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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 Ilmiah Sinus Jurnal ICT : Information Communication & Technology Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Information Systems and Informatics Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JATI (Jurnal Mahasiswa Teknik Informatika) 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 Informasi interaktif : jurnal informatika dan teknologi informasi ILKOMEDIA: Jurnal Ilmu Komputer dan Multimedia Jurnal Sistem Informasi dan Ilmu Komputer Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Endang Purnama Giri
Ilmu Komputer, Sekolah Sains Data, Matematika, Dan Informatika, IPB University
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PENERAPAN METODE IMAGE PROCESSING BERBASIS HISTOGRAM WARNA UNTUK IDENTIFIKASI DAN PENENTUAN TINGKAT KEMATANGAN BUAH PISANG Davino Rizqy Dayan; Muhammad Gibran Anggalana; Muh Fahrul Fahrezi; Naufalih Muzakki Sujono; Rheynesta Hannover; Endang Purnama Giri; Gema Parasti Mindara
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 3 (2026): JATI Vol. 10 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i3.18348

Abstract

Penentuan tingkat kematangan buah pisang merupakan aspek penting dalam proses pascapanen untuk menjaga kualitas sebelum distribusi. Namun, metode manual yang sering digunakan petani masih bersifat subjektif dan kurang akurat. Penelitian ini bertujuan mengembangkan sistem klasifikasi tingkat kematangan pisang menggunakan pengolahan citra digital berbasis histogram warna dan algoritma Support Vector Machine (SVM). Dataset yang digunakan sebanyak 1.865 citra dari empat kategori kematangan, yaitu mentah, setengah matang, matang, dan terlalu matang. Tahapan penelitian meliputi segmentasi citra menggunakan Otsu Thresholding, ekstraksi fitur statistik orde pertama (Mean, Skewness, Energy, dan Smoothness) dari ruang warna RGB, HSV, dan Lab*, serta klasifikasi menggunakan SVM kernel Radial Basis Function (RBF). Hasil pengujian menunjukkan model mampu mengklasifikasikan tingkat kematangan pisang dengan akurasi sebesar 98%. Kelas matang memperoleh precision 93%, recall 95%, dan F1-score 94%, sedangkan kelas mentah mencapai precision 99%, recall 100%, dan F1-score 99%. Kelas setengah matang memperoleh nilai 99% untuk seluruh metrik, sementara kelas terlalu matang mencapai precision 98%, recall 96%, dan F1-score 97%. Hasil ini menunjukkan metode yang digunakan efektif sebagai solusi otomatis dan objektif dalam identifikasi kematangan buah pisang
People Counting in Sample Video Footage Using CNN Integrated with YOLOv5 Ahmad Hasan Faqih Aulia; Carissa Fathinah Balti; Keisyah Zahra Anatasya; Gema Parasti Mindara; Endang Purnama Giri
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.
PERBANDINGAN GAUSSIAN BLUR, MEDIAN, DAN BILATERAL FILTER UNTUK REDUKSI NOISE CITRA DIGITAL Vellisya Afifa Qonita; Keisha Ramadhani; Dwi Febriyanti; Muthiah Hamidah; Achmad Fauzal Khobir; Endang Purnama Giri; Gema Parasti Mindara
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/prosisko.v13i1.11556

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.
Perbandingan Metode Filtering untuk Peningkatan Kualitas Citra Daun Tomat Terinfeksi TMV Azzahra Nabila; Luna Falya Iskandar; Nika Rani Nur Shafa Lubis; Zafira A'idah Gunawan; Pramesyaila Hendri; Endang Purnama Giri; Gema Parasati Mindara
Jurnal Ilmu Komputer dan Multimedia Vol. 2 No. 2 (2025): ILKOMEDIA Edisi Desember 2025
Publisher : Akademi Komunitas Negeri Putra Sang Fajar Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46510/ilkomedia.v2i2.80

Abstract

Penelitian ini telah membandingkan efektivitas tiga metode filtering digital: Median Filter, Gaussian Filter, dan Bilateral Filter dalam meningkatkan kualitas citra daun tomat terinfeksi Tomato Mosaic Virus (TMV) dengan penambahan gangguan Gaussian dan salt-and-pepper noise. Tujuan utama penelitian adalah menentukan metode filtering yang menghasilkan citra paling mendekati aslinya, berdasarkan evaluasi metrik Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), dan Structural Similarity Index Measure (SSIM). Hasil eksperimen menunjukkan Bilateral Filter secara konsisten menghasilkan nilai MSE terendah, PSNR tertinggi, serta SSIM terbaik, baik pada kondisi noise Gaussian maupun salt-and-pepper. Untuk noise Gaussian, Gaussian Filter lebih baik daripada Median Filter, namun belum mampu mempertahankan detail tepi seperti Bilateral Filter. Pada noise salt-and-pepper, Median Filter efektif dalam mereduksi titik-titik noise impulsif, namun Bilateral Filter tetap unggul dalam mempertahankan struktur dan tekstur daun. Kesimpulannya, Bilateral Filter adalah metode yang paling efektif untuk preprocessing citra daun tomat terinfeksi TMV dengan berbagai jenis noise, sehingga dapat meningkatkan akurasi analisis citra dan identifikasi penyakit secara digital.
PERBANDINGAN KINERJA ALGORITMA KNN DAN SVM DALAM KLASIFIKASI KEMATANGAN BUAH JERUK MEDAN BERDASARKAN CITRA DIGITAL Fadilla Julianifa Putri; Siti Laila Nurjannah; Dwi Febrina Wati; Silvia Ariani Daulay; Indira Sistamarien; Endang Purnama Giri; Gema Parasti Mindara
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.
IMPLEMENTASI HOUGH CIRCLE TRANSFORM DAN ORB UNTUK DETEKSI KLASIFIKASI NOMINAL KOIN RUPIAH Maulana Zulfan Azka; Hanin Putri Sholiha; Syahna Aulia Putri; Muhammad Mahardicka Alfattah Zelda; Endang Purnama Giri; Gema Parasti Mindara
Informasi Interaktif : Jurnal Informatika dan Teknologi Informasi Vol 11 No 2 (2026): Jurnal Informasi Interaktif
Publisher : Program Studi Informatika Fakultas Teknik Universitas Janabadra

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Manual coin counting and classification in high-volume transactions are often inefficient and prone to human error. In computer vision implementations, the primary challenges in coin detection are lighting variations and changes in image scale, rendering conventional area-based methods inaccurate. This study aims to develop a coin detection system robust to changes in camera distance and lighting conditions. The proposed method combines the Hough Circle Transform algorithm for detecting geometric coin locations and ORB (Oriented FAST and Rotated BRIEF) for classifying denominations based on surface texture feature matching. The system is equipped with an adaptive learning mechanism (Human-in-the-Loop), allowing users to interactively train the system upon encountering new coin variants or unrecognized coins. Test results indicate that the combination of CLAHE (Contrast-Limited Adaptive Histogram Equalization) pre-processing and Hough detection successfully isolates overlapping circular objects, while the feature matching method effectively distinguishes coin denominations with similar diameters but distinct textures. It is concluded that integrating texture analysis with user manual correction features significantly enhances system accuracy and flexibility compared to static contour-based detection methods.
Pengembangan Aplikasi Berbasis Kecerdasan Buatan untuk Diagnostik Penyakit dan Optimalisasi Ekosistem Akuarium Zolla Perdana Putra Harahap; Maulana Irfan; Farhan Hakim; Adrian Fardan Andi; Aditya Wicaksono; Gema Parasti Mindara; Inna Novianty; Lathifunnisa Fathonah; Endang Purnama Giri
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 01 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i01.235

Abstract

Indonesia boasts a high diversity of ornamental fish species, both freshwater and marine, making it a significant commodity in the global market. However, the high mortality rate of ornamental fish due to a lack of knowledge about disease diagnosis and aquarium ecosystem management remains a major challenge. To address this issue, FishCo was developed as an AI-based application designed to assist ornamental fish owners, particularly beginners, in diagnosing diseases and managing aquarium ecosystems. FishCo offers features such as disease diagnosis using Convolutional Neural Network (CNN) technology, ecosystem setup recommendations tailored to specific fish species, and consultations via FishBot. The application is built as a cross-platform solution, with a backend powered by Laravel and a mobile application developed in Android Studio using the Java programming language. Testing has shown that FishCo can accurately identify diseases, provide appropriate recommendations, and receive positive feedback from users. This application is expected to enhance the success rate of ornamental fish care, reduce mortality rates, and contribute to the preservation of Indonesia’s aquatic biodiversity.
Sistem Pengelolaan Toko Hewan Peliharaan Petify Berbasis Website Zafira A’idah Gunawan; Fadilla Julianifa Putri; Raina Disa Wibowo; Cahaya Elisabet Butarbutar; Hanin Putri Sholiha; Aditya Wicaksono; Gema Parasti Mindara; Inna Novianty; Lathifunnisa Fatonah; Endang Purnama Giri
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 01 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i01.590

Abstract

Manual operational data management in pet shops often causes problems, such as inaccurate stock data, difficulty in tracing transaction history, and inefficient services. The goal of this study is to create and build a website-based pet shop management system called Petify. This system was developed to provide an integrated platform for administrators to centrally manage product data, grooming services, scheduling, transactions, and suppliers. The system development method used is the Waterfall model, which includes the stages of requirements analysis, system design, implementation, and testing. In the system design stage, system modeling was carried out using ERD and DFD, as well as user interface (UI/UX) designs tailored to the needs of administrators. The result of this research is a website-based information system that can replace manual recording and simplify store operations. Based on system testing using the Black Box Testing method with the Equivalence Partitioning approach, all functional features of the system were proven to work well and valid according to user needs. The implementation of Petify is expected to improve store management efficiency and service quality for customers.
Aplikasi Website dengan Flask dan Open CV untuk Filtering Warna Bagi Penderita Buta Warna Mia Putri Yeza; Marsya Halya Alfrida; Anka Luffi Ramdani; Fauzi Adi Saputra; Capriandika Putra Susanto; Endang Purnama Giri; Gema Parasti Mindara
Jurnal Teknik Mesin, Industri, Elektro dan Informatika Vol. 3 No. 4 (2024): Desember : JURNAL TEKNIK MESIN, INDUSTRI, ELEKTRO DAN INFORMATIKA
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jtmei.v3i4.4536

Abstract

Color blindness is a hereditary vision disorder that impairs the ability to distinguish certain colors, significantly affecting daily activities and quality of life. This study aims to develop a web-based application using Flask and OpenCV to assist individuals with color blindness in identifying colors accurately. The application incorporates image processing technology to enhance color contrast and simulate real-time color perception adjustments. Employing the Waterfall model of the Software Development Life Cycle (SDLC), the study encompasses requirements analysis, system design, implementation, testing, and maintenance. Key features include Camify, for real-time color adjustments via device cameras, and Pickerify, for detecting colors in uploaded or live images. Testing reveals the application's effectiveness in providing improved color perception for users with various types of color blindness (e.g., Deuteranopia, Protanopia, Tritanopia). Despite minor limitations under extreme lighting conditions, the intuitive user interface and robust functionality make the application accessible to diverse user groups. Future enhancements include integrating AI for personalized filters and expanding compatibility with emerging technologies.
Co-Authors Abdurrahman, Hasan Achmad Fauzal Khobir Achmad Syahmi Rasendriya Adha, Adzkia Nifa Aditya Rieyza Munif Aditya Wicaksono Adrian Fardan Andi Agus Buono Ahmad Ahmad Ahmad Hasan Faqih Aulia Ahmad Ridha Ahmad Ridha Akbar, Argy Fawwaz Alifia, Faliana Alkautsar, Muhammad Farhan Alma’ Abdhillah Alya Putri Salsabila Anatasya Wenita Putri 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 Atha, Muhammad Sammy Athallah, Ananda Salma Aubrey Nedwin Mantiri Aulia Anggraeni Auzi Asfarian Avanza, Nur’afia Azhar Nadhif Annaufal Aziz Kustiyo Azzahra Nabila Bagaskoro Dwi Adhie Nugroho Bagus Hardika Bima Julian Mahardika Budy Santoso Cahaya Elisabet Butarbutar Capriandika Putra Susanto Carissa Fathinah Balti Daffala Viro Hidayat Daffarizqy Prastowiyono Darmansah, Fadhlan Zaki Davino Rizqy Dayan Denty Nirwana Bintang Desinta Nur Rahma Dhabith, Badzlan Nur Dika Aprilio Wibowo Dini Nurul Azizah Dwi Febrina Wati Dwi Febriyanti Dzulfiqar Azhar Al Ghifari Ester Olivia Silalahi Fadilla Julianifa Putri Faras, Algyon Farhan Hakim Fathonah, Lathifunnisa Faturrahman, Nafis Fauzi Adi Saputra Fauzi Ikhsan Suswanto Fikri Saputra Firman Ardiansyah Fitrah Satrya Fajar Kusumah Fredicia Fredicia Galih Ario Prayudo Gema Parasati Mindara Gema Parasti Mindara Gema Parasti Mindara Gema Parasti Mindara Hafiz Fadli Faylasuf Hakim, Ghaeril Juniawan Parel Hanifah Nur Zahrah Hanifah, Nurrizkyta Aulia Hanin Putri Sholiha Hari Agung Adrianto Hasibuan, Lailan Sahrina Hasna Nabiilah Widiani Hassan Nasrallah Matouq Helena Dewi Hapsari Hendriyan, Amanda Pricillia Ibnu Aqil Mahendar Ibrahim, Arhammirza Indira Sistamarien Inna Novianty Inna Novianty Inyasdi Kahvi, Muhamad Restu Iqna Raidan Abdurrahman 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 Keisha Ramadhani Keisyah Zahra Anatasya Keysha Maulina Halimi Khairunisa, Aulia Kinaya Khairunnisa Komariansyah Kurniawan, Fadly Lailatulrahmi, Puti Aisyah Lasardi, Ekky Mulia Lathifunnisa Fatonah Luna Falya Iskandar Luthfi Dika Chandra Mahesa Dzikri Kurniawan Mahza Aiko Zabrina Manurung, Maryetha Marcelita, Faldiena Marsya Halya Alfrida Maulana Irfan Maulana Zulfan Azka Ma’arief, Denasyah Mia Putri Yeza Mindara, Gema Parasti Mochammad Alwan Al Ataya Mochammad Fadiil Thoriq Muchlisinia, Newi Muh Fahrul Fahrezi Muhamad Ali Imron Muhamad Arifin Fadhila Muhammad Adzka Muhammad Al Amin Muhammad Amin Arsyad Muhammad Asyhar Agmalaro Muhammad Bilal Fauzan Muhammad Farhan Fahrezy Muhammad Fathi Ramdhana Muhammad Fillah Alfatih Muhammad Galuh Gumelar Muhammad Gibran Anggalana Muhammad Ilham Nurfajri Muhammad Mahardicka Alfattah Zelda Muhammad Naufal Ardhani Muhammad Naufal Sutardi Muhammad Rafi Alexander Prayoga Muhammad Rafi' Rusafni Muhammad Rahmat Maryadi Muhammad Ramadhan Muhammad Rizki Pramudya Idris Muhammad Yordi Septian Muhammad, Fadhel Muthia Nurul Sa'adah Muthiah Hamidah Nabil Malik Al Hapid Nabil Raihan Alfarizi Nadhifah, Jauza Najla Amelia Putri Nashwandra, Nakula Bintang Nasywa Shafa Salsabila Naufalih Muzakki Sujono Nelvi, Annisa Amanda Nika Rani Nur Shafa Lubis Nova Sukmawati Novianty, Inna Nur Iman Nugraha Nur Indah Chasanah Nur Rahma Ditta Zahra Nurbadillah, Nurbadillah Nurjihan, Saniyyah Wafa Pramesyaila Hendri Pratama, Dharma Pratiwi, Iswi Nur Prayitno, Lilik Puteri, Khinanti Angelita Qonita, Vellisya Afifa Rabbani, Rafif Raden Mas Galih Pradityo Rafi Hilal Zahir Rafli Damara Rahman, Raden Muhammad Raditya Raina Disa Wibowo Raisa Mutia Thahir Rajhaga Jevanya Meliala Ramadhan, Dean Apriana Rangga Wasita Ningrat Rayhan Ananda Hafiz Pradipta Reksa Prayoga Syahputra Reza Pratama Rheynesta Hannover Rheynesta Hannover Riani, Lutfi Rio Ferddinansya Riupassa, Muhammad Hafidz Sidqi Rivanka Marsha Adzani Rizky Fadlurohman Rizky Kurniawan Saputra, Ananda Pratama Setiady Ibrahim Anwar Sharfina Andzani Minhalina Silvia Ariani Daulay Simangunsong, Gandi Abetnego Siti Farah Fakhirah Siti Laila Nurjannah Sony Muhammad Sri Yusrina Stefanny, Arlyn Sugi Guritman Sugiana, Lili Rahmawati Sukmosuwarno, Rizq Muhammad Surya Agung Syah Bintang Syahna Aulia Putri Thoriq Muhammad Pasya Tiara Ariyanto Putri Toto Haryanto Tyanafisya, Aisya Valenza, Ihsan Lana Vellisya Afifa Qonita Vincentius Argadeo Axel Wahyu Mustika Aji Widhiwipati, David Reza Wiguna, Indra Maki Wildan Holik Zafira A'idah Gunawan Zafira A’idah Gunawan Zafira, Cut Yasmin Zahra, Afnan Zahrah, Hanifah Nur Zolla Perdana Putra Harahap