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Auto Halal Detection Products Based on Euclidian Distance and Cosine Similarity

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@article{IJASEIT7083,
   author = {Nur Aini Rakhmawati and Azmi Adi Firmansyah and Pradita Maulidya Effendi and Rosyid Abdillah and Taufiq Agung Cahyono},
   title = {Auto Halal Detection Products Based on Euclidian Distance  and Cosine Similarity},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {8},
   number = {4-2},
   year = {2018},
   pages = {1706--1711},
   keywords = {halal; ingredients; euclidean distance; cosine similarity.},
   abstract = {Although Indonesia is the world the world's most populous Muslim-majority country, the number of halal-certified products in Indonesia is only 20% of the products on the Indonesian market. Halal certification is voluntary as such there are many food products which are halal but are not certified as halal. In principle, these food products may have similar halal ingredients with halal-certified products.  In this study, we build a system that can compare products that have not been certified halal with halal certified products based on its ingredients.  The food products are collected from Open Food Facts, Institute  For  Foods,  Drugs,  And  Cosmetics Indonesian  Council  Of  Ulama (LPPOM MUI) and our halal system. As of this paper writing, the halal-certified products are obtained from LPPOM MUI.  The system uses the Euclidean Distance and Cosine Similarity that generate top-5 similar products. Those two similarity calculations are based on Term Frequency-Inverse Entity Frequency weighting function.  The weighting function calculates the frequency of a term on a product name and ingredients.  If a similarity value of a product with no halal certification and a halal-certified product is higher than 75%, then the former could be indicated as a halal product. In the end, the system can give a recommendation of unknown products from a related pool of halal-certified products based on similarity of product composition. Cosine similarity accuracy is higher than Euclidean Distance and MoreLikeThis accuracy. Cosine similarity gets the highest precision because the cosine similarity is based on the vector angle of the term in a product.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7083},
   doi = {10.18517/ijaseit.8.4-2.7083}
}

EndNote

%A Rakhmawati, Nur Aini
%A Firmansyah, Azmi Adi
%A Effendi, Pradita Maulidya
%A Abdillah, Rosyid
%A Cahyono, Taufiq Agung
%D 2018
%T Auto Halal Detection Products Based on Euclidian Distance  and Cosine Similarity
%B 2018
%9 halal; ingredients; euclidean distance; cosine similarity.
%! Auto Halal Detection Products Based on Euclidian Distance  and Cosine Similarity
%K halal; ingredients; euclidean distance; cosine similarity.
%X Although Indonesia is the world the world's most populous Muslim-majority country, the number of halal-certified products in Indonesia is only 20% of the products on the Indonesian market. Halal certification is voluntary as such there are many food products which are halal but are not certified as halal. In principle, these food products may have similar halal ingredients with halal-certified products.  In this study, we build a system that can compare products that have not been certified halal with halal certified products based on its ingredients.  The food products are collected from Open Food Facts, Institute  For  Foods,  Drugs,  And  Cosmetics Indonesian  Council  Of  Ulama (LPPOM MUI) and our halal system. As of this paper writing, the halal-certified products are obtained from LPPOM MUI.  The system uses the Euclidean Distance and Cosine Similarity that generate top-5 similar products. Those two similarity calculations are based on Term Frequency-Inverse Entity Frequency weighting function.  The weighting function calculates the frequency of a term on a product name and ingredients.  If a similarity value of a product with no halal certification and a halal-certified product is higher than 75%, then the former could be indicated as a halal product. In the end, the system can give a recommendation of unknown products from a related pool of halal-certified products based on similarity of product composition. Cosine similarity accuracy is higher than Euclidean Distance and MoreLikeThis accuracy. Cosine similarity gets the highest precision because the cosine similarity is based on the vector angle of the term in a product.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7083
%R doi:10.18517/ijaseit.8.4-2.7083
%J International Journal on Advanced Science, Engineering and Information Technology
%V 8
%N 4-2
%@ 2088-5334

IEEE

Nur Aini Rakhmawati,Azmi Adi Firmansyah,Pradita Maulidya Effendi,Rosyid Abdillah and Taufiq Agung Cahyono,"Auto Halal Detection Products Based on Euclidian Distance  and Cosine Similarity," International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 4-2, pp. 1706-1711, 2018. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.8.4-2.7083.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Rakhmawati, Nur Aini
AU  - Firmansyah, Azmi Adi
AU  - Effendi, Pradita Maulidya
AU  - Abdillah, Rosyid
AU  - Cahyono, Taufiq Agung
PY  - 2018
TI  - Auto Halal Detection Products Based on Euclidian Distance  and Cosine Similarity
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 8 (2018) No. 4-2
Y2  - 2018
SP  - 1706
EP  - 1711
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - halal; ingredients; euclidean distance; cosine similarity.
N2  - Although Indonesia is the world the world's most populous Muslim-majority country, the number of halal-certified products in Indonesia is only 20% of the products on the Indonesian market. Halal certification is voluntary as such there are many food products which are halal but are not certified as halal. In principle, these food products may have similar halal ingredients with halal-certified products.  In this study, we build a system that can compare products that have not been certified halal with halal certified products based on its ingredients.  The food products are collected from Open Food Facts, Institute  For  Foods,  Drugs,  And  Cosmetics Indonesian  Council  Of  Ulama (LPPOM MUI) and our halal system. As of this paper writing, the halal-certified products are obtained from LPPOM MUI.  The system uses the Euclidean Distance and Cosine Similarity that generate top-5 similar products. Those two similarity calculations are based on Term Frequency-Inverse Entity Frequency weighting function.  The weighting function calculates the frequency of a term on a product name and ingredients.  If a similarity value of a product with no halal certification and a halal-certified product is higher than 75%, then the former could be indicated as a halal product. In the end, the system can give a recommendation of unknown products from a related pool of halal-certified products based on similarity of product composition. Cosine similarity accuracy is higher than Euclidean Distance and MoreLikeThis accuracy. Cosine similarity gets the highest precision because the cosine similarity is based on the vector angle of the term in a product.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7083
DO  - 10.18517/ijaseit.8.4-2.7083

RefWorks

RT Journal Article
ID 7083
A1 Rakhmawati, Nur Aini
A1 Firmansyah, Azmi Adi
A1 Effendi, Pradita Maulidya
A1 Abdillah, Rosyid
A1 Cahyono, Taufiq Agung
T1 Auto Halal Detection Products Based on Euclidian Distance  and Cosine Similarity
JF International Journal on Advanced Science, Engineering and Information Technology
VO 8
IS 4-2
YR 2018
SP 1706
OP 1711
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 halal; ingredients; euclidean distance; cosine similarity.
AB Although Indonesia is the world the world's most populous Muslim-majority country, the number of halal-certified products in Indonesia is only 20% of the products on the Indonesian market. Halal certification is voluntary as such there are many food products which are halal but are not certified as halal. In principle, these food products may have similar halal ingredients with halal-certified products.  In this study, we build a system that can compare products that have not been certified halal with halal certified products based on its ingredients.  The food products are collected from Open Food Facts, Institute  For  Foods,  Drugs,  And  Cosmetics Indonesian  Council  Of  Ulama (LPPOM MUI) and our halal system. As of this paper writing, the halal-certified products are obtained from LPPOM MUI.  The system uses the Euclidean Distance and Cosine Similarity that generate top-5 similar products. Those two similarity calculations are based on Term Frequency-Inverse Entity Frequency weighting function.  The weighting function calculates the frequency of a term on a product name and ingredients.  If a similarity value of a product with no halal certification and a halal-certified product is higher than 75%, then the former could be indicated as a halal product. In the end, the system can give a recommendation of unknown products from a related pool of halal-certified products based on similarity of product composition. Cosine similarity accuracy is higher than Euclidean Distance and MoreLikeThis accuracy. Cosine similarity gets the highest precision because the cosine similarity is based on the vector angle of the term in a product.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7083
DO  - 10.18517/ijaseit.8.4-2.7083