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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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Perbandingan Metode Naïve Bayes dan Support Vector Machine Untuk Analisis Sentimen Terhadap Vaksin Astrazeneca di Twitter Eva Rahma Indriyani; Paradise Paradise; Merlinda Wibowo
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.v6i3.4220

Abstract

The implementation of Covid-19 vaccination in Indonesia turned out to have various pro and contra opinions from the public. The discovery of disinformation and misinformation about vaccines  spread through social  media content affects a person's absorption of information so which leads to vaccine delays. When in fact, vaccination is one of the biggest and most effective contributions  to preventing the Covid-19 pandemic. Astrazeneca is one of the vaccines provided by the Indonesian government. This vaccine used to be controversial amongst the public regarding its halalness and the safety of the vaccine because of the issue of  the said vaccine  containing swine trypsin. Nowadays Twitter has  become a place for users to express their concerns and opinion regarding the Covid-19 vaccine. Data obtained from Twitter will be useful if it is analyzed, one of which is sentiment analysis. In this study, data collection was carried out using the snscrape library with a total of 3105 tweets obtained from the  period May to June 31, 2021. The dataset that has been  collected is then  preprocessed to optimize the data. After passing the preprocessing stage, the data was labeled as tweet class using a lexicon-based dictionary which resulted in 1275 tweets with positive opinin labels and 1830 tweets  labeled as negative opinion. The  aim of this study is to examines the performance of Naïve Bayes and Support Vector Machine with  adding the weighting method  TF-IDF (Term Frequency – Inverse Document Frequency). The evaluation results  show that the Support Vector Machine has a greater accuracy, precision, recall and f1-score of 87.27%, 90.41%, 77,34% and 83.37% compared to  Naïve Bayes which has an accuracy, precision, recall and f1- of 76.81%, 72.40%, 70.70% and 71.52%.
Identify User Behavior based on Tweet Type on twitter Platform using Mean Shift Clustering Saniyah Nabila Fikriyah; Yuliant Sibaroni
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.v6i3.4329

Abstract

Twitter is a social media where users often get information from various fields. There are many problems with Twitter. For example, in Indonesia's political field, discussing the performance of the President of Indonesia and his staff who are not good, students and the public hold demonstrations in DKI Jakarta. They want the President of Indonesia to step down from office. When the problem is trending, some users have positive (praise) and negative (blasphemous) behavior, which is interesting to discuss in this study. Before the method stage, data preprocessing is carried out so that the data to be used becomes more efficient. Word weighting is also done using the TF-IDF Vectorizer. Then, the clustering method with the Mean Shift algorithm is applied to identify user behavior based on the type of tweet. This method can find information from a vast data set in a short time. Based on this algorithm, the results obtained are 67 clusters from the Mean Shift algorithm. From a total of 67 clusters obtained, 5 clusters were taken to identify user behavior. User behavior in clusters 0, 2, 3, and 4 is negative because it discusses the people who want the President of the Republic of Indonesia to resign from his position immediately. Meanwhile, user behavior in cluster 1 is positive because the topics discussed only information that the people of Lampung are already in Jakarta.
Analisis Sentimen Ulasan Hotel Bahasa Indonesia Menggunakan Support Vector Machine dan TF-IDF Vincentius Westley Dimitrius Thomas; 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.v6i3.4218

Abstract

Reviews of a service, especially hotel services, have an important role to play in consumer decisions. Tripadvisor is a guide and reference platform for travelers in finding information about the hotel services in various countries. There are many reviews about hotels on the platform so that readers are difficult to make decisions so it is necessary to conduct a sentiment analysis that aims to dig up information from existing reviews. The initial stage is by labeling (positive, neutral, negative) to the review. Then the preprocessing stage so that the review can be easily processed, then from that stage continued weighting using Term Frequency - Inverse Document Frequency (TF-IDF) using the best parameters, after weighting the data, then the next is the distribution of data into training data, validation and test. The data are entered into the machine learning process using Support Vector Machine (SVM) and obtained the accuracy of the model by 85%. For testing scenarios if not using slang handling get F1-Score by 80% and if not using stopword get F1-Score by 82%. On the evaluation of the performance of the model using K-Fold obtained the best results on the Fold-7 with a precision value of 87%, recall 86%, F1-score 86%, and accuracy of 87%.
Prediksi Curah Hujan Menggunakan Long Short Term Memory Jamilatul Badriyah; Arna Fariza; Tri Harsono
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.v6i3.4008

Abstract

The importance of predicting rainfall in fields that require rainfall information such as in agriculture, transportation and industry. Prediction of rainfall with statistics is done to solve the problems of this paper, thus this paper proposes prediction of rainfall using Long Short Term Memory in the case study: Surabaya City. The data used is rainfall data at two Surabaya stations, namely the Perak Meteorological Station I and the Tanjung Perak Maritime Meteorology Station from 2015 to 2020. The prediction test was carried out using the Long Short Term Memory algorithm with accuracy measurement results MSE 0.489, MAE 0.537 and R2 0.497. from these results prove that the Long Short Term Memory algorithm is better than previous studies.
Sistem Pendukung Keputusan Dalam Pemilihan Peserta Beasiswa Magister Menggunakan Metode SAW Neni Mulyani; Jeperson Hutahaean; Zulfi Azhar; Aulia Kartika
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.v6i3.4149

Abstract

A scholarship is a gift given by the government or a university to help reduce the cost of education where the higher the level of education taken, the more money that must be provided to fulfill, the opportunity to be able to continue master's education is the dream of many young scholars. Every opportunity that is open to get a master's scholarship makes many prospective scholarship recipients compete to register themselves as participants, but not all participants who apply to receive scholarships get the same opportunity because the quota is limited. It is necessary to make a selection in determining the award of scholarships to the right people and avoid fraud in the selection process, this can be solved by using the application of computer science, namely a decision support system assisted by applying the SAW method in obtaining more systematic results based on previous research. the results obtained in this study
Evaluasi Hasil Pengujian Tingkat Clusterisasi Penerapan Metode K-Means Dalam Menentukan Tingkat Penyebaran Covid-19 di Indonesia Elsa Virantika; Kusnawi Kusnawi; Joang Ipmawati
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.v6i3.4325

Abstract

Coronavirus Diseases 2019, often known as COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Indonesia has a large area so that it is easy to contract COVID-19 and the spread of the Covid-19 virus in Indonesia is growing quite rapidly. Based on the region in Indonesia, it can be grouped into parts of the provinces in Indonesia and generate provincial points for the distribution of Covid-19 cases, aiming to create a strategy for handling the spread of COVID-19 in all provinces in Indonesia. The grouping of the level of spread of COVID-19 is carried out using a data mining method, namely the k-means clustering algorithm by grouping data into several clusters based on the similarity of the data. Based on the results of the study, 3 clusters were identified, namely cluster 0 with a low level of distribution of Covid-19, 12 provinces, cluster 1 with a moderate level of distribution of COVID-19, 18 provinces, and cluster 2 with a high level of distribution of COVID-19, 4 categories. province. Based on the results of this study, it is hoped that it can provide information and support the government to make strategic decisions in each cluster to reduce the level of spread of COVID-19 in Indonesia.
Penerapan metode 7S McKinsey pada Ebay sebagai Strategi E-commerce & Bonus Demography Menghadapi Globalisasi Hasna Widya Pratiwi; Fuad Mas'ud
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.v6i3.4484

Abstract

E-commerce is an important pillar in the changing times and gives a significant influence on everyday human life. E-bay which is one of the largest e-commerce companies in the world has grown very rapidly and provides exceptional service to the world community. The services provided have helped a lot in overcoming problems in facing demographic bonuses, especially absorbing labor; human resource development and information technology development. The strategy used by ebay.com also has a tremendous impact on competitors in the field of e-commerce, a strategy owned by ebay.com despite being very good, still requires innovation in it, this should be done because very fast and inflexible changes in e-commerce strategies, ebay will not be able to cope with those changes. change is a good form, where this transformation can have a positive impact on society and make changes to culture significantly, the change should cover the whole lifestyle and also cover the whole mindset and attitude, therefore ebay.com make many changes positive in the face of globalization, and changes in e-commerce strategy in the face of competition among companies in the world, especially in the field of e-commerce and human resources
Optimasi Naive Bayes dan Cosine Similarity Menggunakan Particle Swarm Optimization Pada Klasifikasi Hoax Berbahasa Indonesia Arfan Yoga Aji Nugraha; 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.v6i3.4170

Abstract

The widespread circulation of hoax news in the information technology era is increasingly troubling, therefore in this era an algorithm to classify hoax news is necessary, in this study researchers focused on optimizing the accuracy of hoax news classification in text documents. The algorithm that will be used is Naive Bayes and cosine Similarity which previously has been applied with particle swarm optimization algorithm. In this study, it was concluded that after feature selection using PSO in the Naive Bayes algorithm the accuracy obtained increased from 0.91 to 0.93 while in the cosine similarity algorithm the accuracy increased from 0.62 to 0.73 after feature selection using PSO
Analisis Tingkat Kematangan Smart City Kabupaten Lombok Utara Menggunakan COBIT 2019 Ari Panen Haster; Kristoko Dwi Hartomo
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.v6i3.4344

Abstract

Indonesia's national smart city program adopts 6 main dimensions of smart cities, namely Governance, Branding, Economy, Living, Society, and Environment. The North Lombok Regency Government also developed the regional smart city concept in 2021 which is stated in the 2021-2026 RPJMD. The results of capability level research on smart city dimensions and process domains show that there are 3 dimensions that are still far from the expected target, namely Smart Branding, Smart Economy and Smart Environment, and there are 3 dimensions that are close to the achievement target, namely Smart Governance, Smart Living, and Smart Society. While the analysis of the 9 COBIT 2019 process domains shows that there are 3 process domains that have not been managed, namely: APO03, APO12 dan DSS04, 4 process domains have been defined and are running well, namely: APO07, APO12, APO14, and BAI01, and 3 process domains that have been managed and have achieved the expected targets, namely: APO07, APO14, and BAI01. The average GAP value of the analysis of smart city dimensions and COBIT 2019 process domains is 1.7 and 1.3, respectively.
Evaluation and Recommendation User Interface of Batamnews Based on User Experience using User-Centered Design Angelino Sandy Kusuma; Indra Lukmana Sardi; Rosa Reska Riskiana
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.v6i3.4424

Abstract

Batamnews is a media that presents news about Batam. Batamnews currently has an application that aims to make it easier for users to continue to be updated regarding news in Batam. But from the results of interviews and the distribution of questionnaires with Batamnews users, there are problems experienced by application users due to the User Interface which makes users uncomfortable using the application. Users feel that Batamnews is not optimal in terms of appearance, and the user's uninterested use of icons (symbols). In addition, Users complain about features that should be available but not in the app. Therefore, this study aims to make a design using the User-Centered Design (UCD) method because UCD focuses on user needs. Then for testing the design, the System Usability Scale (SUS) evaluation method will be used because it can be measured for effectiveness, efficiency, and satisfaction from users. The results of this study are recommendations for the User Interface Design of the Batamnews application and have an increased value on usability from 54 to 74 which is expected to be a reference for Batamnews application developers. So, it can be concluded that the UCD method can increase the SUS usability value in an application as well.

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