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Learning management system for oil palm smallholder-owned plantations

AIP Conference Proceedings 2023年第1期2594卷

作者： E. Firmansyah D. Nurjannah S. I. Dinarti D. Sudigyo T. Suparyanto B. Pardamean Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agricultural Economics Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Departement of Agriculture Engineering Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The production gap between oil palm smallholder-owned plantations and corporations shows a real problem in the national palm oil industry. To reduce the gap, user-friendly assistance for smallholder-owned plantations is needed for the cultivation of sustainable palm oil production. Android-based software can be a platform for bridging the transfer of knowledge and technology, transfer of problems, and transfer of solutions between planters and experts. The platform development was formulated in five stages which began with Learning Management System (LMS) application development, identification of Good Agricultural Practices (GAP) and Best Management Practices (BMP) materials, development of digital contents, uploading the digital content to the LMS server, and dissemination. The curriculum in the application platform was prepared based on the needs of the growers according to the identification results of the farmers' profiles. The curriculum consists of GAP and BMP material blocks. Each block was divided into discussion topics. The digital contents related to GAP and BMP were set in the LMS, which was built using Moodle platform.

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Data Mining for Predicting Customer Satisfaction Using Clustering Techniques

Data Mining for Predicting Customer Satisfaction Using Clust...

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International Conference on Information Management and Technology (ICIMTech)

作者： Kartika Purwandari Join W. C. Sigalingging Muhammad Fhadli Shinta Nur Arizky Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia National Central University Taoyuan Taiwan BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

Managing customer satisfaction has become an important business trend, including restaurants business. This study aims to determine the application of the K-means, Spectral Clustering (SC), and Agglomerative Clustering (AC) method for measuring customer satisfaction on a family restaurant in Taiwan. We contribute the data collection process and application of data mining in a family restaurant. The clustering analysis based on agglomerative clustering approach performs as well as the K-means approach to cluster the same characteristics of the customers. At last, this study shows the measurement result of customer satisfaction and provides improvement suggestion to the restaurant concerned.

关键词： Data mining Customer satisfaction Principal component analysis computer science Reliability Data visualization

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

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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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Facial Recognition Development to Detect Corporate Employees Stress Level

Facial Recognition Development to Detect Corporate Employees...

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2019 IEEE International Conference on Engineering, Technology and Education, TALE 2019

作者： Rahman, Munif Faisol Abdul Vincent Giovanni, Vito Christian Warnars, Harco Leslie Hendric Spits Aryono, Gagah Dwiki Putra Megantoro, Bayu Sasmoko Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Smk Pgri of Serang city Serang42117 Indonesia Sman 2 of Bekasi city Bekasi17144 Indonesia Faculty of Humanities Bina Nusantara University Primary Teacher Education Department Jakarta11480 Indonesia

ISBN: (纸本)9781728126654

Technology has developed so well even to the point where machines can recognize our faces. There many APIs and algorithms out there that can recognize human faces. These APIs or algorithms is called Artificial Intelligence or AI. As modern civilian life in modern society, AI has been used so many times and developed into something more. This time, we proposed an idea to recognize motions and emotions on human faces to detect their stress levels. Companies nowadays need to have something that can know how their employees are feeling or think about their work. This program works by taking an image input from mounted cameras on the office's monitor. However, before that, the program must have been trained first to recognize faces and emotions. For this, the eigenfaces algorithm is used. After the input is received, then the program will process the image received and extract the features from the images such as eyes, nose, mouth, etc. to detect if there are faces and also the emotions of the face. After that, the features extracted will be matched with the features in the database to give an output of the percentage of stress. © 2019 IEEE.

关键词： Neural networks

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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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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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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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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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Mobile application to track people in covid19 monitoring and patients under covid19 supervision

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IOP Conference Series: Earth and Environmental science 2021年第1期729卷

作者： A Chrysler H L H S Warnars W H Utomo Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Department of Magister Science of Information Technology Faculty of Computing President University Bekasi Indonesia 17550

It is crucial for the community including the government and health workers to collaborate to halt the spread of Covid-19. The idea of developing the mobile application surfaced from the previous findings. Previous researches have implemented and developed different features to better the application. The development of a mobile application to provide a platform that will assist people with information regarding patients that are around the perimeter of users to help notify them. With the help of the notification, users will be able to avoid the chances of them being in contact with the people (ODP), patients that are under surveillance (PDP) confirmed patients. All patients including ODP and PDP are required to have the application therefore, the Minister of Health will be able to track the geolocation of patients. Moreover, people will be able to be aware of patients that are around their perimeter. Therefore, with the help of the application, it will be able to help assist the community for them to be aware of and to be able to avoid being in contact with the infected patients.

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