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All Journal Infotech Journal Prosiding National Conference for Community Service Project
Katherine Oktaviani Yap Rui Qi
Universitas Internasional Batam
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education

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SISTEM PEMILIHAN SUARA ONLINE SEDERHANA Natalis Christian; Ria Karina; Iskandar Itan; Katherine Oktaviani Yap Rui Qi; Celine Te; Grace Vaustine; Jason Hirawan; Putri Utami; Riksen Sitorus; Rina Rina; Saphira Evani; Serly Angelica; She Lee Susanti
National Conference for Community Service Project (NaCosPro) Vol 2 No 1 (2020): The 2nd National Conference of Community Service Project 2020 (Accepted Papers)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/nacospro.v2i1.1209

Abstract

This article aims to discuss the performance of the APSON prototype (Aplikasi Pemilihan Suara Online) against the democratic needs of users who need it. The subject of this research is the Student Association of Civil Engineering from Universitas Internasional Batam. The obtained data has derived from various forms of literacy, either physical or online, which has been carefully filtered and reviewed by writers. This research has applied science and technology methods as the method to solve the problem, which also assisted by quantitative data collection techniques, namely documentation techniques, interviews, and online questionnaires. The analysis of this research has given a result as the prototype`s performance is proven to be effective in channeling user`s aspirations without fear of being affected by the risk of COVID-19 pandemic. This decent performance has compiled with various systems, namely the front end(consisting of HTML, Javascript, and CSS) and the back end(consisting of MySQL and PHP). Nevertheless, APSON is still on website-based and in development.
ANALISA EFISIENSI PENDEKATAN DATA-DRIVEN DALAM PROSES SEGMENTASI PASAR DENGAN STUDI KASUS STARTUP Yefta Christian; Katherine Oktaviani Yap Rui Qi
INFOTECH journal Vol. 8 No. 2 (2022)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v8i2.3732

Abstract

Segmentasi pasar mengacu pada karakteristik yang digunakan untuk mengkategorikan customer dalam segmen. Teknokasi Edutech merupakan perusahaan rintisan yang diluncurkan pada awal Juni 2021. Teknokasi fokus pada education technology (ed-tech), terutama di bidang Teknologi Informasi. Perusahaan baru membutuhkan metode segmentasi pasar yang relevan dan akurat karena perusahaan harus memahami pasar sebelum membuat dan memasarkan produknya. Penelitian ini bertujuan untuk menyelidiki efisiensi algoritma pembelajaran mesin yang digunakan dalam segmentasi pasar, terutama untuk perusahaan early startup seperti Teknokasi. Penelitian dilakukan dengan pengembangan tiga model (K-Means dengan AHP-TOPSIS, SVM dengan AHP, dan Decision Tree dengan AHP) dengan tahapan data gathering, data preprocessing dan training, data modelling, evaluasi model, serta komparasi dan seleksi ketiga model. Berdasarkan analisa, didapatkan bahwa metode supervised learning lebih aplikatif, dengan SVM menunjukkan tingkat keakuratan yang relatif lebih tinggi. Penelitian ini membuktikan SVM beserta AHP mampu melakukan prediksi segmen pasar dan dengan begitu membantu decision-making berdasar label yang ditentukan.
Co-Authors Celine Te Christian, Natalis Grace Vaustine Iskandar Itan Jason Hirawan Putri Utami Ria Karina Riksen Sitorus Rina Rina Saphira Evani Serly Angelica She Lee Susanti Yefta Christian