengunjung Mall Menggunakan Metode K-Means dan Particle Swarm Optimization Teuku Muhammad Dista; Ferian Fauzi Abdulloh
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4172

Abstract

This research aims to cluster mall visitors. This is motivated by the mall's income which has decreased since the pandemic. Later from these several clusters we can find out the characteristics of the mall's visitors. Those characteristics will be used later to increase the income from the mall. In this research, we use a dataset from Kaggle named Pengunjung_mall in CSV format which will later be processed using Python language on Jupiter Notebooks using the K-Means method. To ensure how accurate the K-Means method is, optimization is carried out using the PSO (Particle Swarm Optimization) method. After performing clustering and optimization using Jupyter Notebook, the results will then be evaluated with DBI (Davies Bouldin Index) in Microsoft Excel to find out how well the Clustering is generated. The Clustering results obtained are used as a reference to determine the characteristics of mall visitors which is one strategy to increase Mall profits. As a result, we have succeeded in dividing mall customers into 5 clusters based on their annual earned income and expense scores. The cluster has been optimized with PSO and has succeeded in increasing the cluster resulting from the K-Means method which is proven by the Davies Bouldin Index method. This research has concluded that customers who have high income levels and have high spending scores are the targets with the highest priority level for malls.
Penerapan Extreme Programming dalam Pengembangan Fitur Interoperabilitas Pada Aplikasi Bioinformatika Edrian Hadinata; Tantri Hidayati Sinaga
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4238

Abstract

Extreme Programming or XP Model is an application development framework used to create applications that require rapid adaptation. Even in the condition that the application is still in use, the use of this development model is quite appropriate if it is applied to the development of Several features of Bioinformatics Applications. The widespread use of interoperability features found in many applications has made it easier for many system developers to develop applications on various system devices. Likewise, bioinformatics applications that carry out various in silico studies. Application development using the XP Model for special applications that are rarely used for feature additions, the difficulty is implementing rapid adaptation to interoperability feature development without design and analysis of specific applications. The contribution of this paper is to make notes and blueprints for adding interoperability features for bioinformatics applications. Besides that, the XP model is a different method when used to carry out the application development process, especially if it is used to add features to special applications. The success obtained in the development of this application cannot be separated from the advantages of the XP model itself, because the XP model can be implemented in conditions of lack of human resources and the possibility of applying practical principles and techniques in the process. As a result, development is carried out quickly and the application runs well through testing results and then applications can be used to perform processes in silico through interaction between applications to applications to the dataset provider.
Penerapan Clustering K-Means untuk Pengelompokan Tingkat Kepuasan Pengguna Lulusan Perguruan Tinggi Dikky Praseptian M; Abdul Fadlil; Herman Herman
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4191

Abstract

One way to evaluate the quality of graduates is to provide questionnaires to graduate users, namely agencies / companies in the world of work in order to assess the quality of graduates of each university. Questionnaires for graduates are generally carried out by filling out the questionnaire form physically and then returning to the college. The K-Means method is one of several non-hierarchical clustering methods. Data clustering techniques are easy, simple and fast. Many approaches to creating clusters or groups, such as creating rules that dictate membership in the same group/group based on the level of similarity between the members of the group. Other approaches such as creating a set of functions to measure multiple criteria from grouping as a function of some parameters of clustering/grouping. From the results and discussions, K-Means clustering succeeded in grouping graduate user satisfaction data into three clusters where the results shown by manual calculations and applications showed the same results where clusterS C1 as many as 48 alternatives, C2 as many as 1 alternative, and C3 as many as 2 alternatives. In the sense that the application that is built successfully implements K-Means clustering is evidenced by the comparison of applications with weka tools has similar percentage results. In terms of the percentage of graduate users or alumni from STMIK PPKIA Tarakanita Rahmawati 94.12% Very satisfied, 1.96% Satisfied and 3.92% Quite Satisfied.

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