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Information system model for maize research to anticipate the impact of climate change on Indonesian food security

AIP Conference Proceedings 2023年第1期2594卷

作者： A. S. Perbangsa F. A. Munawwar M. B. Pabendon B. Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Indonesia Cereals Research Institute Maros South Sulawesi 90514 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

As a rich country in genetic resources, Indonesia has the potential for strengthening food security. Maize is one of the commodities that greatly affect food security in Indonesia. Maize is also widely used as animal feed. The high demand for maize requires efforts to increase the productivity of sustainable cultivation. Various research efforts that have been carried out require the support of a comprehensive information system. This study aimed to build a research model for maize genetic resource information systems that can help search for superior varieties. The data collection method used was collecting genotype, phenotype, climate data and interviewing the maize breeders or researchers. As for the design of the model, this study used the Object-Oriented Analysis & Design (OOAD) method with notation from Unified Modelling Language (UML). The result of this research was a web-based model of information systems for maize research.

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Detecting Hemileia vastatrix using Vision AI as Supporting to Food Security for Smallholder Coffee Commodities

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Teddy Suparyanto Erick Firmansyah Tjeng Wawan Cenggoro Digdo Sudigyo Bens Pardamean Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia 55282 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 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 Jakarta Indonesia 11480

The loss of food crops during the COVID-19 pandemic threatens food security in Indonesia as one of the world's top coffee producers. This affects the insecurity of coffee commodities which is influenced by several factors other than the pandemic such as pests, plant diseases, and extreme weather. Plant diseases, such as leaf rust, are a significant factor in the insecurity problem in coffee commodities. The fungus Hemileia vastatrix B et Br causes leaf rust disease, which is a pest that frequently damages coffee plants. This disease not only interferes with plant growth but also causes a decrease in coffee quality and quantity. This initial research aims to carry out early prevention of these diseases as supporting to food security in smallholder coffee commodities. An AI-based visual detection application is the result of this research. We collected 100 images of coffee leaves from various coffee plants. The image is reshaped to 256 x 256 pixels and randomly trimmed to 224 x 224 pixels to fulfill the size requirements of a standard Deep Learning technique. Each image was classified into two classes by a plant pest and disease specialist. The dataset was divided into training, validation, and testing series with a ratio of 60:20:20 for training procedures. The Convolutional Neural Network (CNN) research method used a variation of the ResNet CNN model with 18 layers. The best model validation was 59%.

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Improving Rice Productivity in Indonesia with Artificial Intelligence 7

Improving Rice Productivity in Indonesia with Artificial Int...

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7th International Conference on Cyber and IT Service Management, CITSM 2019

作者： Liundi, Nicholas Darma, Aditya Wirya Gunarso, Rivaldi Warnars, Harco Leslie Hendric Spits School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728129099

Indonesia is one of the biggest agriculture countries in the world with one of its major commodities, rice. Despite its vast paddy field and tropical resources, Indonesia still has not achieved food security. This paper explores ideas about how to present artificial intelligence that may increase rice productivity in Indonesia. Current situations of rice cultivation are mentioned start from existing technologies and systems used in Indonesia. Some artificial intelligence concepts are also introduced. Artificial Intelligence use in rice farming is then analyzed which ideas may be applicable to increase efficiency further. Those that are already implemented in rice farming include diseases and pest detection, prediction and estimation, and automated intelligent systems. In the proposed idea section, ideas to increase production are explored. Using advanced technologies may not be suitable for Indonesia's rice farmers. One argument is to minimize the differences in rice yield in different areas or so what we called yield gap. Factors such as weather, water, and harvest date are those with most impacts on the yield gap. It is beneficial to have a model that can predict the optimal planting date of rice since that will maximize the factors before. Among those implemented, a planting calendar prediction on rice-based on rainfall is already developed. However, rainfall is not the only factor. El-Nino occurrences and temperature changes also have a role in deciding when best to plant rice. The author suggests adding those two new variables, making three in total to the earlier neural network model may improve the overall result. © 2019 IEEE.

关键词： Productivity

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A Strategy using Social Media to Increase Students Engagement in E-Learning for the Equivalency Education program (E-LEEP) 3

A Strategy using Social Media to Increase Students Engagemen...

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3rd International Conference on Computing and Applied Informatics 2018, ICCAI 2018

作者： Betty Yel, Mesra Sfenrianto, Sfenrianto Wang, Gunawan Fajar, Ahmad Nurul Faculty of Computer Science STIKOM CKI Jakarta Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia

Social media can be used to support learning activities in non-formal education. The utilization of social media by students can be seen as a strategy to improve the students' engagement in e-Learning. It is an important goal for the e-Learning of non-formal education. This study aims to propose a strategy using social media to increase students engagement in E-Learning for the Equivalency Education program (SosMed E-LEEP strategy). This strategy has several key components. These components are students, social media, E-LEEP, and Students Engagement. SosMed E-LEEP Strategy consists of sharing activities, getting information, discussing, and interacting with students and Tutor, and for learning purpose. Each of these strategic activities is expected to improve students engagement, namely: access to learning materials, forum discussion activities, and assignment uploading. © 2019 Published under licence by IOP Publishing Ltd.

关键词： E-learning

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Ten-Year Compilation of #SaveKPK Twitter Dataset

Ten-Year Compilation of #SaveKPK Twitter Dataset

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International Conference on Information Management and Technology (ICIMTech)

作者： Reza Rahutomo Arif Budiarto Kartika Purwandari Anzaludin Samsinga Perbangsa Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia School of Computer Science Bina Nusantara University Jakarta Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

Politic is one of the most favorite topics to discuss in social media for people in Indonesia. It was proven when a people movement to support the Commission of Corruption Eradication (KPK) named #SaveKPK has been enlived for ten years and become a trending topic on Twitter for several times. In this research, all tweets contain `#SaveKPK' are crawled and compiled using an alternative algorithm to get twitter historical data instead of using Twitter API. The result described the characteristic of the dataset statistically, from the most frequently used words to the most active users. A clustering algorithm named Latent Dirichlet Allocation (LDA) was run over the gathered text dataset to discover most relevant keywords using unsupervised learning approach.

关键词： Twitter computer science Resource management Natural language processing Information management Databases

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Learning Culture Framework in E-Learning for the Equivalency Education program (E-LEEP Culture) 3

Learning Culture Framework in E-Learning for the Equivalency...

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3rd International Conference on Computing and Applied Informatics 2018, ICCAI 2018

作者： Betty Yel, Mesra Sfenrianto, Sfenrianto Wang, Gunawan Faculty of Computer Science STIKOM CKI Jakarta Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia

Understanding learning culture based on learning activities is important for learning success in an e-Learning. As an effort that can be done for this purpose, it is necessary to build a student learning culture based on students activities. The study proposes a Cultural Framework for Learning in an E-Learning. It is called E-LEEP Culture because used in an e-Learning the Equality Education program (E-LEEP). This framework has five key components, namely: Students, E-LEEP, Learning Activities, Learning Culture, and Digital Contents. Learning culture component in E-LEEP Culture Framework consists of sharing activities, getting information, discussingIndividualistic Learning, Collaborative Learning, Learning Time (learn a little), and Learning Time (learn a lot). Each of the components is expected as a basis for providing learning material that suits the needs of students. © 2019 Published under licence by IOP Publishing Ltd.

关键词： E-learning

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Big Data Governance for Building A Smart Cities 10

Big Data Governance for Building A Smart Cities

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10th International Conference on ICT for Smart Society, ICISS 2019

作者： Zulkarnain, Novan Kosala, Raymond Ranti, Benny Supangkat, Suhono Harso Bina Nusantara University Computer Science Department BINUS Graduate Program Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia Universitas Indonesia Computer Science Department Depok16424 Indonesia Institute of Technology Bandung School of Electrical and Informatics Engineering Bandung40132 Indonesia

ISBN: (纸本)9781728148809

There have been many studies conducted related to Smart City, IT Governance and Big Data. In this study aims to find out how the relationship between the three and how to form a framework to explain it. The methodology used is qualitative by looking for some literature on smart city framework, IT Governance framework, and a Big Data framework. From these results, an overall picture of the relationship between the three is concluded, where Big Data has a role in IT Governance. and also the relationship of IT Governance to the realization of Smart City. And the final results of this study produce a framework to explain the relationship between Smart City, IT Governance and Big Data. © 2019 IEEE.

关键词： Big data

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Real-time Weed Identification Using Machine Learning and Image Processing in Oil Palm Plantations

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Erick Firmansyah Teddy Suparyanto Alam Ahmad Hidayat Bens Pardamean Agricultural Engineering Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The effectiveness and efficiency of the operation of oil palm plantations are considered to be the most crucial factor to develop the productivity and profitability of the palm oil business. One of the major obstacles for the plants to optimally produce crops based on their capacity is caused by the presence of noxious weeds in the plantation area. However, weed control via chemical processes may potentially harm the surrounding environment if it is not properly managed. Therefore, an automatic system to assist the farmers to identify and control the weeds is required to minimize harmful impacts on the environment. Machine Learning (ML) and Artificial Intelligence (AI)-based systems provide powerful tools to perform such tasks. In this work, we aim for an ML-based system design to perform an automatic weed recognition task. The methodology can provide an effort for environmental sustainability in oil palm plantations. The weed identification involves the description, the local names, and tolerance class of the weeds as well as suggestions to control them. The flow of this work consists of weed and herbicide data acquisition, data labeling, model configurations, and data training. Further, the proposed system can be adopted as an android-based application in mobile devices that can deploy the trained model to predict weed category in both real-time and non-real-time tasks.

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Smart IoT CPO Storage Tank

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Rengga Arnalis Renjani Hermantoro Prima Okta Adi Nugraha Kartika Purwandari Teddy Suparyanto Bens Pardamean Department of Agricultural Engineering Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Indonesia's crude palm oil (CPO) production from year to year continues to increase, at the end of 2020 it reached 17.35 million tons, up 3.6% from the previous year. Increasing production will result in more CPO stock and require good storage. The storage process that occurs is to maintain the temperature of the CPO so that the quality is not damaged. This temperature regulation is still done manually and raises the risk of work accidents. The purpose of this research is to create a temperature control system and automatic volume measurement that can be monitored from a smartphone. The manufacture of a control system used ESP8266 NodeMCu microcontroller, temperature sensor, proximity sensor, and 1000-Watt heater. programming used the Arduino IDE and C++. The result of this study was an IoT CPO Storage Tank design equipped with sensors and microcontrollers. The temperature was measured with the DS18B20 sensor had a data accuracy of 99.19% while the volume measured with the HC-SR04 sensor had an accuracy of 99.78%. Data obtained from the sensor could be seen through the Thingspeak application from a computer or smartphone.

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LC-MS analysis: Mini review frequently used open source softwares 6

LC-MS analysis: Mini review frequently used open source soft...

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6th International Conference on Information Technology, computer and Electrical Engineering, ICITACEE 2019

作者： Binanto, Iwan Warnars, Harco Leslie Hendric Spits Sianipar, Nesti Fronika Abbas, Bahtiar Saleh Sanata Dharma University Informatics Department Yogyakarta Indonesia Bina Nusantara University BINUS Graduate Program Doctor Computer Science Computer Science Department Jakarta11480 Indonesia Research Interest Group Biotechnology Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728126890

This paper provides information about open source softwares that most often used as a tool to analyze data generated from the Liquid chromatography-mass spectrometer (LC-MS) instrument and including a little discussion about how LC-MS works. LC-MS consists of Liquid Chromatography and Mass Spectrometer analytical instruments. This device extensively used in Metabolomics, because it provides more comprehensive information about the metabolites. It also shows the breadth of the diversity of chemical compounds in metabolites that make difficult and time-consuming to identification of metabolite's structures. This is an obstacle in efficient and accurate identification. So, many open source softwares developed to simplify and speed up the analysis and interpretation of LC-MS result. There are popular open source softwares. We compiling mini review of this open source softwares. The conclusion is open source softwares quite helpful in terms of data analysis and interpretation of compounds contained, but no one has provided a single interpretation, still need experts for reliable interpretation. © 2019 IEEE.

关键词： Liquid chromatography

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