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Analysis of Acoustic Features in Gender Identification Model for English and Bahasa Indonesia Telephone Speeches

Procedia Computer Science 2019年 157卷 199-204页

作者： Muhamad Fitra Kacamarga Tjeng Wawan Cenggoro Arif Budiarto Reza Rahutomo Bens Pardamean Eureka.ai Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

One of the most interesting topics in auditory problem is determining gender of the speaker. In recent years, machine learning has gained significant attentions as a way to build a classifier from labeled data which also can be implemented to build a gender classifier. In this study we develop gender classifier using two different datasets with different languages, English and Bahasa Indonesia. Each data from both datasets is represented by 20 acoustic features. Multi Layer Perceptron (MLP) is used to build the classification model using all these features and trained only on English dataset. This model is evaluated in both dataset to get the performance matrices consist of accuracy, AUROC, precision and recall. Ultimately, using this model we also identify and compare important features from both dataset to see the different characteristics of English and Bahasa Indonesia speeches.

关键词： Gender Identification Acoustic Feature Speech Recognition

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Database Design for Pre-Eclampsia Case Administration 8

Database Design for Pre-Eclampsia Case Administration

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8th International Conference on Information Technology and Digital Applications, ICITDA 2023

作者： Asadi, Faisal Elwirehardja, Gregorius Natanael Trinugroho, Joko Pebrianto Rahutomo, Reza Pardamean, Bens School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350344691

The Ministry of Health of Indonesia has referred to pre-eclampsia as one of the most severe diseases affecting women. As an urgency, it is crucial to administrate pre-eclampsia cases for disease prevention as a long-term national healthcare strategy. Regarding health science, case data was significant in developing research and innovation. However, the main problem regarding pre-eclampsia case administration is data handling, recording, and management incompetence. Hence, this research proposed a conceptual design of a database for pre-eclampsia case administration. The proposed design covered conceptual, logical, and physical design. We elaborate the concept into three concepts of pre-eclampsia disease: pre-treatment, treatment, and post-treatment. This study proposed a solution to gain more data and study pre-eclampsia disease in Indonesia. © 2023 IEEE.

关键词： Medical informatics

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Ecological Show Cave and Wild Cave: Negative Binomial Gllvm's Arthropod Community Modelling 3

Ecological Show Cave and Wild Cave: Negative Binomial Gllvm'...

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3rd International Conference on Computer Science and Computational Intelligence, ICCSCI 2018

作者： Caraka, Rezzy Eko Shohaimi, Shamarina Kurniawan, Isma Dwi Herliansyah, Riki Budiarto, Arif Sari, Shinta Purnama Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia Department of Statistics Padjadjaran University Indonesia Department of Biology UIN Sunan Gunung Djati Bandung Indonesia Kalimantan Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Ecology is a branch of biology that studies the interaction and relationship between organisms and their environment. Abundance, distribution of organisms and patterns of biodiversity are great interests for many ecologists. One of interesting ecosystems to be studied is a cave. A cave has a typical environment character with a vulnerable ecosystem. Many caves in Indonesia, particularly in Gunungsewu karst area have been developed into tourist objects (show caves) and managed imprudently. Such cave management has potential to harm the environment and leads to ecosystem destruction. Arthropods are the most abundance fauna in cave that play critical roles in maintaining cave ecosystems equilibrium. In the heart of statistical ecology, we need to analyze the differences on Arthropods community and abiotic (climatic-edaphic) parameters among show caves and wild caves. Statistical techniques are needed for the extraction of such information. GLLVM is one method that is able to explain spatial-based information and is particularly suitable for ecology. In this paper, we use negative binomial models to see the differences on spatial patterns of predator and decomposer Arthropods, also characteristic of edaphic and climatic in each cave. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Caves

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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning 7

Impact of CLAHE-based image enhancement for diabetic retinop...

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7th International Conference on Computer Science and Computational Intelligence, CSCI 2022

作者： Hayati, Mira Muchtar, Kahlil Roslidar Maulina, Novi Syamsuddin, Irfan Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-Aided diagnosis of diabetic retinopathy. © 2022 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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Sleep Quality and Daily Activity Association Assessment From Wearable Device Data

Sleep Quality and Daily Activity Association Assessment From...

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International Conference on Information Management and Technology (ICIMTech)

作者： Bharuno Mahesworo Arif Budiarto Alam Ahmad Hidayat Haryono Soeparno Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia School of Computer Science Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

Having good sleep is important to maintain physical and mental health. However, many young adults suffer poor sleep due to work or academic demands. Even more, sleep is associated with stress which is many young adults experienced it. To solve this problem, many epidemiologic studies suggest that exercise may improve sleep quality. On the other hand, several studies show that daily activity has no association with sleep quality. In this study, we tried to find more evidence of whether the daily activity has an association with sleep quality or not. We used a commercially available wearable device to capture the subject's heart rate, step count and deep sleep duration for 7 months. Then we used linear regression, XGBoost, and Long Short-Term Memory to find an association between these variables. The results show that there was no association between these variables.

关键词： Sleep Heart rate Biomedical monitoring Computer science Predictive models bioinformatics Data science

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Smart Dome 4.0: Low-Cost, Independent, Automated Energy System for Agricultural Purposes enabled by Machine Learning 2

Smart Dome 4.0: Low-Cost, Independent, Automated Energy Syst...

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2021 2nd International Symposium on Automation, Information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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AI-Based Ripeness Grading for Oil Palm Fresh Fruit Bunch in Smart Crane Grabber 3

AI-Based Ripeness Grading for Oil Palm Fresh Fruit Bunch in ...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Harsawardana Rahutomo, Reza Mahesworo, Bharuno Cenggoro, Tjeng Wawan Budiarto, Arif Suparyanto, Teddy Surya Atmaja, Don Bosco Samoedro, Bayu Pardamean, Bens Institut Pertanian STIPER Yogyakarta55281 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mechatronics and AI Research and Development Center Biotechnic Innovation Yogyakarta55281 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Indonesia is one of the biggest palm oil exporters in the world. For Indonesia to stay competitive in thepalm oil industry, the harvesting and evacuating process in its oil palm plantation need to be optimized. This research introduces machinerycalled as Smart Crane Grabber. This machinery can be used for automatic harvesting and evacuation process of oil palm fresh fruit bunch. To enable automation, Smart Crane Grabber is equipped with an Artificial Intelligencesystem for automatic ripeness sorting. TheArtificial Intelligence system developed for Smart Crane Grabber achieves 71.34% accuracy by using only 400 images as preliminary training data. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Palm oil

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CCTV traffic congestion analysis at Pejompongan using case based reasoning 1

CCTV traffic congestion analysis at Pejompongan using case b...

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1st International Conference on Information and Communications Technology, ICOIACT 2018

作者： Surjandy Anindra, Firman Soeparno, Haryono Napitupulu, Togar Alam Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Departement BINUS Graduate Program - Master of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Departement of Informatics and Communication Universitas Nasional Jakarta12520 Indonesia Information Systems Department BINUS Graduate Program - Master of Information Systems Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538609545

Congestion in Jakarta is a chronic problem and is still being sought. The growing city of Jakarta is facing various international events, increasing the complexity of these congestion problems. This research is aimed to propose an alternative solution for congestion problem in Jakarta using Cased-Based Reasoning (CBR) approach on Pejompongan area. The CBR integrated model analyzes congestion at a given point in a particular hour to identify the same case. The Cased-Based Management (CBM) algorithm approach is used to capture transient problems and ultimately reduce case base cases for their contribution to long-term maintenance. Congestion data is obtained from CCTV cameras via website *** in some strategic areas. With that data, we use CBR's integrated model to adjust traffic time for traffic flow to be optimized so that congestion will decompose. The results of this study can be used by relevant government agencies and policymakers in Jakarta to formulate the best solution to solve the congestion problem in the Indonesian capital. Image analysis at nights and bad weather conditions become a limitation and also the integration with traffic lights control in this paper that can be a new topic for further research. © 2018 IEEE.

关键词： Traffic congestion

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Systematic Literature Review on Statistics and Machine Learning Predictive Models for Rice Phenotypes

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Procedia Computer Science 2023年 227卷 1054-1061页

作者： Nicholas Dominic Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Predicting the best-quality of rice phenotypes is the priority among agricultural researchers to fulfill worldwide food security. Trend development of predictive models from statistics to machine learning is the subject of this review. Gathered from the Google Scholar database, 14 appropriate papers (2016-2020) related to the rice phenotypes prediction were selected through title and abstract content filtering. The outputs show that Support Vector Machine, Multi-layer Perceptron, and regression are the most used models, while yield is the priority prediction point besides tiller, panicle, and 1000-grain weight of rice. However, finding the accurate predictor is invariably challenging due to distinct rice varieties in the world and high confounding factors. Thus, developing an advanced deep learning model that accommodates these needs is worth considering further.

关键词： rice phenotypes prediction statistics machine learning

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Parameter Optimization of Support Vector Regression Using Harris Hawks Optimization

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Procedia Computer Science 2021年 179卷 17-24页

作者： I Nyoman Setiawan Robert Kurniawan Budi Yuniarto Rezzy Eko Caraka Bens Pardamean Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Department of Information Management College of Informatics Chaoyang University of Technology Taiwan (ROC) Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia 11530 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia. 11530.

Support Vector Regression (SVR) is often used in forecasting. Adjustment of parameters in the SVR affects the results of forecasting. This study aims to analyze the SVR method that is optimized using Harris Hawks Optimization (HHO), hereinafter referred to as HHO-SVR. The HHO-SVR was evaluated using five benchmark datasets to determine the performance of this method. The HHO process is also compared based on the type of kernel and other metaheuristic algorithms. The results showed that the HHO-SVR has almost the same performance as other methods but is less efficient in terms of time. In addition, the type of kernel also affects the process and results.

关键词： SVR harris hawks optimization parameter optimization kernel forecasting

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