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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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Comprehensive Sentiment Analysis of Religious Content Naive Bayes Algorithm Model Listiyono, Hersatoto; Budiarso, Zuly; Susilowati, Susi; Windarto, Agus Perdana
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.v8i1.7062

Abstract

This paper delves into sentiment analysis of online religious content utilizing the Naive Bayes algorithm to decipher the array of sentiments present in religious discussions. By tailoring this algorithm to the complexities of religious language, the study reveals hidden sentiments, offering valuable insights for researchers, policymakers, and communities. The findings demonstrate that the sentiment analysis model performs robustly, with a precision of 84.78%, a recall of 82.98%, and a balanced F1 Score of 83.87%, indicating high accuracy in sentiment identification and effectiveness in capturing a significant portion of actual sentiments. The overall accuracy of the model stands at 75.10%, affirming its successful adaptation to the intricacies of religious discourse. These results not only deepen our understanding of sentiment analysis in the realm of faith and spirituality but also have practical implications for enhancing interfaith dialogue, fostering mutual understanding, and guiding decision-making in religious and social organizations. This research makes a significant contribution to the growing field of sentiment analysis, providing a methodological framework for exploring the nuanced sentiment landscape within the domain of faith and spirituality.
Comparative Analysis of Transformer Models in Object Detection and Relationship Determination on COCO Dataset Hafizh, Raihan Atsal; Wiharja, Kemas Rahmat Saleh; Fikriansyah, Muhammad Arya
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.v8i1.7158

Abstract

This research investigates the integration of object detection and relationship prediction models to enhance image interpretability, addressing the core question: What challenges necessitate a Comparative Analysis of Object Detection and Transformer Models in Relationship Determination? A robust object detection model exhibits commendable performance, especially at lower Intersection over Union (IoU) thresholds and for larger objects, laying a solid foundation for subsequent analyses. The transformer models, including GIT, GPT-2, and PromptCap, are evaluated for their language generation capabilities, showcasing noteworthy performance metrics, including novel keyword-based metrics. The study transparently addresses limitations related to dataset constraints and potential challenges in model generalization, offering a clear rationale for the research. The evaluation of both object detection and transformer models provides valuable insights into the dynamic interplay between visual and linguistic understanding in image comprehension. By candidly acknowledging limitations, including data constraints and model generalization, this research paves the way for future refinements, addressing identified limitations and exploring broader application domains. The comprehensive approach to understanding the interplay between visual and textual elements contributes to the evolving landscape of computer vision and natural language processing research.
Win Probability of Heroes in Mobile Legends MPL ID S12 Competitions Using Nave Bayes Algorithm Putra, Angga Permana; Andono, Pulung Nurtantio
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.v8i1.7185

Abstract

The development of the gaming industry into digital formats has become a rapidly growing trend. E-Sports, particularly in Indonesia, has shown significant growth alongside technological advancements. The increased interest in E-Sports is evidenced by the higher quality tournaments organized by local game developers, such as Moonton, a subsidiary of ByteDance, hosting tournaments for Mobile Legends: Bang Bang. This article aims to analyze the probability of winning heroes in the Mobile Legends Professional League Season 12 using the Naive Bayes algorithm. The results of calculating the probabilities for various hero roles show varying levels of winning potential. By utilizing this method, it becomes possible to predict hero victories or losses more systematically, aiding players in developing more effective strategies during matches. The results obtained from predicting hero victories and losses indicate that for the jungler role, the win rate is 0.145 and the loss rate is 0.088. For midlaners, the victory rate reaches 0.492 and 0.661 for losses. As for roamers, the win rate is 0.120 and the loss rate is 0.102. For goldlaners and explaners, they achieve win rates of 0.528 and 0.177, respectively, while their loss rates are 0.339 and 0.132. Furthermore, after testing the data, the accuracy obtained for the roles is as follows: jungler role 67.61%, midlaner role 67.5%, roamer 67.65%, goldlaner 67.29%, and explaner 67.71%.
Klasifikasi Penyakit Daun Padi Menggunakan KNN dengan GLCM dan Canny Edge Detection Verawati, Ike; Aunurrohim, Ridwan Al Akhyar
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.v8i1.6906

Abstract

Rice plants have an important role in human survival, especially in Indonesia where rice plants are the staple food source for most of the population. The Central Statistics Agency reported that rice consumption in Indonesia reached 28.69 million tons in 2019. In the same year, rice production in Indonesia reached 31.31 million tons. However, production results decreased compared to the previous year, which amounted to 33.94 million tons. One of the factors causing the decline in quality and even death of rice plants is pests and disease. According to the International Rice Research Institute, every year farmers lose an average of 37 percent of their harvest due to pest and disease attacks. The Food and Agriculture Organization also reported a similar thing, where 20 to 40 percent of world food production failures were caused by pests and diseases. Farmers' lack of knowledge and the limited number of experts result in ineffective disease diagnosis. Therefore, a step or method is needed so that the disease detection process in rice plants becomes more effective. This research uses the K-Nearest Neighbor classification algorithm with Gray Level Co-Occurrence Matrix and Canny Edge Detection to classify diseases in rice plants. The result is that Canny Edge Detection has a positive influence on method performance with accuracy reaching 91.67%, precision 87.37% and recall 87.50% at k=7.
Enhancing Machine Learning Accuracy in Detecting Preventable Diseases using Backward Elimination Method Dliyauddin, Muhammad; Shidik, Guruh Fajar; Affandy, Affandy; Soeleman, M. Arief
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.v8i1.7073

Abstract

In the current landscape of abundant high-dimensional datasets, addressing classification challenges is pivotal. While prior studies have effectively utilized Backward Elimination (BE) for disease detection, there is a notable absence of research demonstrating the method's significance through comprehensive comparisons across diverse databases. The study aims to extend its contribution by applying BE across multiple machine learning algorithms (MLAs)Nave Bayes (NB), k-Nearest Neighbors (KNN), and Support Vector Machine (SVM)on datasets associated with preventable diseases (i.e. heart failure (HF), breast cancer (BC), and diabetes). This study aims to elucidate and recommend significant differences observed in the application of BE across diverse datasets and machine learning (ML) methods. This study conducted testing on four distinct datasetsraisin, HF, BC, and early-stage diabetes risk prediction datasets. Each dataset underwent evaluation with three MLAs: NB, KNN, and SVM. The application of BE successfully eliminated non-significant attributes, retaining only influential ones in the model. In addition, t-test results revealed a significant impact on accuracy across all datasets (p-value < 0.05). In specific algorithmic evaluations, SVM exhibited the highest accuracy for the raisin dataset at 87.22%. Additionally, KNN attained the utmost accuracy in the heart failure dataset with an accuracy of 86.31%. In the breast cancer dataset, KNN again excelled, achieving an accuracy of 83.56%. For the diabetes dataset, KNN proved the most accurate, reaching 96.15%. These results underscore the efficacy of BE in enhancing the execution of MLAs for disease detection.
Integrating LightGBM and XGBoost for Software Defect Classification Problem Airlangga, Gregorius
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.v8i1.7267

Abstract

Software defect classification is a crucial process in quality assurance, pivotal for the development of reliable software systems. This paper presents an innovative approach that synergizes traditional software complexity metrics with advanced machine learning algorithms, namely Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost), to enhance the accuracy and efficiency of software defect classification. Leveraging a dataset characterized by McCabe's and Halstead's metrics, this study embarks on meticulous data preprocessing, feature engineering, and hyperparameter optimization to train and evaluate the proposed models. The LightGBM and XGBoost models are fine-tuned through the Optuna framework, aiming to maximize the ROC-AUC score as a measure of classification performance. The results indicate that both models perform robustly, with XGBoost demonstrating a slight superiority in predictive capability. The integration of machine learning with traditional complexity metrics not only enhances the defect classification process but also provides deeper insights into the factors influencing software quality. The findings suggest that such hybrid approaches can significantly contribute to the predictive analytics tools available to software engineers and quality assurance professionals. This research contributes to the field by offering a comprehensive methodological framework and empirical evidence for the effectiveness of combining machine learning algorithms with traditional software complexity metrics in software defect classification.
Perbandingan Metode K-Nearest Neighbor dan Support Vector Machine Untuk Memprediksi Penerima Beasiswa Keringanan UKT Enggar Novianto; Arief Hermawan; Donny Avianto
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.v8i1.6913

Abstract

Scholarships are financial assistance provided to individuals, pupils, or scholars to extend their education. These may be provided by government agencies or the colleges themselves to students. One component that ensures quality human resources is formal education. The purpose of scholarships is to help disadvantaged or underprivileged students. Scholarship providers usually give some consideration to the student's level of difficulty, such as parents' salary and number of siblings. Due to the large number of applications for relief scholarships and strict assessment criteria, not all students who apply can be accepted. Scholarship application selection officers often have difficulty determining which students are worthy of receiving a scholarship. While the quota for scholarship recipients for this study program is always limited, applications for student UKT relief scholarships continue to increase every semester. This application came from students with poor economic conditions. To select UKT relief scholarship application documents, you have to consider various criteria and use manual methods which are less effective and require more time to determine the results. This research aims to make a comparison between the K-Nearest Neighbor and Support Vector Machine classification algorithms in determining recipients of UKT relief scholarships for undergraduate students in the Legal Sciences Study Program, Faculty of Law, Sebelas Maret University using the RapidMiner application. The accuracy results obtained using the RapidMiner application that have been carried out, the K-NN method produces an accuracy of 92.92%, while the SVM method produces an accuracy of 85.84%, so the K-NN method is the best method in classification for predicting recipients of UKT relief scholarships for students in the program. Bachelor of Law studies.
Perbandingan Optimasi Metode Grid Search dan Random Search dalam Algoritma XGBoost untuk Klasifikasi Stunting Pramudhyta, Nirvan Adam; Rohman, Muhammad Syaifur
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.v8i1.6965

Abstract

Stunting is a condition of stunted physical growth in children due to chronic nutritional deficiencies with serious impacts on health and psychological aspects. The impacts include decreased self-esteem, learning difficulties, impaired concentration, critical thinking problems, and lower economic contributions as adults. This study aims to optimize the XGBoost classification model using the Grid Search and Random Search methods, thereby improving the accuracy of detecting stunting and obtaining an accurate and efficient diagnosis. Seeing the danger and alarming prevalence rate of stunting, signaling the urgency of handling this problem for the welfare of future generations, an automatic classification model is needed to avoid subjectivity and potential errors in the manual decision-making process. XGBoost needs optimization because it has parameters that require adjustment to maximize accuracy. Comparison of two optimization models is important to understand the advantages and disadvantages of each because they have different approaches in finding the best combination. The study used 10,000 data from Krobokan Health Center with attributes of gender, age, birth weight, birth height, weight at measurement, height at measurement, and category. The largest increase in accuracy was obtained by the Grid Search model with an increase in XGBoost accuracy of 5.81% from 83.28% to 89.09%. The Random Search model increased the accuracy by 5.43%, reaching an accuracy of 88.71%. The choice of both models depends on time and resource preferences. Random Search provides higher time efficiency than Grid Search. This research can contribute to identifying children at risk of stunting so that intervention actions can be carried out more efficiently.
Ensemble Klasifikasi Penyakit Tuberculosis Pada Hasil Pengobatan Menggunakan Metode Hybrid K-Nearest Neighbor (K-NN), Decision Tree dan Support Vector Machine (SVM) Alya Nurfaiza Azzahra; Junta Zeniarja; Ardytha Luthfiarta; Mufida Rahayu
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.v8i1.7021

Abstract

Tuberculosis (TB) is an infectious disease with the highest cause of death in the world. This disease can be transmitted through the air and attacks the pulmonary respiratory system. The increase in TB cases from year to year is due to little information about the treatment of this disease. This requires the process of diagnosing and treating TB requiring accurate data analysis. From these problems, classification of tuberculosis disease is needed to improve better treatment results. In this study, experiments were used with the Hybrid model classification algorithm with a method that combines three approaches, namely K-Nearest Neighbor (K-NN), Decision Tree, Support Vector Machine (SVM) to classify treatment results using the Ensemble classification method and aims to combine each method in order to create a stronger Ensemble model and increase accuracy in treatment results, using data from the Semarang City Health Service or what is called Tuberculosis Information System (SITB) data in 2020-2023 with 80% training data and test data 20%. Based on the results of testing and analysis using the confusion matrix, the highest accuracy value was obtained at 78.55% using K-Fold Cross validation, namely k equals 7 and the Ensemble model obtained high results for treatment outcomes.
Evaluasi Perbandingan Kinerja Convolutional Neural Networks untuk Klasifikasi Kualitas Biji Kakao Indra Riyana Rahadjeng; Muhammad Noor Hasan Siregar; Agus Perdana Windarto, M.Kom
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6533

Abstract

The assessment of cocoa bean quality plays a crucial role in the chocolate industry, and automated approaches utilizing image processing techniques and classifiers have become increasingly appealing. In this study, we implemented and compared the performance of image classifiers using Convolutional Neural Network (CNN) architectures for cocoa bean quality classification. By employing this approach, we developed a system capable of accurately and efficiently classifying cocoa bean images, reducing dependence on human evaluation. We compared several CNN architectures, including VGGNet, to evaluate their performance in cocoa bean image classification. Experimental results demonstrated that CNN-based classifiers can provide accurate assessments of cocoa bean quality, with significant success rates. This research contributes to the development of efficient and accurate image classification systems for cocoa beans, which can enhance efficiency in the chocolate industry and ensure product quality. Additionally, our testing results indicate that the model with a batch size of 64 achieved the highest accuracy of 98.44%, outperforming the other three tested batch sizes in cocoa bean classification performance.

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