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All Journal Techno.Com: Jurnal Teknologi Informasi SOSCIED
Eko Prasetyo
Universitas Stikubank
Computer Science & IT Education Engineering Social Sciences

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ANALYSIS OF USER EXPERIENCE TESTING STMIK AKI WEBSITE USING SUPR-Q IN PERSPECTIVE HUMAN-COMPUTER INTERACTION Eko Prasetyo; Kristiawan Nugroho; Kristophorus Hadiono
SOSCIED Vol 6 No 1 (2023): SOSCIED - Juli 2023
Publisher : LPPM Politeknik Saint Paul Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32531/jsoscied.v6i1.645

Abstract

Human-Computer Interaction (HCI) is a scientific field that studies human-computer interaction in order to design systems that satisfy user requirements and enhance the user experience (UX). HCI is very important in the development of websites because a good user experience is necessary to improve user satisfaction, efficiency, and effectiveness. The motivation behind this study is to recognize the qualities and shortcomings of the STMIK AKI site as far as client experience, as the need might have arisen to further develop client UX. From a HCI perspective, SUPR-Q is one way to use a questionnaire to measure user experience. To analyze various UX dimensions, UX testing will use Task Level Satisfaction methods like Single Ease Question (SEQ), Scenario Testing, and System Usability Scale (SUS) in addition to the SUPR-Q method. The SUPR-Q Loyalty variable had a low value, but the SEQ, SUS, and SUPR-Q methods produced results with an average grade of B, supporting the hypothesis that respondents were satisfied with their use of the STMIK AKI website. The worth got was 59.1% with Grade C and the speculation was adequate in utilizing and suggesting the STMIK AKI site page. Furthermore, in the Situation Testing strategy, the typical worth got is 57.5% with Grade C, with the speculation that the respondent or client is very able in finishing jobs or working the STMIK AKI page framework.
Optimasi Klasifikasi Data Stunting Melalui Ensemble Learning pada Label Multiclass dengan Imbalance Data Eko Prasetyo; Kristiawan Nugroho
Techno.Com Vol 23, No 1 (2024): Februari 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i1.9779

Abstract

Salah satu permasalahan kesehatan yang sering ditemui di banyak negara termasuk Indonesia adalah stunting. Stunting telah mendapat banyak perhatian di Indonesia, terlihat dari alokasi APBN masing-masing sebesar Rp48,3 triliun dan Rp49,4 triliun pada tahun 2022 dan 2023 untuk bidang ini. Pada tahun 2022, Kementerian Kesehatan merilis temuan dari Survei Status Gizi Indonesia (SSGI) yang menyatakan bahwa angka stunting di Indonesia mencapai 21,6% pada saat Rapat Kerja Nasional BKKBN pada 25 Januari 2023.Hal ini menunjukkan pentingnya untuk mengerti pemahaman mendalam tentang faktor-faktor yang mengidentifikasi anak-anak berisiko tinggi terkena stunting. Banyak penelitian sebelumnya yang membahas faktor resiko stunting, namun masih sedikit penerapannya dalam metode machine learning, dalam data yang kompleks dan tidak seimbang.Penelitian ini mengevaluasi kinerja dari berbagai metode machine learning yang bertujuan dapat memberikan kontribusi penting dalam bidang kesehatan anak dan analisis data. Diantara metode machine learning yang dipilih metode Bagging Decision Tree mendapatkan nilai accuracy yang terbaik sebesar 78,93%, precision 78% dan recall sebesar 77,99%. Dalam penelitian ini menunjukkan bahwa metode ensemble learning mampu bekerja dengan baik dalam atribut multiclass dan data yang tidak seimbang pada dataset pertumbuhan balita.
Co-Authors Kristiawan Nugroho Kristophorus Hadiono