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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 FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Esensi Infokom: Jurnal Esensi Sistem Informasi dan Sistem Komputer Jurnal Informatika Progres Internet of Things and Artificial Intelligence Journal Jurnal Pengabdian Pada Masyarakat KOMPUTASI Jurnal Serina Abdimas
Teny Handhayani
Universitas Tarumanagara
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Journal : Jurnal Informatika Progres

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i1.426

Abstract

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 Adithya Putra, Farhan; Handhayani, Teny
PROGRESS Vol 17 No 1 (2025): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i1.438

Abstract

This study analyzes the performance of two algorithms, Long Short-Term 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 Adithya Putra, Farhan Afrial, Farhan Andre Andre, Andre Andrian, Gion Andry Winata Angelica Christina Arya Bintang Saputra Arya Dwi Saputra Brando Dharma Saputra Cecillia Chung Chairisni Lubis Cherissa Aeryn Djaya Christina, Angelica Daffa Hilmi Aji Dara Kharisma Limparan David Jansen Dayanti, Afina Putri Desi Arisandi Desi Arisandi Djoenaedi, Owen Duncan Ariel Dwi Saputra, Arya Dyah Erny Herwindiati Dyah Erny Herwindiati Ericko, Teddy Faradila Herfiyana Fawaz Georgia Sugisandhea Hendryli, Janson Herfiyana, Faradila Huang, Jervis Irvan Lewenusa Irvan Lewenusa, Irvan Janson Hendryli Janson Hendryli Jason Jaya, Jefri Jayadi, Bryan Valentino Jeanny Pragantha Jeanny Pragantha Jeremia Pinnywan Immanuel Jochsen, Erico Jong, Fenny Jordi Pradipta Kusuma Jourdan Stanley Julius Juan Karnadi, Benny Kelvin Wijaya Kusuma, Jordi Pradipta Lely Hiryanto Lim, Maggie Lubis, M.Kom., Chairisni Mahendra, Izam Susilo Mahendra, Izam Susilo Manatap Dolok Lauro, Manatap Dolok Manatap Sitorus Marchel Yusuf Rumlawang Arpipi Mathew Judianto Matthew Oni Matthew Russel Paul Mohammad Faraditya Eka Putra Monica Ong Muhammad Isnaini Syaifudin Nicko Kurniawan Novario Jaya Perdana Owen Maytrio Phratama Paulus Samotana Zalukhu Phratama, Owen Maytrio Purba, Andrew Castello Putra, Tommy Wijaya Sandy Permadi Sormin Sitorus Dolok Lauro , Manatap Sopany, Mikael Reichi Sumarlie , Devid Sumarlie, Aurellia Clearesta Tanudy, Clara Tasya Syamsudin Tedja, Peter James Tony Tony Veri Wasino Wasino Wasino Wasino, Wasino William William Winata, Andry Zyad Rusdi