JURNAL MEDIA INFORMATIKA BUDIDARMA
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"Vol 5, No 4 (2021): Oktober 2021"
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<![CDATA[Implementasi Metode SMART Untuk Penentuan Platform Pembelajaran Jarak Jauh Masa Pandemi ]]>
<![CDATA[Rajiansyah, Rajiansyah]]>;
<![CDATA[Fajri, Ahmad]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i4.3317
<![CDATA[The pandemic period is difficult times for all nations and countries around the world because the pandemic period is a period that limits many activities carried out as usual due to an outbreak of the Covid-19 virus as the most contagious and quite deadly virus, including in learning activities for an educational institution for the sake of education. break the rope of the spread of a larger virus so that a platform is used that helps in carrying out the teaching and learning process, the many types of platforms currently available make it difficult to determine the most appropriate platform for its use based on several criteria such as usability, affordability, maintability, accessibility and compatibility. the selection using a decision support system to facilitate the decision-making process appropriately, the process of using a decision support system using the SMART (Simple Multi Attribute Rating Technique) method, the results obtained are ah A2 with a value of 0.92]]>
<![CDATA[Implementasi Penerapan Metode C4.5 dan Naïve Bayes Dalam Tingkat Kelulusan Akreditasi Lembaga PAUD Pada Badan Akreditasi Nasional ]]>
<![CDATA[Genisa, Lenggo]]>;
<![CDATA[Mulyana, Dadang Iskandar]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i4.3267
<![CDATA[Education from an early age is one way to stimulate children's potential. This is explained in the Law of the Republic of Indonesia Number 20 of 2003 concerning the National Education System which states that Early Childhood Education (PAUD) is a coaching effort aimed at children from birth to the age of six which is carried out through the provision of educational stimuli to help growth and physical and spiritual development so that children have readiness to enter further education. The National Accreditation Board for Early Childhood Education and Non-Formal Education, hereinafter referred to as BAN PAUD and PNF, is an independent evaluation body that determines the feasibility of PAUD and PNF programs and/or units. BAN PAUD and PNF were formed based on Permendikbud Number 52 of 2015 concerning the National Accreditation Board for Early Childhood Education and Non-Formal Education which is a substitute for Permendikbud 59 of 2012. Improving the quality of the implementation of PAUD and PNF Accreditation can be done by increasing the availability of non-formal education accreditation services. Other things that can be done to improve the quality of the implementation of PAUD accreditation are by providing certainty and guarantee of obtaining non-formal education accreditation services and improving a reliable governance system in ensuring the implementation of non-formal education accreditation services. This study uses data mining techniques in predicting the accreditation status of PAUD education units. First, preprocessing is used to get a quality dataset. Second, the data is processed to get a series of predictions. In this step, two data mining algorithms are applied, namely the Naïve Bayes Algorithm and the C4.5 Algorithm with the aim of knowing the performance of the two algorithms with a greater level of accuracy will be recommended in solving the problem of predicting the accreditation of PAUD education units in BAN PAUD and PNF DKI Jakarta Province. Then the third, the results will be in the Conffusion Matrix to validate the accuracy of the prediction results. And the results of the assessment show that the C4.5 and Naïve Bayes Algorithm methods can be used to predict the accreditation status of PAUD education units with an accuracy of 99.00%]]>
<![CDATA[Klasifikasi Physical Activity Berbasis Sensor Accelorometer, Gyroscope, dan Gravity menggunakan Algoritma Multi-class Ensemble GradientBoost ]]>
<![CDATA[Aziz, Firman]]>;
<![CDATA[Usman, Syahrul]]>;
<![CDATA[Jeffry, Jeffry]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i4.3222
<![CDATA[The current generation of smartphones is increasingly sophisticated, equipped with several sensors such as accelerometer, gravity sensor, and gyroscope that can be used to recognize human activities such as going up stairs, going down stairs, running and walking. To get information, the data will be grouped using statistical methods. The performance of statistical methods has shortcomings in classifying data because of the procedures that must be met. To cover this shortcoming, the ensemble technique is used. In this paper, we propose to apply the Multi-Class Ensemble Gradientboost algorithm to improve the performance of the logistic regression method in classifying such as climbing stairs, descending stairs, running and walking. The process of taking data using a smartphone by designing an Android-based .apk system. Then, the entire dataset was separated into training data and test data with a comparison percentage of 70:30. The results obtained show that the Multi-Class Ensemble Gradientboost algorithm succeeded in increasing the logistic regression performance by 27.93%]]>
<![CDATA[Penerapan Model Pembelajaran dengan Metode Reinforcement Learning Menggunakan Simulator Carla ]]>
<![CDATA[Dharma, Arie Satia]]>;
<![CDATA[Tambunan, Veronika]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i4.3169
<![CDATA[Artificial Intelligence is the study of how to make machines or computer programs have the intelligence or ability to do things that humans can do. The application of AI is currently in various ways, one of which is for self-driving cars. To be able to do a self-driving car, the AI that is implanted in a car must applied to the method to be able to walk on its path and be able to adapt to its environment. Reinforcement learning is one type of machine learning where agents learn something by doing certain actions and the results of those actions and try to maximize the gifts received through interactions with the environment that are reward negative or positive. In this research, we applied of the reinforcement learning method on the Carla Car simulator. The simulator is used to collect data using an RGB sensor, then modeling experiments which produce several models to be used in simulation experiments. The model is obtained by using the Convolutional Neural Network (CNN) algorithm with the NVIDIA architectural model. From the results of research based on experiments conducted obtained the best model obtained from the experimental model by comparing the maximum reward value, high accuracy and low loss is model 1 in the experimental model A with 100 episodes and model 4 in model B experiment with 150 episodes]]>
<![CDATA[Sistem Informasi Penjualan Daster Handmade Berbasis Multiplatform Menggunakan WhatsApp Gateway ]]>
<![CDATA[Asyhadi, Ahmad]]>;
<![CDATA[Naibaho, Ronald]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i4.3297
<![CDATA[Duratu Jambi is a business unit that utilizes Instagram for sales activities. The product offered is a handmade negligee. Based on the analysis of the current system, the limitations of Duratu's system hamper the performance of the admin staff and require a system that can be used to manage all sales transactions, and generate reports. The system design is implemented based on progressive web applications that can run on multiplatform, and the system development is based on the waterfall method. The system testing method uses integrated testing. The results of this study are a new system to provide convenience in transacting and managing reports in Duratu Jambi]]>
<![CDATA[Sistem Pendukung Keputusan Pemilihan Supplier Dengan Metode AHP Pada Apotek & Laboratorium Klinik Interna Berbasis Web ]]>
<![CDATA[Awanda Octavianti Putri]]>;
<![CDATA[Eka Prasetyaningrum]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i4.3236
<![CDATA[Inventory of products or goods in the form of medicines and others is one of the work activities. In procuring products, in this case drugs, the selection of drug suppliers is very important for a drugstore business, pharmacy or regional hospital. Interna Pharmacy & Clinical Laboratory is a private pharmacy located in Sampit, East Kotawaringin, Central Kalimantan. The problem that exists at the Internal Pharmacy & Clinical Laboratory is the selection of suppliers by comparing several suppliers or only based on the thoughts of the owner, pharmacist or pharmacist assistant. Suppliers are selected based on price, number of discounts given, speed of delivery of goods, completeness of drugs and drug packaging. Selection of suppliers with a system that has been running with various criteria resulted in the selection of suppliers is not accurate and takes time. Based on the problems above, it is very necessary to have a decision support system website using the Analytical Hierarchy Process method. By using a decision support system with the Analytical Hierarchy Process method, the Pharmacy & Internal Clinical Laboratory can make an assessment by comparing each criterion. The output of this research is a decision support system using the Analytical Hierarchy Process method. This study aims to make it easier to select drug suppliers from various criteria, save time in selecting drug suppliers, get good suppliers for the procurement of the necessary drugs]]>
<![CDATA[Mobile E-Learning Mata Pelajaran Natural Sciences (IPA) Berbasis User Centered Design ]]>
<![CDATA[Olha Musa]]>;
<![CDATA[Abdul Malik I Buna]]>;
<![CDATA[Yulanda Yunus]]>;
<![CDATA[Zainudin Sidik]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i4.3126
<![CDATA[In this study, designing a system based on mobile e-learning which is a necessity for formal educational institutions in the era of globalization which is increasingly inevitable. Systematically implement a program of guidance, teaching, based on observations and interviews with one of the science subject teachers at MTs Al-Khairaat Kwandang that the learning process, especially about the excretory system, is still explanatory without any teaching aids where students can see the organs included in the excretory system. . The purpose of this study is to create a mobile e-learning system about science subjects for class VIII 3 at MTs Al-Khairaat Kwandang that is easy to use by students and teachers. The method used is the UCD method. This application is designed using PHP and HTML programming language software, namely Sublime, Java Programming Language Using Android Studio, and modeling tools using UML (Unified Modeling Language). The results of this system research use whitebox and blackbox where with this test the correct graph flow is obtained as a sample, the researchers conduct tests on the flowchart in the evaluation. The conclusion obtained: Region (R) = 4 Independent Paths (Independent path) = 4 Cyclometic Complexity (CC) = 4 provided that this research can be implemented]]>
<![CDATA[Conversational Recommender System for Impromptu Tourists to Recommend Tourist Routes Using Haversine Formula ]]>
<![CDATA[Liviandra, Monica]]>;
<![CDATA[Baizal, Z K Abdurahman]]>;
<![CDATA[Dharayani, Ramanti]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i4.3229
<![CDATA[In this paper, we use two terms to describe tourists, i.e. planned tourists and impromptu tourists. Planned tourists are tourists who intentionally travel. Meanwhile, impromptu tourists are those who accidentally become tourists because they are in a new area for an activity. Previously, tourists who were going to travel usually relied on the services of travel agents to get recommendations for tourist attractions, different from impromptu tourists this was not done before. Impromptu tourists sometimes do not have much time to carry out tourism activities so that impromptu tourists only visit the closest tourist attractions from their location. Lack of experience in a new area and only relying on information on the internet makes it difficult for tourists to find tourist attractions based on their preferences. One solution to this problem is that a system is needed that can recommend tourist attractions in terms of distance by considering tourist preferences. In this study, we developed a conversational recommender system (CRS) to obtain user preferences. For the method we use the Haversine Formula to calculate the distance. The results of this study are a web application that recommends tourist attractions and routes to several tourist attractions, which can be done at one time. Based on the evaluation of the time complexity in the route search, linear complexity is obtained which shows good performance with optimal conditions.]]>
<![CDATA[Klasifikasi Varietas Buah Kiwi dengan Metode Convolutional Neural Networks Menggunakan Keras ]]>
<![CDATA[Aldi Jakaria]]>;
<![CDATA[Sofiyatul Mu’minah]]>;
<![CDATA[Dwiza Riana]]>;
<![CDATA[Sri Hadianti]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i4.3166
<![CDATA[Kiwi fruit is known as a fruit rich in benefits because it contains many nutrients, sources as well as high antioxidants. In Indonesia, there are two varieties of kiwi fruit sold in the market, namely green kiwi and golden kiwi and there is one more variety, namely red kiwi. The content of the three varieties is different and the price is also different. Gold kiwi has the highest nutritional content so that the price is above other kiwi varieties, but from the outside the appearance of this kiwi fruit at a glance is the same and many people do not recognize the kiwi variety they will buy even though these three kiwi varieties have different tastes and nutritional content. For this reason, the researcher proposes a classification system for kiwi fruit varieties using the hard CNN method. The CNN method is one of the deep learning methods that can be used to recognize and classify an object in a digital image. Then the preprocessing process is carried out using labeling on the data. Then the CNN architecture is designed with Input containing 320x258x3 neurons. The data was then trained using 25 epochs with an accuracy rate of 0.98. Then the test data using test data get an average accuracy value of 0.987, while for precision and recall it is also the same at 0.987]]>
<![CDATA[Pembobotan Kriteria Dalam Prediksi Meningitis Tuberkulosis Menggunakan Metode SWARA dan Nearest Neighbor ]]>
<![CDATA[Dwika Assrani]]>;
<![CDATA[Pahala Sirait]]>;
<![CDATA[Andri Andri]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/mib.v5i4.3276
<![CDATA[Weights greatly affect the value and results of decisions or predictions of a test data, a problem that often occurs in the results of the prediction process is the weighting of symptom attributes which is less certain of the value of the weight, thus affecting the prediction results and the level of accuracy of a prediction itself. This study predicts a data using the Nearest Neighbor method where in the process of predicting the attribute weight value does not yet have a definite value for testing. Then we need an attribute weighting for each test attribute to get a definite weight value result. One method that can be applied to attribute weighting is the SWARA method. Based on research conducted to compare the prediction of Meningitis Tuberculosis without SWARA weighting and with SWARA weighting, testing with a ratio of 90:10, 80:20, 70:30 results in disease prediction using the Nearest Neighbor method, there are differences in results and levels of prediction accuracy and the process in prediction helps shorten the time to find prediction results, the highest prediction result using the swara method is 100% accurate and without weighting method is 91%.]]>
