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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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Sentiment Analysis on Indonesian Movie Review Using KNN Method With the Implementation of Chi-Square Feature Selection Imam Prayoga; Mahendra Dwifebri Purbolaksono; Adiwijaya Adiwijaya
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5522

Abstract

The advancement and development of the internet is used by the people to support various sectors, one of which is the film industry. Nowadays, people can easily access various movies from available sites. This convenience had led to many reviews about a movie that can be obtained easily. This movie review is very influential on the variety of movies. Freedom of expression on the internet, makes the reviews of a movie vary. For this reason, it is necessary to analyze the sentiment of he movie reviews that are positive or negative. In this research, a sentiment analysis model is build using chi-square selection feature with the KNN algorithm. The final result of this research is able to provide the best classification model with the implementation of stemming. The value of k = 267 in selectkbest at the feature selection stage using chi-square, and using the value of K = 11 in the KNN parameter. This model produces f1 score value of 86.98%.
Simulasi Pengukuran Mutu Perguruan Tinggi: Principal Component Analysis (PCA) pada Model Integrasi BANPT - COBIT Faradillah Faradillah; Muhammad Fadhiel Alie
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5190

Abstract

This study integrates two measurement instruments, namely the BAN PT assessment matrix as a measure of quality and COBIT 5 as a measurement of higher education IT governance through the application of Principal Component Analysis to reduce variables with the same components in order to obtain a measurement model that can accommodate IT quality and governance equally. Based on the PCA results obtained 2 Main Components with variables VMTS, Civil Service and Curriculum on Main Component Factor 1 and Build, Acquire and Implement, Deliver, Service and Support, Monitor, Evaluate, and Score on Main Component Factors 2 with a total of 44 indicator items. Furthermore, a simulation of measuring higher education accreditation was carried out with the variable using the average value. This research was conducted at a private university in South Sumatra, the simulation results obtained showed a score of 3.62 or at the "Established" level. The results of this simulation can then be used by these universities in preparing the actual accreditation process.
Pengelompokan Hasil Survei MBKM Menggunakan K-Mean dan K-Medoids Clustering Mayadi Mayadi; Siti Setiawati; Wowon Priatna
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5003

Abstract

In order to categorize data from the findings of the MBKM survey using machine learning, the goal of this research is to determine how well the implementation of MBKM has been comprehended by Education staff, lecturers, and students at the university level. Artificial intelligence, which is frequently employed to address a variety of issues, includes machine learning. K-Mean and K-Medoids clustering algorithm models were used to group the data for suggestions on how to apply MBKM at Bhayangkara Jakarta Raya University. This study uses databases and machine learning approaches to categorize MBKM survey data at the level of the relevant study program. In order to examine the machine in accordance with the learning algorithm, the K-Mean and K-Medoids clustering algorithms will be utilized. This research will train a machine or system using filtered data that can predict the outcomes of the MBKM survey, which was constructed using machine learning. The study's findings come from the application of MBKM into two groups, with cluster 1 showing a high degree of comprehension and cluster 2 showing a low level of understanding, as produced by K-Mean. In the meantime, K-Medoids created cluster 2 for high comprehension of MBKM and cluster 1 for low understanding of MBKM implementation. The results of the comparison evaluation of clustering between K-Mean and K-Medoids obtained cluster evaluation values using the Davies Bouldin Index by conducting trials starting from K=2, K=3, K=4 and K=5 showing lower K-Mean values compared to K-Medoids, so that K-Mean is recommended as a clustering algorithm for grouping the results of the MBKM survey implementation in Higher Education.
DevOps Implementation with Enterprise On-Premise Infrastructure Muhammad Alvin; Rizal Fathoni Aji
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5500

Abstract

Digital transformation is essential in today's VUCA era (volatile, uncertain, complex, ambiguous). As the primary driver of digital transformation, the software has widely adopted agile concepts with agile software development. Its short and iterative cycle makes it adaptable to change. Instead of producing significant changes simultaneously, the development team produces small but frequent changes. The operations team is overwhelmed with implementing these changes, and bottlenecks arise. DevOps comes to remove these bottlenecks and allow the development and operations teams to work together to release the software to users quickly. As part of the digital transformation, PT Logistik Pangan has started to implement DevOps with on-premise infrastructure, which is yet to be optimal. This qualitative research aims to understand the steps taken by the company for implementing DevOps with on-premise infrastructure using GitLab and offers suggestions on how to maximize its implementation. The results show that implementing DevOps with on-premises infrastructure requires additional works to manage the supporting infrastructures for DevOps. Implementation was done incrementally, by adopting DevOps practices one by one at a time. Version control (also known as source control or source code management) is implemented by using GitLab, and requires self-managed GitLab as supporting infrastructure. Continuous integration and continuous delivery are implemented by using GitLab CI/CD, and requires GitLab Runner as supporting infrastructure. Besides the DevOps practices, the company also implement container technology by using Docker that is upgraded to Docker Swarm later, and requires local Docker Registry as supporting infrastructure. All the supporting infrastructures are installed on-premise in company’s data center. It includes servers, storage, and networking that must be managed separately. Some improvements are ensuring mindset and culture have been adjusted, implementing other principles alongside automation, and should continuing beyond these stages: Code, Build, Test, Release, and Deploy. This research has limitation of using GitLab products only. Future research can use other DevOps tools or combine GitLab products with other products.
Penerapan Data Mining Dalam Analisis Penilaian Kinerja Pegawai Menerapkan Metode K-Means Supriadi Sahibu; Rismawati Bambang; Imran Taufik; Agusriandi Agusriandi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5100

Abstract

The organization, including local government agencies, in the face of increasingly fierce competition, must improving employee performance if they want to exist. As an effort to improve employee performance, various strategies were needed, one of which is clustering analysis of employee performance. Clustering analysis is very important in an effort to gain knowledge from employee activities quickly compared to manual methods. Therefore, in this study an analysis of employee performance was carried out based on the Employee Performance Targets (SKP), which was divided into 2, namely the 2020 SKP Printout and the 2021 SKP questionnaire. From the results of the K-Means Cluster it produced a more convincing questionnaire SKP cluster because there was a distribution that was more dominated by employee performance that good and the outliers are of little value, and the difference in the Sum of Square values of the SKP printout and questionnaire clusters that not too far away, only 21% so that the characteristics of the cluster results are almost identical. Cluster results described the real conditions of employee performance.
Fisher Kolmogorov Equation Theory Simulation Using Deep Learning Conny Tria Shafira; Putu Harry Gunawan; Aditya Firman Ihsan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5432

Abstract

Neural Networks (NNs), a powerful tool for identifying non-linear systems, derive their computational power through a parallel distributed structure. The Physics-Informed Neural Network (PINN) technique can solve the Partial Differential Equation (PDP) in the Fisher Kolmogorov equation. By testing several hyperparameter changes, the formula is correct, and the visualization results can be consistent. Shows that an accurate value can be obtained from the results of the Mean Squared Error (MSE) on the formula loss value (loss f) and data loss (loss u). In experiment 1 the MSE obtained was 0.00001657 (Loss f) and 0.00000038 (loss u), as well as the MSE values obtained in experiment 4, is 0.00005865 (Loss f) and 0.00000216 (Loss u). It can be said to be accurate if the MSE value is close to 0. A formula is proven correct if it displays consistent data in random input data, but with the condition that it uses the same parameters. The author conducted research to simulate the Fisher-Kolmogorov equation with deep learning using the PINN technique. So the purpose of the research conducted was to simulate the Fisher-Kolmogorov equation with the deep learning method using the PINN technique. From the research, it can be concluded that Fisher-Kolmogorov's equation proves to be true if the simulation is carried out in deep learning and produces a visualization that is consistent with the functions used for visualization.
Penerapan Fuzzy Logic Untuk Menentukan Indeks Massa Tubuh (IMT) Berbasis Internet of Things (IoT) Ardi Apriansyah; Ahmad Fauzi; Sutan Faisal
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5470

Abstract

A person's Body Mass Index (BMI) can be determined using the parameters of body weight and height. In manual measurements, to determine BMI, measurements of body weight and height must be carried out and then the BMI results are calculated, so it is felt that it still requires effort and is less effective. Then a tool is made to determine BMI using the Fuzzy Logic method by measuring body weight and height in one step. This Fuzzy Logic method is used to determine the membership status of weight and height. Fuzzy set of weight and height that has been adjusted to the threshold in Indonesia, including weight 35-55 low status, 44-66 normal status, 54-80 heavy status, and height 145-165 low status, 149-176 status normal, 159-190 high status. Software testing results have successfully displayed the results of both sensors, namely, body weight 69.43 Kg with Fuzzi weight status and 161 cm height with normal Fuzzi status. The results of the measurements of the two sensors will also be stored in a database and then displayed on a web containing historical information to find out when the body was last weighed. Then the test results on the LCD Hardware have been able to display a person's weight with a weight of 53 Kg and a height of 171 cm. After testing, these scales have been able to determine the results of a person's body BMI and the two sensors on the scales have been able to determine a person's weight and height.
Comparison of IndoBERTweet and Support Vector Machine on Sentiment Analysis of Racing Circuit Construction in Indonesia Hanvito Michael Lee; Yuliant Sibaroni
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5380

Abstract

The construction of the circuit is one of the policies made by the Indonesian government to advance the tourism sector and improve the national economy. This policy triggers various opinions given by the public, primarily through social media Twitter, both in the form of positive and negative opinions. This study compares machine learning and deep learning algorithms, Support Vector Machine and IndoBERTweet, that will be used as a model to predict the sentiment of racing circuit construction tweets. These models are built with K-Fold cross-validation to obtain the overall model’s performance for the entire dataset. Based on the experiments that have been carried out, it shows that IndoBERTweet performs better than the Support Vector Machine, with an overall accuracy score of 86%, a precision score of 88.2%, a recall score of 88.6%, and an f1-score of 88.4% for the entire dataset. Meanwhile, the Support Vector Machine model only achieves 82% for the accuracy score, 87.3% for the precision score, 84.3% for the recall score, and 85.8% for the f1-score. In addition, the best accuracy value from each iteration for IndoBERTweet is 94%, and the Support Vector Machine is 93%.
Aplikasi Augmented Reality Geometri Sekolah Dasar Untuk Bangun Datar dan Ruang Menggunakan Metode Marker Based Tracking Syahrizal Dwi Putra; Diah Aryani; Harlinda Syofyan; Verdi Yasin
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5281

Abstract

Geometry is an important part of students' mastery of mathematics. In its application, students are still constrained in imagining objects abstractly. It takes the right learning media that is used by teachers and keeps abreast of current technological developments. This study aims to describe flat and spatial geometric objects in teaching geometry using augmented reality learning media based on Android applications so that learning becomes more interesting, concrete and visual equipped with a quiz game feature that contains quizzes that are puzzle games. This application was built using Unity3D and Vuforia SDK and 3D objects created using Blender. This application utilizes the marker method used to determine the point of emergence of 3D objects. The results of application testing using the black box method state that the detection of markers on objects, features and the speed level of devices using the application is running well. The Marker Based Tracking method can be used to recognize flat object markers and spatial planes very well.
Collaborative Filtering with Dimension Reduction Technique and Clustering for E-Commerce Product Daffa Barin Tizard Riyadi; Z K A Baizal
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5538

Abstract

The rapid development of internet users over the last decade has led to an increase in the use of electronic commerce (e-commerce). The existence of a recommender system influences the success of e-commerce. Collaborative Filtering (CF) is one of the most frequently used recommender system methods. However, in real cases, sparsity problems generally occur. This is generally caused because only a small number of users give ratings to items. In this study, we propose the combination of clustering and dimension reduction methods on the Amazon Review Data to overcome the sparsity problem. The clustering method with K-Means is used to group users based on item preferences. Meanwhile, we used Singular Value Decomposition (SVD) for dimension reduction to improve the performance of the recommender system in sparse data. The results show that the combination of SVD and K-Means is successful in predicting ratings with an RMSE value of less than 2, significant performance increase compared to previous study. The use of SVD is proven to be able to overcome sparsity, with a decrease in RMSE of 9.372%.

Page 76 of 119 | Total Record : 1182

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