A Comparison Tsukamoto and Mamdani Methods in Fuzzy Inference System for Determining Nutritional Toddlers

Wulandari, Dewi; Prihatin, Titin; Prasetyo, Arfhan; Merlina, Nita

doi:10.1109/citsm.2018.8674248

Cited by 13 publications

(10 citation statements)

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“…In this method, each IF-THEN rule must be represented by a fuzzy set that has a membership function. As a result, the inference output from each rule will be given certainty based on the D-Predicate [15]. In the Fuzzy Tsukamoto method there must be a relationship between the rules in the form of cause and effect, besides that there must be a relationship between input and output so that the relationship between action and condition can be seen.…”

Section: Application Of the Tsukamoto Fuzzy Methodsmentioning

confidence: 99%

“…Previous research related to this problem includes the Application of an Expert System for Selection of Food Based on Nutritional Needs Using the Forward Chaining Method [14], A Comparison Tsukamoto and Mamdani Methods in Fuzzy Inference System for Determining Nutritional Toddlers [15], Decision Support System for Determining the Nutritional Status of Toddlers Using the Fuzzy Tsukamoto Method [16], Education on Feeding Infants and Children to Fulfill Children's Nutrition Intake [17].…”

Section: Introductionmentioning

confidence: 99%

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Implementation of the Tsukamoto Fuzzy Method for Infant Food Recommendations According to the Needs and Akg

Panjaitan¹,

Suryanata²,

Ibnutama³

2022

JURTEKSI

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Choosing the right foods for babies to eat is easy. Age 0 to 5 years is a golden period for babies in their growth and development, both physically and intellectually. Incorrectly choosing foods that do not meet the needs can lead to the risk of obesity, while foods that do not meet the nutritional adequacy of infants can also cause stunting. An intelligent system taken from the knowledge of baby nutrition experts can be used as a solution to accompany parents in choosing the right food menu for babies during their growth and development. Fuzzy Tsukamoto can be used to group babies by age and weight, to decide which foods are suitable for consumption by the baby, based on the evaluation Images table compiled by the ministry of health as a guide for appropriate nutrition. This research resulted in a system that is able to assist parents in choosing the appropriate food menu for babies so as to prevent stunting. Keywords: fuzzy tsukamoto; stunting; nutrition Abstrak: Memilih makanan yang tepat untuk dikonsumsi oleh bayi bukanlah hal yang mudah. Usia 0 sampai 5 tahun merupakan masa-masa emas bagi bayi dalam tumbuh kembangnya, baik fisik maupun kecerdasan. Salah memilih makanan yang tidak sesuai kebutuhan dapat menyebabkan resiko obesitas, sedangkan makanan yang tidak memenuhi angka kecukupan gizi bayi juga dapat menyebabkan stunting. Sistem cerdas yang diambil dari pengetahuan para pakar gizi bayi dapat dijadikan solusi pendamping orang tua dalam memilih menu makanan yang tepat bagi bayi di masa tumbuh kembangnya. Fuzzy Tsukamoto dapat digunakan untuk mengelompokkan bayi berdasarkan usia dan berat badan, untuk memutuskan makanan mana yang cocok dikonsumsi oleh bayi tersebut, berdasarkan tabel angka kecukupan gizi yang telah disusun oleh kementrian kesehatan sebagai panduan pemberian nutrisi yang sesuai. Dari penelitian ini dihasilkan sistem yang mampu membantu orang tua dalam memilih menu makanan yang sesuai bagi bayi sehingga dapat mencegah terjadinya stunting. Kata kunci: Fuzzy Tsukamoto; stunting; nutrisi

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Section: Application Of the Tsukamoto Fuzzy Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implementation of the Tsukamoto Fuzzy Method for Infant Food Recommendations According to the Needs and Akg

Panjaitan¹,

Suryanata²,

Ibnutama³

2022

JURTEKSI

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“…These statements are impossible to quantify using traditional mathematics. The FL system is used in many controlling applications such as aircraft control (Rockwell Corp.), Sendai subway operation (Hitachi), and TV picture adjustment (Sony) [16,26,35].…”

Section: Outline Of Fuzzy Logicmentioning

confidence: 99%

FSSLA: A Fuzzy-based scheme for slice service level agreement in 5G wireless networks and its performance evaluation

Ampririt

Qafzezi

Bylykbashi

et al. 2022

JHS

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The Software-Defined Network (SDN) with Network Slicing will be a good approach for admission control in the Fifth Generation (5G) wireless network, which is planned to be adaptable to meet user requirements. The system’s resources are limited, and the number of devices is growing much faster than it can handle. So, the overloading problem will be a very critical problem. To deal with these problems, this paper presents a Fuzzy-based Scheme for Service Level Agreement (SLA) evaluation (FSSLA). We compare two models: FSSLA1 and FSSLA2. The FSSLA1 considers three input parameters: Reliability (Re), Availability (Av), Latency (La) and the output parameter is SLA. In FSSLA2, we consider Traffic Load (Tl) as a new parameter. From simulation results, we conclude that the considered parameters have different effects on the SLA. When Re and Av are increasing, the SLA parameter is increased but when La and Tl are increasing, the SLA parameter is decreased. When the Tl value is changed from 20% to 80%, the SLA is decreased by 16.62% when Re 10%, Av 90% and the La value is 1 ms. When the Re is increased from 10% to 90% and 50% to 90% and the Tl value is 50%, the Av is 50% and the La is 1 ms, the SLA is increased by 22.76% and 11.38%, respectively.

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“…Based on the results of the 2021 Indonesian Nutritional Status Survey (SSGI) conducted by the Ministry of Health, the prevalence rate of stunting in Indonesia in 2021 was 24.4%, or decreased by 6.4% from 30.8% in 2018 [5]. Therefore, the Government is trying to reduce the prevalence of malnutrition by monitoring nutritional status periodically once a year, Nutritional Status Monitoring has been carried out in 34 provinces and 514 regencies/cities [3]. The National Medium-Term Development Plan (RPJMN) in 2021 of 24.4% of toddlers experiencing nutritional problems and is targeted to decrease to 14% by 2024 [5].…”

Section: Introductionmentioning

confidence: 99%

“…The Tsukamoto method uses the implications of MIN on the inference evaluation engine and the Centralized Average for Defuzzification. The measurement results show that the Fuzzy Tsukamoto method obtained 82.35% from Fuzzy Mamdani [3]. The machine learning method researched by Afifaturahman in 2021 for prediction uses a comparison of the K-Nearest Neighbour (KNN) and Naive Bayes algorithms, the Naïve bayes method to measure the optimal level of accuracy with fewer training datasets, while the KNN algorithm can group datasets with higher-feature space [11].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Naïve bayes Method for Predictor Prevalence Level for Malnutrition Toddlers in Magelang City

Arumi

Subrata

Rahmawati

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Nutritional status is an important factor in assessing the growth and development rate of babies and toddlers. Cases of malnutrition are increasing, especially in magelang city. Because nutritional problems (Malnutrition) can affect the health of toddlers. Therefore, this study aims to predict the level of prevalence of malnutrition with the Naïve Bayes method. This research uses an observational design, a single center study at the Magelang City Office, using the Naïve bayes method which is used as an application of time series data, and is most widely used for prediction, especially in data sets that have many categorical or nominal type attributes. The Naïve bayes method is used to predict such cases of malnutrition. The results of this study show that the Naïve Bayes method succeeded in predicting the magnitude of cases of malnourished toddlers in Magelang City with an accuracy percentage of 75% due to the very minimal amount of training data, and the areas that have the most malnutrition are in three areas, namely Magersari, North Tidar and Panjang.

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A Comparison Tsukamoto and Mamdani Methods in Fuzzy Inference System for Determining Nutritional Toddlers

Cited by 13 publications

References 1 publication

Implementation of the Tsukamoto Fuzzy Method for Infant Food Recommendations According to the Needs and Akg

Implementation of the Tsukamoto Fuzzy Method for Infant Food Recommendations According to the Needs and Akg

FSSLA: A Fuzzy-based scheme for slice service level agreement in 5G wireless networks and its performance evaluation

Implementation of Naïve bayes Method for Predictor Prevalence Level for Malnutrition Toddlers in Magelang City

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