JURNAL MEDIA INFORMATIKA BUDIDARMA
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<![CDATA[Penggunaan Text Modeling Untuk Identifikasi Kesalahan Penulisan Kata Pada Teks Pidato Bupati Banggai Sulawesi Tengah ]]>
<![CDATA[Suparno, Daffa Setiawan]]>;
<![CDATA[Rosyda, Miftahurrahma]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i3.3051
<![CDATA[Typing errors or typography are errors made when typing a document or text, typing errors can occur due to mechanical failure or slipping of the hand or finger. Generally, typing errors are something that often occurs when someone is typing and is considered normal, but this typing error in some contexts can change the meaning of the word or even the meaning of the sentence itself, This causes the need for correction again after someone has finished typing, but the correction process is mostly still manually so the results of the correction depend on how carefully someone makes corrections and how many documents will be corrected. Therefore we need a system that can make corrections quickly and accurately, the correction process can be done by various methods, one of which is using the text modeling method. In this study, the test data used 10 documents of the Banggai Regent's important speech, Central Sulawesi. The text modeling method can be combined with other supporting methods such as word2vec, where word2vec will be used as a recommendation for corrected words. This study creates a system that can correct word errors in important speech documents of the Banggai Regent, Central Sulawesi by using text modeling and Word2Vec methods, the results obtained from the system that has been made are the system has good performance and gets maximum test results]]>
<![CDATA[Perbandingan Metode Klasifikasi Data Mining Untuk Rekomendasi Tanaman Pangan ]]>
<![CDATA[Wibowo, Merlinda]]>;
<![CDATA[Ramadhani, Rafian]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i3.3086
<![CDATA[Determination of the right food crops needs to be done to improve the community's economy in the agricultural sector. The use of traditional cropping patterns needs to be changed by utilizing information technology. The utilization of data from local governments can be used to assist in providing recommendations for types of food crops by processing them with several data mining methods. This method can extract information to find patterns and knowledge from the data. The classification method approach is used as a grouping of data based on data attachment to sample data. This study uses several classification methods, namely Naïve Bayes, Decision Tree, Support Vector Machine (SVM), Neural Network, Random Tree, Random Forest, dan K Nearest Neighbor (KNN). These methods were successfully compared to find out which method is the best to help recommend appropriate and accurate food crops based on the results of the classification performance of each method. Random Tree was chosen as the best method for the results of this performance comparison using discretization and normalization methods at the pre-processing stage of the data. It can be seen based on the results of the Accuracy, Precision, Recall, and F1-Score values on the use of discretization of 98%, respectively. Meanwhile, normalization showed that the results of the Accuracy, Precision, Recall, and F1-Score values are 99%, respectively.]]>
<![CDATA[Model Pengembangan Sistem Informasi Akademik Berbasis User Centered Design Menerapkan Framework Flask Python ]]>
<![CDATA[Ngantung, Ronaldo Kristoforus]]>;
<![CDATA[Pakereng, M A Ineke]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i3.3054
<![CDATA[Along with the rapid development of the times, one of which is in the field of information technology, this is increasingly demanding that we have to work faster. The ineffective technological development at SMK Negeri 1 Bitung makes students and teachers still using the manual system in academic activities, such as inputting grades, in providing information on school activities and so on. SMK 1 Negeri Bitung needs technology development solutions as school infrastructure. In this study, an Academic Information System was designed by implementing the Flask Python Framework technology. This study shows the performance of the system to help the process of academic activities to be better]]>
<![CDATA[Penerapan Metode K-Means Untuk Menganalisis Minat Nasabah ]]>
<![CDATA[Hutagalung, Juniar]]>;
<![CDATA[Sonata, Fifin]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i3.3113
<![CDATA[Insurance is a mechanism of protection or protection from the risk of loss by transferring the risk to another party. Sometimes a product that has just emerged becomes a product that is superior in terms of sales, so that interest in a product is not absolutely measured from the year the product was released. The constraint factors include the marketing of the product when it was launched. Offering products with low premiums along with the benefits that customers want. However, insurance companies still have difficulty in classifying superior products that are in great demand by prospective customers. For this reason, a technique for grouping insurance products is needed to make it easier for companies to see superior products and choose products that suit the needs of their customers. Analyzing and processing data using the K-Means method in the clustering of insurance products is the aim of this study. The application of the K-Means algorithm is to help calculate the purity value from the results of the clustering carried out so that the clustering of insurance products is in accordance with the needs of its customers. The application of the K-Means method with clustering techniques for data mining produces information on insurance products that are more attractive to potential customers. This is very appropriate in grouping data types because it is easier to implement and its application can filter quickly and precisely. Calculations using the K-Means method with a data sample of 55 customers obtained 3 clusters, namely cluster 1 for fire insurance which has 30 customers, cluster 2 for accident insurance 24 people and cluster 3 for health insurance 1 person.]]>
<![CDATA[Fuzzy Neural Network (FNN) Pada Proses Identifikasi Penyakit ISPA ]]>
<![CDATA[Saputra, Dhio]]>;
<![CDATA[Yanto, Musli]]>;
<![CDATA[Safitri, Wifra]]>;
<![CDATA[Mayola, Liga]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i3.3020
<![CDATA[ISPA is a disease that can affect anyone from children, adolescents, adults, and even the elderly. The causes experienced by sufferers of this disease are quite simple, such as fever, runny nose, and cough. The discussion in this paper describes the process of ISPA disease identification by developing a Fuzzy Neural Network (FNN) model. The process will be optimized using Fuzzy Logic to form rules for the diagnostic process, then proceed with an Artificial Neural Network (ANN). This model can maximize the performance of ANN in the identification process so that the output given is quite precise and accurate. The results provided by Fuzzy Logic can describe the clarity of the rules in diagnosis by presenting several rules (rules) that are presented from the Fuzzyfication process to the Defuzzyfication process. The output obtained from the ANN process also shows quite perfect results with an average error value based on MSE of 0.00912 and accuracy value of 91.96%. With these results, it can be stated that the FNN model can be used in the ISPA diagnosis process so that the presentation of this paper aims to provide an alternative in the identification process]]>
<![CDATA[Penerapan Pengenalan Wajah Untuk Aplikasi Absensi dengan Metode Viola Jones dan Algoritam LBPH ]]>
<![CDATA[Buana, I Komang Setia]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i3.3008
<![CDATA[The human face can be used to assess because of its uniqueness based on certain parameters. To perform facial recognition, the first thing that needs to be done is face detection. The author uses the Viola-Jones method to detect faces. The Viola-Jones method is known to have high speed and accuracy because it combines several concepts (Haar Features, Integral Image, AdaBoost, and Cascade Classifier) into the main method for handling objects. The principle of camera face recognition itself is that the captured face object will be processed and compared with all face images in the existing data set so that the identity of the face is known. One of the applications of face recognition is to do attendance with individual faces. The attendance process does not need physical contact interactions between humans and devices such as the fingerprint system so that during the current COVID-19 pandemic, the spread of the virus can reduce. In this research, a system that can be checked and a person's face is used as a leverage medium for arrival and return attendance using the Viola-Jones method and the LBPH algorithm. The language used is python with the OpenCV library. The PHP language is used for the user interface so that users perform attendance via a browser with the MySQL database to store attendance data. The result of the research is that using the Viola-Jones method and the LBPH algorithm faces are identified and the data is stored in the database used for data attendance. Distance and slope affect the results of face recognition. The distance is too close about 30 cm from the camera, the face cannot be detected. Instead of face position is too far approximately 200 cm, the face can still be detected but could not be identified. For a face tilt level of about 20o from perpendicular, it can still be recognized, but at a tilt degree of about 30o up or to the right, faces cannot be detected.]]>
<![CDATA[IoT-Based Kobela Teaching Aid for Mathematics Learning Multiplication and Division Materials for Grade II Elementary School Students ]]>
<![CDATA[Setiawan, Muhammad Ilham]]>;
<![CDATA[Suwastika, Novian Anggis]]>;
<![CDATA[Prabowo, Sidik]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i3.3107
<![CDATA[Kotak Belajar Ajaib (Kobela) is props for elementary school math class II which can help learn to calculate multiplication and division. Based on research conducted by Sugeng Harnanto, Kobela can improve concentration, increase creativity and student learning outcomes. This tool has been tested in low-grade learning and extracurricular learning activities. The average student success in learning without using teaching aid is 54.56 (56.77%), after using teaching aid the average learning success rate reaches 90.52 (94.19%). The level of mastery learning for Basic Competencies: 3.1 Doing Multiplication of Two Numbers have increased by 37.42. In previous studies, the application of Kobela teaching aid in all learning activities was still manual-based. Potential or opportunities for development, especially for reading assessments and automatic data storage are possible to be achieved by implementing the Internet of Things (IoT). In this study, Kobela was built which implements IoT technology for reading, assessment, and recording based on learning activities. Then evaluate the system by testing the functionality of all the learning activities. From the test results, it was found that the system was running 100% by the specified function. The results of system performance testing in terms of sensor readings are on average 3 seconds with 8 Watt room lighting conditions and the average value of the assessment accuracy is 84.]]>
<![CDATA[Deep Learning dalam Mengindetifikasi Jenis Bangunan Heritage dengan Algoritma Convolutional Neural Network ]]>
<![CDATA[Winiarti, Sri]]>;
<![CDATA[Saputro, Mochammad Yulianto Andi]]>;
<![CDATA[Sunardi, Sunardi]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i3.3058
<![CDATA[A heritage building is a building that has a distinctive style or tradition from a culture whose activities are carried out continuously until now and are used as a characteristic of that culture. The problems that occur in the community are the lack of knowledge to recognize the types of heritage buildings and the lack of digital documentation. Another problem that occurs in identifying heritage buildings is that there are similarities between heritage buildings and new buildings that imitate the architectural style of heritage buildings from ornaments. This can raise doubts in the information related to the original history of heritage buildings for the public or visitors. This study aims to apply the Convolutional Neural Network (CNN) to identify the types of heritage buildings. The benefits of this research can be found in the characteristics of a building based on ornaments so that it can be used to obtain information about the types of heritage buildings in Indonesia. A dataset of 7184 images of ornaments from heritage buildings were used which were taken directly at the Yogyakarta location, namely; Mataram Grand Mosque, Taqwa Wonokromo Mosque, Kalang House, Joglo KH Ahmad Dahlan and Ketandan. It is necessary to identify the heritage building because the object of the building can become extinct at any time, so to maintain it, documentation is needed as an effort to preserve culture and for education. Based on the evaluation of the performance of the tests carried out using the confusion matrix method from 391 ornamental images, the results obtained are 98% accuracy]]>
<![CDATA[Deep Learning on Game Addiction Detection Based on Electroencephalogram ]]>
<![CDATA[Pangistu, Lalu Arfi Maulana]]>;
<![CDATA[Azhari, Ahmad]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i3.3061
<![CDATA[Playing games for too long can be addictive. Based on a recent study by Brand et al, adolescents are considered more vulnerable than adults to game addiction. The activity of playing games produces a wave in the brain, namely beta waves where the person is in a focused state. Brain wave activity can be measured and captured using an Electroencephalogram (EEG). Recording brain wave activity naturally requires a prominent and constant brain activity such as when concentrating while playing a game. This study aims to detect game addiction in late adolescence by applying Convolutional Neural Network (CNN). Recording of brain waves was carried out three times for each respondent with a stimulus to play three different games, namely games included in the easy, medium, and hard categories with a consecutive taking time of 10 minutes, 15 minutes, and 30 minutes. Data acquisition results are feature extraction using Fast Fourier Transform to get the average signal for each respondent. Based on the research conducted, obtained an accuracy of 86% with a loss of 0.2771 where the smaller the loss value, the better the CNN model built. The test results on the model produce an overall accuracy of 88% with misclassification in 1 data. The CNN model built is good enough for the detection of game addiction in late adolescence. ]]>
<![CDATA[Perbandingan Metode Certainty Factor dan Theorema Bayes dalam Mendiagnosa Penyakit Kandidiasis pada Manusia Menggunakan Metode Perbandingan Eksponensial ]]>
<![CDATA[Panjaitan, Zaimah]]>;
<![CDATA[Hafizah, Hafizah]]>;
<![CDATA[Ginting, Rico Imanta]]>;
<![CDATA[Amrullah, Amrullah]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i3.3078
<![CDATA[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.]]>
