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All Journal JSM (Jurnal SIFO Mikroskil) Jurnal Ilmiah Matrik KOMPUTA : Jurnal Ilmiah Komputer dan Informatika
Gunawan, Erin
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Computer Science & IT Decision Sciences, Operations Research & Management

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Rancang Bangun Aplikasi Penyedia Pekerjaan Lepas Trivial Menggunakan Metodologi Agile Halim, Apriyanto; Antoline, William; Arbecco, Arbecco; Gunawan, Erin; Wijaya, Leonardi Rendy; Wijaya, Leonardo Randy
Jurnal Ilmiah Matrik Vol. 26 No. 1 (2024): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/jurnalmatrik.v26i1.2993

Abstract

Freelancers are individuals who perform specific tasks for a number of different organizations, rather than committing to a single one. By the mean of freelance marketplace technology, it is now easier for recruiters and freelancers to meet and discuss. However, jobs on most freelance marketplaces today are categorized, focusing on more highly-skilled jobs. Thus, jobs such as daily and trivial ones cannot be found. Therefore, this research aims to develop a freelance marketplace application to help solve the existing problem of freelance work supply demand. This application, by accommodating trivial freelance jobs, is expected to promote inclusivity by providing job opportunities for freelancers with general skill sets. This research is performed using agile methodology, consisting of requirement analysis, design, application development, and black box testing. It was obtained that the web application for admin and the mobile application for user are functioning properly and could met the users’ need.
Pengembangan Aplikasi Deteksi Kematangan Buah Pisang Berbasis Web Menggunakan Model CNN-LSTM Sintiya, Cindy; Gunawan, Erin; Marpaung, Dhea Romantika; Fa, Farrell Rio; Sinaga, Frans Mikael
Jurnal Sifo Mikroskil Vol. 26 No. 1 (2025): JSM VOLUME 26 NOMOR 1 TAHUN 2025
Publisher : Fakultas Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55601/jsm.v26i1.1500

Abstract

Klasifikasi tingkat kematangan buah merupakan salah satu tantangan dalam penerapan teknologi kecerdasan buatan di sektor pertanian. Penelitian ini mengusulkan sistem deteksi tingkat kematangan pisang menggunakan arsitektur deep learning berbasis Convolutional Neural Network (CNN) dan Long Short-Term Memory (LSTM). Dataset citra pisang diproses melalui tahapan preprocessing yang mencakup normalisasi, segmentasi warna (mask kuning dan hijau), serta deteksi tepi, untuk menonjolkan fitur visual yang relevan. Model yang diimplementasikan mampu mengklasifikasikan tingkat kematangan pisang ke dalam kategori "matang" dan "mentah". Sistem ini diintegrasikan dengan antarmuka berbasis web menggunakan Streamlit, memungkinkan prediksi dilakukan secara real-time. Hasil pengujian menunjukkan bahwa model mencapai akurasi 100% pada dataset uji, dengan precision, recall, dan F1-score sempurna. Penelitian ini membuktikan efektivitas pendekatan CNN-LSTM dalam klasifikasi tingkat kematangan buah yang diharapkan dapat membantu memberikan kontribusi terhadap otomatisasi di sektor pertanian.
Klasterisasi Negara Dunia Berdasarkan Data Sosioekonomi dan Demografi Tahun 2023 dengan PCA dan K-Means Marpaung, Dhea Romantika; Gunawan, Erin; Fa, Farrel Rio; Christianto, Albert
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.15249

Abstract

The development of social, economic, and demographic factors is an important indicator for assessing the progress of a country. These factors reflect the quality of life, economic conditions, and population dynamics that can influence policies and development planning. Therefore, to better understand a country's conditions, it is important to cluster countries based on similar characteristics in these various aspects. The purpose of this study is to identify clusters of countries worldwide based on the analysis of socio-economic and demographic data for 2023 using Principal Component Analysis (PCA) and K-Means Clustering methods. This analysis examines the relationship between GDP, birth rate, death rate, population, and CO2 emissions. The results reveal three clusters with distinct characteristics. Cluster 0 shows high GDP with low infant mortality and controlled CO2 emissions. Cluster 1 shows lower GDP, high infant mortality, and challenges in the health and economic sectors. Cluster 2, which includes countries like China, India, and the US, has high GDP but faces high CO2 emission issues. These findings indicate the need for integrated policies to improve global well-being by considering economic, health, and environmental factors in a sustainable manner.
Co-Authors Antoline, William Apriyanto Halim Arbecco, Arbecco Christianto, Albert Fa, Farrel Rio Fa, Farrell Rio Marpaung, Dhea Romantika Sinaga, Frans Mikael Sintiya, Cindy Wijaya, Leonardi Rendy Wijaya, Leonardo Randy