Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika
Muhammad Iyad Irviansyah
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Gap Analysis Matrix dan Roadmap Smart Transportation Bandar Lampung Berbasis Rujukan Jakarta dan Bogor Anabella, Marshanda; Claresta, Vanesya; Muhammad Iyad Irviansyah; Siregar, Master Edison
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 9 No. 3 (2025): IKRAITH-INFORMATIKA Vol 9 No 3 November 2025
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

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

Abstract

The rapid population growth and urbanization in Bandar Lampung have generated substantial pressure on the city’s transportation system, which remains dominated by private vehicles and traditional public transport services. This condition contributes to increasing congestion, travel inefficiency, pollution, and a decline in service quality. This study aims to assess the readiness of Bandar Lampung to implement smart transportation using the seven layers of the Smart City Ecosystem Framework. The research employed a qualitative approach through literature analysis and the application of a Gap Analysis Matrix, with Jakarta and Bogor serving as benchmarks to define the desired state of smart transportation development. The findings indicate that Bandar Lampung is positioned in the emerging category, with the most significant gaps identified in the technology infrastructure, data and information, service integration, and digital innovation layers. The city lacks adequate IoT infrastructure, multimodal integration, and a unified mobility data system. Based on these findings, a 2025–2030 smart transportation roadmap is proposed, focusing on regulatory development, service digitalization, IoT enhancement, multimodal and digital payment integration, fleet modernization, and multi-stakeholder collaboration. This study is expected to support strategic decision-making for promoting a more efficient, integrated, and sustainable urban mobility system in Bandar Lampung.
Analisis Faktor Penentu Kategori Harga Rumah di Kota Tangerang Selatan Menggunakan Web Crawling dan Regresi Logistik Multinomial Muhammad Iyad Irviansyah; Claresta, Vanesya; Anabella, Marshanda; Saputra, Muhammad Rifqo; Kurniawan, Rido Dwi; Sari, Muh. Masri
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 9 No. 3 (2025): IKRAITH-INFORMATIKA Vol 9 No 3 November 2025
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

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

Abstract

This study aims to identify factors influencing housing price categories in South Tangerang City using digital data obtained through web crawling from online property platforms. The research addresses how physical attributes, facilities, and location affect the probability of a house belonging to a specific price category. Data were automatically collected via web crawling, and after data cleaning and validation, 1,264 housing records were retained for analysis. Housing prices were classified into four categories—Economical, Standard, Luxury, and Exclusive—using a quartile-based approach. Multinomial Logistic Regression (MLR) was applied to model relative probabilities based on land area, building area, number of bedrooms, number of bathrooms, garage availability, and district location. The results indicate that land area, building area, number of bathrooms, and garage availability significantly influence housing price categories, while the number of bedrooms and district location are not significant after controlling for physical characteristics. The model is statistically significant and achieves a classification accuracy of 64.8%. The main contribution of this study lies in the integration of web crawling and Multinomial Logistic Regression for housing price classification, offering a data-driven framework to support housing market analysis and automated property valuation systems.
Co-Authors Anabella, Marshanda Claresta, Vanesya Rido Dwi Kurniawan Saputra, Muhammad Rifqo Sari, Muh. Masri Siregar, Master Edison