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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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Conversational Recommender Systems Based on Mobile Chatbot for Culinary Ghazi Ahmad Fadhlullah; 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.v5i4.3242

Abstract

Culinary places are one of the tourists attractions in a place that makes many new culinary places appear. Various types of new foods and drinks are present along with the addition of culinary places. However, this can be a problem when tourists visit a new destination and look for a culinary place that suits their tastes. In the previous research on the recommendation system for culinary places, users only gave their preferences at the beginning of the recommendation process and ignored the operating hours of the recommended culinary places. Therefore, we developed a recommendation system for culinary places by utilizing the context of time from users. We use the Conversational Recommender System on the chatbot platform with the Personalized PageRank algorithm to generate recommendations. In addition, we also use the explanation facility to get an explanation of the recommended items. We use questionnaires and the accuracy of recommendation results to measure user satisfaction and system performance. The evaluation results with a questionnaire involving 81 respondents concluded that users are pretty satisfied with the system built. However, testing with accuracy yields a value of 40%, proving that the system performance is low
Klasifikasi SMS Spam Berbahasa Indonesia Menggunakan Algoritma Multinomial Naïve Bayes Herwanto Herwanto; Nuke L Chusna; Muhammad Syamsul Arif
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.v5i4.3119

Abstract

Based on a report submitted by Truecaller Insights Report 2020, Indonesia placed sixth position with the most spam messages, one of the spam applications is SMS. Spam SMS contains unwanted or unsolicited messages, including advertisements, scams and so on. The existence of this spam message causes inconvenience from the user's side when receiving spam SMS, and some even become victims of crime after responding to the SMS. To minimize inconvenience and crime caused by spam messages, the purpose of this study is to filter SMS spam or SMS filtering by classifying SMS spam using the Multinomial Naïve Bayes algorithm by looking for the best combination of parameters to improve the performance of the model that is formed. The results of model testing get the highest precision value in the MNB and SVM models by 93%, the highest recall value in the SVM model at 94%, the highest f1-score value in the SVM model at 94%, the highest accuracy value in the SVM model at 95%, and the fastest test time on the MNB model is 2.66 ms
Penerapan Simple Additive Weighting Pada Sistem Otomatisasi Surat Teguran SAMSAT Mataram Melalui SMS Gateway Muhammad Reza Syachrani; Nisa Hanum Harani; M Harry K Saputra
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.v5i4.3264

Abstract

Remind or notify the Taxpayer of unnecessary obligations so that a Reprimand letter arises. Choosing a Reprimand letter and sending a Reprimand letter is not effective because it takes a lot of time and money. Therefore, to be able to overcome the existing problems, it is necessary to automate the selection of Reprimand letters using the Simple Additive Weighting (SAW) method which is a multi-criteria decision support method. As well as sending automatic Reprimand letters using Short Message Service (SMS) using the Gammu library. So that it can make it easier for users to choose and send letters so that it can be done faster and at lower costs. The results of the tests carried out in terms of time, SAW has a faster time with conventional systems by only taking 8,620 seconds. Furthermore, testing is carried out in terms of the cost of the SMS gateway, which is more affordable than the conventional system that was previously applied with a cost difference of 99.7%
Analisis Sentimen E-Wallet di Twitter Menggunakan Support Vector Machine dan Recursive Feature Elimination Elza Fitriana Saraswita; Dian Palupi Rini; Abdiansah Abdiansah
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.v5i4.3118

Abstract

Grouping of positive or negative sentiments in text reviews is increasingly being done automatically for identification. The selection of features in the classification is a problem that is often not solved. Most of the feature selection related to sentiment classification techniques is insurmountable in terms of evaluating significant features that reduce classification performance. Good feature selection technique can improve sentiment classification performance in machine learning approach. First, two sets of customer review data are labeled with sentiment and then retrieved, processed for evaluation. Next, the supports vector machine (svm-rfe) method is created and tested on the dataset. Svm-rfe will be run to measure the importance of the feature by rating the feature iteratively. For sentiment classification, only the top features of the ranking feature sequence will be used. Finally, performance is measured using accuracy, precision, recall, and f1-score. The experimental results show promising performance with an accuracy rate of 81%. This level of reduction is significant in making optimal use of computing resources while maintaining the efficiency of classification performance
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.

Page 32 of 119 | Total Record : 1182

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