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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 62 Documents
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Search results for , issue "Vol 5, No 4 (2021): Oktober 2021" : 62 Documents clear
Trading Strategy on Market Stock by Analyzing Candlestick Pattern using Artificial Neural Network (ANN) Method Ni Putu Winda Ardiyanti; Irma Palupi; Indwiarti Indwiarti
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.v5i4.3266

Abstract

Technical analysis plays an important role in a stock market. Traders using technical analysis to find the trading strategy on the market stock. There are some technical indicators tools that can support the technical analysis, such as Moving Average, Stochastic, and others. Candlestick pattern also parts of the tools that used in technical analysis to develop the trading strategy since Candlestick represents the stock behavior. Therefore, understanding the Candlestick pattern and technical indicator tools will be valuable for the traders to predict the trading strategy. This study performs the prediction of trading strategy by analyzing the Candlestick pattern using an Artificial Neural Network (ANN). The technical indicator tools and Candlestick pattern will be generated as the features and label data in the modeling process. The method is applied to four stocks from IDX through their technical indicators for a certain period of time. We find that in the period of 28 days, the model generates the highest accuracy that reached 85.96%. We also used K-Fold Cross-Validation to evaluate the result of model performance that generates
Social Media Network Analysis (SNA): Identifikasi Komunikasi dan Penyebaran Informasi Melalui Media Sosial Twitter Novia Amirah Azmi; Aqil Teguh Fathani; Delila Putri Sadayi; Ismi Fitriani; Muhammad Rayhan Adiyaksa
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.v5i4.3257

Abstract

This article aims to identify the level of communication, information dissemination, and the dominant status of online media in Indonesia in disseminating information about COVID-19 circulating to the public, especially Twitter users. Qualitative research methods are used in this study to describe the findings obtained from Social Network Analysis on national online media. They are Twitter accounts @CNN Indonesia, @Detikcom, @KompasTV, and @VIVAcoid, and see the intensity of the interaction using #lawancovid and #saatnyaNyalakanTandaBahaya. The analysis was carried out using NVivo 12+ software in looking for graphs, interactions, and network intensity which were grouped into recipient actors, namely government, non-government organizations (NGOs), and the community, and saw the level of Centrality in SNA, which consisted of degree centrality, betweenness centrality, and closeness centrality regarding the spread of COVID-19. The study results stated: (1) the dissemination of information through Twitter media carried out by the national media became dominant and became a prominent tagline in the news summary during June 2021. (2) the highest recipients of information and responses were the public with an average of (0.574), NGOs (0.228), and government (0.2). (3) community interaction patterns and responses related to COVDI-19 also increased compared to other news taglines. (5) A good centrality measurement result is the @KompasTV account, with a degree centrality value of 63, closeness centrality 0.016, and betweenness centrality 3906.000. (6) for the overall framing carried out by the media, it is increasingly making people afraid to do activities outside the home to increase awareness in suppressing the spread of COVID-19 in Indonesia
Visualisasi Data Program Vaksinasi Covid-19 di Kota Depok dengan Big Data Analytics Rizki Elisa Nalawati; Dewi Yanti Liliana
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.v5i4.3330

Abstract

Indonesia and various country in the world are facing the problem of the Covid-19 pandemic. In Indonesia, the suspect Covid-19 was found in March 2020 in Depok City, West Java. Until February 2021, the number of positive COVID patients was 1,527,524. The need for supervision of the administration of vaccines is carried out by the Depok City Health Office on a number of health facilities that are trusted to administer vaccines to the public. This supervision is carried out using surveillance through visualization of vaccine administration data from the community. So far, the number of vaccines given to all health facilities in Depok City from January to August is around 613276 times which includes the administration of dose 1, dose 2 and dose 3. The amount of existing data can be managed and visualized properly using big data analytics. To get a good shape and visualization in decision making, several data cleansing processes are carried out up to the visualization stage. The use of big data analytics can be used to visualize descriptive data that is able to describe the rhythm of vaccination in Depok City, categorization of vaccine recipients, the type of vaccine given to the number of doses given. So it can be estimated that every month, vaccine recipients will continue to increase, both receiving dose 1, dose 2 and dose 3. This is in line with the Depok government's target which will complete the provision of vaccines to the people of Depok by the end of 2021
Optimalisasi Kinerja Klasifikasi Melalui Seleksi Fitur dan AdaBoost dalam Penanganan Ketidakseimbangan Kelas Tanti Tanti; Pahala Sirait; Andri Andri
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.v5i4.3280

Abstract

One of the problems in data mining classification is class imbalance, where the number of instances in the majority class is more than the minority class. In the classification process, minority classes are often misclassified, because machine learning prioritizes the majority class and ignores the minority class so that this can cause the classification performance to be not optimal. The purpose of this study is to provide a solution to overcome class imbalances so as to optimize classification performance using chi-square and adaboost on one of the classification algorithms, namely C5.0. In this study, the majority class in the dataset used is dominated by the negative class, so the performance appraisal should focus more on the positive class. Therefore, a more suitable assessment is recall/sensitivity/TPR because the resulting value only depends on the positive class. The results showed that both methods were able to increase the recall/sensitivity/TPR value, meaning that the application of chi-square and adaboost was able to improve the classification performance of the minority class
Pemanfaatan Algoritma K-Means untuk Pengelompokkan Angka Partisipasi Sekolah di Jawa Tengah Jati Sumarah; Ajeng Tiara Wulandari
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.v5i4.3277

Abstract

In Indonesia, the School Participation Rate (APS) is recognized as one of the indicators of the success of developing education services in regions, whether Province, Regency, or City. The higher the rate of school enrollment, the more successful an area is at providing access to educational services. The dataset was obtained from the Central Statistics Agency (BPS) of Central Java Province's website. The object studied is the percentage of APS in the Central Java region from 2017 to 2019 for ages 7 to 12, 13 to 15, and 16 to 18. The study's goal was to conduct an analysis in the form of mapping the School Participation Rate in the districts and cities of Central Java, the third most populous province after West and East Java. RapidMiner software is used in the analysis process. The research output is a map of clusters of areas in the Regency and City areas. The k-means method, which is part of clustering data mining, is the solution method offered. The number of mapping clusters in this study was divided into two categories: high (C1) and low (C2) clusters. According to the study's findings, the mapping of the 7-12 year old cluster was 24 provinces in the high cluster (cluster 0) and 11 provinces in the low cluster (cluster 1); the mapping of the 13-15 year old cluster is 23 provinces in the high cluster (cluster 0) and 12 provinces in the low cluster (cluster 1); and the mapping of the 16-18 year old cluster is 15 provinces in the low cluster (cluster 1). Cluster determination is based on the final centroid value, with the final centroid value of the 7-12 year old cluster being high (cluster 0) 99.81, 99.87, 99.75; low (cluster 1) 99.73, 99.43, 99.25; and the centroid value of the 13-15 year old cluster being high (cluster 0) 97.52, 97.12, 96.93; low (cluster 1) 93.78, 93.58 Overall, the mapping results show a high percentage for all age groups, which is greater than 50% in the high cluster. In detail, 24 provinces (57 percent) are in the low cluster for the 16-18 year age group. The research findings can provide a macro picture of the level of development of the School Enrollment Rate over the last few years
Conversational Recommender Systems Based on Criticism for Tourist Attractions using TF-IDF Rayhan M Auliarahman; Z K Abdurahman Baizal; Nurul Ikhsan
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.v5i4.3245

Abstract

Tourist attractions are one of the attractions of tourist interest. There are many types of tourist attractions in an area, but this becomes a problem in itself because tourists will find it hard to find or determine a tourist attractions that suit their tastes. Many researches on recommendation systems based on criticism have been carried out with the aim of obtaining user preferences. However, only a few studies have conducted a critique-based recommendation system using the Conversational Recommender System (CRS). With this research, we will discuss a recommendation system based on criticism using natural language or CRS for tourist attractions in Bandung. In this study, we add assistance from the system to help users choose preferences or what can be called System-suggested Critiques (SC), users more easily determine preferences for the system. We use the Term Frequency-Inverse Document Frequency (TF-IDF) to determine critiques submitted to users. Based on the results of an evaluation involving 88 respondents who were asked to fill out a questionnaire after trying the system built, it was found that users were quite satisfied with the system we built. And obtained 62.06% system accuracy which proves that the system performance is quite satisfactory.
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%.

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