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All Journal Indonesian Journal of Engineering and Technology (INAJET) Ranah Research : Journal of Multidisciplinary Research and Development
Alpiana, Intan
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Transportation Other

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PENERAPAN METODE KNA (KOMBINASI K-MEANS DAN AGGLOMERATIVE HIERARCHICAL CLUSTERING) DENGAN PENDEKATAN SINGLE LINKAGE UNTUK MENENTUKAN STATUS GIZI PADA BALITA Alpiana, Intan; Anifah, Lilik
Indonesian Journal of Engineering and Technology (INAJET) Vol 1, No 2 (2019): April
Publisher : Fakultas Teknik Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/inajet.v1n2.p61-68

Abstract

The nutrition fulfillment for children under five years old is a factor that must be considered in maintaining their health, because toddlers are in a developmental period that is vulnerable to nutrition. The attention of parents and officers of the Public Health Service Center (PUSKESMAS) is very necessary to monitor toddler nutrition. Therefore we need a system that can help to classify the nutritional status of children. The system is designed using the KnA method (a combination of K-means and Agglomerative Hierarchical Clustering) so that clustering results are better by combining the advantages of bottom-up clustering (agglomerative) and top-down clustering (k-means). This study attempts to categorize the data from Pulung Ponorogo health center with 7336 data (testing 3641 and learning 3695) with variables of sex, age, weight and height into 4 clusters of each toddler's nutritional status, namely for BB / U there 4 clusters BB S.Krg, BB Krg, BB Normal, BB Lbh, 4 clusters TB / U consisting of S. Short, Short, Normal, High and BB / TB 4 clusters S.Kurus, Skinny, Normal and Fat. In this study, the KnA method was able to properly classify toddlers' data in large numbers. The learning data is as much as 3695 produces an accuracy of BBU = 94%, TBU = 98%, and BBTB = 93%. From the results of the grouping conducted, it is used to determine the nutritional status of status according to BB / U, TB / U, and BB / TB, each of which i s equipped with a handling solution. From the testing with the black-box method as a whole the system is running well, as well as the questionnaire method and as well as validation of nutri
Otomatisasi Klasifikasi Tingkat Urgensi Keluhan E-Layanan Unesa Berbasis TF-IDF dan Logistic Regression Alpiana, Intan; Yustanti, Wiyli; Yamasari, Yuni
Ranah Research : Journal of Multidisciplinary Research and Development Vol. 8 No. 1 (2025): Ranah Research : Journal Of Multidisciplinary Research and Development
Publisher : Dinasti Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/rrj.v8i1.1912

Abstract

Perkembangan teknologi digital menuntut perguruan tinggi untuk menghadirkan layanan akademik yang cepat, tepat, dan responsif. Universitas Negeri Surabaya (Unesa) melalui platform E-Layanan memberikan sarana bagi civitas akademika untuk menyampaikan keluhan terkait kendala penggunaan sistem informasi dan jaringan. Namun, proses klasifikasi tingkat urgensi keluhan masih dilakukan secara manual oleh admin, yang berpotensi menyebabkan keterlambatan penanganan, inkonsistensi penilaian, serta meningkatnya beban kerja. Penelitian ini bertujuan untuk mengembangkan sistem otomatisasi klasifikasi tingkat urgensi keluhan dengan memanfaatkan Term Frequency-Inverse Document Frequency (TF-IDF) sebagai representasi fitur teks, serta Logistic Regression berbobot (class_weight) sebagai model klasifikasi utama. Dataset yang digunakan terdiri dari 79.303 keluhan, dibagi menjadi data latih (70%), validasi (15%), dan uji (15%). Evaluasi model dilakukan menggunakan metrik akurasi, presisi, recall, F1-score, dan confusion matrix. Hasil penelitian menunjukkan bahwa kombinasi TF-IDF dan Logistic Regression berbobot mampu memberikan kinerja yang baik dengan akurasi 92,54% pada data uji. Selain itu, model menunjukkan kemampuan yang tinggi dalam mendeteksi keluhan kritis secara akurat, memastikan prioritas penanganan terjaga secara optimal. Temuan ini menegaskan bahwa penerapan model berbasis pembelajaran mesin dapat meningkatkan efisiensi operasional dan konsistensi klasifikasi dibandingkan pendekatan manual. Sistem yang dikembangkan diharapkan dapat diintegrasikan lebih lanjut ke dalam platform E-Layanan Unesa, mendukung proses penanganan keluhan secara otomatis dan real-time, serta membantu administrasi fokus pada resolusi masalah yang paling mendesak.
Co-Authors Lilik Anifah Wiyli Yustanti YUNI YAMASARI