Machine learning is a method used to create programs that can learn from data or applications. In contrast to ordinary static computer programs, machine learning programs are programs designed to be able to learn on their own. Machine learning technology has been widely applied in various fields, including in chemistry. Currently the introduction of basic chemical objects is a study that has many concepts that are easy for beginners to understand and imagine, such as atomic structure and molecular shape in chemical bonds. So that an alternative learning media is needed in order to improve understanding of the concept of chemical objects interactively. Machine learning technology is expected to provide a more realistic interaction and is an advancement of a promising technological method that can motivate users to engage in a more active learning system

, Christopher Halim

Rostianingsih, Silvia

Setiawan, Alexander

English

Scientific Repository

IOP Conference Series: Materials Science and EngineeringPAPER • OPEN ACCESSDevelopment of Machine Learning Applications for Chemical ObjectsTo cite this article: A Setiawan et al 2020 IOP Conf. Ser.: Mater. Sci. Eng. 990 012001 View the article online for updates and enhancements.This content was downloaded from IP address 203.189.123.33 on 11/01/2021 at 02:37Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distributionof this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.Published under licence by IOP Publishing LtdThe 5th Engineering Science & Technology International ConferenceIOP Conf. Series: Materials Science and Engineering 990 (2020) 012001IOP Publishingdoi:10.1088/1757-899X/990/1/0120011Development of Machine Learning Applications for ChemicalObjectsASetiawan1, S Rostianingsih1, CI Halim11Informatics Department, Petra Christian University, Surabaya, IndonesiaE-mail: alexander@petra.ac.idAbstract. Machine learning is a method used to create programs that can learn from data orapplications. In contrast to ordinary static computer programs, machine learning programs areprograms designed to be able to learn on their own. Machine learning technology has beenwidely applied in various fields, including in chemistry. Currently the introduction of basicchemical objects is a study that has many concepts that are easy for beginners to understandand imagine, such as atomic structure and molecular shape in chemical bonds. So that analternative learning media is needed in order to improve understanding of the concept ofchemical objects interactively. Machine learning technology is expected to provide a morerealistic interaction and is an advancement of a promising technological method that canmotivate users to engage in a more active learning system.1. IntroductionThe development of a technology is getting faster and faster. Technology and the development ofmachine learning is one of the most frequently developing fields of science today. Without realizing it,almost everyone uses products produced by machine learning applications. The term in machinelearning is basically the computer's process of learning from data. All technology in machine learningwill inevitably involve data. The data can be the same, but an algorithmic concept and a differentapproach to get the optimal level of ability and results. Presenting chemical processes for students withvisualization is highly emphasized in chemistry education, because it can help students develop anunderstanding of chemical concepts to find the implications of using machine learning in chemistrylearning, which is to encourage active, organized, and able learners to integrate the informationobtained to understand the concepts and difficult principles and can be used to solve problems.Therefore, important steps that need to be taken are providing learning resources for students toovercome difficulties in understanding chemical concepts.One technique for applying machine learning is supervised learning. As discussed earlier, machinelearning without data will not work. Therefore, the first thing prepared is data. Data will usually bedivided into 2 groups, namely training data and testing data. The training data will be used to train thealgorithm to find a suitable model, while the testing data will be used to test and determine theperformance of the model obtained at the testing stage.Machine Learning is used to recognize a pattern as is done by our brains. Therefore, we need input,model, and output. When Machine Learning does a new job, it will see what work has previously beendone so that it can provide the correct output. Google Cloud Platform has developed tools for users to1 alexander@petra.ac.idThe 5th Engineering Science & Technology International ConferenceIOP Conf. Series: Materials Science and Engineering 990 (2020) 012001IOP Publishingdoi:10.1088/1757-899X/990/1/0120012develop Machine Learning in applications. Therefore, to continue previous research, namely 3DModelling of Chemical Elements with Augmented Reality [1], an introduction to chemical objects wasmade using machine learning. The system has stored a set of datasets containing chemical objects andtheir element names. When a new object is scanned with this application, it will be matched with theprevious dataset. If it turns out to be recognized it will appear the name of the chemical element. If notrecognized, a new dataset can be added based on the results of the object recognition training. Thedesired end result is to display the chemical element names for the recognized objects.2. Machine LearningIn machine learning, pattern recognition or feature extraction starts from the initial set of data andbuilds features that are meant to be informative, facilitating learning and subsequent generalization ofsteps, not superfluous. For some other cases, machine learning leads to better human interpretations.Several features are closely related to dimensional reduction. When inputting data into an algorithm,the size may be too large to be processed and may be overkill, the measurements are the same inmeters and feet. Image repetition is represented as pixels. These can be turned into a reduced featureset (known as a vector pattern). In this process, it is known as pattern extraction. It is hoped that theextracted pattern will contain a relevant information from the input data. Therefore, the desired resultis that it can be used for scaled down representations, not the initial complete data [2].All of this means it's possible to quickly and automatically generate models that can analyst larger,more complex data, and provide faster and more accurate results - even at very large scales. And bybuilding the right model, organizations stand a better chance of identifying profitable opportunities -or avoiding unknown risks. Lots of machine learning the researchers performed their work on single-purpose and general-purpose computers [3,4], and multiple single-machine frameworks supported thisscenario. Caffe [5] is a high performances framework for training that is described ina declarativemanner neural networks on multicore and general purposes computer purposes. As discussed above,the programming model is similar to DistBelief, so it is easy to model from existing layers, butrelatively difficult to add new layers or optimizers. Torch [6] offers a powerful imperativeprogramming model for scientific computation and machine learning. This allows fine grained controlover command execution and memory usage, allowing power users to optimize performance theirprogram. While this flexibility is beneficial for research, Torch does have the advantages of graphicaldata flow as a portable representation across small-scale experiments, production training, anddeployment [7].3. Research MethodologyThe research methodology of machine learning application is very important for the success.Therefore, machine learning applications at student must be designed and developed so that they canbe used. Testing machine learning must be done, and to received feedback from participants so that thelevel of application. Synthetic chemistry methodology refers to the methods used for the syntheticdataset objects can be seen in Figure 1.Figure 1. The synthetic datasets.The 5th Engineering Science & Technology International ConferenceIOP Conf. Series: Materials Science and Engineering 990 (2020) 012001IOP Publishingdoi:10.1088/1757-899X/990/1/01200134. Implementation SystemThe implementation can be used Jupyter Notebook, Tensorflow, Python 3.6, CUDA Toolkit 10.0 andCUDNN 7.4.1. For machine learning development, Google Cloud Platform provides a machinelearning API, while for image or voice recognition it uses TensorFlow. The programming languageused is Python. Machine learning API is used to detect images (Cloud Vision API) such as logos orphotos, video, natural language (text), voice, and translators. The five of them can be combined intoone. From a set of datasets, machine learning (training, deployment, server) is carried out, thenpredictions are generated using the REST API.The training dataset, you can use two ways, namely transfer learning or training from scratch.Transfer learning means updating existing data using and conducting training with your own data.While training from scratch is doing training from scratch with only your own data. After the trainingis complete, two hooks are tested: Testing of synthetic images / combined images, and testing withreal images / real photos. Displays several sample images to confirm whether the neural network canread the image correctly can be seen Figure 2.Figure 2. The testing of synthetic dataset.Training Process:Training: 4 Epochs, each 1000x cycles (total 4000 cycles)Fine-tuning: 4 Epochs, each 1000x cycles (total 4000 cycles)Total 8 epochs or 8000 cycle7.The prototype that uses Google Machine Learning as the basis of this research, keep in mind thatthis neural network is being trained using a synthetic dataset, and according to result The Synthetic ofBurner and Spektrofotometer can be seen Figure 3, The Synthetic of Weight Balance can be seenFigure 4, The Synthetic of Bulb Pipette can be seen Figure 5, and The Real Synthetic of WeightBalance can be seen Figure 6, when tested to recognize real photos, the results are quite satisfying,namely being able to recognize some of the objects/objects correctly.The 5th Engineering Science & Technology International ConferenceIOP Conf. Series: Materials Science and Engineering 990 (2020) 012001IOP Publishingdoi:10.1088/1757-899X/990/1/0120014Figure 3. The synthetic of burner andspektrofotometer.Figure 4. The synthetic of weight balance.Figure 5. The synthetic of bulb pipette. Figure 6. The real synthetic of weight balance.5. ConclusionThe machine learning applications for chemical object have the problem is that most of the labequipment is made of glass / transparent, such as: erlenmeyer flask, florence flask. It is more difficultfor the neural network to recognize correctly, because the atmosphere / colour behind the glass objectsaffects the performance of the training data. The manual annotation process requires a lot of labour(human labour) and takes a long time to get meaningful results. Because machine learning requires atleast thousands of unique images. Using synthetic datasets can speed up the creation of datasets, whentraining, can produce results that are not bad.AcknowledgmentsThis Research Project was funded by a Internal Research Grant, received in 2020. We thank theCentre of Research Petra Christian University for the supports and guidance.References[1] Setiawan A, Rostianingsih S and Widodo T R 2018MATEC Web Conf. 24805007The 5th Engineering Science & Technology International ConferenceIOP Conf. Series: Materials Science and Engineering 990 (2020) 012001IOP Publishingdoi:10.1088/1757-899X/990/1/0120015[2] Yadaf S K and Tiwari A K 2015Mach. Learn. Appl.2(3/4)30-49[3] Krizhevsky A 2014 One weird trick for parallelizing convolutional neural networksarXiv:1404.5997[4] Krizhevsky A, Sutskever I and Hinton G E 2012 Adv. Neural Inf. Process. Syst. 25(2)1097-1105[5] JiaY,Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S and Darrel T2014Proc. 22nd ACM International Conference on Multimedia 675-678[6] Collobert L, Bengio S and Marithoz J2002IDIAP Research Report[7] Abadi M et al. 2016Proc. 12th USENIX Symposium on Operating Systems DesignandImplementation 265-283.

Development of Machine Learning Applications for Chemical Objects

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Development of Machine Learning Applications for Chemical Objects

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