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All Journal Jurnal Pengabdian Informatika (JUPITA) Algoritma: Jurnal Matematika, Ilmu Pengetahuan Alam, Kebumian dan Angkasa
Ni Kadek Trisnawati
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Humanities Computer Science & IT Earth & Planetary Sciences Mathematics Other

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Pengembangan Aplikasi (OCR) Optical Character Recognition Berbasis Snipping Di Kantor Pertanahan Kabupaten Karangasem Ni Kadek Trisnawati; I Gede Santi Astawa
Jurnal Pengabdian Informatika Vol. 3 No. 3 (2025): JUPITA Volume 3 Nomor 3, Mei 2025
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

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Abstract

Optical Character Recognition merupakan sebuah teknologi yang digunakan untuk mengekstrak teks dari suatu objek atau gambar. Aplikasi Optical Character Recognition digunakan pada saat validasi surat ukur di kantor Badan Pertanahan Nasional Kabupaten Karangasem. Dimana proses validasi berkas perlu melakukan penyesuaian data dari data digital berupa hasil scan surat ukur kedalam data baru yang akan divalidasi. Aplikasi ini menggunakan bahasa pemrograman python yang terdiri dari fitur snip and copy untuk menangkap teks pada gambar, teks yang berhasil ditangkap bisa diedit atau langsung di paste pada tempat yang diinginkan. Aplikasi ini dapat meningkatkan efisiensi waktu dalam melakukan proses validasi surat ukur. Kata kunci : Optical Character Recognition , Python, Badan Pertanahan Nasional Kabupaten Karangasem.
Prediksi Jumlah Sepeda yang Melintasi Willianmsburg Bridge Menggunakan Regresi Binomial Negatif Berdasarkan Variabel Cuaca: Suhu dan Curah Hujan Mulyani, Luh Sukma; Stefani Putri Wulandari; Marcellina Layata; Ni Kadek Trisnawati; I Wayan Sumarjaya
Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa Vol. 3 No. 6 (2025): Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/algoritma.v3i6.859

Abstract

Negative Binomial Regression is a statistical modeling approach used to analyze count data with overdispersion, where the variance exceeds the mean. This study applies the method to examine the influence of weather factors on the daily number of cyclists crossing the Williamsburg Bridge in New York City. The independent variables used in the analysis include maximum temperature, minimum temperature, and precipitation. The dataset was obtained from the NYC Department of Transportation through the Kaggle platform and covers the period from April 1 to April 30, 2016. The analysis began with a Poisson Regression model; however, the presence of overdispersion was identified, indicated by a high AIC value of 8598.19, suggesting that the model was not suitable. The alternative Negative Binomial Regression model was then employed and produced a significantly lower AIC value of 518.77, demonstrating a superior fit. The findings indicate that maximum temperature has a positive effect on the number of cyclists, while precipitation shows a significant negative effect. Conversely, minimum temperature does not exhibit a meaningful influence. These results highlight the importance of considering weather conditions when planning bicycle-based transportation systems and support the development of sustainable mobility strategies in urban environments.
Co-Authors I Gede Santi Astawa I Wayan Sumarjaya Marcellina Layata Mulyani, Luh Sukma Stefani Putri Wulandari