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All Journal Jurnal Sistem dan Informatika Jurnal Teknologi Informasi dan Komputer Jurnal Eksplora Informatika Jurnal Informasi dan Komputer Jurnal aplikasi IPTEK
Ni Luh Ratniasih
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Mathematics Public Health Social Sciences Other

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Journal : Jurnal Informasi dan Komputer

 1 
PENENTUAN STATUS GIZI BALITA PADA POSYANDU MENGGUNAKAN METODE K-NEAREST NEIGHBOR Ni Luh Ratniasih; Ni Komang Sri Julyantari; Ni Wayan Ninik Jayanti; Ni Luh Mas Elma Yuniawati
Jurnal informasi dan komputer Vol 11 No 01 (2023): Jurnal Informasi dan Komputer yang terbit pada tahun 2023 pada bulan 04 (April)
Publisher : STMIK Dian Cipta Cendikia Kotabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35959/jik.v11i01.398

Abstract

Posyandu is one of the village/kelurahan community institutions that accommodates community empowerment in basic social services whose implementation can be synergized with other services according to regional potential. One of the basic social activities at Posyandu is monitoring nutrition for toddlers. During the COVID-19 pandemic, posyandu cadres had difficulty bringing in experts or nutritionists to help check the nutritional condition of toddlers who came to the posyandu. So that posyandu cadres need a decision support information system that can assist in categorizing the nutritional status of toddlers, whether the toddlers are classified as malnourished, undernourished, and well-nourished. In this study, a decision support system will be built using the website-based K-Nearest Neighbor (KNN) method using the Bootstrap framework. The main function of the bootstrap framework is to create responsive websites. The website interface will work optimally on all screen sizes, both on smartphone screens and computer/laptop screens. The flow stage in this research is data collection, data pre-processing, which is then carried out by the mining process to find out the final results of this research. In this study, the classification model starts from the dataset which will be divided into training and testing data using split data which is then mined with a classification algorithm so that a classification model is generated and generates evaluation parameters. The model in this study was generated from the implementation of the K-NN (K-Nearest Neighbor) method. The software development method uses the waterfall method. The developed application is targeted to achieve the product readiness level indicator at level 2 where the technology data equipment to be developed allows it to be applied and the basic elements of the technology to be developed are known.
ANALISIS SENTIMEN KEPUASAN PEMANGKU KEPENTINGAN MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER DAN K-NEAREST NEIGHBOUR Ni Luh Ratniasih; Larasati Nabila Putri
Jurnal informasi dan komputer Vol 11 No 02 (2023): Jurnal Informasi dan Komputer yang terbit pada tahun 2023 pada bulan 10 (Oktobe
Publisher : STMIK Dian Cipta Cendikia Kotabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35959/jik.v11i02.378

Abstract

Pemangku kepentingan merupakan individu atau kelompok yang memiliki kepentingan dan dapat memberikan pengaruh kepada suatu objek. Pengukuran kepuasan pemangku kepentingan (stakeholder) sangat penting dilakukan untuk mendapatkan umpan balik dan masukan bagi keperluan pengembangan dan implementasi strategi peningkatan kepuasan pemangku kepentingan, sehingga perlu diketahui opini dari pemangku kepentingan. Metode penelitian terdiri dari beberapa tahap diantaranya tahap pertama dilakukan identifikasi masalah dan studi pustaka, tahap kedua pengumpulan data kepuasan pemangku kepentingan (Mahasiswa), tahap ketiga preprocessing data, tahap keempat adalah ekstrasi fitur agar dapat mempermudah klasifikasi menggunakan metode Naïve Bayes Classifier (NBC) dan K-Nearest Neighbour (KNN). Tahap keempat merupakan tahap pengujian dan evaluasi model. Tahap kelima adalah pengujian tingkat akurasi metode. Tujuan dari penelitian ini adalah mendapatkan perbandingan tingkat akurasi antara metode Naïve Bayes Classifier (NBC) dan K-Nearest Neighbour (KNN) pada analisis sentimen dari komentar hasil pengukuran kepuasan pemangku kepentingan. Hasil tingkat akurasi menggunakan metode Naïve Bayes Classifier (NBC) sebesar 91,13% dan K-Nearest Neighbour (KNN) sebesar 83,06% sehingga performance metode Naïve Bayes Classifier (NBC) lebih tinggi dalam analisis sentiment kepuasan pemangku kepentingan.
Co-Authors I Gede Arya Suartika I Gede Putu Adhitya Prayoga I Kadek Hedy Sugiantara I Ketut Suardika Artana Larasati Nabila Putri Ni Komang Sri Julyantari Ni Luh Mas Elma Yuniawati Ni Luh Putu Silvia Dewi Ni Made Astiti Ni Wayan Ninik Jayanti