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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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Implementation Graph Sampling and Aggregation (GraphSAGE) Method for Job Recommendation System Wijaya, Dewa Made; Wiharja, Kemas Rahmat Saleh
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7105

Abstract

Finding job is currently a challenge, especially for final-year students. Career Development Centre (CDC) is a service that is provided by a university for its students. However, a more sophisticated system is needed that not only provides job information but provides job recommendations based on their interests, skills, and experience. Developing a GraphSAGE-based job recommendation system can help provide suitable jobs according to user preferences. GraphSAGE works by embedding nodes or feature vectors at each node or node in a graph. GraphSAGE aggregates information from neighbouring nodes and propagates that information using different model layers. By combining the feature information of each node, the resulting representation can be richer in information and also more accurate. The development of the GraphSAGE system uses a dataset from the "Job Recommendation Challenge" from Kaggle which consists of 3 data, namely job data, user dataset, and applicant dataset. This study also uses GAT to provide a value or weight for each node before GraphSAGE process the graph. Based on experimental results, this GraphSAGE model has an accuracy value of 97.5% and this value is 13% greater than its comparison, namely FNN (Feedforward Neural Network) commonly used at tabular dataset. This comparison helps us know that which the best model we have to use to the dataset. The model also tested on the Movie dataset, Food dataset, and Epinions dataset.
X Spotify Cares Clustering Analysis using K-Means and K-Medoids Pangestu, Citra; Shaufiah, Shaufiah; Wijaya, Rifki
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7279

Abstract

The rise of social media platforms, particularly Twitter, has transformed how individuals express opinions and concerns. Companies, like Spotify, leverage platforms such as Twitter for customer support and feedback gathering. This research delves into the world of Spotify Cares tweets using K-Means and K-Medoids clustering methods, aiming to enhance customer support analysis. The study employs the silhouette coefficient and the Davies-Bouldin Index (DBI) to evaluate clustering quality. With an extensive dataset covering more than 3 million Twitter customer service interactions, including 29,479 notes specific to Spotify Cares, this investigation uncovered latent patterns and themes. The versatility of K-Means and K-Medoids, proven effective in a wide range of applications, is highlighted. Therefore, K-means and K-medios were implemented in this research. The results show that K-Means, with 10 clusters (K = 10), with a DBI value of 1.76, shows moderate dispersion, indicating the potential for improvements for better segmentation precision. In contrast, K-Medoids, with 2 clusters (K = 2) and a lower DBI of 1.48, present a clearer and more compact clustering structure. This implies simplified customer categories, which is beneficial for targeted support. In conclusion, although both methods have strengths and weaknesses, K-Medoids with two clusters emerges as a promising method for Spotify Cares, offering cohesive customer groupings for efficient intervention. Future research efforts could focus on refining parameters and exploring the complex relationships between response time, sentiment analysis, and customer satisfaction to achieve a more nuanced analysis.
Implementasi Metode CRISP DM dan Algoritma Decision Tree Untuk Strategi Produksi Kerajinan Tangan pada UMKM A Asyraf, Haekal; Prasetya, Machmudin Eka
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7050

Abstract

In industry 4.0, the utilization of information technology and data analysis is very important in helping business decision making. The use of data mining and the Decision Tree algorithm in analyzing data can help to classify products that best suit customer preferences for Micro, Small and Medium Enterprises (MSMEs) handicraft products. In this research we use the Cross Industry Standard Process for Data Mining methodology to analyze data in classifying the types of craft products produced by MSME A to determine production strategy that suits market demand after the Covid-19 pandemic. Covid-19 influenced MSME A as a handicraft producer to temporarily stop production due to decreased demand and produce a special order only. Changes in consumer behavior due to the Covid-19 pandemic mean that MSME A must determine the right production strategy so that products are sold and can reuse the capital. We succeeded in building a fairly effective model with CRISP-DM and the Decision Tree algorithm which has an accuracy rate of 74.2%. This model found as many as 21 types of products that were still selling well during the pandemic, making it useful for MSME A for making production decisions based on market conditions during the Covid-19 pandemic.
Malware Detection Using K-Nearest Neighbor Algorithm and Feature Selection Supriyanto, Catur; Rafrastara, Fauzi Adi; Amiral, Afinzaki; Amalia, Syafira Rosa; Al Fahreza, Muhammad Daffa; Abdollah, Mohd. Faizal
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6970

Abstract

Malware is one of the biggest threats in today’s digital era. Malware detection becomes crucial since it can protect devices or systems from the dangers posed by malware, such as data loss/damage, data theft, account break-ins, and the entry of intruders who can gain full access of system. Considering that malware has also evolved from traditional form (monomorphic) to modern form (polymorphic, metamorphic, and oligomorphic), a malware detection system is needed that is no longer signature-based, but rather machine learning-based. This research will discuss malware detection by classifying the file whether considered as malware or goodware, using one of the classification algorithms in machine learning, namely k-Nearest Neighbor (kNN). To improve the performance of kNN, the number of features was reduced using the Information Gain and Principal Component Analysis (PCA) feature selection methods. The performance of kNN with PCA and Information Gain will then be compared to get the best performance. As a result, by using the PCA method where the number of features was reduced until the remaining 32 PCs, the kNN algorithm succeeded in maintaining classification performance with an accuracy of 95.6% and an F1-Score of 95.6%. Using the same number of features as the basis, the Information Gain method is applied by sorting the features from those with the highest Information Gain score and taking the 32 best features. The result, by using this Information Gain method, the classification performance of the kNN algorithm can be increased to 96.9% for both accuracy and F1-Score.
Sentiment Classification of The Capsule Hotel Guest Reviews using Cross-Industry Standard Process for Data Mining (CRISP-DM) Singgalen, Yerik Afrianto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7329

Abstract

Technology advancements empower hotel accommodation service managers to undertake innovative initiatives to enhance guest appeal and ensure safety and comfort. One manifestation of such innovation is exemplified by The Capsule Hotel, which offers novel experiences to both domestic and international tourists. This research seeks to assess the sentiments of guests at The Capsule Malioboro, employing the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology and the Support Vector Machine (SVM) technique with Synthetic Minority Over-sampling Technique (SMOTE) operators. The findings demonstrate that when operated without SMOTE, the SVM algorithm yields a confusion matrix displaying an accuracy of 99.01%, precision of 99.00%, recall of 100%, AUC of 0.944, and an f-measure of 99.49%. With the integration of SMOTE, there is a notable enhancement across all metrics, with accuracy, precision, recall, AUC, and f-measure, all achieving perfect scores of 100%. In addition, an analysis of the top 10 frequently used words in guest reviews, such as "solo," "good," "place," "staff," "comfortable," "room," "clean," "hotel," "capsule," and "Malioboro," provides additional insights. Examining guest profiles within the dataset uncovers a strong inclination among Indonesian individuals to opt for The Capsule Malioboro's services, with solo travelers being the predominant guest type and most stays lasting only a single day. The capsule accommodations cater to various gender preferences, and an examination of overnight data indicates a rising trend, particularly in December 2022 and 2023. These insights enable the hotel to discern guest preferences, offering valuable information for enhancing service ratings and addressing specific needs.
Alat Penghitung Jumlah Gerakan Pull Up dan Push Up Menggunakan Sudut Kemiringan pada Sensor MPU6050 Berbasis Internet Of Things Putra, Dimas Ade; Jufrizel, Jufrizel; Ullah, Aulia; Maria, Putut Son
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6937

Abstract

The pull-up and push-up counter device is designed to automatically count the number of pull-up and push-up repetitions performed by an individual. The problem faced is errors in performing pull-up and push-up movements and the limitation of manual counting, which solely relies on remembering the total count of movements from the previous day. This can lead to uncertainty in monitoring the progress of the exercise. The purpose of this research is to develop a pull-up and push-up counter device that utilizes the Internet of Things (IoT) and is connected to the Blynk application on a smartphone to provide ease and accuracy in recording exercise repetitions, eliminate the difficulties of manual counting, and provide real-time data to users for monitoring and tracking their exercise progress. This device is equipped with an MPU 6050 sensor that can detect changes in angular acceleration in the arms. The methodology applied in this research is the Research and Development (R&D) method, which includes product efficiency testing and product development. The research results show that this device can connect to Wi-Fi networks effectively, and its status can be viewed through the LCD display on the device as well as the Blynk application, which displays an online status when the device is connected and an offline status when it is not. The MPU 6050 sensor works effectively in detecting the user's inclination at the specified angles, forming a 70 angle for pull-up movements and a 90 angle for push-up movements. Testing was conducted using a comparative method between manual counting and counting using the device. In the tests conducted at three different movement speeds, it was found that fast movements in pull-ups resulted in a difference in counting of 7.6%, while in push-ups, there was a difference in counting of 6.8%. These differences were due to the device's inability to detect the intended angles of inclination accurately. On the other hand, movements at moderate and slow speeds showed a 0% difference in counting for both pull-ups and push-ups.
Classification of Company Level Based on Student Competencies in Tracer Study 2022 using SVM and XGBoost Method Revandi, Tyo; Gunawan, Putu Harry
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7237

Abstract

Assessing the quality level of companies where graduates are employed is crucial for understanding the impact of academic programs on career placements. The use of methodologies that do not match the research objectives may lead to inaccurate or irrelevant analysis. When company classification methods are not aligned with the nature of the data collected in a tracking study, the risk of misinterpretation and the formulation of invalid generalizations becomes apparent. This study utilizes the 2022 Tracer Study Data from Telkom University, encompassing responses from 4306 graduates working across Local, National, and Multinational companies. The research employs support vector machine (SVM) and XGBoost algorithms to analyze and classify the company levels of the surveyed graduates. The primary objective is to enhance the accuracy of company level classification, thereby facilitating a more precise analysis of the Tracer Study dataset. The SVM and XGBoost algorithms are rigorously tested, and the results indicate an accuracy improvement with the XGBoost method, yielding a 2% increase over the SVM method. The evaluation is conducted with a data separation of 20% test data and 80% training data. This research not only contributes to the refinement of company level classification in the context of Tracer Studies but also underscores the potential of machine learning algorithms, specifically SVM and XGBoost, in providing valuable insights into graduates' professional trajectories. The findings of this study pave the way for more informed decision-making processes in academic and career development initiatives.
Perbandingan Metode Dempster Shafer Dan Teorema Bayes Untuk Mendeteksi Penyakit Ensefalitis Mustaqim, M.; Rakasiwi, Galih; Iskandar, Agus
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7339

Abstract

The aim of this study was to evaluate how well Bayes' Theorem and the Dempster-Shafer Method identify encephalitis. Inflammation of the brain, or encephalitis, can be caused by several things, such as bacterial or viral diseases. The main aim of this study was to assess how well both approaches perform in identifying this disease using clinical data. The main problem faced in detecting encephalitis is the complexity of the variations in symptoms and causal factors. This research focuses on analyzing clinical data of encephalitis patients, including medical history, laboratory test results, and clinical symptoms. The Dempster-Shafer method, a belief theory approach that allows the integration of information from uncertain sources, will be compared with Bayes' Theorem, a classical statistical approach frequently used in medical diagnostics. The research method involves collecting clinical data from medical records of patients diagnosed with encephalitis. This data will then be analyzed using the Dempster-Shafer Method and Bayes' Theorem to compare their accuracy in detecting disease. In addition, evaluation of method performance will also be carried out by comparing the sensitivity, specificity, and positive and negative predictive values of each method. The results of this research are expected to provide better insight into the effectiveness of the Dempster-Shafer Method and Bayes' Theorem in detecting encephalitis. The implications of these findings can be used to improve existing diagnostic methods and increase the ability of early detection of this disease. This research has the potential to make an important contribution to the development of the field of diagnostic medicine and can help medical practitioners make better decisions in the management of encephalitis patients. Using the Dempster Shafer method, the encephalitis diagnosis rate reached 99.8%, while applying Bayes' Theorem gave a diagnosis rate of only 3.5%. From these results it can be concluded that the application of Dempster Shafer is more powerful and provides a higher level of confidence in the encephalitis diagnosis process compared to the Bayes Theorem method.
Decision Support System for Manufacturing Division Priority using AHP Method Kencana, Christian
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7079

Abstract

In the competitive manufacturing landscape, especially in stamping and automobile parts production, optimizing production processes is a paramount challenge. This study addresses a manufacturing company's vulnerability to production reporting manipulation and inefficiencies in manual report generation. To tackle these challenges, we propose a vital system to combat the complexities and inaccuracies in existing reporting methods that significantly impede decision-making and operational efficiency. We introduce a novel, integrated system combining a production information system with an Analytical Hierarchy Process (AHP)-based decision support system. This hybrid solution aims to streamline report generation and boost decision-making within production divisions. The approach involves deploying a web-based system to simplify report creation and ensure accuracy and timeliness. The decision support system utilizes AHP to facilitate division prioritization, addressing critical issues promptly with data-driven insights. AHP's practicality and reliability assist in evaluating criteria for effective division prioritization. The implementation of this system marked significant improvements in production data management, evident from a substantial 68.17% user satisfaction rating. These results demonstrate the system's efficacy in enhancing decision-making, refining production processes, and boosting overall productivity. Furthermore, the system provides valuable insights into operator performance, fostering management recognition and appreciation, thus promoting transparency and accountability. The integration of an information system with AHP-based decision support emerges as a potent solution for manufacturing companies confronting similar challenges.
Sistem Deteksi Anomali Pada Transformator Menggunakan Dissolved Gas Analysis Dengan Metode K-Nearest Neighbour Kurniawan, Andre; Purnomo, Hindriyanto Dwi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7034

Abstract

The transformer is the most important part of the electric power system, therefore maintenance needs to be carried out to prevent the emergence of anomalies in the transformer. Dissolved Gas Analysis (DGA) is a method for detecting anomalies in transformers. DGA is used to test the condition of the insulating oil in transformers by taking samples of the insulating oil. If an anomalous event occurs in a transformer, the resulting gas concentration will vary depending on the type of anomalous event in the transformer. The main problem underlying this research is the inability of previously existing anomaly detection systems to provide an optimal level of accuracy, traditional methods or approaches used also face obstacles in interpreting complex data from dissolved gas analysis. The aim of the research carried out is to be able to design an anomaly detection system on Transformers using DGA and to see the level of accuracy of the existing DGA method using KNN. In this research, the anomaly detection system on the transformer resulted in the highest level of accuracy being 94% using the key gas method and the lowest level of accuracy being 79% using the Doernenburg Ratio method. The conclusion of this research is that it is able to create a system that can make it easier to analyze anomalies in transformers, and can be used as an alternative method for determining the condition of transformers.

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