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Expert System Diagnosis Corn Pests And Diseases Using Certainty Factor Method

Journal of Physics: Conference Series 2019年第1期1230卷

作者： Sulindawaty Muhammad Zarlis Zakarias Situmorang Hengki Tamando Sihotang Doctoral Program Faculty of Computer Science and InformationTechnology Universitas Sumatera Utara Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia

The development of technology has been very significant, not only in the fields of information, industry, education, but in agriculture. Therefore technological sophistication is also utilized by corn farmers to obtain information about corn crops. Corn is included in plantation commodities in Indonesia. However, in corn cultivation there are various problems associated with the disease that is likely to attack the plant. Therefore, the authors designed the expert system of corn disease diagnosis using certainty method. This system will analyze based on symptoms - symptoms that are in the input by the user so that in obtaining a result of disease identification on corn in the name of the disease, the cause of the disease, the percentage value and how to handle it. This expert system is developed by using the visual basic application and Microsoft Access database with Certainty Factor method to measure the value of certainty of a hypothesis against a fact.

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids

Integrating Noise Classification and Speech Enhancement Mode...

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International SoC Design Conference, ISOCC

作者： Ying-Hsiu Hung Yen-Ching Chang Shin-Chi Lai Wen-Ho Juang Ming-Hwa Sheu Jeng-Dao Lee Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering National Formosa University Huwei Taiwan

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm.

关键词： Accuracy Noise Noise reduction Speech recognition Speech enhancement Hearing aids Classification algorithms Convolutional neural networks Human voice Older adults

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Analysis of Application of the SAW, WP and TOPSIS Methods in Decision Support System Determining Scholarship Recipients at University

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Journal of Physics: Conference Series 2021年第1期1830卷

作者： Murni Marbun Muhammad Zarlis Zulkifli Nasution Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Teknik Informatika STMIK Pelita Nusantara Medan Indonesia

This research is an analysis of the application of the SAW, WP and TOPSIS methods to the support system for decision making at university. The types of scholarships provided are PPA education scholarships given by the Kementerian Riset, Teknologi dan Pendidikan Tinggi. The research variables are the attributes or criteria specified by the Directorate General of Learning and Student Affairs as contained in the 2018 Academic Achievement Improvement (PPA) guidebook. Decision Support Systems apply the classic FMADM method SAW, WP and TOPSIS. The results of this study are the application of the both methods of SAW and WP produce the same ranking and TOPSIS giving different rankings.

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A model to optimize palm oil biomass supply chain by considering production traceability information

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AIP Conference Proceedings 2023年第1期2714卷

作者： Heru Pranoto Muhammad Zarlis Syahril Efendi Herman Mawengkang Doctoral Program of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia

Due to the global situation, it is necessary for an industrial company to integrate production scheduling and traceability planning, in a way to get more reliable stand and to make economics advantages. This paper presents biomass supply chain company which could produce a renewable energy. Traceability is a tracking requirement to check whether the quality of the product is satisfied. The supply chain of biomass planning problem aims to meet customer demand subject to traceability and other restrictions. The problem is modelled as a mixed integer linear program, and then it is solved using neighborhood search approach.

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A robust optimization approach for measuring efficiency in stochastic DEA

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AIP Conference Proceedings 2023年第1期2714卷

作者： Hengki Tamando Sihotang Syahril Efendi Muhammad Zarlis Herman Mawengkang Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara. FEBO Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara. FEBO Indonesia Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara. FEBO Indonesia

The classic data envelopment analysis (DEA) concept assumes that decision making unit (DMU) inputs and outputs can be clearly measured because they have definite values. However, there are times when there is uncertainty in a situation. This study looks at how to build a DEA model with input and output uncertainty. In the DEA model, the uncertainty situation is approached using a stochastic concept. Furthermore, the robust optimization method, which can solve the uncertainty problem, is introduced. As a result, at the conclusion of this study, a robust optimization model based on the Stochastic DEA concept is introduced.

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Framework for tackling stochastic DEA based on data-driven approach

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AIP Conference Proceedings 2023年第1期2714卷

作者： Hengki Tamando Sihotang Syahril Efendi Muhammad Zarlis Herman Mawengkang Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia

Data Envelopment Analysis (DEA) is a linear programming-based technique used to evaluate the relative efficiency of multi-input and multi-output decision-making units based on observed data by comparing one decision-making unit (DMU) with another DMU. The efficacy assessment process sometimes involves a stochastic approach due to the uncertainty inherent in many real life problems, so the DEA model discussed in this paper is the stochastic DEA (SDEA) model. Data-driven decision-making is based entirely on the data involved. This paper proposes a framework for the completion of SDEA based on a data-driven approach.

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SPEAKER ADAPTATION USING SPECTRAL INTERPOLATION FOR SPEECH RECOGNITION

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ELECTRONICS AND COMMUNICATIONS IN JAPAN PART III-FUNDAMENTAL ELECTRONIC SCIENCE 1994年第10期77卷 1-11页

作者： SHINODA, K ISO, KI WATANABE, T Information Technology Research Laboratories NEC Corporation Kawasaki Japan 216 Ken-ichi Iso received his B.S. and M.S. from the Department of Physics Faculty of Science University of Tokyo in 1983 and 1985 respectively. He retired from the Doctoral Program in 1986. He affiliated with NEC Corporation and is engaged in research and development of speech recognition. He was a Visiting Researcher in 1991–1992 at Carnegie-Mellon University Pittsburgh PA USA. Presently he is with the Inf. Technology Research Lab. and is a member of the Acoustic Society of Japan.

This paper proposes a technique for supervised speaker adaptation for speech recognition based on a continuous density hidden Markov model (HMM). When the number of utterances for the adaptation words is decreased in order to reduce the burden on the user, the ratio of demisyllables (recognition units) not appearing in the uttered words is increased. In the proposed method, the HMM parameters for the demisyllables contained in the adaptation data are corrected and then the HMM parameters for the demisyllables not contained in the adaptation data are corrected by interpolation in the parameter space (spectral interpolation). To avoid a biased estimation of the parameters that depend on the adaptation data set, a correction is made based on large-scale uttered data by a large number of speakers. A word recognition experiment, executed for 100 similar words, simulates a 5000-word large-vocabulary speech recognition;and the proposed method is then evaluated. When the recognition rate by the speaker-independent HMM reaches 81.2 percent, the recognition rate is improved up to 85.2 percent by using 50 words for adaptation. Thus, the effectiveness of the proposed method is verified.

关键词： SPEAKER ADAPTATION SPEAKER-INDEPENDENT RECOGNITION HMM SPECTRAL INTERPOLATION VOCABULARY DEPENDENCY

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Machine learning-based global air quality index development using remote sensing and ground-based stations

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Environmental Advances 2024年 15卷

作者： Anggraini, Tania Septi Irie, Hitoshi Sakti, Anjar Dimara Wikantika, Ketut Center for Environmental Remote Sensing Chiba University 1-33 Yayoicho Inage-Ku Chiba 263-8522 Japan Doctoral Program in Geodesy and Geomatics Engineering Faculty of Earth Science and Technology Institut Teknologi Bandung Bandung 40132 Indonesia Center for Remote Sensing Institut Teknologi Bandung Bandung 40132 Indonesia Remote Sensing and Geographic Information Sciences Research Group Faculty of Earth Sciences and Technology Institut Teknologi Bandung Bandung 40132 Indonesia

Air pollution refers to the presence of hazardous substances in the air that has adverse effects on health, causing millions premature deaths annually. Ground-based stations can provide accurate measurements for monitoring air pollution. However, the spatial coverage of air pollution measurements is limited by the number of measurement instruments available in specific hotspot areas. Satellite remote sensing can reduce spatial uncertainty;however, the measurement results are mostly limited to the upper atmosphere with high measurement sensitivity. To better represent surface conditions, this study aims to model the Air Quality Index for pollutants CO, NO₂, SO₂, PM_2.5, and PM₁₀ in the global region using remotely sensed data. To support this study, 425 data points from air pollution stations distributed globally are combined using Machine Learning Linear Regression methods. Furthermore, socioeconomic and environmental data are combined with air pollution satellite to form Multiple models. According to the results of this study, the Multiple Models are more accurate than the single models, showing that the addition of socioeconomic and environmental data can enhance accuracy. The results of this study are expected to help regions without air pollution monitoring stations to estimate the air quality index using satellite data, in turn, preventing air pollution disasters. © 2023 The Author(s)

关键词： Air pollution Air quality index Machine learning linear regression Monitoring stations Satellite

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Digital Dictionary Using Binary Search Algorithm

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Journal of Physics: Conference Series 2019年第1期1255卷

作者： Riski Muhamad Fitrian Insan Taufik Muhammad Sabir Ramadhan Neni Mulyani Jeperson Hutahaean Arjon Samuel Sitio Hengki Tamando Sihotang Teknik Informatika STMIK Pelita Nusantara Medan Sumatera Utara Indonesia Doctoral Program Vocational Technology Education Postgraduate Engineering Faculty Padang State University Padang West Sumatera Indonesia Sistem Informasi STMIK Royal Kisaran Sumatera Utara Indonesia Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan-Indonesia

Digital Dictionary of Computer and Network Engineering can be used in the learning process of computer and nerwork enginering students. Dictionaries are generally book-shaped is hard to carry because of their thick and heavy, now can be accessed anywhere by development of web technology. Digital dictionaries can be accessed through computers, laptops dan cellphones, this is make student easily in learning process. The digital dictionary can not only be used by students of computer and network engineering, but can also by general public, as long as you have an internet connection, anyone can be use this digital dictionaries. The main process in this digital dictionary is the search process. Binary search algorithm used in the search process of binary search algoritm. Binary search algoritm search is applied to word search in this digital dictionary, because this algoritm is intended for sequintal data. program language that will be used to build this digital dictionary is PHP and MySQL.

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ANN: Prediction of Per Capita Income Rural Community on Poverty Line Based on Province

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Journal of Physics: Conference Series 2019年第1期1255卷

作者： Solikhun Mochamad Wahyudi M. Safii Syahril Efendi Suci Ramadhani Yogi Yunefri Ahmad Zamsuri Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Sumatera Utara Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Universitas Pembangunan Panca Budi Medan North Sumatera Indonesia Universitas Lancang Kuning Pekanbaru Indonesia

The problem of poverty is a fundamental problem that is of concern to every country. The Indonesian state has established a poverty reduction program as the main program. Poverty occurs in urban and rural communities. This research raises the problem of poverty in rural communities. The contribution of research to the government is to predict the per capita income of rural communities according to the poverty line based on the future provinces. The data used is data from the National Statistics Agency. These data are 2015 semester 1 data up to 2018 semester 1. The algorithm for its completion uses the artificial neural network backpropagation method. Input data is 2015 data for the 1 to 2017 semester 2. The training and testing architecture model is 4, namely 6-2-1, 6-3-1, 6-2-3-1, and 6-3-2-1. Target data is 2018 semester 1 data. The best architectural model is 6-2-1 with 79 epoch, MSE 0,004801 and 100% accuracy rate. From this model, a prediction of rural income per capita in the poverty line is based on the provinces of each province in Indonesia.

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