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Journal : Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)

Klasifikasi Status Gizi Balita Menggunakan Algoritma K-Nearest Neighbor (KNN) Hafsah. HS; Nurul Azmi; Hazriani, Hazriani; Yuyun, Yuyun
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Nutrition is an important component contained in food which includes carbohydrates, protein, vitamins, minerals, fat and water which are needed by the body for growth, development and maintenance which are used directly by the body to repair body tissue. Nutritional needs are an important factor in the growth and development of children, especially children aged under five years (toddlers), because what happens in the first five years determines their growth and development year after year. To achieve good growth and development, strong nutrition is needed. Assessment of the nutritional status of toddlers can be determined through human body measurements known as anthropometry. The reference standards for toddler nutritional status are Body Weight according to Age (WW/U) which describes the child's relative weight for age, Body Weight according to Height (WW/TB) which describes whether the child's weight is in accordance with his height growth and Height according to Age (TB/U) describes a child's height growth based on age. The method used to determine the nutritional status of toddlers is the KNN method, which is to find the closest distance between the evaluated data and a number of K neighbors closest to the test data. Toddler nutrition data uses 4 classifications, namely insufficient, normal, poor and more. The amount of data used in this research was 170 data with a data composition of 90% consisting of 150 training data and 10% testing data totaling 20 data. Data that is normalized using Z-Score gets an accuracy of 95%, class precision of 98.08% and class recall of 93.75%, data normalized using the min-max technique gets an accuracy of 85%, class precision of 95.00% and class recall of 79.17%, Meanwhile, KNN modeling without normalization produces an accuracy of 80%, precision of 82.4% and a class recall value of 75%.
Penerapan Metode K-Means Clustering Dalam Mengelompokkan Data Penjualan Obat pada Apotek M23 Nurul Azmi; Hafsah HS; Yuyun, Yuyun; Hazriani, Hazriani
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Planning for the need for the right medicines can make the procurement of medicines efficient and effective so that the medicines can be sufficiently available as needed and can be obtained when needed. At the current M23 Pharmacy, sometimes there is a shortage or overstock of medicines. To overcome these problems a data mining method is applied by analyzing drug use to produce information that can be used as drug inventory control and planning. The method used in this study is the K-Means method. The K-Means clustering method aims to group data that has the same characteristics into the same cluster and data that has different characteristics are grouped into other clusters. As for determining the best number of clusters using the Davies Bouldin Index (DBI) method. The results of this study determined that the best number of clusters was 2 clusters, the drug data grouping consisted of cluster 1 with low drug use consisting of 144 types of drugs and cluster 2 with high drug use consisting of 6 types of drugs.
Co-Authors Aditiya Mario Zai Putra Agista Ariyani Saripudi Ahmad Fadli Ahmad Fauzul Hakim Ahmad Muhtadi Amanda Berlia Bersky AMSIR AMSIR Anthony Anggrawan Anwar Apriliani Apriliani Asriani Asriani Ariani Asriani Asriani Auzora Vidya Nataha Baderi Cindi Pricilia Cindy Claudia* Cut Faizah Cut Fauziah Cut Nyakdhin CUT ZAHARA ROZA DEDE RUSLAN Deyana Manapo Dina Hidayati Dinda Saputri Ekawati, Dessy Eli Nurliza Erfinawati Erfinawati Ersi Sisdianto Faisal Faisal Faisal Fatikah Rahma Dewi Feny Nursyahwa Aulia Tarigan Fikria Nur Ramadani Fitrahwaty Fitrahwaty Fitrahwaty, Fitrahwaty Fitri Nurhaliza Ridwan Grace Cleosa Panesa Pelmelay Hafsah HS Hafsah. HS Hairani Hairani Hayati Hazriani, Hazriani Helmi Apriliyatmi Hapwiyah Heni Pujiastuti Hery Syahrial Ichsan Ichsan Ika Febriana Imas Nurjanah Indani Indani Indonesia, Hafizhatu Nadia Indriana Dachi Indriana Dachi Isbadar Nursit Ismawirna isti, Nuraqsa Istifarni Joko Suhianto Jullimursyida Junaidi Junaidi Junaidi Junaidi Kaiesa, Kaiesa Raihatul Muntaza Khairani Alawiyah Matondang Kurnia Shandi Lailani Fitria Lazim. N Lazim. N Lisa Rahmania Listari, Nening Lora Theresia Lora Theresia Panggabean M. Manugeren Margaretha Sembiring Maslena Maslena Mawardati Melani Adila Putri Meutia Nanda* Mhd Rayyan Fahadi MIFTAHUL JANNAH Muhammad Zubaidi Mujiburrahman Mujiburrahman Mukhlis Mukhlis Mukhlish Muhammad Nur Mukhriza Harahap Munardi Munardi Munawarah Mursyidah Muspiroh, Novianti Mutia Budhi Utami Nainggolan, Icha Riska Gloria Nasrudin Nayla Husna Afila Nazli Hasan Nur Anisa Ali Nur Asyah Nur Laila Meilani Nuraini Nuraiza Nuraiza Nurhabibah Nasution Nurhasanah Nurhasanah Nurliza, Eli Nursafiah, Nursafiah Nusaindah Nurul Amin Puji Prastowo Purwarno, Purwarno Rahmadini, Annisa Fitri Rajab Bahry Rajudin Ratna Mutia Ria Yulia Gloria Rini Nurul Amla Rozana Putri RR. Ettie Rukmigarsari Rusmin Husain Saadiah Saadiah saryati Siti Komariah Siti Naila Fauzia Siti Wardatul Jannah Suci Rakhmawati Tasya Amalia Tiok Wijanarko Vera Devani Vera Wardani Wisman Hadi Yofita Intan Putri Kasih Yosua Bintang Halomoan Yunasfi Djayus Yuyun, Yuyun Zakia Masrurah Zariul Antosa Zaskia Dyayu Larasati