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All Journal JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Jurnal Informatika dan Teknik Elektro Terapan E-Dimas: Jurnal Pengabdian kepada Masyarakat Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Computatio : Journal of Computer Science and Information Systems Jurnal CoreIT Jurnal Eksplora Informatika SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Sebatik KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Simtek : Jurnal Sistem Informasi dan Teknik Komputer Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat ComTech: Computer, Mathematics and Engineering Applications Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan JISKa (Jurnal Informatika Sunan Kalijaga) INTI Nusa Mandiri Jurnal Mnemonic Jurnal Bakti Masyarakat Indonesia Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Informatika Progres Internet of Things and Artificial Intelligence Journal Jurnal Pengabdian Pada Masyarakat Edu Komputika Journal Jurnal Serina Abdimas Jurnal Esensi Infokom : Jurnal Esensi Sistem Komputer dan Informasi
Teny Handhayani
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Journal : jurnal informatika progres

 1 
PERBANDINGAN KINERJA K-NEAREST NEIGHBORS DAN CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI CITRA KONDISI PERMUKAAN JALAN Fenny Jong; Teny Handhayani
PROGRESS Vol 17 No 1 (2025): April
Publisher : P3M STMIK Profesional Makassar

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Improving road infrastructure quality is an important aspect of transportation development and road user safety. Automatically assessing road surface conditions can accelerate maintenance and repair efforts. This study compares two classification methods, K-Nearest Neighbors (KNN) and Convolutional Neural Network (CNN), to evaluate road surface conditions based on digital images. Texture features are extracted using the Gray Level Co-occurrence Matrix (GLCM), including Contrast, Homogeneity, Energy, and others, to enhance the classification accuracy in KNN, while feature extraction and classification in CNN are performed automatically. The dataset used in this research consists of 1500 images of road surfaces with three different conditions: smooth, cracked, and potholes. Each condition contains 500 images with a resolution of 300x300 pixels. The results show that the KNN algorithm achieves an accuracy of 57.2%, while CNN demonstrates the best performance with an accuracy of 93.8%. for 80% training data and 20% testing data
PREDIKSI HARGA DAGING SAPI DI KOTA JAKARTA PUSAT MENGGUNAKAN LSTM DAN GRU Farhan Adithya Putra; Teny Handhayani
PROGRESS Vol 17 No 1 (2025): April
Publisher : P3M STMIK Profesional Makassar

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Abstract

This study analyzes the performance of two algorithms, Long ShortTerm Memory (LSTM) and Gated Recurrent Unit (GRU), in predicting data from the PIHPS website, focusing on beef commodity prices. The dataset was divided into two proportions: 80:20 and 70:30, and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and coefficient of determination (R²). The experimental results showed that GRU with 128 units and a 70:30 proportion achieved the best performance, with metrics of MAE at 170, RMSE at 390.2889, and R² at 0.902. The goal of this research is to determine the most suitable algorithm and unit configuration for this dataset. Future research is expected to integrate additional data with more complex models to improve prediction accuracy.
Co-Authors Adela Calista Adela Tania Agus Budi Dharmawan Akmal Farouqi Andre Andre Andre Andre, Andre Andrew Castello Purba Andrian, Gion Andry Winata Angelica Christina Arya Bintang Saputra Arya Dwi Saputra Arya Dwi Saputra Brando Dharma Saputra Cecillia Chung Chairisni Lubis Cherissa Aeryn Djaya Christina, Angelica Daffa Hilmi Aji Dara Kharisma Limparan Darius Andana Haris David Jansen Dayanti, Afina Putri Desi Arisandi Desi Arisandi Desi Arisandi Desi Arisandi Desi Arisandi Duncan Ariel Dwi Saputra, Arya Dyah Erny Herwindiati Ericko, Teddy Faradila Herfiyana Farhan Adithya Putra Farhan Afrial Fawaz Fenny Jong Gabriella Adeline Halim Georgia Sugisandhea Gion Andrian Gion Andrian Hendryli, Janson Herfiyana, Faradila Huang, Jervis Irvan Lewenuisa Irvan Lewenusa Irvan Lewenusa Irvan Lewenusa Irvan Lewenusa, Irvan Janson Hendryli Janson Hendryli Jason Jason Sunaryo Jaya, Jefri Jayadi, Bryan Valentino Jeanny Pragantha Jeanny Pragantha Jeanny Pragantha Jeanny Pragantha Jefri Jaya Jefri Jaya Jeremia Pinnywan Immanuel Jochsen, Erico Jordi Pradipta Kusuma Jourdan Stanley Julius Juan Karnadi, Benny Kelvin Wijaya Kelvin Wijaya Kusuma, Jordi Pradipta Lely Hiryanto Lim, Maggie Lubis, M.Kom., Chairisni Mahendra, Izam Susilo Mahendra, Izam Susilo Manatap Dolok Lauro Manatap Dolok Lauro, Manatap Dolok Manatap Sitorus Manatap Sitorus Dolok Lauro Marcel Yusuf Rumlawang Arpipi Marchel Yusuf Rumlawang Arpipi Mathew Judianto Matthew Oni Matthew Russel Paul Mikael Reichi Sopany Mohammad Faraditya Eka Putra Monica Ong Muhammad Isnaini Syaifudin Naufal Firdausyan Nicholas Eugene Supardi Nicko Kurniawan Novario Jaya Perdana Oni, Matthew Owen Djoenaedi Owen Maytrio Phratama Paulus Samotana Zalukhu Peter James Tedja Phratama, Owen Maytrio Purba, Andrew Castello Raffy Sonata Reinerius S. D. Kristiantoro Sandy Permadi Sormin Sitorus Dolok Lauro , Manatap Sopany, Mikael Reichi Sumarlie , Devid Sumarlie, Aurellia Clearesta Tanudy, Clara Tasya Syamsudin Tommy Wijaya Putra Tony Tony Veri Vincent Alexander Wasino Wasino Wasino Wasino . Wasino Wasino William William Winata, Andry Yudistira Permana Zyad Rusdi