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All Journal Jurnal Informatika dan Teknik Elektro Terapan ARTEKS : Jurnal Teknik Arsitektur Jurnal Responsif : Riset Sains dan Informatika Indonesian Journal of Community Diversity and Engagement SWARNA Philosophiamundi : Journal of Global Humanistic Studies Submit : Jurnal Ilmiah Teknologi Informasi dan Sains SWARNA: Jurnal Pengabdian Kepada Masyarakat Jurnal Bakti Dirgantara
Ahmad Syaifuddin
Universitas Islam Majapahit
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Komparasi Algoritma Regresi Linear dan Random Forest untuk Prediksi Tingkat Pengangguran Terbuka di Jawa Timur Wijayanto; Ronny; Ahmad Syaifuddin
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.10539

Abstract

Tingkat Pengangguran Terbuka (TPT) merupakan indikator penting dalam mengevaluasi kondisi ketenagakerjaan suatu daerah. Prediksi TPT diperlukan untuk mendukung penyusunan kebijakan ketenagakerjaan yang lebih efektif. Penelitian ini bertujuan membandingkan kinerja algoritma Linear Regression dan Random Forest Regression dalam memprediksi TPT di Provinsi Jawa Timur. Dataset yang digunakan berasal dari Badan Pusat Statistik (BPS) periode 2021–2025 yang mencakup 38 kabupaten/kota dengan total 190 data. Variabel prediktor meliputi Produk Domestik Regional Bruto (PDRB), Upah Minimum Regional (UMR), Tingkat Partisipasi Angkatan Kerja (TPAK), inflasi, dan rata-rata lama sekolah. Tahapan penelitian meliputi preprocessing data, standardisasi menggunakan StandardScaler, pembagian data dengan rasio 80:20, pembangunan model, serta evaluasi menggunakan MAE, MSE, RMSE, dan R². Hasil penelitian menunjukkan bahwa Random Forest Regression memiliki performa lebih baik dibandingkan Linear Regression, dengan nilai MAE 0,6768, MSE 0,8211, RMSE 0,9062, dan R² 0,7295, sedangkan Linear Regression memperoleh MAE 0,8518, MSE 1,2439, RMSE 1,1153, dan R² 0,5902. Variabel rata-rata lama sekolah menjadi faktor yang paling berpengaruh terhadap prediksi TPT. Dengan demikian, Random Forest Regression lebih efektif untuk memprediksi TPT di Provinsi Jawa Timur.
Integrasi Aplikasi Web dan Mobile untuk Otomasi Transaksi dan Transparansi Layanan Bank Sampah Andika Wira Nugraha; Ronny Makhfuddin Akbar; Ahmad Syaifuddin
Philosophiamundi Vol. 4 No. 3 (2026): Philosophiamundi June 2026
Publisher : PT. Kreasi Karya Majakata

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study develops a web- and mobile-based Waste Bank application to address manual recording problems in customer management, waste types, deposit transactions, balances, withdrawals, and reporting. The system provides two main roles: Admin and Customer. Admin manages operational data through the web application, while Customers use the mobile service to monitor balances, transaction history, waste prices, and submit withdrawal requests. The system was developed using the Waterfall approach covering requirements analysis, system design, implementation, and testing. The web frontend uses React.js, the backend uses Laravel, and MySQL is used as the database, with Tailwind CSS supporting the user interface. Deposit recording applies automatic calculation based on waste weight and price per kilogram, followed by balance updating after the transaction is stored. Black Box Testing was conducted on 25 scenarios covering the main functions, and all scenarios were successful. The results indicate that the application integrates waste bank administration, improves transaction recording, structures operational data, and provides customers with more transparent access to savings and transaction information.
Integration of Artificial Intelligence in inclusive urban design to enhance gender and disability safety: A design-based research in Surabaya Ainin Bashiroh; Ahmad Syaifuddin; Faizatus Sholikhah; Komang Ayu Laksmi Harshinta Sari; Ade Fitriyanti Ulul Azmi; Ionocki Prasetyo
ARTEKS : Jurnal Teknik Arsitektur Vol 11 No 1 (2026): ARTEKS : Jurnal Teknik Arsitektur | January 2026 ~ March 2026
Publisher : Program Studi Arsitektur Fakultas Teknik Universitas Katolik Widya Mandira

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30822/arteks.v11i1.4813

Abstract

Limited accessibility and safety for women and persons with disabilities remain critical issues in urban development in Indonesia, particularly in public spaces that are insufficiently responsive to vulnerable groups. This study aims to develop an inclusive urban design concept based on gender and disability perspectives through the integration of Artificial Intelligence (AI) and participatory approaches as an evidence-based design foundation. The research adopts a Design-Based Research (DBR) approach using mixed methods that combine AI-based quantitative analysis and participatory qualitative methods. Quantitative data were obtained through YouTube data crawling (853 entries, 729 valid datasets) and analyzed using Named Entity Recognition (NER) to identify potentially unsafe urban locations, then visualized through WebGIS. Qualitative data were collected through Focus Group Discussions (FGDs) involving disability organizations, women’s groups, and policymakers. The results identified three vulnerable locations in Surabaya: Kedung Cowek Street, Wonorejo Timur Street, and Kupang Indah Street. The study formulates inclusive urban design elements, including standardized pedestrian pathways, guiding blocks, gentle ramps, lighting levels of at least 12 lux, accessible transit stops, signage, and gender-sensitive public facilities. The novelty lies in integrating AI-based social media analysis with participatory approaches within a DBR framework to support data-driven inclusive urban design.
Co-Authors Ade Fitriyanti Ulul Azmi Ainin Bashiroh Andhika C. Putra Andika Wira Nugraha Anita Fahrul Amirudin Faizatus Sholikhah Fajar Indra Kurniawan Fatihatul Lailiyah Fianto Putra, Hafizh Hafizh Fianto Putra Ionocki Prasetyo Komang Ayu Laksmi Harshinta Sari Luki Ardiantoro Makhfuddin Akbar, Ronny Masnia Ningsih Moh. Muslimin Mohammad Muslimin Muhammad Nurfadhli Nur Sukmaningtyas, Yanuarini Ronny Ronny Makhfuddin Akbar Wahyu P. Indarwati Widya Ayu K. Wijayanto Yanuarini Nur Sukmaningtyas Yensi K.S Zahara, Soffa