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All Journal Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Informatika dan Teknik Elektro Terapan Jurnal Informatika Upgris Dinamisia: Jurnal Pengabdian Kepada Masyarakat JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal ULTIMATICS Krea-TIF: Jurnal Teknik Informatika Jurnal ICT : Information Communication & Technology Journal of Information Systems and Informatics SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Jurnal Sains Terapan : Wahana Informasi dan Alih Teknologi Pertanian PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Scientech Research and Development Jurnal Teknik Mesin, Industri, Elektro dan Informatika Prosiding Seminar Nasional Universitas Ma Chung Scientica: Jurnal Ilmiah Sains dan Teknologi AI dan SPK : Jurnal Artificial Intelligent dan Sistem Penunjang Keputusan Jurnal Rekayasa Sistem Informasi dan Teknologi International Journal of Multilingual Education and Applied Linguistics International Journal of Computer Technology and Science International Journal of Information Engineering and Science International Journal of Electrical Engineering, Mathematics and Computer Science ILKOMEDIA: Jurnal Ilmu Komputer dan Multimedia Jurnal Sistem Informasi dan Ilmu Komputer
Endang Purnama Giri
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PERBANDINGAN AKURASI TESSERACT DAN EASYOCR SEBELUM DAN SESUDAH PRAPEMROSESAN PADA CITRA NOTA Puteri, Khinanti Angelita; Alifia, Faliana; Lailatulrahmi, Puti Aisyah; Mindara, Gema Parasti; Giri, Endang Purnama
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8603

Abstract

Pengenalan teks pada citra nota menggunakan Optical Character Recognition (OCR) masih relevan diteliti karena tingginya variasi kualitas citra. Penelitian ini mengevaluasi kinerja Tesseract dan EasyOCR dalam mengenali teks pada citra nota dengan beberapa metode prapemrosesan. Dataset berasal dari Kaggle dengan 50 sampel citra yang dipilih menggunakan stratified sampling. Pengujian dilakukan dengan menghitung Character Error Rate (CER) antara hasil OCR dan ground truth. Hasil menunjukkan nilai CER berada pada kisaran 18%–25%, dengan performa terbaik Tesseract pada mode denoise dan EasyOCR pada mode grayscale. Metode threshold memberikan penurunan akurasi paling signifikan. Kualitas citra dan jenis prapemrosesan terbukti memengaruhi kinerja OCR, sehingga pemilihan prapemrosesan yang tepat sangat penting dalam meningkatkan akurasi pengenalan teks pada citra nota.
Fire Detection Berbasis Computer Vision Menggunakan YOLOv8 Secara Real-Time Sukmosuwarno, Rizq Muhammad; Islam, Muhammad Faris Fadhil; Rahman, Raden Muhammad Raditya; Mindara, Gema Parasti; Giri, Endang Purnama
Jurnal ICT: Information Communication & Technology Vol. 25 No. 2 (2025): JICT-IKMI, December , 2025
Publisher : LPPM STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36054/jict-ikmi.v25i2.330

Abstract

This study presents the development of a fire detection system using image processing techniques based on the YOLOv8 object detection algorithm to achieve fast, accurate, and real-time performance. A dataset of fire images with various visual characteristics was preprocessed, converted into YOLO annotation format, and used to train the model for 30 epochs. Evaluation results demonstrate that the YOLOv8 model performs effectively, achieving an mAP50 of 0.646, a precision of 0.889, and an inference speed of 282.5 ms per frame. The system is integrated with OpenCV to process webcam input and display bounding boxes and confidence scores in real time. The implementation confirms that YOLOv8 is a reliable solution for early fire detection, offering faster and more adaptive responses compared to conventional sensor-based methods. This approach can be applied to modern safety monitoring systems to enhance fire prevention efforts.
PERBANDINGAN KINERJA ALGORITMA KNN DAN SVM DALAM KLASIFIKASI KEMATANGAN BUAH JERUK MEDAN BERDASARKAN CITRA DIGITAL Putri, Fadilla Julianifa; Nurjannah, Siti Laila; Wati, Dwi Febrina; Daulay, Silvia Ariani; Sistamarien, Indira; Giri, Endang Purnama; Mindara, Gema Parasti
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3661

Abstract

As a regional flagship commodity with a promising selling value, the process of grouping the maturity level of Medan Orange is still dominated by manual visual techniques. This often triggers data inconsistency and requires a long duration of processing due to personnel subjectivity factors. This research aims to compare the performance of two machine learning algorithms, namely KNN and SVM, in classifying the maturity level of Medan Orange fruit based on digital images. The dataset used is a primary dataset collected directly from Medan Orange farmers in field conditions. The research stages include image acquisition, pre-processing, extraction of HSV-based color features and GLCM-based textures, as well as classification of maturity levels into three classes, namely raw, semi-cooked, and mature. The performance of both algorithms is evaluated using accuracy, precision, and recall metrics. The research results show that the KNN algorithm has a superior performance compared to SVM, with an accuracy rate of 96,25%, while SVM produces an accuracy of 91,25%. This result shows that KNN is effective and more suitable to be applied to the automation system of classification of the maturity of Medan Orange fruit based on digital images.
People Counting in Sample Video Footage Using CNN Integrated with YOLOv5 Aulia, Ahmad Hasan Faqih; Balti, Carissa Fathinah; Anatasya, Keisyah Zahra; Mindara, Gema Parasti; Giri, Endang Purnama
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1933

Abstract

Accurate people counting in dynamic environments remains challenging due to variations in lighting, complex backgrounds, and occlusion. This study proposes a video-based people counting system leveraging a Convolutional Neural Network (CNN) integrated with the YOLOv5 object detection model. The system applies a structured preprocessing pipeline, including frame extraction, normalization, and noise reduction, to enhance data consistency before detection. The model was evaluated using ten real-world campus video sequences to assess detection reliability and counting accuracy. Experimental results demonstrate that the proposed method achieves high precision and recall for real-time detection across diverse scenarios. Performance degradation was observed in frames containing dense crowds or low illumination, indicating limitations under extreme conditions. These findings validate the feasibility of lightweight CNN-based detectors for surveillance and monitoring applications, while highlighting the need for larger datasets and optimized training strategies to improve robustness in more complex environments.
Implementasi Sistem Deteksi Kantuk Secara Real-Time Bagi Pengemudi Menggunakan Metode Eye Aspect Ratio Mochammad Fadiil Thoriq; Muhammad Fathi Ramdhana; Desinta Nur Rahma; Najla Amelia Putri; Rafi Hilal Zahir; Gema Parasti Mindara; Endang Purnama Giri
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 2 No. 4 (2024): November : Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v2i4.4226

Abstract

Traffic accidents are one of the leading causes of death worldwide, where drowsiness while driving is a significant factor that reduces driver alertness. This study develops a real-time driver drowsiness detection system using the Eye Aspect Ratio (EAR) method to avoid this. EAR calculates the ratio of the upper and lower eyelid distances to detect signs of drowsiness based on changes in eye shape. This system utilizes the OpenCV and Dlib libraries to identify faces and measure EAR, with a threshold of 0.25 as a warning trigger. If the EAR value drops below the threshold in several consecutive frames, the system automatically activates an alarm to increase driver alertness. With the advantages of cost efficiency and ease of implementation without additional hardware, this system is suitable for various types of vehicles. The results show that this system is effective in providing early warnings, thus helping to reduce the risk of accidents due to drowsiness.
Penerapan Klasifikasi Gambar Buah dalam Aplikasi FruityLens Menggunakan Metode CNN Bagus Hardika; Mahesa Dzikri Kurniawan; Muhammad Adzka; Daffarizqy Prastowiyono; Apik Banyubasa; Gema Parasti Mindara; Endang Purnama Giri
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 2 No. 4 (2024): November : Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v2i4.4275

Abstract

This research develops a fruit classification system using Convolutional Neural Network (CNN) in the educational application FruityLens, which helps children recognize different types of fruits through image recognition. The application can identify four types of fruits: apple, banana, orange, and watermelon, utilizing an image dataset from open sources. The research methods include dataset collection, image pre-processing, CNN model training, and classification accuracy evaluation. The results indicate that the developed CNN model achieves high accuracy, supporting children's learning about fruits. This implementation is expected to contribute to the advancement of artificial intelligence technology, specifically in the field of fruit object recognition.
Sistem Deteksi Bahasa Isyarat Alfabet Menggunakan Dataset American Sign Language (ASL) dan Algoritma Random Forest Siti Farah Fakhirah; Muhammad Fillah Alfatih; Hasna Nabiilah Widiani; Thoriq Muhammad Pasya; Endang Purnama Giri; Gema Parasti Mindara
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 2 No. 4 (2024): November : Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v2i4.4321

Abstract

Introducing alphabetical sign language is necessary to bridge communication between deaf and hard-of-hearing people and their surrounding environment. This research aims to develop a sign language alphabet letter detection system based on American Sign Language (ASL). The research methods include data collection, feature extraction with OpenCV and Mediapipe, model development with Random Forest algorithm, and real-time system testing. The test results show that the developed system can achieve 97% prediction accuracy in recognizing hand patterns that represent ASL letters. The system uses a webcam as real-time input, providing accurate responses in various environmental conditions. This research contributes significantly to developing communication support technology for the deaf community, with implications for increased inclusivity and social engagement.
PERBANDINGAN GAUSSIAN BLUR, MEDIAN, DAN BILATERAL FILTER UNTUK REDUKSI NOISE CITRA DIGITAL Qonita, Vellisya Afifa; Ramadhani, Keisha; Febriyanti, Dwi; Hamidah, Muthiah; Khobir, Achmad Fauzal; Giri, Endang Purnama; Mindara, Gema Parasti
PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer Vol. 13 No. 1 (2026): Prosisko Vol. 13 No. 1 Maret 2026
Publisher : Pogram Studi Sistem Komputer Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/zf6dvz09

Abstract

Reduksi noise merupakan tahapan krusial dalam pengolahan citra digital. Hal ini karena reduksi noise dapat menurunkan kualitas visual dan akurasi analisis citra. Permasalahan utama dalam reduksi noise adalah memilih metode filtering paling efektif untuk jenis noise tertentu dengan tetap mempertahankan detail dan tepi objek. Penelitian ini bertujuan untuk membandingkan efektivitas Gaussian Blur, Median Filter, dan Bilateral Filter dalam mereduksi Gaussian noise dan salt and pepper noise, serta mengevaluasi kualitas visual citra hasil filter melalui penilaian subjektif. Metode pada penelitian ini adalah eksperimen kuantitatif dan kualitatif, dimana citra uji (grayscale) diolah dengan ketiga filter dan diukur menggunakan tiga metrik objektif yaitu Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE), dan Structural Similarity Index (SSIM). Kemudian penelitian dilengkapi dengan survei penilaian visual oleh responden.
Co-Authors Abdurrahman, Hasan Achmad Syahmi Rasendriya Aditya Wicaksono Agus Buono Ahmad Ridha Ahmad Ridha Alifia, Faliana Alkautsar, Muhammad Farhan Alya Putri Salsabila Anatasya Wenita Putri Anatasya, Keisyah Zahra Andisa, Gany Anggito Rangkuti Bagas Muzaqi Anka Luffi Ramdani Apik Banyubasa Aprilianti, Dhila Ar Rachman, Muhammad Aqil Musthafa Ari Dian Prastyo Aria Wrdana Ariya Pratama Adjie Nugroho Arya Dimas Wicaksana Asa Yuaziva Athallah, Ananda Salma Aulia Anggraeni Aulia, Ahmad Hasan Faqih Auzi Asfarian Azhar Nadhif Annaufal Aziz Kustiyo Bagaskoro Dwi Adhie Nugroho Bagus Hardika Balti, Carissa Fathinah Bima Julian Mahardika Budy Santoso Capriandika Putra Susanto Daffala Viro Hidayat Daffarizqy Prastowiyono Darmansah, Fadhlan Zaki Daulay, Silvia Ariani Denty Nirwana Bintang Desinta Nur Rahma Dini Nurul Azizah Dwi Febriyanti Ester Olivia Silalahi Faras, Algyon Fathonah, Lathifunnisa Faturrahman, Nafis Fauzi Adi Saputra Fauzi Ikhsan Suswanto Fikri Saputra Firman Ardiansyah Fitrah Satrya Fajar Kusumah Fredicia Fredicia Galih Ario Prayudo Gema Parasti Mindara Gunawan, Zafira A'idah Hafiz Fadli Faylasuf Hakim, Ghaeril Juniawan Parel Hamidah, Muthiah Hanifah, Nurrizkyta Aulia Hari Agung Adrianto Hasibuan, Lailan Sahrina Hasna Nabiilah Widiani Hassan Nasrallah Matouq Helena Dewi Hapsari Hendri, Pramesyaila Hendriyan, Amanda Pricillia Ibnu Aqil Mahendar Ibrahim, Arhammirza Inna Novianty Inyasdi Kahvi, Muhamad Restu Iqna Raidan Abdurrahman Iskandar, Luna Falya Islam, Muhammad Faris Fadhil Jasmine Aulia Mumtaz Jonathan Cristiano Rabika Jonser Steven Rajali Manik Jovita Nabilah Azizi Juliansyah, Rizki Ka-sasi, R.I. Damai Kanaya Sabila Azzahra Keysha Maulina Halimi Khairunisa, Aulia Khobir, Achmad Fauzal Kinaya Khairunnisa Komariansyah Kurniawan, Fadly Lailatulrahmi, Puti Aisyah Lasardi, Ekky Mulia Lubis, Nika Rani Nur Shafa Luthfi Dika Chandra Mahesa Dzikri Kurniawan Manurung, Maryetha Marcelita, Faldiena Marsya Halya Alfrida Ma’arief, Denasyah Mia Putri Yeza Mindara, Gema Parasati Mindara, Gema Parasti Mochammad Alwan Al Ataya Mochammad Fadiil Thoriq Muchlisinia, Newi Muhamad Ali Imron Muhammad Adzka Muhammad Al Amin Muhammad Asyhar Agmalaro Muhammad Bilal Fauzan Muhammad Farhan Fahrezy Muhammad Fathi Ramdhana Muhammad Fillah Alfatih Muhammad Galuh Gumelar Muhammad Ilham Nurfajri Muhammad Naufal Ardhani Muhammad Naufal Sutardi Muhammad Rafi Alexander Prayoga Muhammad Rafi' Rusafni Muhammad Rahmat Maryadi Muhammad Yordi Septian Muhammad, Fadhel Muthia Nurul Sa'adah Nabil Malik Al Hapid Nabil Raihan Alfarizi Nabila, Azzahra Nadhifah, Jauza Najla Amelia Putri Nashwandra, Nakula Bintang Nasywa Shafa Salsabila Nelvi, Annisa Amanda Nova Sukmawati Novianty, Inna Nur Iman Nugraha Nur Indah Chasanah Nur Rahma Ditta Zahra Nurbadillah, Nurbadillah Nurjannah, Siti Laila Nurjihan, Saniyyah Wafa Pratama, Dharma Pratiwi, Iswi Nur Prayitno, Lilik Puteri, Khinanti Angelita Putri, Fadilla Julianifa Qonita, Vellisya Afifa Rabbani, Rafif Rafi Hilal Zahir Rafli Damara Rahman, Raden Muhammad Raditya Raisa Mutia Thahir Rajhaga Jevanya Meliala Ramadhan, Dean Apriana Ramadhani, Keisha Rangga Wasita Ningrat Rayhan Ananda Hafiz Pradipta Reza Pratama Rheynesta Hannover Riani, Lutfi Rio Ferddinansya Riupassa, Muhammad Hafidz Sidqi Rivanka Marsha Adzani Rizky Fadlurohman Saputra, Ananda Pratama Setiady Ibrahim Anwar Sharfina Andzani Minhalina Simangunsong, Gandi Abetnego Sistamarien, Indira Siti Farah Fakhirah Sri Yusrina Stefanny, Arlyn Sugi Guritman Sugiana, Lili Rahmawati Sukmosuwarno, Rizq Muhammad Surya Agung Syah Bintang Thoriq Muhammad Pasya Tiara Ariyanto Putri Tyanafisya, Aisya Valenza, Ihsan Lana Wahyu Mustika Aji Wati, Dwi Febrina Widhiwipati, David Reza Wiguna, Indra Maki Wildan Holik Zafira, Cut Yasmin Zahra, Afnan