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All Journal Jurnal Teknologi Sistem Informasi dan Aplikasi GEMBIRA (Pengabdian Kepada Masyarakat)
Anggiq Karisma Aji Restu
Universitas Muhammadiyah Kalimantan Timur
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Law, Crime, Criminology & Criminal Justice Social Sciences

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OPTIMASI DATA GEOSPASIAL PEMBARUAN PEMETAAN KECAMATAN SUNGAI PINANG DENGAN PENDEKATAN QGIS Andi Nur Halim; Anggiq Karisma Aji Restu; Arbansyah
Jurnal Gembira: Pengabdian Kepada Masyarakat Vol 1 No 06 (2023): DESEMBER 2023
Publisher : Media Inovasi Pendidikan dan Publikasi

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Abstract

Kecamatan Sungai Pinang, sebagai salah satu dari sepuluh kecamatan di Kota Samarinda, memiliki lima kelurahan yang membentuk lanskap administratifnya, termasuk kelurahan Bandara, Sungai Pinang Dalam, Temindung Permai, Mugirejo, dan Gunung Lingai. Peran penting kecamatan ini tidak hanya terbatas pada aspek administratif dan pemerintahan, tetapi juga mencerminkan kehidupan masyarakat serta kekayaan budaya dan geografis Kalimantan Timur. Namun, pertumbuhan wilayah, pemukiman, dan jumlah penduduk yang tidak seimbang memunculkan kebutuhan akan pembaruan informasi geografis menggunakan teknologi Sistem Informasi Geografis (SIG). kegiatan ini bertujuan memperbaharui dan meningkatkan akurasi informasi geografis Kecamatan Sungai Pinang, khususnya menghadapi dinamika perkembangan Kota Samarinda. Melalui pemanfaatan QGIS, diharapkan dapat mengoptimalkan pengelolaan data geospasial dan memberikan dampak positif yang signifikan dalam mendukung perencanaan pembangunan yang berkelanjutan dan responsif terhadap kebutuhan masyarakat lokal.
Model Optimasi KNN-PSORF dalam Menangani High Dimensional Data Banjir Kota Samarinda Anggiq Karisma Aji Restu; Taghfirul Azhima Yoga Siswa; Wawan Joko Pranoto
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.41587

Abstract

Floods are a natural phenomenon that frequently occurs in Indonesia, including in Samarinda City which has faced flood issues over the past three years, affecting thousands of homes and around 27,000 residents. Predicting flood disasters requires machine learning technology using data mining classification methods. However, classification processes often encounter issues related to high-dimensional data, which can lead to overfitting and class imbalance, thereby biasing dominant classes while neglecting minority classes. This research aims to enhance classification accuracy in Samarinda City's flood data using the K-Nearest Neighbor (KNN) algorithm combined with Relief feature selection and Particle Swarm Optimization (PSO) optimization. The validation method employed is 10-fold cross-validation, with performance evaluation using a confusion matrix. Data sourced from Samarinda City's Disaster Management Agency (BPBD) and Meteorology, Climatology, and Geophysics Agency (BMKG) spans from 2021 to 2023, comprising 19 features and a total of 1095 records. Relief feature selection identified four crucial features: maximum wind direction, wind speed, average wind speed, and maximum wind speed direction. Average evaluations with k values of 3, 5, 7, 11, 13, and 15 demonstrate that Relief feature selection and PSO optimization effectively enhance accuracy in the K-Nearest Neighbor algorithm for flood data, with KNN and PSO yielding improvements of 2-5%. Relief feature selection alone improves accuracy by 1-2%, while combining Relief with PSO provides a 2-5% enhancement. The combined KNN, Relief, PSO model is expected to deliver optimal performance in classifying Samarinda City's flood data.
Co-Authors Andi Nur Halim Arbansyah Arbansyah Taghfirul Azhima Yoga Siswa, Taghfirul Azhima Yoga Wawan Joko Pranoto