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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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Sistem Pendukung Keputusan Penilaian Kinerja Mekanik Menggunakan Metode AHP dan TOPSIS Noprida Arianto; Nurahman Nurahman
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3235

Abstract

Decision Support (DSS) is a system that was built to solve problems that are managerial or corporate organization designed to develop the effectiveness and productivity of managers to solve problems. PT. Auto Mobil Prima is one of the most popular Hino dealers in Sampit. This dealer is located in Central Kalimantan, precisely on Jl. General Sudirman Km. 2 Sampit and has branches in Pangkalan Bun and Palangka Raya. This study aims to assess the performance of the mechanical employees at PT. Prime Cars. In the assessment process, it is difficult for management to give weight to each criterion. So that it takes quite a long time, there is no system for decision makers that supports the performance appraisal of employees in the mechanics department. From the problems above and based on previous research, it is necessary to have a decision support system for evaluating the performance of employees in the mechanics department at PT. Auto Mobil Prima, one of the methods that can be used is AHP (Anailitycal Hierarchy Process) and TOPSIS (Technique For Others Reference by Similarity to Ideal Solution). This method was chosen because it is able to complete employee performance appraisals based on predetermined criteria. The results of this study are a website for a mechanical performance appraisal decision support system using the AHP (Anailitycal Hierarchy Process) and TOPSIS (Technique For Others Reference by Similarity to Ideal Solution) methods. So it is hoped that it can help PT. Auto Mobil Prima is a more objective mechanical performance decision maker
Analisis Faktor Penerimaan Media Internet sebagai Sumber Informasi Kesehatan dengan Model UTAUT dan HBM Heru Widianto; Ahmad R Pratama; Rahadian Kurniawan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3292

Abstract

People are increasingly turning to the internet for health-related information. It is very easy to perform or obtain health-related services or information on the internet using different types of devices. This study's goal is to assess public acceptance of the internet as a source of health information by combining the Unified Theory of Acceptance and Use of Technology (UTAUT) with the Health Belief Model (HBM). SEM techniques were used to analyze data collected from 324 respondents via online questionnaires. Two factors from the HBM model: self efficacy (SE) and cues to action (CA) were found as the strongest factors behind the acceptance of the internet as a source of health information. Two other factors from UTAUT model; Social Influence (SI) and Performance Expectancy (PE) also had significant effects, albeit not as strong, on the acceptance of internet media as a source of health information. This study's findings also point to the possibility of incorrect self-medication when excessive perceived self-efficacy (SE) is combined with symptoms as cues to action (CA) and social influence (SI) when looking up health information online
Tourist Places Recommender System Using Cosine Similarity and Singular Value Decomposition Methods Theriana Ayu Waskitaning Tyas; Z K Abdurahman Baizal; Ramanti Dharayani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3151

Abstract

Tourism in the city of Bandung has various potentials in the field of culture, regional specialties, buildings, and other tourist attractions. On the Tripadvisor page there are many reviews from users who have visited tourist attractions in the city of Bandung. In this case, user reviews are an important element for analysis. The analysis process is carried out using rule-based sentiment analysis. In conducting the review analysis, we use vaderSentiment to weight the positive and negative values. Positive values are subtracted from negative values to get a compound value and converted to a rating value. The rating value obtained is then processed using the Cosine Similarity and Singular Value Decomposition methods to obtain recommendations for tourist attractions in the city of Bandung. For this method, we use the Root Mean Square Error method as a measure of the level of accuracy between the predicted values. The results of the measurement of the level of accuracy produce a value of 3,489 in the Cosine Similarity method, while the Singular Value Decomposition method gets a value of 1,231. The value in the Singular Value Decomposition method is smaller than the Cosine Similarity method with a difference of 2,258 values
Klasifikasi Motif Citra Batik Menggunakan Convolutional Neural Network Berdasarkan K-means Clustering Amin Padmo Azam Masa; Hamdani Hamdani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3246

Abstract

Batik has several motifs and patterns so it is necessary to identify certain objects in an image, one of which is the recognition of the image of Yogyakarta batik using the Convolutional Neural Network (CNN) method which is already popular in the use of image data classification. The introduction of batik imagery aims to contribute to the digitization of batik image data and at the same time provide information on types of batik to the public. The batik image recognition process using CNN in this study combines the image segmentation process and the enhancement process with median filters and sharpening. The segmentation process carried out before CNN aims to help separate foreground objects from objects that are not needed in the background. The segmentation process that is commonly used is using K-means Clustering. Where K-means Clustering is used to group data in the same category. Furthermore, the enhancement process using the median filter and sharpening was carried out separately to compare the batik image classification process using CNN based on K-means Clustering from the median filter results and the sharpening results. The batik image classification process with CNN based on K-means Clustering on the median filter resulted in an accuracy value of 100%. Meanwhile, the batik image classification process with CNN based on K-means Clustering from the sharpening results resulted in an accuracy value of 80%.
Analisis Sentimen Pada Isu Vaksin Covid-19 di Indonesia dengan Metode Naive Bayes Classifier Fitria Septianingrum; Jajam Haerul Jaman; Ultach Enri
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3260

Abstract

The Covid-19 pandemic that has occurred in Indonesia and even in the world has not yet ended. Various efforts have been made by the Indonesian government to minimize the spread of this virus, such as the implementation of a lockdown, Large-Scale Social Restrictions (PSBB), a ban on going home during the Eid al-Fitr holiday, and so on. One of the new policies issued by the government is the vaccination program, where the government has started implementing the program since early 2021 for the people of Indonesia, which aims to increase antibodies to avoid exposure to the Covid-19 virus. To find out opinions, comments, or feedback given by the public on this new policy, sentiment analysis can be done. The process of this sentiment analysis includes data collection, namely the crawled tweet data originating from the Twitter social media. The data is then selected for further pre-processing stage so that the data is clean and ready for classification. Furthermore, sentiment weighting is carried out for data labeling using a lexicon dictionary and negative words. Then after that, the terms or words are weighted with tf-idf and followed by the feature selection process using Information Gain. Furthermore, the classification process is carried out using the Naive Bayes Classifier algorithm to classify the data into 3 classes, namely positive, negative, and neutral sentiments. The results of this study are to produce a model accuracy rate of 78%, recall 80%, and an AUC score of 0.904.
Klasifikasi Data Malaria Menggunakan Metode Support Vector Machine Nur Ghaniaviyanto Ramadhan; Azka Khoirunnisa
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3347

Abstract

Malaria is a life-threatening disease, caused by a parasite that is transmitted to humans through the bite of an infected female Anopheles mosquito. In 2019, there were an estimated 229 million cases of malaria worldwide and the death toll reached 409,000. The area most frequently affected by malaria, according to WHO, is the African region. Malaria can be detected beforehand by using the information inpatient data and applying machine learning techniques. This study aims to detect and classify severe malaria based on the history of examining patient data using the Support Vector Machine (SVM) method with a normalization technique using min-max on the dataset and a cross-validation technique with several experiments on the K value of the results. This study also compares the Support Vector Machine method with Naïve Bayes (NB) where the accuracy of the SVM model is superior to Nave Bayes with an average accuracy gap of 25%. The accuracy generated by the application of the proposed method is 92.3%.
Analisis Aritmia (Gangguan Irama Jantung) Menerapkan Metode Certainty Factor Masyuni Hutasuhut; Tugiono Tugiono; Asyahri Hadi Nasyuha
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3289

Abstract

The heart is one of the most important organs for humans that functions to pump oxygenated blood, the heart can experience problems, one of which is arrhythmia, arrhythmia is a heart rhythm disorder or pattern of rapid changes from a normal heart rate. Heart rhythm disturbances (arrhythmias) are patterns of rapid change from the normal heart rate. This becomes a problem when not handled properly and correctly because it can cause disruption of the heart's function, even in more severe cases it can cause sudden death. An expert system is one of the artificial intelligence of humans that studies how an expert thinks in solving a problem, making decisions or drawing conclusions from a number of facts. An expert system is a system that adopts the expertise of an expert that can be used to overcome certain problems, one of which is diagnosing arrhythmias. This study applies Certainty Factor analysis which can provide additional knowledge to the public, especially knowledge on heart disease. So that arrhythmia analysis with this certainty factor method can be applied to a system that helps the general public in preventing and overcoming heart rhythm disorders
Pemetaan Tingkat Kemiskinan di Provinsi Jawa Tengah Berdasarkan Kabupaten/Kota dengan Metode K-Medoids Fitriani Dwi Ratna Sari; Sotya Partiwi Ediwijojo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3278

Abstract

Poverty describes a condition of lack of ownership and low income, or in more detail describes a condition that basic human needs cannot be fulfilled, namely food, shelter, and clothing. In the last ten years, Central Java's poverty reduction performance has had its ups and downs, with rural poverty still dominating. The purpose of this research is to conduct a mapping analysis in the form of clusters on the number of poverty levels in districts or cities in the province of Central Java using artificial intelligence techniques. Given that Central Java is the third most populous province after West Java and East Java. This needs to be done in order to obtain a macro picture of the poverty level over the last few years through regional mapping. The dataset used is sourced from the Central Java Statistics Agency (BPS) website on the subject of the number of poor people (thousands of people) in 2017-2019. The solution given in conducting mapping in the form of clusters is the K-Medoids method which is part of clustering data mining. The number of clusters used are high and low clusters in mapping the number of poverty levels. The mapping analysis process uses the help of RapidMiner software. The results showed that 6 provinces (17%) were in the high cluster and 29 provinces (83%) were in the low cluster. The final centroid values for each cluster are {293.2, 309.2, 343.5} in the high cluster (cluster_1) and {18.6, 19.4, 20.1} in the low cluster (cluster_0). The results of the mapping can be useful information for tackling the poor where the high cluster (cluster_1) is a priority for the government in the province of Central Java, namely Cilacap Regency, Banyumas Regency, Kebumen Regency, Grobogan Regency, Pemalang Regency, Brebes Regency
Optimization of Piezoelectric Sensor Based Lighting Power Management Using Fuzzy Logic Mamdani Devani Adi Permana; Rahmat Yasirandi; Dita Oktaria
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3248

Abstract

Information is a very important thing from time to time. Lighting is needed by humans to support their daily activities. The problem of the lighting sector today is energy efficiency which is still not resolved. Where the use of electricity for lighting is still using old technology that is not environmentally friendly and costs a lot of money per month. One of the concepts of IoT, namely smart lighting, emerged as a solution to overcome problems in the lighting sector. However, the current smart lighting still requires a direct power source from the government (PLN), In addition, sectors such as tourist attractions in rural areas still need to be considered because there are still many areas that do not have a power source that can be used for street lighting. the tourist attraction. So we need a smart lighting technology that can produce its own power source to reduce the electricity costs that must be incurred. This study aims to build a tool or device that can manage and optimize the energy expended by building an IoT device using a piezoelectric sensor as the main material to generate an electric field that will produce electrical energy for lighting or lighting in rural tourist destinations and using fuzzy algorithms. Mamdani logic is a determinant of the intensity of the light obtained from the light sensor on the IoT device. The overall results of the system that has been built can work properly and the Mamdani fuzzy algorithm can be used properly with an accuracy of 93% power saving at the time of testing. In addition, the monitoring system built is also following the data obtained from the system. The suitability between the fuzzy system and Matlab as a whole is in accordance with the value of 100%
E-Travel Riau Berbasis Mobile Menggunakan Metode Dijkstra Marni, Prina; Asnal, Hadi; Erlinda, Susi; Agustin, Agustin
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3261

Abstract

Travel is one of the transportation that is often used by study tours or tourism in Riau Province. Travel is not only engaged in ordering but also in the field of delivery of goods. But what often happens to travel is that bookings are still done manually. The ordering process is carried out by telephone, then the admin records the address of the passenger to be picked up and the admin immediately confirms to the driver to pick up the passenger. The purpose of this study is to assist passengers in ordering travel online and drivers can monitor and determine the location of prospective passengers. In this study, an android-based online travel booking application was created using the Dijkstra algorithm. The dijkstra algorithm is an algorithm used to solve the shortest path problem for a directed graph with non-negative edge weights. This algorithm is used by drivers to determine the fastest route in the process of picking up prospective passengers. The advantage of this Dijkstra method is that it can find the closest route from the starting point to the end point by comparing the smallest value between points that will be used as a route that will be passed by the travel driver in order to get to the destination faster. The results of this study are a travel application that makes it easier for users to book travel and makes it easier for drivers to determine the fastest route in picking up passengers

Page 33 of 119 | Total Record : 1182

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