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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal SOLMA JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Ilmiah Sinus Jurnal Informatika Kaputama (JIK) Jurnal Mantik Jurnal Informasi dan Teknologi JTIK (Jurnal Teknik Informatika Kaputama) G-Tech : Jurnal Teknologi Terapan Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) Jurnal Tika Multica Science and Technology JUSTIN (Jurnal Sistem dan Teknologi Informasi) Brilliance: Research of Artificial Intelligence Indonesian Community Journal Sisfo: Jurnal Ilmiah Sistem Informasi SAGA: Journal of Technology and Information Systems Journal Of Artificial Intelligence And Software Engineering Jurnal Medika: Medika Science: Indonesian Journal of Science
Ananda Faridhatul Ulva
Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : Multica Science and Technology

 1 
IMPLEMENTATION OF MACHINE LEARNING USING THE K-NEAREST NEIGHBOR CLASSIFICATION MODEL IN DIAGNOSING MALNUTRITION IN CHILDREN Mutammimul Ula; Ananda Faridhatul Ulva; Ilham Saputra; Mauliza Mauliza; Ivan Maulana
Multica Science and Technology Vol 2 No 1 (2022): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v2i1.326

Abstract

The problem faced today is the lack of nutrition for children which causes stunting. One way to prevent stunting problems is to provide input to the community in Aceh for the importance of providing adequate nutrition for children. This study classifies toddlers who are identified as stunting with the K-NN model technology which is modeled in machine learning, the results are grouped. The purpose of this study was to determine the detection of malnutrition in toddlers and to classify data on malnutrition in toddlers using the k-means clustering method and the system that was built could be used as a reference to monitor the growth and development of children. Then in classifying malnutrition in children based on the results of the nutritional status criteria in toddlers, it can be known based on the index of Body Weight for Age (W/U), Height for Age (TB/U), and Weight for Height (W/TB). by entering data values ​​from weight, height and gender of toddlers. The purpose of this study was to determine the detection of malnutrition under five at the Cut Meutia Hospital Kab. North Aceh. The process in the initial data analysis of Mr. ID, baby's name, gender, age, weight (kg), height (cm), the data to be classified for training data are 40 children in each region / village. In the assessment of nutritional status, it is classified as Malnutrition less than 3 SD or 70%, Malnutrition - 3 SD to < - 2 SD or 80%, Good Nutrition -2 SD to +2 SD, Over Nutrition >+2 SD. The results of the final score obtained are euclidean distance with a value of 1.3 with a ranking of malnutrition, age 1.6 months, weight (weight) 0.852, TB (height) 4.556 with euclidean distance with a value of 1.3 with a low ranking. For the second test data, age is 2.8 months, BB (weight) 0.222, TB (height) 4.556 with Euclidean distance with a value of 1.3 with a good rating of 0.778. The results of this study can be classified in children to children for each region in each region, village and sub-district of each Puskesmas in North Aceh Regency
APPLICATION OF MACHINE LEARNING IN PREDICTING CHILDREN'S NUTRITIONAL STATUS WITH MULTIPLE LINEAR REGRESSION MODELS Mutammimul Ula; Ananda Faridhatul Ulva; Mauliza; Muhammad Abdullah Ali; Yumna Rilasmi Said
Multica Science and Technology Vol 2 No 2 (2022): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v2i2.363

Abstract

Forecasting is an important part of making plans and making decisions that can predict future events. Forecasting techniques in this study used multiple linear regression. This study aims to predict the number of cases of child nutritional status in children in each region. The purpose of this study was to see the results of predicting the number of children's nutritional status in each region and to make it easier to predict children's nutrition. The research method includes the analysis of the system built and the design of machine learning applications using the Multiple Linear Regression method. Then the system built can help predict the nutritional status of children in Aceh quickly, precisely, and accurately. The data used is data on the nutritional status of children in 2018, 2019, and 2020. Based on the results of forecasting for 2021 based on data obtained in previous years, the predicted results of total nutritional status in 2021 are 449,0912126. The results of this study indicate that the linear regression method obtains the best model results by being able to predict the implementation of machine learning.
IMPLEMENTATION OF MACHINE LEARNING USING THE K-NEAREST NEIGHBOR CLASSIFICATION MODEL IN DIAGNOSING MALNUTRITION IN CHILDREN Mutammimul Ula; Ananda Faridhatul Ulva; Ilham Saputra; Mauliza Mauliza; Ivan Maulana
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 2 No. 1 (2022): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v2i1.326

Abstract

The problem faced today is the lack of nutrition for children which causes stunting. One way to prevent stunting problems is to provide input to the community in Aceh for the importance of providing adequate nutrition for children. This study classifies toddlers who are identified as stunting with the K-NN model technology which is modeled in machine learning, the results are grouped. The purpose of this study was to determine the detection of malnutrition in toddlers and to classify data on malnutrition in toddlers using the k-means clustering method and the system that was built could be used as a reference to monitor the growth and development of children. Then in classifying malnutrition in children based on the results of the nutritional status criteria in toddlers, it can be known based on the index of Body Weight for Age (W/U), Height for Age (TB/U), and Weight for Height (W/TB). by entering data values ??from weight, height and gender of toddlers. The purpose of this study was to determine the detection of malnutrition under five at the Cut Meutia Hospital Kab. North Aceh. The process in the initial data analysis of Mr. ID, baby's name, gender, age, weight (kg), height (cm), the data to be classified for training data are 40 children in each region / village. In the assessment of nutritional status, it is classified as Malnutrition less than 3 SD or 70%, Malnutrition - 3 SD to < - 2 SD or 80%, Good Nutrition -2 SD to +2 SD, Over Nutrition >+2 SD. The results of the final score obtained are euclidean distance with a value of 1.3 with a ranking of malnutrition, age 1.6 months, weight (weight) 0.852, TB (height) 4.556 with euclidean distance with a value of 1.3 with a low ranking. For the second test data, age is 2.8 months, BB (weight) 0.222, TB (height) 4.556 with Euclidean distance with a value of 1.3 with a good rating of 0.778. The results of this study can be classified in children to children for each region in each region, village and sub-district of each Puskesmas in North Aceh Regency
Co-Authors Abdi Zulfikri Alimul Haq, Nur Amalia, Fatihah Indah Andriani, Dela Angga Pratama Angga Pratama Apriyulida, Fitri Aulia, Keysha Bahrul Ulumul Haq Chairil Anwar Chairul Akbar Cut Meurah Nurul ‘Akla Dahlan Abdullah Desvina Yulisda Dhani, Saniah Eka Susanti Eka Susanti Eza Pradanta Sitepu Fadhliani Fadhliani, Fadhliani Fajar Tri Tri Anjani Fidyati, Fidyati Habib Muharry Yusdartono Hafizh Al Kautsar Aidilof Himmatur Rijal Ikhwani, Muhammad Ikhwani ilham - sahputra Ilham Saputra Ivan Maulana Kurniawati Kurniawati Lianita, Ervi M Taufiq Hariadi Masriadi Maulida Hasbi Mauliza Mauliza Mauliza Mauliza Mauliza Mauliza Mauliza Mauliza, Ade Mochamad Ari Saptari Muhammad Abdullah Ali Muhammad Abdullah Ali Muhammad Zikri Mukti Qamal Munirul Ula Mutammimul Ula Muzakir Nur Nazimah Nazimah Nunsina, Nunsina Nur Alimul Haq Nur, Muzakir Nurdin Nurdin Nurhasanah Nurhasanah Nurhasanah Rahma Fitria Rahma Fitria, Rahma Reyhan Achmad Rizal Ridwan Ridwan Rizki Setiawan Rizky Putra Fhonna Saptari, Mochamad Ari Setiawan, Rizki Sinambela, Reza Syahputra Syafitri, Siska Aulia Ulumul Haq, Bahrul Wardana, Ade Bagus WULANDARI Yesy Afrillia Yulisda, Desvina Yumna Rilasmi Said Yumna Rilasmi Said Yunia Silfitri yusliana, yusliana Zahratul Fitri, Zahratul Zuhra, Elviza