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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
 92   93   94   95   96 
A Comparison of C4.5 and K-Nearest Neighbor Algorithm on Classification of Disk Hernia and Spondylolisthesis in Vertebral Column Irma Handayani; Suprapto Suprapto
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.v7i3.6394

Abstract

Good spinal health is needed to carry out daily activities. Trauma to the vertebral column can affect the spinal cord's ability to send and receive messages from the brain to the body's sensory and motor control systems. Disk hernia and spondylolisthesis are examples of pathology of the vertebral column. Research on pathology or damage to bones and joints of the skeletal system is rare. Whereas the classification system can be used by radiologists as a "second opinion" so that it can improve productivity and diagnosis consistency from that radiologist. This study compared the accuracy values of the C4.5 and K-NN algorithms in the classification of herniated disc disease and spondylolisthesis as well as a comparison of the speed of time in the classification process. Tests were carried out using data from 310 patients with normal conditions (100 patients), herniated disks (60 patients), and spondylolisthesis (150 patients). The results showed that the accuracy of the C4.5 classifier was 89% and the K-NN classifier was 83%. The average time needed to classify the C4.5 classifier is 0.00912297 seconds and the K-NN classifier is 0.000212303 seconds.
Meningkatkan Kemampuan Model dalam Memprediksi Penyakit Jantung dengan Algoritma NCL dan GridSearchCV Zulfan Ahmadi; Asrul Abdullah; Izhan Fakhruzi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6142

Abstract

Heart disease is the main cause of death in the world. To reduce this high mortality rate, accurate prediction capabilities are needed in warning people with heart disease to prevent and manage this condition. This study uses a machine learning model to predict heart disease. The purpose of this research is to improve the ability of a machine learning classification model, namely Logistic Regression (LR), in predicting heart disease. So that prediction errors that can harm patients can be significantly reduced. To achieve this goal, research is carried out using two important approaches, namely data preparation and model optimization. At the data preparation stage, data imbalance problems were found between people with heart disease and non-heart disease sufferers. To deal with this problem, the Neighborhood Cleaning Rule (NCL) algorithm is used to correct data imbalances. The use of NCL in the data preparation stage has a significant impact on improving the performance of the prediction model. Furthermore, at the model optimization stage, the GridSearchCV method is used to find the best hyperparameter combination in the Logistic Regression (LR) algorithm. By finding optimal hyperparameters, the performance of the prediction model can be improved. In addition, this study also implemented Weighted Logistic Regression which allows setting class weights, which also contributes to improving model performance. The results of testing the model using the evaluation metrics Accuracy, Recall, and Area Under Curve (AUC) show an increase in the ability of the model. The recall score increased from 0.10 to 0.93, and the AUC score increased from 0.83 to 0.98. This study used a dataset obtained from Kaggle from the Centers for Disease Control and Prevention (CDC). With better predictive ability in identifying heart disease, it is hoped that it can provide accurate early warning to individuals at risk, thereby significantly reducing mortality from heart disease.
Hidroponik Pintar Menggunakan Fuzzy Logic Berbasis Internet of Things Pada Tanaman Selada Aris Martin Kobar; Jamaludin Indra; Yana Cahyana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6731

Abstract

Changes in land use have become one of the drivers of innovation in modern agriculture, so research and development has been carried out on hydroponic systems. Hydroponic systems require special treatment on several parameters for optimal results and good quality. Manual verification is considered less effective because agents have to do it more often, so there are often delays in monitoring and adding nutrient solutions to the water. Previous researchers have controlled hydroponic systems using various methods. To regulate the nutrition of the hydroponic system in the hydroponic management system using Fuzzy Logic Control based on Electrical Conductivity (EC) used in the fuzzy input is the EC error and determining the duration of turning on the nutrient valve which shows the results that the fuzzy logic control can maintain the EC value range according to plant needs with water depth. In this research, monitoring and managing nutrition, temperature and water level in a hydroponic system is designed using Fuzzy Logic based on the Internet of Things so that monitoring and setting Parts Per Million (PPM) can be done automatically so that you can monitor lettuce plants remotely according to their age. can be viewed via the web application. The test results in this research obtained that the accuracy of the TDS sensor in detecting water TDS values was 97.78%, and the accuracy of the DS18B20 sensor in detecting water temperature conditions was 98.37%. The fuzzy test obtained an error value of 10%.
Analisis Perbandingan metode Teorema Bayes dan CF dalam Mendiagnosa Gejala Penyakit Demam Berdarah Dengue Bartolomius Harpad; Reza Andrea
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6776

Abstract

Dengue Hemorrhagic Fever (DHF) is a potentially hazardous condition and a health concern in various tropical countries. To accurately and swiftly detect this disease, various diagnostic methods have been developed. The incidence of DHF can fluctuate from year to year, influenced by factors such as weather changes, vector control efforts, and socio-economic aspects. In Indonesia, there have been significant outbreaks of DHF in certain years. In this study, the Author conducted a comparison between the Bayesian Theorem method and the Certainty Factor (CF) method to diagnose DHF symptoms. The Bayesian Theorem calculates the probability of the disease based on symptoms, while the Certainty Factor employs a confidence level to link symptoms with the disease. Symptom data from previous DHF patients were collected, and both methods were utilized to diagnose these cases. The analysis results indicate that both methods have their respective strengths and limitations in terms of accuracy and speed. The Bayesian Theorem is accurate when complete symptom data is available, while the Certainty Factor is useful when data is incomplete or uncertainty exists. Both methods can be used concurrently based on context. This research illustrates the application of statistical analysis and data-driven approaches to enhance DHF diagnosis, also stimulating the development of advanced combined methods in the future. This study provides insights into the use of probabilistic approaches and confidence-based logic in DHF diagnostic development. Both methods can be applied interchangeably or in conjunction, depending on data and case characteristics. The results of applying the Bayesian Theorem and Certainty Factor show that the Bayesian Theorem yields 57.29%, while the Certainty Factor achieves 94.47% accuracy in diagnosing DHF.
Perbandingan Kinerja Algoritma Clustering Data Mining Untuk Prediksi Harga Saham Pada Reksadana dengan Davies Bouldin Index Gatot Soepriyono; Agung Triayudi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6623

Abstract

Mutual funds are a container that can be used to accommodate funds from the public which will later be distributed to the owners of the company. The ease of investing in share prices cannot be separated from the ease of obtaining information. The share price that is very popular with the public is the share price for banks, whether privately owned or government owned. However, even though banks are very close and popular with capital market players, this does not rule out the possibility of a decline in share prices. This problem is not a problem that can be considered trivial and ignored, if you continuously experience losses from the capital market it will certainly give rise to distrust or a lack of interest in the public to participate in investing in companies. Predictions for stock prices must be done well and correctly and get accurate results, therefore it is necessary to use a special technique or method to help carry out the prediction process until results are obtained with a good level of accuracy. The expected prediction process is in line with the concept of data mining. The process of applying clustering for predictions is also considered very suitable, this is because in stock prices there is no target class for each data. The K-Means algorithm and K-Medoids algorithm are part of cluster data mining to be used to make predictions based on cluster formation. The purpose of the comparison is to get more reliable results, where these results can be seen from better algorithm performance. The performance measurement process for the K-Means and K-Medoids algorithms will later be assessed based on the Davies Bouldin Index (DBI). The results of the research show that the performance results of the K-Means algorithm are better than the K-Medoids algorithm. This is proven by the DBI value obtained from the K-Means algorithm being no more than 0.6, while in the K-Medoids algorithm the DBI value obtained is up to 5.822. Overall, each stock data has an optimal cluster based on the clustering process with the K-Means algorithm. The optimal cluster results in BMRI stock data, the optimal cluster is at K=4 with a DBI value of 0.501. In the BBNI stock data, the optimal cluster is at K=4 with a DBI value of 0.500. In the BBCA stock data, the optimal cluster is at K=3 with a DBI value of 0.441. In the BNGA stock data, the optimal cluster is at K=2 with a DBI value of 0.263. In the BDMN stock data the optimal cluster is at K=2 with a DBI value of 0.028 and in the MEGA stock data the optimal cluster is at K=4 with a DBI value of 0.353.
Implemantasi Case Based Reasoning (CBR) Untuk Pengembangan Sistem Pakar Diagnosa Penyakit Gigi Rosmini Rosmini; Ummi Syafiqoh; Asmah Asmah
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.v7i3.6261

Abstract

Dental disease is a condition that can interfere with the teeth in carrying out their functions properly. Dental disease in humans ranks first on the list of the top 10 diseases that most people complain about in Indonesia. Dental disease can affect anyone of any age and gender. Dental disease initially does not cause problems and if allowed to continue can become a big problem. Dental disease has almost similar symptoms, therefore an expert system application is needed that can help diagnose dental disease so that you can get the right treatment before the dental disease develops into a more serious disease. The Case Based Reasoning (CBR) method is a method for solving problems by remembering the same/similar (similar) events that have occurred in the past then using that knowledge/information to solve new problems or in other words solve problems by dealing with solutions. - solutions that have been used. The results of the CBR method test resulted in a diagnosis of dental and oral disease with a similarity rate of 82%. So this method can be used to diagnose dental disease experienced by patients, thereby helping specialist doctors make decisions in disease management.
Penerapan Metode Hybrid Case Based dalam Diagnosis Penyakit pada Tanaman Karet Lucky Suriyah Ningsih; Sahat Parulian Sitorus; Rahmadani Pane
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.v7i3.6551

Abstract

Rubber cultivation utilizing Hevea brasiliensis as the primary source of raw material has established itself as a profitable commercial crop. However, diseases can damage rubber plants and hinder their growth and latex production. To mitigate the resulting losses, it is crucial to develop an efficient expert system for identifying infections in rubber plants. An expert system is a computer program designed to simulate human intelligence in problem-solving and analysis within a specific domain. In this scenario, diseases in rubber plants are identified using an expert system. The Hybrid Case-Based technique, which combines the benefits of Case-Based Reasoning (CBR) method with other methodologies, has proven to be a successful strategy for constructing expert systems. In the Hybrid Case-Based approach, CBR is integrated with other approaches such as Rule-Based Reasoning (RBR). The Hybrid Case-Based technique utilizes information from the existing case base, as well as recognized rules and models, in the context of disease detection in rubber plants. This enables the expert system to diagnose with high accuracy and provide appropriate solutions. The advantages of the Hybrid Case-Based method include its ability to handle complex situations and find optimal solutions. By integrating various approaches, this method overcomes the weaknesses of each individual method. Furthermore, the Hybrid Case-Based approach allows for an overall improvement in the performance of the expert system, resulting in better outcomes in diagnosing diseases in rubber plants. The objective of this research is to utilize the hybrid case-based approach to create an expert system for identifying diseases in rubber plants. It is expected that this research will yield a useful expert system in identifying diseases in rubber plants, which will support farmers in addressing rubber plant health issues. The significance of this research stems from the importance of preserving rubber resources and creating a sustainable rubber economy. When a readily available expert system is capable of accurately identifying infections in rubber plants, farmers can swiftly take necessary steps to halt disease spread and reduce production losses. Additionally, this research can serve as a foundation for developing expert systems in the field of agriculture and other plant diseases. The results of this research indicate that the Hybrid Case-Based method is capable of diagnosing diseases in rubber plants, revealing a low likelihood of White Root Rot disease in the rubber plants with a percentage value of 76%.
Perbandingan Metode Naïve Bayes dan K-NN dengan Ekstraksi Fitur GLCM pada Klasifikasi Daun Herbal A. Nurjulianty; Purnawansyah Purnawansyah; Herdianti Darwis
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6262

Abstract

Indonesia is a country with various types of herbal plants that have the potential to be very effective medicines. Herbal plants have been used since ancient times as natural medicines. One part that has health benefits is the leaves, however, there are many similarities between the different types of leaves. This research aims  to classify digital images of herbal leaves implementing the Naïve Bayes and K-Nearest Neighbor (KNN) methods with Gray Level Co-occurrence Matrix (GLCM) feature extraction. The dataset consisted of sauropus androgynus and moringa leaves with data collection in bright and dark scenarios. A total of 480 data which was divided into two parts, namely 80% for training data and 20% for testing images. The KNN distances used for comparison are Euclidean, Manhattan, Chebyshev, Minkowski, and Hamming. Meanwhile, Naïve Bayes uses Gaussian, Multinomial, and Bernoulli kernels. The results of the study showed that the KNN method with the Manhattan distance obtained the best results with an accuracy rate of up to 94% in bright scenarios.
Pengelompokan Status Stunting Pada Anak Menggunakan Metode K-Means Clustering Intan Saleha Tinendung; Ilka Zufria
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6908

Abstract

Stunting is a physical health disorder caused by a deficiency or imbalance of nutrients necessary for the growth and development of children. This article examines the problem of stunting in children in Kerajaan sub-district, with a focus on the Sukaramai village of Pakpak Bharat district. In 2021, Pakpak Bharat District saw an increase in the number of toddlers in North Sumatra, who were diagnosed with stunting, reaching 21.25%. This occurs due to socio-economic factors and socio-cultural background which have a lot to do with diet and nutrition. According to the Indonesian Toddler Nutrition Statutes (SSGBI), in 2019, the stunting rate in Indonesia increased to 27.7%. The impact of stunting on children includes physical growth disorders, delayed brain development, and the risk of chronic disease in adulthood. Recognizing the urgency of this problem, the government has taken various steps, including the designation of funds for stunt prevention programs. This study uses the K-Means Clustering method to group stunting status in children into three categories: normal, stunting, and rapid growth. Therefore, a method is needed to group stunting status in children, namely using the Clustering method with the K-Means algorithm. The aim is to assist the government in adopting appropriate policies related to reducing the prevalence of stunting in children based on the status and problems of each cluster. The data used primarily comes from the Sukaramai village. The results of the research showed that around 30% of the 101 children studied experienced stunting in the Kerajaan sub-district, including 0 total, 43 children with normal status, 1 total, 31 children with stunting, and 2 total children. 27 children who had not yet developed rapidly did not start early his umur. This study makes an important contribution to the management of data on the nutritional status of children in the remote area and can be a reference for future research. By applying the K-Means Clustelring algorithm, this study helps understand stunting patterns and design more targeted solutions.
Implementasi K-Means Clustering untuk Analisis Tingkat Pemahaman Computational Thinking Siswa Sekolah Dasar Herlina Herlina; Zeny Ernaningsih
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.v7i3.6132

Abstract

The rapid development of technology impacted the world of education worldwide, including in Indonesia. Good problem-solving skills are needed in this era. Computational thinking (CT) or computational thinking is considered capable of training students in problem-solving skills. This study aims to group students based on CT abilities to help teachers more easily determine learning methods that suit the characteristics of students. The data processing uses the K-Means algorithm with data taken from the results of the Bebras Challenge 2022 for the elementary school level. In the clustering process, the most optimal number of clusters was determined using the Elbow method. The optimal cluster is 3 clusters: namely high, medium and low levels of understanding of CT. The cluster for the SiKecil category has an average value of 81.52 and a duration of 15.07 minutes for the high cluster, an average value of 43.02 and a duration of 24.59 minutes for the medium cluster, and an average value of 34.96 and a duration of 15.28 minutes for the low cluster. The clusters formed for the Siaga category are clusters with a high level of understanding of CT with an average value of 67.13 and a duration of 23.88 minutes, an average value of 56.91 and a duration of 34.76 minutes for the medium cluster, and low cluster with an average value of 32.14 and a duration of 20.79 minutes.

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