Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal Bulletin of Information Technology (BIT) Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia APPA : Jurnal Pengabdian Kepada Masyarakat
Dahlan Supriatna
Universitas Pamulang
Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Transportation

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
RANCANG SISTEM PENDUKUNG KEPUTUSAN UNTUK KELAYAKAN PENERIMA BANTUAN SOSIAL TUNAI (BST) PADA MASYARAKAT MISKIN BERBASIS WEBSITE MENGGUNAKAN METODE FUZZY (FMADM) ARAS (STUDI KASUS : KELURAHAN PORIS PLAWAD INDAH, KECAMATAN CIPONDOH) Dahlan Supriatna; Agung Perdananto
BINER : Jurnal Ilmu Komputer, Teknik dan Multimedia Vol. 1 No. 2 (2023): BINER : Jurnal Ilmu Komputer, Teknik dan Multimedia
Publisher : CV. Shofanah Media Berkah

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

Abstract

Poris Plawad Indah Sub-District is an institution whose duties include collecting community data to determine the eligibility of the recipient community of cash social assistance (BST). Therefore, in its implementation, Poris Plawad Indah Village has a program to select the eligibility of recipients of social assistance (BST). In conducting community eligibility elections, this agency has not used a system that makes it easy for the election committee to determine who is a suitable candidate. As a result, the election runs ineffectively because the time used is very long. The DSS system for selecting community feasibility uses the Fuzzy ARAS method with the waterfall system development method which aims to get an overview of the system that is in accordance with what is needed in Poris Plawad Indah Village. Therefore, with the DSS website system, selecting community eligibility, the community eligibility selection committee in Poris Plawad Indah Village will be greatly facilitated.
Penerapan Machine Learning Menggunakan Teachable Machine untuk Mendeteksi Pengelompokan Gambar di SMK Letris 1 Cheby Raka Sadewa; Ariyansyah; Faros Fadillah Robinson; Fajar Ramadhan; Ali Imron; Frans Wendi M.C.P; Eka Muhammad Guntur; Dahlan Supriatna
APPA : Jurnal Pengabdian Kepada Masyarakat Vol 4 No 1 (2026): APPA : Jurnal Pengabdian kepada Masyarakat 
Publisher : Shofanah Media Berkah

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

Abstract

Pesatnya perkembangan teknologi Artificial Intelligence (AI) dan Machine Learning (ML) menuntut siswa Sekolah Menengah Kejuruan (SMK) untuk menguasai digital literacy yang relevan. Namun, pemahaman siswa mengenai implementasi praktis teknologi ini masih terbatas. Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan untuk mengenalkan konsep dasar Machine Learning beserta penerapannya melalui pelatihan klasifikasi gambar menggunakan platform Google Teachable Machine di SMK Letris 1 Indonesia. Metode pelaksanaan yang digunakan meliputi empat tahapan sistematis, yaitu persiapan dan survei, desain solusi dan penyusunan materi, implementasi interaktif melalui workshop praktik, serta evaluasi menyeluruh. Hasil kegiatan menunjukkan bahwa siswa peserta memiliki antusiasme yang tinggi dan berhasil membangun model pengelompokan gambar sederhana secara mandiri tanpa kendala pemrograman yang rumit. Pelatihan ini terbukti efektif meningkatkan digital literacy, kemampuan computational thinking, serta kesiapan siswa dalam menghadapi perkembangan teknologi di era industri.
Komparasi Model LSTM dan CNN-LSTM untuk Peramalan Curah Hujan di Kota Tangerang Selatan Uliyatunisa; Dahlan Supriatna
Bulletin of Information Technology (BIT) Vol 6 No 3: September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i3.2235

Abstract

This study compares the performance of Long Short-Term Memory (LSTM) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) models for daily rainfall forecasting in South Tangerang City using meteorological data from January 2005 to July 2025. Data from official meteorological stations was processed with mean imputation for missing values and MinMaxScaler normalization. Models were evaluated based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and coefficient of determination R². Results show CNN-LSTM outperforms with RMSE 0.79, MAE 0.63, MSE 0.62, and R² 0.61, compared to LSTM (RMSE 0.83, MAE 0.60, MSE 0.68, R² 0.58). Prediction visualizations confirm CNN-LSTM's accuracy in capturing extreme patterns, with statistically significant differences via t-test. The novelty lies in using a long-term (20-year) dataset for tropical Indonesia, demonstrating the hybrid model's efficacy for complex spatio-temporal predictions. Findings support flood early warning systems and water resource management, recommending additional climate variable integration for further development.
Co-Authors Agung Perdananto Ali Imron Ariyansyah Cheby Raka Sadewa Eka Muhammad Guntur Fajar Ramadhan Faros Fadillah Robinson Frans Wendi M.C.P Uliyatunisa