Traditional food knowledge of Indonesia: a new high-quality food dataset and automatic recognition system

Wibisono, Ari; Wisesa, Hanif Arief; Rahmadhani, Zulia Putri; Fahira, Puteri Khatya; Mursanto, Petrus; Jatmiko, Wisnu

doi:10.1186/s40537-020-00342-5

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…The traditional food images are divided into testing, training and validation sets. CNN model is used for performing the automatic recognition and analysis with high accuracy [13].…”

Section: Related Workmentioning

confidence: 99%

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Recognition of food type and calorie estimation using neural network

et al. 2021

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Across the globe, health cognizant among the people is increasing and everyone wants to maintain a healthy and normal life. But due to the fast moving world, obesity and other related issue becomes the major health problem among the human beings. According to medical experts, a person is defined as obese when their BMI is greater than 30 kg/m 2. Obesity leads to many diseases like high cholesterol, liver failure, breathing issues, heart problems, diabetes and sometimes cancer. By eating healthy foods with high nutrition and low calorie values, we can control the obesity among the people. Human cannot control their appetite and have the nature of eating food which they like the most which leads to obesity. Many people have the difficulty in choosing the food items that have good nutrient and low calorific values. If a system can help the people and give them suggestions about the food and its calorific values, we can find a solution for this obesity problem. In this paper, identifying the food type and its calorific value estimation is done using multilayer perceptron model and the results are discussed. From the mixed food items, region of interest is selected from which the features are extracted. Extracted features are fed as the input to the MLP. Based on the food volume, the calories present in the food are calculated. Implementation of the algorithm is done in MATLAB environment for fruits and food items. The results showed that the level of detection of food item and accuracy of estimation of calorific level was acceptable.

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“…The traditional food images are divided into testing, training and validation sets. CNN model is used for performing the automatic recognition and analysis with high accuracy [13].…”

Section: Related Workmentioning

confidence: 99%

“…The fixed 150 * 150 resolution of the image has been used. The DCCN model is used to perform food item recognition and compared the output results with other networks for better performance [31].…”

Section: Related Workmentioning

confidence: 99%

Recognition of food type and calorie estimation using neural network

et al. 2021

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“…Dalam penelitian [2] dinyatakan bahwa terdapat ribuan makanan tradisional yang ada di Indonesia, beberapa diantaranya merupakan makanan tradisional yang memanfaatkan proses pengolahan fermentasi. Popularitas makanan fermentasi etnik/tradisional terus meningkat beberapa tahun terakhir sehingga membuat tingkat kesadaran dan pemahaman masyarakat dunia tentang makanan fermentasi etnik/tradisional meningkat [3] Fermentasi merupakan salah satu metode pengolahan pangan yang telah lama digunakan dan merupakan metode ekonomis yang digunakan dengan tujuan memperpanjang umur simpan dan meningkatkan kualitas produk. Tak hanya itu, pengolahan pangan dengan menggunakan metode fermentasi sering dimanfaatkan karena mampu memperpendek waktu masak ( [4], [5] menyediakan kandungan gizi yang lebih baik [6], menghilangkan kandungan toksin pada pangan [7], meningkatkan rasa dan aroma [8], dan dapat dikategorikan sebagai pangan fungsional yang memberikan manfaat pada kesehatan.…”

Section: Pendahuluanunclassified

Produk Fermentasi Tradisional Indonesia Berbahan Dasar Pangan Hewani (Daging dan Ikan): A Review

Mulyani

Prakoso

Yang

2023

JAHT

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Indonesia as an archipelagic country is rich in ethnicity and culture. This condition causes Indonesia to have a lot of uniqueness, one of which is traditional food. Traditional foods are varied from traditional non-fermented foods to traditional fermented foods. Based on the essential ingredients, traditional fermented foods from Indonesia can be grouped into 2 types, plant-based, and animal-based fermented foods. Information about plant-based fermented foods has been widely discussed, one of which is tempeh. However, a comprehensive discussion of traditional animal-based fermented food products, especially meat and fish, is rarely found. Therefore, the discussion on this topic can be a new thing to provide information to the public about Indonesia's treasures from a culinary perspective. This review discusses several traditional fermented foods from Indonesia, such as shrimp paste (terasi), urutan, budik, bekasam, naniura, buntilan/bontot, and ikan peda. In most cases, these foods utilize lactic acid bacteria (LAB) for their manufacture. This article is expected to be able to complete information about traditional fermented food products from Indonesia, as well as to promote the treasures of culinary origins from Indonesia to the societies.

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“…Other challenges and opportunities are to collect universal big data in order to generate patterns, reveal the hidden structure and gain insights that might be useful to a variety of stakeholders, and, ultimately, make decisions [ 6 ] in a variety of real-life applications and services, including health care [ 7 ], business [ 8 ], online learning [ 9 , 10 ] and sociocultural contexts [ 11 ]. This study puts big data ideas into practice in the higher education system in Indonesia.…”

Section: Introductionmentioning

confidence: 99%

‘Everything is data’: towards one big data ecosystem using multiple sources of data on higher education in Indonesia

2022

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Big data is increasingly being promoted as a game changer for the future of science, as the volume of data has exploded in recent years. Big data characterized, among others, the data comes from multiple sources, multi-format, comply to 5-V’s in nature (value, volume, velocity, variety, and veracity). Big data also constitutes structured data, semi-structured data, and unstructured-data. These characteristics of big data formed “big data ecosystem” that have various active nodes involved. Regardless such complex characteristics of big data, the studies show that there exists inherent structure that can be very useful to provide meaningful solutions for various problems. One of the problems is anticipating proper action to students’ achievement. It is common practice that lecturer treat his/her class with “one-size-fits-all” policy and strategy. Whilst, the degree of students’ understanding, due to several factors, may not the same. Furthermore, it is often too late to take action to rescue the student’s achievement in trouble. This study attempted to gather all possible features involved from multiple data sources: national education databases, reports, webpages and so forth. The multiple data sources comprise data on undergraduate students from 13 provinces in Indonesia, including students’ academic histories, demographic profiles and socioeconomic backgrounds and institutional information (i.e. level of accreditation, programmes of study, type of university, geographical location). Gathered data is furthermore preprocessed using various techniques to overcome missing value, data categorisation, data consistency, data quality assurance, to produce relatively clean and sound big dataset. Principal component analysis (PCA) is employed in order to reduce dimensions of big dataset and furthermore use K-Means methods to reveal clusters (inherent structure) that may occur in that big dataset. There are 7 clusters suggested by K-Means analysis: 1. very low-risk students, 2. low-risk students, 3. moderate-risk students, 4. fluctuating-risk students, 5. high risk students, 6. very high-risk students and, 7. fail students. Among the clusters unreveal, (1) a gap between public universities and private universities across the three regions in Indonesia, (2) a gap between STEM and non-STEM programmes of study, (3) a gap between rural versus urban, (4) a gap of accreditation status, (5) a gap of quality human resources distribution, etc. Further study, we will use the characteristics of each cluster to predict students’ achievement based on students’ profiles, and provide solutions and interventions strategies for students to improve their likely success.

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Traditional food knowledge of Indonesia: a new high-quality food dataset and automatic recognition system

Cited by 10 publications

References 23 publications

Recognition of food type and calorie estimation using neural network

Recognition of food type and calorie estimation using neural network

Produk Fermentasi Tradisional Indonesia Berbahan Dasar Pangan Hewani (Daging dan Ikan): A Review

‘Everything is data’: towards one big data ecosystem using multiple sources of data on higher education in Indonesia

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