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

Procedia Computer Science 2018年 135卷 377-384页

作者： Rezzy Eko Caraka Shamarina Shohaimi Isma Dwi Kurniawan Riki Herliansyah Arif Budiarto Shinta Purnama Sari Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Department of Statistics Padjadjaran University Indonesia Department of Biology UIN Sunan Gunung Djati Bandung Department of Mathematics Kalimantan Institute of Technology (ITK) Kalimantan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

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.

关键词： Ecology GLLVM Negative Binomial Show cave Wild Cave

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

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Procedia Computer Science 2023年 216卷 57-66页

作者： Mira Hayati Kahlil Muchtar Roslidar Novi Maulina Irfan Syamsuddin Gregorius Natanael Elwirehardja Bens Pardamean Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh 23111 Indonesia Telematics Research Center (TRC) Universitas Syiah Kuala Banda Aceh 23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar 90245 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.

关键词： Diabetic retinopathy Deep Learning CNN Image Enhancement CLAHE

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Database Design for Pre-Eclampsia Case Administration

Database Design for Pre-Eclampsia Case Administration

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

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

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.

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Machine Learning Implementations in Childhood Stunting Research: A Systematic Literature Review

Machine Learning Implementations in Childhood Stunting Resea...

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International Conference on Information Management and Technology (ICIMTech)

作者： Reza Rahutomo Gregorius Natanael Elwirehardja Mahmud Isnan Faisal Asadi Bens Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University 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

Childhood stunting is a condition anticipated to affect the growth potential of children under the age of five. With numerous stunting researches that have been conducted, stunting datasets are now widely available to facilitate stunting research. This provides an opportunity to implement machine learning (ML) principles to produce a broader insight or a novel technique in stunting prediction. A systematic literature review is necessary to discover the landscape of machine learning implementation in the application domain as a preliminary study for creating an effective research roadmap. This paper presents a systematic literature review (SLR) of 22 curated manuscripts that focuses on identifying the ML models applied in stunting research, as well as the datasets used in such studies that were published during 2017–2022. The SLR process found that ML principles have been applied in stunting research since 2017, and the diversity of ML implementation has become more varied in 2021–2022. In terms of ML models, XGBoost and Random Forest are recognized as the two most utilized models, and stunting prediction is the most common ML implementation. The majority of stunting research utilizing ML has been conducted in Indonesia. Although national survey data has been the most commonly utilized dataset in stunting research, researchers in Indonesia have shown a preference for utilizing data from regional or independent surveys. This study will be followed by developing a classifier model for stunted children using XGBoost and Random Forest algorithms. The model will be trained on a dataset generated from StuntingDB.

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IoT for Water Quality Categorization

IoT for Water Quality Categorization

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Hermantoro Sastrohartono Andreas Wahyu Krisdiarto Arief Ika Uktoro Reza Rahutomo Teddy Suparyanto Bens Pardamean Agricultural Engineering Department Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results.

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Confidentiality. Integrity, and Availability (CIA) Approach for Private Dataset Management System Design

Confidentiality. Integrity, and Availability (CIA) Approach ...

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Mahmud Isnan Gregorius Natanael Elwirehardja Dedy Ariansyah Reza Rahutomo Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Dataset management systems are essential for assisting research and development (R&D) organizations in applying data governance protocols, especially in managing the utilization of datasets. In R&D, datasets are vital sources for producing analysis, machine learning prediction, and supporting decision-making. As such, it is important to deploy dataset management systems with considerations regarding the aspects of Confidentiality, Integrity, and Availability (CIA) which is a design principle that emphasized information protection and collaboration facilitation among dataset users. The study proposed an analysis and a design for a dataset management system that takes CIA aspects into account. In the future, the proposed design will be utilized as a benchmark for building a dataset management system. In this study, the Waterfall method was adopted. A use case diagram, an ERD (Entity Relationship Diagram), and an activity diagram were constructed in the design stage. In the project implementation stage, the Laravel framework including Laravel Jetstream was considered to be used.

关键词： Protocols Data security Decision making Organizations Machine learning Encryption Protection Information technology Research and development System analysis and design

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FAR-RED INSENSITIVE 219 and phytochrome B corepress shade avoidance via modulating nuclear speckle formation

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Plant physiology 2023年第2期192卷 1449-1465页

作者： Kai-Chun Peng Wei Siao Hsu-Liang Hsieh Institute of Plant Biology College of Life Science National Taiwan University Taipei 106 Taiwan. Department of Plant Biotechnology and Bioinformatics Ghent University 9052 Ghent Belgium. Center for Plant Systems Biology VIB 9052 Ghent Belgium. Department of Life Science College of Life Science National Taiwan University Taipei 106 Taiwan. Master Program in Global Agriculture Technology and Genomic Science National Taiwan University Taipei 106 Taiwan.

Plants can sense the shade from neighboring plants by detecting a reduction of the red:far-red light (R:FR) ratio. Phytochrome B (phyB) is the primary photoreceptor that perceives shade light and regulates jasmonic acid (JA) signaling. However, the molecular mechanisms underlying phyB and JA signaling integration in shade responses remain largely unknown. Here, we show the interaction of phyB and FAR-RED INSENSITIVE 219 (FIN219)/JASMONATE RESISTANT1 (JAR1) in a functional demand manner in Arabidopsis (Arabidopsis thaliana) seedling development. Genetic evidence and interaction studies indicated that phyB and FIN219 synergistically and negatively regulate shade-induced hypocotyl elongation. Moreover, phyB interacted with various isoforms of FIN219 under high and low R:FR light. Methyl jasmonate (MeJA) treatment, FIN219 mutation, and PHYBOE digalactosyldiacylglycerol synthase1-1 (dgd1-1) plants, which show increased levels of JA, altered the patterns of phyB-associated nuclear speckles under the same conditions. Surprisingly, PHYBOE dgd1-1 showed a shorter hypocotyl phenotype than its parental mutants under shade conditions. Microarray assays using PHYBOE and PHYBOE fin219-2 indicated that PHYB overexpression substantially affects defense response-related genes under shade light and coregulates expression of auxin-responsive genes with FIN219. Thus, our findings reveal that phyB substantially crosstalks with JA signaling through FIN219 to modulate seedling development under shade light.

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Ramadhan short-term electric load: A hybrid model of cycle spinning wavelet and group method data handling (CSW-GMDH)

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2019年第4期46卷 670页

作者： Caraka, Rezzy Eko Chen, Rung Ching Toharudin, Toni Pardamean, Bens Bakar, Sakhinah Abu Yasin, Hasbi College of Informatics Chaoyang University of Technology Taichung City41349 Taiwan Department of Statistics Padjadjaran University Bandung Indonesia College of Informatics Chaoyang University of Technology Taichung City41349 Indonesia Bioinformatics Data Science Research Center Bina Nusantara University Indonesia BINUS Graduate Program-Master of Computer Science Bina Nusantara University. Indonesia School of Mathematical Sciences FST The National University of Malaysia Malaysia Department of Statistics Diponegoro University Semarang Indonesia

In general, performing a nonlinearity time series analysis in the modeling of data can reach a robust and increase the quality of the results. Wavelet methods have successfully been applied in a great variety of applications for modeling also forecasting. Wavelet Transform divided into two categories. There is continuous wavelet (CWT) and a discrete wavelet transform (DWT). Cycle spinning unlike the discrete wavelet transform (DWT), is highly redundant, non-orthogonal, also defined naturally for all sample sizes. There is a Group Method of Data Handling (GMDH) algorithm, which is a multivariate analysis method can be used in modeling and identifying uncertainty on linear also nonlinearity systems. In this paper, we aim to explain the combination of A-Trous wavelet transforms applied on cycle spinning and group method of data handling (GMDH) in data of short-term electric load holy month of Ramadhan from 2014 to 2015. © 2019, International Association of Engineers.

关键词： Discrete wavelet transforms

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Latent Regression and Ordination Risk of Infectious Disease and Climate

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Procedia Computer Science 2021年 179卷 25-32页

作者： Rezzy Eko Caraka Rung Ching Chen Youngjo Lee Prana Ugiana Gio Arif Budiarto Bens Pardamean College of Informatics Chaoyang University of Technology 168 Jifong East Road Wufong Dist. Taichung City 41349 Taiwan (R.O.C.). Department of Statistics College of Natural Science Seoul National University Gwanak-gu Seoul 08826 Korea Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan 20222 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Indonesia.

Global warming arising from climate change can increase the spread of deadly diseases. Effort is needed to develop a set of policies for the government to stem or reduce health risks from global warming. The purpose of this paper is to examine more detail and comprehensively about the relationship among climate and event disease count in Taiwan using the partial least square latent regression model. The results obtained that of the 17 types of diseases in Taiwan, that has the most significant loading factor is Amoebiasis, Malaria and Chikungunya. At the same time, climate variables that have the biggest most significant factor are Number day with max temp more than 30, Number day Temp more than 25, and Rainfall PH. Cronbach’s Alpha infectious disease 0.9696 and climate 0.2813. At the same time, the value of Dillon Goldstein’s rho infectious disease 0.974 and climate 0.6404, respectively.

关键词： Disease Counts Climate Latent PLS Regression

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Deep Learning with Greedy Layer-Wise Compound Scaling for Temperature and Humidity Prediction in Solar Dryer Dome

SSRN

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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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