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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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Perbandingan Metode Certainty Factor dan Theorema Bayes dalam Mendiagnosa Penyakit Kandidiasis pada Manusia Menggunakan Metode Perbandingan Eksponensial Panjaitan, Zaimah; Hafizah, Hafizah; Ginting, Rico Imanta; Amrullah, Amrullah
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3078

Abstract

Candidiasis is an infectious disease caused by the fungus candida. Research on this fungus has been widely carried out until several types of candida fungi are found that can attack and cause infections in humans. Types of candidiasis also vary, but can be classified in general into three types, namely attacking the mouth (Candidiasis Thrush), vagina (Vulvoginal Candidiasis), and skin (Cutaneous Candidiasis). Candidiasis is very susceptible to infection and infection, therefore a study is needed to diagnose candidiasis. Today, expert systems are often used to diagnose diseases. There are several methods commonly used in expertise, including the Certainty Factor method and the Bayes Theorem. However, the problem faced in implementing an expert system in any field is uncertainty. This is caused by the user's hesitation in answering questions during the consultation session or even the inaccuracy of the methods used in building the system. Therefore, it is necessary to study and compare the methods that can be used to build the system. Exponential is a simple comparison that can reduce bias in the analysis process. This study aims to apply and analyze both methods and the results compare with an exponential comparison in detecting candidiasis in humans. The results of this study showed that both methods achieved the same results, namely the lowest percentage level was Candidiasis Truth, then Vuvoginal Candidiasis, and the highest was Candidiasis Cutaneous. Of these two methods, Certanty Factor is more accurate in diagnosing candidiasis.
Analisis Sentimen Terhadap Review Film Menggunakan Metode Modified Balanced Random Forest dan Mutual Information Firdausi Nuzula Zamzami; Adiwijaya Adiwijaya; Mahendra Dwifebri P
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 2 (2021): April 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i2.2844

Abstract

Information exchange is currently the most happening on the internet. Information exchange can be done in many ways, such as expressing expressions on social media. One of them is reviewing a film. When someone reviews a film he will use his emotions to express their feelings, it can be positive or negative. The fast growth of the internet has made information more diverse, plentiful and unstructured. Sentiment analysis can handle this, because sentiment analysis is a classification process to understand opinions, interactions, and emotions of a document or text that is carried out automatically by a computer system. One suitable machine learning method is the Modified Balanced Random Forest. To deal with the various data, the feature selection used is Mutual Information. With these two methods, the system is able to produce an accuracy value of 79% and F1-scores value of 75%.
Penerapan Algoritma C4.5 Dalam Memprediksi Ketersediaan Uang Pada Mesin ATM Firman Syahputra; Hartono Hartono; Rika Rosnelly
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 2 (2021): April 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i2.2933

Abstract

This study aims to provide an evaluation of the availability of money in ATM machines using data mining. Data mining with the C4.5 algorithm is used to predict cash demand or total cash withdrawals at ATMs. To determine the need for ATM cash based on cash transaction data. It is hoped that this forecasting can help the monitoring department in making decisions about the money requirements that must be allocated to each ATM machine. The results of this study are expected to assist the ATM management unit in optimizing and monitoring the availability of money at an ATM machine for cash needs, so that it can provide optimal service to customers. Algortima C4.5 is an algorithm that is able to form a decision tree, where the decision tree will then generate new knowledge. The results of the test matched the data on the availability of money at the ATM machine. The results of implementing the C4.5 method on the availability of money at the ATM machine are seen from the travel time to the ATM location and also the remaining balance in the machine. The resulting decision tree model is to make the balance variable as the root, then the travel time as a branch at Level 1 with the variables fast, medium, long, and the bank becomes a branch at the last level (Level 2). Then the C4.5 algorithm was tested using the K-Fold Cross validation method with the value of fold = 10, it can be seen that the accuracy rate is 85%, the Precision value is 80% and the Recall value is 66.67%. While the AUC (Area Under Curve) value is 0.833, this shows that if the AUC value approaches the value 1, the accuracy level is getting better
Clustering Kanker Serviks Berdasarkan Perbandingan Euclidean dan Manhattan Menggunakan Metode K-Means Widodo, Slamet; Brawijaya, Herlambang; Samudi, Samudi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 2 (2021): April 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i2.2947

Abstract

K-means a fairly simple and commonly used cluster of clusters to partition datasets into multiple clusters. Distance calculations are used to find similar data objects that lead to developing powerful algorithms for datamining such as classification and grouping. Some studies apply k-means algorithms using distance calculations such as Euclidean, Manhattan and Minkowski. The study used datasets from gynecological patients with a total of 401 patients examined and as many as 205 patients detected cervical cancer, while 196 other patients did not have cervical cancer. The results were shown with the help of confusion matrix and ROC curve, accuracy value obtained by 79.30% with ROC 79.17% on K-Means Euclidean Metric while K-Means Manhattan Metric by 67.83% with ROC 65.94%. Thus it can be concluded that the Euclidean method is the best method to be applied in the K-Means Clustering algorithm on cervical cancer datasets.
Analisa Penentuan Saham Terbaik Menggunakan Metode Analytic Hierarchy Process (AHP) Romindo Romindo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.2978

Abstract

The number of stock investors has increased from the past to the present. Likewise, the number of companies listed on the Indonesia Stock Exchange (IDX). What makes investors confused about choosing the best stock from the hundreds of stocks that are regulated on the IDX. The method used for the best stock is the Analytic Hierarchy Process (AHP) method. Investors will enter criteria that take into account the specified criteria and criteria. The criteria used to determine stocks are C1 = PER, C2 = PBV, C3 = DER, C4 = ROE and C5 = ROA. The results of research conducted in the second rank show that alternative A1 = CPIN is in the first rank with a Priority Vector of 0.6536, alternative A2 = JFPA is in the second rank with a Priority Vector of 0.2013 and alternative A3 = UTAMA is in the second rank. second rank. second rank. second rank. ranked third with a Priority Vector of 0.1452.
Penerapan Metode Technique for Orders Preference by Similarity to Ideal Solution (TOPSIS) dan Rank Order Centroid (ROC) Dalam Pemberian Beasiswa Kartu Indonesia Pintar (KIP) Ita Arfyanti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3048

Abstract

The provision of KIP scholarships, which are aid scholarships given when a person is still in high school/vocational school, or assistance for high school/vocational high school graduates to continue their education. This assistance is given to students who have good academic potential and have economic limitations. When searching for and determining recipients of scholarship assistance for KIP scholarship recipients by implementing a decision support system using the Technique for Orders Preference by Similarity to Ideal Solution (TOPSIS) method approach where the selection criteria have been determined previously, the criteria are given or weighted using the ROC method ( Rank Order Centroid), so that the election data process will get more optimal results using the TOPSIS method, the results obtained are 0.66061 which is the highest value of all alternative data and in this study shows the data is more shaped to the selection of potential alternatives. get a KIP scholarship with a more accurate weight and ranking process.
Data Mining Menggunakan Algoritma K-Nearest Neighbor Dalam Menentukan Kredit Macet Barang Elektronik Silvilestari Silvilestari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3100

Abstract

Business is an activity that is routinely carried out by many people, one of the promising businesses is a business that provides the needs of electronic goods to meet the needs of daily life, high demand causes business people to be more careful and selective in seeing the pattern of customers who want perform transactions in order to avoid business risks appropriately. Business people use a credit system to increase sales for a long time, but the obstacle that often occurs is that many customers who use credit services often delay payments to make the company's finances unstable so someone needs to predict someone who has the potential to do bad credit for electronic goods. using a system that helps business owners to make it easier to process data and predict patterns of bad credit for electronic goods that have been formed from previous data using the KNN algorithm approach, so that the results show the closeness of the value to the data of old customers, both those who pay properly and those who make payments in default and it can be obtained that the processing process accelerates data processing with precise results in the problem solving process
Penerapan Algoritma C5.0 Untuk Prediksi Kelulusan Pembelajaran Mahasiswa Pada Matakuliah Arsitektur Sistem Komputer Muchamad Sobri Sungkar; M Taufik Qurohman
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3116

Abstract

Computer system architecture is one of the subjects that must be taken in the informatics engineering study program. In the study program the graduation of each student in the course is one of the important aspects that must be evaluated every semester. Graduation for each student / I in the course is an illustration that the learning process delivered is going well and also the material presented by the lecturer in charge of the course can be digested by students. Graduation of each student in the course can be predicted based on the habit pattern of the students. Data mining is an alternative process that can be done to find out habit patterns based on the data that has been collected. Data mining itself is an extraction process on a collection of data that produces valuable information for companies, agencies or organizations that can be used in the decision-making process. Prediction of graduation with data mining can be solved by classifying the data set. The C5.0 algorithm is an improvement algorithm from the C4.5 algorithm where the process is almost the same, only the C5.0 algorithm has advantages over the previous algorithm. The results of the C5.0 algorithm are in the form of a decision tree or a rule that is formed based on the entropy or gain value. The prediction process is carried out based on the classification of the C5.0 algorithm by using the attributes of Attendance Value, Assignment Value, UTS Value and UAS Value. The final result of the C5.0 algorithm classification process is a decision tree with rules in it. The performance of the C5.0 algorithm gets a high accuracy rate of 93.33%
Manufacturing a Plotter Printer with Computer Numerical Control Based Pen Ink Using CoreXY Mechanisms Veshea Falerie Goszal; Hilal Hudan Nuha; Maman Abdurohman
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 2 (2021): April 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i2.2783

Abstract

In the process of printing images, a high-quality plotter printer is required to obtain better printed images. This causes the costs incurred to purchase a plotter printer become even higher along with the quality of the printout. In addition, the use of original plotter printer ink is significantly more expensive than that of the non-original ink. Therefore, this paper proposes a pen based computer plotter printer design using the CoreXY framework. This framework is based on a 2D in-axis motion control technique using a single continuous belt for both axes. To evaluate the performance of the proposed design, the level precision for vector and raster modes are measured using the Mean Absolute Percentage (MAPE). Based on the results of precision measurements, it was found that the printer prototype designed to produce high-precision images with affordable pen ink costs and can process digital instructions from a computer using Computer Numerical Control (CNC).
Analisis Komparasi Algoritma Naïve Bayes dan K-Nearest Neighbor Untuk Memprediksi Kelulusan Mahasiswa Tepat Waktu Muhammad Gunawan; Muhammad Zarlis; Roslina Roslina
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 2 (2021): April 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i2.2925

Abstract

Students are one of the important pillars in the life cycle of a university. In the process of developing, a university can be influenced by how many bachelor degree (S1) graduates from the university are. The number of graduations of a college sometimes has a low ratio when compared to the number of students admitted in the same school year. This low passing rate of students can be caused by several factors, such as the number of student activities that are participated in, economic factors, and several other unexpected factors. This makes a university must have a scheme or a formula that can predict whether the student can graduate on time. Normally, a bachelor (S1) student takes 8 semesters of education. But the existence of several factors that have been mentioned can make the time to take S1 education to be more, or even fail to graduate. This study will try to compare the results of the analysis of the two methods in the classification algorithm to predict student graduation. The algorithm used is the K-Nearest Neighbor and Naïve Bayes Algorithm. This study also aims to identify the best algorithm among the two classification algorithm choices. This research concluded that the Naïve Bayes algorithm has the same level of accuracy as the KNN algorithm in predicting the graduation of students in the Medical Education study program, which is 90%

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