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Integration of GIS for Mapping Distributed Online Learning

IOP Conference Series: Materials science and Engineering 2021年第1期1088卷

作者： Jenny Ohliati Arief Agus Sukmandhani Emny Harna Yossy Computer Science Department School of Computer Science Bina Nusantara University Indonesia

This study was carried out to overcome one of the main obstacles encountered, namely the lack of information on the location of student residences in private universities that provide online learning system. Quantum GIS can display the distribution of students in all regions of Indonesia. The method used in this study is the descriptive analysis method. The results of this study are presented in the form of a distribution map of online learning students across Indonesia, which shows information about student data based on a regional boundary map.

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Application of expert system for oil palm smallholder-owned plantations

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. Umami H. G. Mawandha D. D. Puruhito Purwadi T. Suparyanto D. Sudigyo B. Pardamean Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agricultural Economics Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agriculture 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 Program Bina Nusantara University Jakarta Indonesia 11480

Most of the oil palm smallholders operate independently. As a result of this lack of support and assistance, oil palm owned by independent smallholders has the lowest productivity compared to large private (corporation) plantations and large state plantations. In addition, smallholder-owned plantations often operate without paying attention to sustainability aspects. There is a need for an effective, efficient, and user-friendly mentoring tool for oil palm farmers that are accessed independently. The Android-based platform was developed by applying an expert system to support increased production of oil palm cultivation for smallholders. Several stages of the expert system implementation, including the identification of planters and land profiles, were carried out using the direct interview method. The expert system includes land preparation management, planting material selection, seeding, weeds control, pests and diseases, fertilization, harvesting and transport, plantation administration, chat platforms, and data scrapping from data providers. The type of expert system is a data-driven Decision Support System (DSS).

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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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Learning management system for oil palm smallholder-owned plantations

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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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An algorithm finding the smallest mean square error in the exponential smoothing method for forecasting time series data

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AIP Conference Proceedings 2024年第1期2982卷

作者： Alfian Muh. Kabil Djafar Nerru Pranuta Murnaka Sulistiawati Rinda Nariswari Samsul Arifin Mathematics Department Halu Oleo University Kendari 93232 Indonesia Mathematics Education Department STKIP Surya Tangerang 15115 Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

One of the measurements for evaluating time series forecasting performances is the mean square error (MSE). This paper proposes an algorithm to find the smallest MSE. To capture the four components of the time series data (namely, seasonal variation, trend variation, cyclical variation, and random variation), the exponential smoothing method was used. This method uses three factors for smoothing where the data are the smoothing factor, 0 < α < 1, the trend smoothing factor, 0 < γ < 1, and the seasonal change smoothing factor, 0 < β < 1. First, all possible combination values of smoothing factors will be generated to one decimal digit. After that, the smallest MSE of those combinations will be determined by using an algorithm and observing their convergence pattern.

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Door Security System for Home Monitoring Based on ESP32

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Procedia computer science 2019年 157卷 673-682页

作者： Andreas Cornelio Revelivan Aldawira Handhika Wiratama Putra Novita Hanafiah Surya Surjarwo Aswin Wibisurya Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Door plays an important role in home security. To secure the house, the occupants of the house will always have the door locked. However, sometimes the house occupants forget to lock the door due to hurry when leaving the house, or they may doubt whether they have locked the door or not. We propose an application called Door Security System which is based on Android using Internet of Things (IoT) technology to monitor the status of the door, controlling the door and increasing security in a house. MQTT cloud is utilized as the communication protocol between smartphone and door lock system. PIR sensor is implemented in the door lock to detect the movement near the door, while touch sensor is installed on the door handle to recognize the human hand. Should the door is opened by force, the alarm will ring and send notification to alert the house occupant on the existence of intruder in the house. The evaluation results show that motion detection sensor can detect movement accurately up to 1,6 meters ahead, and messages published between smartphone and door lock are encrypted properly so messages are safely sent.

关键词： Home Security Internet of Things Lock Door IoT Encrypted MQTT Cloud

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Mobile Application Development to Mitigate the Risk of Earthquake

Mobile Application Development to Mitigate the Risk of Earth...

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

作者： Yuwono, Adeliana Chrysler, Audrey Puteri, Triladias Warnars, Harco Leslie Hendric Spits Adnandi, Muhammad Arba Maulana, Ihsan Sasmoko Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Muhammadiyah University of Tangerang Faculty of Tourism and Creative Industries Tangerang15118 Indonesia Faculty of Humanities Bina Nusantara University Primary Teacher Education Department Jakarta11480 Indonesia

ISBN: (纸本)9781728126654

Indonesia is prone to natural disasters such as an earthquake. Many people are clueless regarding what to do when an earthquake occurs. An earthquake has caused a more devastating outcome due to it being able to lead to other hazards. The idea of developing a mobile application surfaced by reading and receiving inspiration from the previous research. Previous research has been implementing different kinds of ways to alert people regarding the upcoming earthquake. The development of a mobile application to provide a platform that will assist people with information on what to do in times of before, during, and after the earthquake. Therefore, people will have a source that they can turn to when seeking information about the steps to be done when an earthquake occurs. Besides, providing information apropos to earthquakes, the mobile application will send further alerts to the people that are in that specific area. Moreover, people will be able to be aware of the particular areas that they are in. The alerts sent will give information to the people so that they could evacuate to safety as soon as possible before the upcoming earthquake occurs. © 2019 IEEE.

关键词： Earthquakes

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A tabu search approach with embedded nurse preferences for solving nurse rostering problem

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International Journal for Simulation and Multidisciplinary Design Optimization 2020年第1期11卷

作者： Ramli, Razamin Ahmad, Siti Nurin Ima Abdul-Rahman, Syariza Wibowo, Antoni Department of Decision Science School of Quantitative Sciences Universiti Utara Malaysia Sintok Kedah06010 Malaysia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

This paper presents an intelligent tabu search (TS) approach for solving a complex real-world nurse rostering problem (NRP). Previous study has suggested that improvement on neighborhoods and smart intensification of a TS could produce faster and fitted solution. In order to enhance the TS, this paper introduces an improvement to the neighborhoods and explores on the neighborhoods exploitations of TS to solve the NRP. The methodology consists of two phases: Initialization and neighborhood. The semi-random initialization is employed for finding a good initial solution during the initialization phase which avoids the violation of hard constraints, while the neighborhood phase is established for further improving the solution quality with a special representation and innovative neighborhood generations within TS algorithm. The aim is to move sample points towards a high-quality solution while avoiding local optima by utilising a calculated force value. It is observed that the enhancement strategy could improve the solution quality of the constructed roster. It is concluded that the TS with enhancements approach is able to assign effective and efficient shift duties for the NRP especially when related with real-world working regulations and nurses preferences. © 2020 R. Ramli et al., published by EDP sciences.

关键词： Tabu search

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Optimum Nutrition Intake from Daily Dietary Recommendation for Indonesian Children using binary Particle Swarm Optimization Algorithm

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Procedia computer science 2019年 157卷 16-24页

作者： Fanny Lili Ayu Wulandhari Sani Muhamad Isa 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

Optimum nutrition intake in daily dietary habit has a significant role for children growth. Nevertheless, the mistakenness in the fulfillment of nutrition still concerned. It happens because an individual does not have much knowledge about the energy content of food and food combination to meet the nutrition requirement. The objectives of this research are to facilitate an individual to obtain the optimum nutrition intake from their daily dietary habit. This paper proposes a binary Particle Swarm Optimization (BPSO) algorithm to find the optimum combination of food portion and food option for an individual daily dietary habit. The food data is obtained from ’Tabel Komposisi Pangan Indonesia ’ book which contains more than 1600 kind of Indonesian food. The results show that BPSO provides an optimum nutrition intake accuracy of 99.14%. Moreover, the nutritionist is already validated that this experiment is succeed in recommending a variation of daily dietary habit that meet an optimum nutrition intake for an individual. Based on this result it can be conducted that BPSO can provide the better accuracy of optimum nutrition intake than Genetic Algorithm (GA), while GA can only provide an optimum nutrition intake accuracy of 97.87%.

关键词： Optimum nutrition Energy requirement Energy intake Dietary habit recommendation binary Particle Swarm Optimization

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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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