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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 54 Documents
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Search results for , issue "Vol 7, No 4 (2023): Oktober 2023" : 54 Documents clear
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.
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.
Metode MICE Support Vector Machine (MICE-SVM) untuk Klasifikasi Performance Mahasiswa Merdeka Belajar Kampus Merdeka Angga Apriano Hermawan; Galuh Wilujeng Saraswati; Etika Kartikadarma
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.v7i4.6821

Abstract

The Ministry of Education and Culture established a Merdeka Belajar Kampus Merdeka (MBKM) program with the aim of improving the competency of student graduates, both soft skills and hard skills, so that they are better prepared and relevant to the needs of the times, preparing graduates as future leaders of the nation who are superior and have personality. However, the MBKM program is not always effective in improving the quality of a student because there are still several shortcomings. It is also felt that some students have not received maximum results when participating in the MBKM program. In fact, not all programs offered by MBKM partners receive an assessment in the form of soft skills scores. The aim of this research is to classify whether the MBKM program influences the performance of MBKM program students by applying the Multivariate Imputation by Chained Equation (MICE) method to overcome missing values in the classification of MBKM student performance at the Faculty of Computer Science, Dian Nuswantoro University. The qualification of MBKM student performance is very important because we need to know whether the program is deemed effective or not to be continued in the future. In this study, researchers used a dataset originating from the MBKM report from students at the Faculty of Computer Science, Dian Nuswantoro University. Researchers obtained data by collecting data from MBKM student certificates and reporting the results. The data taken was 277 pieces for training and 69 pieces for testing. Next, the researchers used the Support Vector Machine (SVM) algorithm for the classification process. The research results show that the performance of the Support Vector Machine (SVM) algorithm model with MICE missing value handling has better accuracy results, with an accuracy value of 98.07% compared to using the Mean Imputation method, which only obtains an accuracy of 97.34%.
Analisis Perbandingan Algoritma SVM, Naïve Bayes, dan Perceptron untuk Analisis Sentimen Ulasan Produk Tokopedia Muhammad Aulia; Arief Hermawan
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.v7i4.6839

Abstract

The rapid growth of the online market in Indonesia has changed the business landscape. Tokopedia, one of the leading E-commerce platforms, serves millions of users with a variety of products. In the fierce E-commerce competition, understanding customer reviews is very important. However, performing review analysis manually is a complex and time-consuming task. Sentiment analysis is needed to understand customer preferences, improve service quality, and maintain Tokopedia's competitiveness in the competitive E-commerce market. This study carried out a comparison between three algorithms, Support Vector Machine, Perceptron, and Multinomial Naïve Bayes to evaluate and determine the most effective and accurate algorithm in conducting sentiment analysis of product reviews on Tokopedia. The results of research using 2000 Tokopedia product review data show that Multinomial Naïve Bayes has the highest level of accuracy, reaching 84.00% and precision of 96.00%. Support Vector Machines has an accuracy rate of 80.00% and a precision value of 95.00%. Meanwhile, Perceptron provides 81.00% accuracy and 95.00% precision. Evaluation using the confusion matrix also indicates that Multinomial Naïve Bayes provides superior results with a truth level of 1011 for positive sentiment labels and 860 for negative sentiment labels. This research provides valuable insights regarding sentiment analysis of product reviews on Tokopedia, and the results can be a reference for further research exploring more innovative sentiment analysis methods or the application of technology to increase the efficiency of sentiment analysis in the context of E-commerce.
Perbandingan Keefektifan Metode Case-Based Reasoning dan Certainty Factor dalam Sistem Pakar Diagnosis Penyakit Multiple Sclerosis Hanifah Ekawati; Ita Arfyanti; Tommy Bustomi
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.v7i4.6574

Abstract

The management of complex neurological diseases such as Multiple Sclerosis (MS) requires accurate and efficient diagnostic approaches. To enhance diagnostic precision, a study has conducted a comparison between two approaches within the framework of an expert system, namely the Case-Based Reasoning (CBR) Method and the Certainty Factor (CF) Method. The primary objective of this study is to evaluate the effectiveness of these two methods in supporting the diagnosis process of Multiple Sclerosis. The Case-Based Reasoning Method is an approach that relies on past experiences to address new issues. Within an expert system, CBR utilizes knowledge from previous cases to identify diagnoses that align with the current situation. On the other hand, the Certainty Factor Method is an approach that measures the confidence level in a statement based on rules and associated confidence factors. This study makes use of a dataset containing information from previous cases related to the diagnosis of Multiple Sclerosis. By employing both of these methods, an expert system is developed to provide diagnostic recommendations based on inputted symptoms and data. The effectiveness of both approaches is evaluated through diagnostic accuracy, computational speed, and confidence levels in the generated results. Research findings indicate that both methods have their respective strengths and weaknesses. The CBR method tends to yield accurate results by referring to similar cases in the past, but it may encounter challenges in unique or rare cases. On the other hand, the Certainty Factor Method has the ability to handle uncertainty and can produce results with measurable confidence levels. However, dependence on predefined rules may limit adaptation to new cases. In conclusion, this study underscores that there is no singular perfect approach within expert systems for diagnosing Multiple Sclerosis. Both the CBR and Certainty Factor methods contribute in their own ways to improving accuracy and confidence in the diagnosis process. Therefore, integrating these two methods could be a promising direction for the development of expert systems in the future.
Sentiment Analysis of the Jakarta - Bandung Fast Train Project Using the SVM Method Muhammad Daffa Dhiyaulhaq; Putu Harry Gunawan
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.v7i4.6855

Abstract

Web growth contributes greatly to user-generated content such as user feedback, opinions and reviews. The construction of the Jakarta-Bandung High Speed Train is both an icon and a momentum for Indonesia to modernize mass transportation in an era of continuous progress. Sentiment analysis is one of the text-based research field solutions suitable for addressing satisfaction issues based on user reviews. In this research, the system will be made with review sentences from users and produce output in the form of positive and negative classes. The method used by the author is classification using the Support Vector Machine (SVM) method and Word2Vec extraction features. In addition, a comparison of the accuracy value between the Support Vector Machine method, Naïve Bayes method and TF-IDF extraction features is carried out. The data studied came from several news websites containing user reviews of the Jakarta-Bandung High Speed Train. This method is used because it represents words in a vector, besides that the training process is faster when compared to other extraction features. This research resulted in the performance of accurasy, precision, recall, and f1-score, namely accurasy of 82.74%, precision of 75.68%, recall of 97.67%, and f1-score of 85.28%. These results were obtained using the best tuning hyperparameters, namely ('C': 10, 'gamma': 0.1, 'kernel': 'rbf'). Then in the second scenario a comparison is made with the Naïve Bayes method. It was found that the accuracy of the Support Vector Machine method using the TF-IDF extraction feature obtained better and stable performance results than the other three performance results, which amounted to 86.90%. So the author concludes that the Support Vector Machine method using the TF-IDF extraction feature is better when compared to the Naïve Bayes method and the Word2vec extraction feature.
Peningkatan Akurasi Pembacaan Lembar Jawaban Komputer dengan Memperbaiki Ketidaksimetrisan Citra Hasil Pemindaian Menggunakan Transformasi Homografi Prayitno Prayitno; Guruh Fajar Shidiq; Ahmad Zainal Fanani; M. Arief Soeleman
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.v7i4.6651

Abstract

Answer Sheet Reading Computers are one of the technologies for converting images into information which continues to develop until now. Some of the applications of this technology include correcting various school exams, psychological tests, surveys and voting. Accuracy is something that is often a problem in various studies on reading computer answer sheets. Accuracy is greatly influenced by the scanned image. In the process of scanning computer answer sheets, images often produce asymmetry, such as tilting, shifting and dilatation.The process of scanning computer answer sheets often produces asymmetrical images, such as tilted, shifted and dilated.  This incident will affect the accuracy of the results of reading the computer answer sheet, due to deformation of the shape between the reference image and the scanned image. This study aims to improve asymmetric images to become symmetrical with the Homografi transformation in order to get better reading accuracy. The results showed that the improvement of image symmetry with Homografi transformation was better than the skew correction method. This is shown from the respective RMSE values, the Homografi transformation method produces an RMSE value of 51.54 and the skew correction method produces a value of 67.04. The results of the study also stated that the accuracy of reading computer answer sheets with the Homografi transformation method was better than skew correction. The skew correction accuracy is 95.8%, while the Homografi transformation is 99.3%.

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