Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JSiI (Jurnal Sistem Informasi) LEADER: Civil Engineering and Architecture Journal
Asep Dian Heryadiana
Universitas Majalengka
Computer Science & IT Decision Sciences, Operations Research & Management

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

Found 2 Documents
Search

 1 
The Impact of Road Section Damage on Alternative Transportation Routes: A Systematic Literature Study Iip Faturohman; Andi Nugraha Saputra; Mushthofa; Asep Dian Heryadiana; Muhammad Isradi
LEADER: Civil Engineering and Architecture Journal Vol. 3 No. 4 (2025): August
Publisher : Fakultas Teknik Sipil dan Perencanaan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/leader.v3i4.10919

Abstract

This study examined the effect of road damage on the operation of alternate transportation routes using the systematic literature review (SLR) method. This study focused on the impact of road geometry, various forms of road damage, and societal repercussions. Major academic databases were searched for literature from 2013 to 2023 using the predetermined inclusion and exclusion criteria. According to general studies, geometric elements, including lane width, curvature, slope, and shoulder conditions, are directly associated with different types of damage, such as cracks, potholes, grooves, and drainage failures. These damages result in decreased route capacity, longer delivery times, higher rates of vehicle damage, higher accident risks, and restricted access to essential services. The social effects are substantial in places with little redundancy in the infrastructure. The findings demonstrate the importance of integrated planning that takes preventative maintenance and geometric design into account to guarantee transportation resilience and community welfare.
Prediksi Perubahan Luas Perkebunan Aren di Jawa Barat Berbasis Geospasial dengan Algoritma ARIMA dan Machine Learning Dadan Zaliluddin; Asep Dian Heryadiana; Dimar Pateman
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/djx30932

Abstract

Aren palm (Arenga pinnata) plays a significant role as an economic commodity and a renewable energy source in West Java, Indonesia. However, fluctuations in plantation areas caused by land use change, climate variability, and socio-economic factors have created challenges for sustainable management. Accurate prediction of aren plantation area dynamics is required to support decision-making and policy design for renewable energy development and environmental sustainability.This study aims to predict changes in aren plantation areas in West Java using a combination of Autoregressive Integrated Moving Average (ARIMA) for time-series forecasting and Machine Learning algorithms for enhanced prediction accuracy. Historical data of aren plantation areas from 2013 to 2023 were collected from official government databases. ARIMA was applied to model temporal trends, while Machine Learning approaches such as Random Forest and Long Short-Term Memory (LSTM) were employed to capture non-linear relationships and integrate external factors such as rainfall, soil characteristics, and urbanization patterns. In addition, a geospatial approach using Geographic Information System (GIS) was adopted to visualize spatial changes in plantation areas.Preliminary results indicate that ARIMA successfully models short-term trends with relatively low forecasting errors (RMSE < 15%). Machine Learning models demonstrate the potential to improve robustness and predictive accuracy by incorporating multidimensional variables. The integration of spatial visualization enables stakeholders to identify high-risk regions for land conversion and areas with strong potential for sustainable aren cultivation. The findings of this research provide a foundation for developing a decision support system to enhance sustainable plantation management and bioethanol policy planning in West Java. The proposed predictive framework contributes not only to the field of computational forecasting but also to the strategic alignment of renewable energy development with local socio-economic priorities. Keywords: ARIMA, Machine Learning, Geospatial, Aren Plantation, Forecasting  
Co-Authors Andi Nugraha Saputra Dadan Zaliluddin Dimar Pateman Iip Faturohman Muhammad Isradi Mushthofa