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Informatics Engineering, science & Technology (INCITEST), International Conference on

作者： Prasetyo Mimboro Benfano Soewito Haryono Soeparno Widodo Budiharto Computer Science Departement BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Departement School of Computer Science Bina Nusantara University Jakarta Indonesia

Artificial neural network (ANN)-based computer vision techniques are becoming increasingly popular for palm oil disease detection and classification. Deep learning models' capacity to automatically learn and extract relevant image features has enabled accurate and efficient detection and classification of palm oil diseases. In this research, research was conducted to test the deep learning method to predict the condition of oil palm plantations based on the visible atmospherically resistant index on the Unmanned Aerial Vehicle Image. Some diseases that can attack oil palm trees are root disease or oil palm root rot (blast disease), basal stem rot (ganoderma), bud rot (spear rot), yellow line disease (patch yellow). This study aims to predict the condition of oil palm trees based on the VARI so that the process of detecting the spread of disease in oil palm trees can be accelerated. In this study, the prediction model for the condition of oil palm trees using the ANN algorithm succeeded in predicting the condition of oil palm trees and provided satisfactory prediction results, namely an accuracy rate of 94.7% and a loss of 21.58%.

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Diffusion models applied to skin and oral cancer classification

arXiv

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arXiv 2025年

作者： Uliana, José J.M. Krohling, Renato A. Labcin - Nature-inspired Computing Lab Federal University of Espírito Santo Vitória Brazil Labcin - Nature-inspired Computing Lab Graduate Program in Computer Science PPGI UFES Federal University of Espírito Santo Vitória Brazil

This study investigates the application of diffusion models in medical image classification (DiffMIC), focusing on skin and oral lesions. Utilizing the datasets PAD-UFES-20 for skin cancer and P-NDB-UFES for oral cancer, the diffusion model demonstrated competitive performance compared to state-of-the-art deep learning models like Convolutional Neural Networks (CNNs) and Transformers. Specifically, for the PAD-UFES-20 dataset, the model achieved a balanced accuracy of 0.6457 for six-class classification and 0.8357 for binary classification (cancer vs. non-cancer). For the P-NDB-UFES dataset, it attained a balanced accuracy of 0.9050. These results suggest that diffusion models are viable models for classifying medical images of skin and oral lesions. In addition, we investigate the robustness of the model trained on PAD-UFES-20 for skin cancer but tested on the clinical images of the HIBA dataset. © 2025, CC BY.

关键词： Lung cancer

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Evaluation of Machine Learning on Smart Home Data for Prediction of Electrical Energy Consumption

Evaluation of Machine Learning on Smart Home Data for Predic...

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Mochammad Haldi Widianto Alexander Agung Santoso Gunawan Yaya Heryadi Widodo Budiharto Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Electrical energy consumption is always increasing, and this causes the supply of electrical energy to be increased to compensate. One solution is to predict electricity energy consumption using Artificial Intelligence (AI) technology in Smart Homes. Several studies' solutions for predicting electrical energy consumption usually focused only on performance but rarely evaluated Machine Learning (ML) by correlation for feature selection and utilized interpretability model. This study uses an ML model for predicting utilization (Linear Regression, Decision Tree, Random Forest, and XGBoost). Then, Feature Selection utilizes correlation to choose the best feature. After that, the interpretability model utilizes Local Interpretable Model-agnostic Explanations (LIME). The results show that XGBoost has the best Root Mean Squared Error (RMSE) value (0.318) with a percentage of the number of train and test data (90/10). After that, by eliminating features that correlate with 0.01, XGBoost improves with an increase of (0.018) to become (0.3). Then from LIME. This work also gets positive feature from XGBoost such as: "Furnance, Well dan Living Room".

关键词： Energy consumption Correlation Linear regression Smart homes Predictive models Feature extraction Data models

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Transfer Learning Method for Malaria Identification

Transfer Learning Method for Malaria Identification

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International Conference on Informatics and Computing (ICIC)

作者： Cecilia Audrey Herli Daniel Zerge Wijaya Hansen Michelle Angela Zakaria Berlam Pragusma Ajeng Wulandari Maria Susan Anggreainy Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

Malaria is a severe disease caused by parasites of the genus Plasmodium, which are transmitted to humans through the bite of an infected female Anopheles mosquito. Symptoms of malaria begin to appear at least within 10 to 15 days. If malaria is not treated immediately, it is feared that it will cause respiratory problems, shortness of breath, and death. To avoid the occurrence of these events, the idea arose to create an AI (Artificial Intelligence) project that can recognize the presence of malaria parasites in blood cells. Thus, the main objective of this project is to find out how to create a Machine Learning model that can efficiently identify malaria parasites in the human body. The AI project uses CNN (Convolutional Neural network) as an algorithm to recognize the presence or absence of parasites in blood cell images that will be inputted by the user. Process of implementing CNN, using VGG19 which is an advanced CNN that has pre-trained layers and a good understanding of describing an image, both the shape, color, and structure of the image. After implementing the Transfer Learning algorithm on the dataset, the result is a Transfer Learning algorithm that can detect the presence of Malaria parasites in blood cells with an accuracy rate of 92 percent a specificity of 95 percent, and a sensitivity of 89 percent. The accuracy can still increase depending on the diversity of the data provided. The more often we train and input test data as train data, the accuracy of AI will also increase.

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Analyzing Company’s Share Purchase Value Based on Financial Reports Using Artificial Neural Network (ANN)

Analyzing Company’s Share Purchase Value Based on Financial...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Jeremy Shawn Jonathan Gunawan Chen Zi Hao Maria Susan Anggreainy Afdhal Kurniawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

Because financial time series forecasting is sensitive to political, economic, and social factors, it is not a simple task. As a result, those who make investments in currency exchange and financial markets typically search for reliable models that can guarantee they will maximize their profile and minimize their losses. Fortunately, many studies have used a method from Artificial Neural Networks (ANNs) called Backpropagation, could improve the predictive accuracy of the behavior of the financial data over time. This paper aims to forecast stock share prediction from closing value of PT. Bank Central Asia Tbk, and PT. Bank Maybank Indonesia Tbk. The results show that the using Backpropagation gives the closest result. And for the rating of judgement for cast accuracy, it exceeded 10% accuracy, which means high accurate from the prediction. For further checking, comparing the results of research from Victor’s results, it almost hits the same accuracy percentage. Which means, these prediction are accurate enough to do time series forecasting.

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Biodiversity or ultra-processed food: an analysis of school meals offered in a riverside school in the Brazilian Amazon

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The Lancet. Planetary health 2024年 8卷 S14页

作者： Humbelino de Melo, Paula Regina Alves, Pericles Vale Souza de Camargo, Tatiana Graduate Program in Science Education Federal University of Rio Grande do Sul Porto Alegre Brazil Graduate Program in Environmental Sciences Federal University of São Carlos São Paulo Brazil

BACKGROUND: Rural schools in Amazonas, Brazil, often offer ultra-processed foods in school meals for students, which can lead to health problems and loss of regional food culture. We show an analysis of the menu offered in a riverside school in the Brazilian Amazon and the acceptability of students regarding the consumption of the food they are served with. METHODS: Data were collected in situ, in a riverside school in southern Amazonas, through the analysis of the school menu and the application of an investigative questionnaire to 37 students in the 9th grade of Junior High School. FINDINGS: The research revealed that the foods most consumed by students in school meals are canned beef, canned meatballs, canned sardines, sausage, biscuits, juice, rice porridge, corn porridge, pasta, meat soup, and rice with beans. In the questionnaire that was applied to students, there is a wide variation in the acceptability of the foods offered. However, 57% of students reported not liking the lunch offered at the educational institution. INTERPRETATION: To tackle this problem, it is essential that, local food culture and biodiversity food can be more valued, elements that are often excluded from school menus. This work showed that is also essential to fully adhere to the National School Meal program (PNAE) in Brazil, which recommends that at least 30% of food intended for school meals must come from family farming, highlighting that quality food is crucial for cognitive development of students. Therefore, the meals offered in the chosen riverside school not only do not meet the PNAE guidelines but are also not well accepted by students. This study shows a significant need to consider the direct relationship between planetary health, school meals food security, and food sovereignty, given the various negative effects of foods that are rich in fat, sodium, preservatives, and other substances. Furthermore, it is imperative to integrate food into the students' context, valuing regi

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ANALYSIS OF NITROGEN FLOW IN FOOD WASTE: A CASE STUDY OF THE university RESTAURANT-UNB 32nd

ANALYSIS OF NITROGEN FLOW IN FOOD WASTE: A CASE STUDY OF THE...

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32nd European Biomass Conference and Exhibition, EUBCE 2024

作者： Ramos, Arthur Augusto Frasson De Brites, Eneida Campos Felipe Soares, Raquel Moraes Silveira, Edgar A. Contreras, Francisco University of Brasilia Department of Civil and Environmental Engineering DF Brasilia70910-900 Brazil University of Brasilia Graduate Program on Environmental Technology and Water Resources Department of Civil and Environmental Engineering DF Brasilia70910-900 Brazil Federal Institute of Education Science and Technology of Brasília Estrutural Campus DF Brasília71250-000 Brazil University of Brasília Mechanical Sciences Graduate Program Laboratory of Energy and Environment DF Brasilia70910-900 Brazil

With the projected global population reaching 9.7 billion by 2050, the production and consumption of food continue to escalate. Nitrogen is pivotal in this landscape due to its significance in living organisms and the environmental repercussions associated with nutrient-rich waste. Inappropriate disposal of such residues contributes to nitrogen release into the environment, impacting aquatic ecosystems and precipitating the generation of greenhouse gases. This study tackles the global challenge of effectively managing nitrogen in food waste by utilizing Material Flow Analysis (MFA) as a tool to comprehend this cycle. The analysis uncovered that vegetables, legumes, and fruits constitute the primary sources of waste generation, while meats, despite their lower mass, account for a substantial proportion of total nitrogen depletion. In the surveyed month of October 2023, 174,834 meals were served, resulting in an average food consumption N-footprint of 0.003 kg of nitrogen discarded per individual meal within the restaurant’s organic waste. These findings indicate that 65% of nitrogen is consumed in meal form, while 35% is discarded as organic solid waste. However, 53% of the nitrogen in the residues originates from food preparation processes, with food preparation responsible for over half of this figure. A deeper process analysis reveals that vegetables have low nitrogen concentrations, although they significantly contribute to waste at all stages. In contrast, meats and eggs, with higher nitrogen concentrations, emerge as noteworthy contributors to the overall nitrogen content in waste. Vegetables and meats contribute about 50% and 45% of the total nitrogen, respectively. These outcomes substantially enhance our comprehension of waste generation dynamics and nutrient utilization at the university restaurant, assisting with waste management, design of sustainable menus, reduction of food waste, and optimized resource utilization, contributing to sustainability and re

关键词： Effluent treatment

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Objective Sleep Quality Measurement based on Fuzzy Logic and Wearable Device

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Procedia computer science 2023年 227卷 1153-1162页

作者： Jason A. Wijaya Pauw D. Andersen Nico Surantha Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Electrical Electronic and Communication Engineering Tokyo City University Japan

Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to restore itself and prepare for the next day. The standard method for evaluating sleep quality was subjective evaluation. Actigraphy devices, which can measure the sleep cycle, are now widely available. This study developed a method using Fuzzy Logic and an actigraphy device to measure and classify sleep quality. The fuzzy logic method was developed in several stages, which are determining the sleep quality measurement parameters, constructing the fuzzy set for each input variable, and developing the fuzzy rules. To evaluate the proposed fuzzy model, five individuals were invited to participate in the experiment and required to complete the PSQI subjective sleep questionnaire. The evaluation result shows that our proposed Fuzzy model achieves lower error compared to the existing method.

关键词： sleep sleep quality actigraphy artificial intelligences fuzzy logic

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Dagang Yuk Yuk As Smartphone Application for MSME Product Sales 2

Dagang Yuk Yuk As Smartphone Application for MSME Product Sa...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Erinda, Muhammad Fandi Warnars, Harco Leslie Hendric Spits Sumardin, Winters Arianto, Jimmy Vinarycha, Devi Wahyu Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara Univesity Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

Many MSMEs manually close their businesses in today's competition, especially those caused by the COVID19 pandemic, and besides that, the lack of technology implementation is even more aggravating. Like it or not, Micro, Small, and Medium Enterprises (MSMEs) must equip their businesses with the application of technology, and unfortunately, there are still many MSMEs that have not been able to take advantage of the benefits of technological developments. Many companies see this condition as an opportunity;for example, Gojek and Grab partner with MSME players to make it easier to trade their products or services. In this paper, a smartphone application for MSMEs called 'Dagang Yuk Yuk, ' which consists of activities such as Login, Create Order, Input Delivery Location, Select Courier, and Select Payment Method has been proposed. Dagang Yuk Yuk tries to give different ideas for implementing MSME actors where this application is only for MSME players as part of an MSME business unit. This paper discusses the application's design using use case diagrams to describe the business process, while class diagrams model the database design. This application was developed using Arduino studio and MySQL database for database storage. © 2022 IEEE.

关键词： Competition

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Data mining for epidemiology: The correlation of typhoid fever occurrence and environmental factors 7

Data mining for epidemiology: The correlation of typhoid fev...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Asadi, Faisal Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information System Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Typhoid fever is an endemic disease that burdens Indonesia and has a potentially fatal infection multisystem. Salmonella typhi bacterium is responsible for typhoid fever disease. Poor sanitation, crowding, and slums are the main factors of increasing typhoid fever incidences. Environmental factors directly connected to meteorological factors are the main factor in breeding the Salmonella typhi bacterium. This study aims to identify the correlation between meteorological parameters and typhoid fever disease occurrence. The study was carried out in Jakarta, Indonesia, and the Bureau of Meteorological, Climatology, and Geophysics (BMKG) provided the meteorological parameter data. In addition, the Jakarta health surveillance office provided information on typhoid fever hospitalizations from 2019 to 2021. Pearson's concept was utilized d to investigate the correlation between typhoid fever incidences and the meteorological parameters. Humidity, precipitation, and wind speed are the meteorological parameters that significantly affect in contribute to the occurrence of typhoid fever disease. These findings might be used as a reference for Indonesia's government in making public policy to prevent typhoid fever in Indonesia. © 2022 Elsevier B.V.. All rights reserved.

关键词： Data mining

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