Article Per Year (5 Year)
p-Index From 2021 - 2026
3.212
P-Index
This Author published in this journals
All Journal Pelita : Jurnal Penelitian dan Karya Ilmiah M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Matematika UNAND Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam Jurnal Statistika dan Matematika (Statmat) Indonesia Berdaya International Journal Software Engineering and Computer Science (IJSECS) KOMMAS: Jurnal Pengabdian Kepada Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Creative Student Research Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika Bulletin of Network Engineer and Informatics (BUFNETS) CSRID Tesseract: International Journal of Geometry and Applied Mathematics
Choirul Basir
Universitas Pamulang
Arts Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 22 Documents
Search

 1   2   3 
Application of a 1D-CNN Model for Flood Forecasting Using Weather Parameters and Flood History in the Tangerang Region Nely Ramah Kurniawati; Kahfi Heryandi Suradiradja; Choirul Basir
Bulletin of Network Engineer and Informatics Vol. 4 No. 2 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) October 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/cvxgp523

Abstract

Flooding frequently affects the Tangerang region and causes substantial social and economic losses. Conventional forecasting methods may not adequately capture the nonlinear relationships between dynamic urban weather conditions and flood events. This study applies a one-dimensional convolutional neural network (1D-CNN) to classify flood potential using multivariate weather parameters and historical flood-event records. Temperature, relative humidity, and rainfall observations from three Automatic Weather Stations were combined with flood-event data from 2022 to 2024. Preprocessing included quality-control checks, outlier treatment, Multiple Imputation by Chained Equations (MICE), moving-average and lag features, sliding-window sequence construction, Min-Max normalization, and an 80:20 training-testing split. The model used 40 convolutional filters, max pooling, a 40-neuron hidden layer, and a three-class Softmax output representing low, medium, and high flood potential. The 1D-CNN achieved 83.59% accuracy, 83.52% precision, 83.59% recall, and an F1-score of 83.52%. The confusion matrix showed strong identification of low- and high-risk cases, although the medium class and some high-risk events remained difficult to distinguish. These results indicate that integrating weather observations with historical disaster records can support the development of a data-driven flood early warning framework for the Tangerang region.
Implementation of the K-Means Algorithm for Clustering Urban Heat Island Intensity in Banten Province Tri Ratna Rahayu Rahmawati; Makhsun; Choirul Basir
Bulletin of Network Engineer and Informatics Vol. 4 No. 2 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) October 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/89hw9546

Abstract

Urban Heat Island (UHI) is a phenomenon of increased urban temperatures that affects thermal comfort, energy demand, and environmental quality. In Indonesia, UHI studies are still dominated by satellite-derived land-surface temperature, whereas continuous in-situ meteorological observations are less frequently used for multivariate data-driven grouping. This study addresses that gap by implementing K-Means clustering to identify station-based UHI-related thermal regimes in Banten Province using observations from 17 Automatic Weather Stations (AWS) during 2024. The source data were recorded at 10-minute intervals and processed through range and step quality-control checks, removal of invalid observations, Multiple Imputation by Chained Equations (MICE) for missing-data treatment, station-level aggregation, and Min-Max normalization. The final clustering features were annual mean air temperature, relative humidity, and solar radiation; rainfall was excluded from clustering because its correlations with the principal thermal variables were comparatively weak. Candidate cluster counts from K = 2 to K = 5 were evaluated using the Elbow method, Silhouette Coefficient Index (SCI), and Davies-Bouldin Index (DBI). Although the Elbow curve shows a structural transition around K = 3, internal validation identifies K = 2 as the most defensible final solution, with SCI = 0.79 and DBI = 0.33, compared with SCI = 0.65 and DBI = 0.58 for K = 3. The selected solution separates a higher-thermal regime concentrated in the northern and eastern urban-industrial corridor from a lower-thermal regime concentrated mainly in southern and western Banten. Because the AWS network is spatially sparse, the result should be interpreted as station-based thermal-regime clustering that complements, rather than replaces, spatially continuous remote-sensing UHI analysis. The resulting framework can support targeted heat monitoring and provide a data-driven basis for subsequent urban-climate assessment.
Co-Authors Ade Sekarningsih Ade Soekarno Putra Santoso Aditama Aditama Aditama Aditama Agung Budi Susanto Arya Adhyaksa Waskita Didin Sahrudin DWI RAMADHANI Enggar Prasetyawan Fauziyah Fauziyah Ferhat Aziz Gerry Sastro Ilmadi Ilmadi Imamatun Nisa Intan Idaman Halawa Iwan Muttaqin Kahfi Heryandi Suradiradja Makhsun Maolana, Cucu Nely Ramah Kurniawati Nina Valentika Nursyirwan , Vivi Iswanti Nurwijayanti Rahmat, Usep Salma, Anca Almira Sanubari Tansah Tresna Sudarno Tabah Heri Setiawan Tri Ratna Rahayu Rahmawati Usep Rahmat Winarni Winarni Yosep, Herman Yuni Permata Sari