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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) SKANIKA: Sistem Komputer dan Teknik Informatika PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Informasi interaktif : jurnal informatika dan teknologi informasi ILKOMEDIA: Jurnal Ilmu Komputer dan Multimedia
Endang Purnama Giri
IPB University
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering

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Perancangan Sistem Informasi Alumni Menggunakan Pendekatan OOP untuk Meningkatkan Konektivitas Lulusan dan Efisiensi Administrasi Prodi Teknologi Rekayasa Perangkat Lunak Alma’ Abdhillah; Raden Mas Galih Pradityo; Muhammad Ramadhan; Dika Aprilio Wibowo; Aditya Wicaksono; Endang Purnama Giri
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 3 (2026): JULY 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i3.6122

Abstract

The management of alumni data for the Software Engineering Technology (SET) Study Program is currently facing challenges, including dispersed data, difficulty in updating, and administrative inefficiencies. This study aims to design a structured and centralized web-based Alumni Information System. The method used is Object-Oriented Analysis and Design (OOAD), which includes literature review, requirements identification, UML modeling (use case, activity, class, sequence diagrams), and interface design. The design results indicate that the system provides nine main functional features, which address the data management problems. The Object Oriented Programming (OOP) approach is applied to produce a system architecture that is modular, maintainable, and scalable. The designed system is estimated to increase administrative process efficiency by more than 90%, instantly reducing data search and verification time. Overall, this design is expected to enhance data accuracy, alumni connectivity, and operational efficiency of the study program.
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.
Co-Authors Achmad Fauzal Khobir Aditya Wicaksono Ahmad Hasan Faqih Aulia Alma’ Abdhillah Azzahra Nabila Carissa Fathinah Balti Dika Aprilio Wibowo Dwi Febrina Wati Dwi Febriyanti Fadilla Julianifa Putri Gema Parasati Mindara Gema Parasti Mindara Hanin Putri Sholiha Indira Sistamarien Keisha Ramadhani Keisyah Zahra Anatasya Luna Falya Iskandar Maulana Zulfan Azka Muhammad Mahardicka Alfattah Zelda Muhammad Ramadhan Muthiah Hamidah Nika Rani Nur Shafa Lubis Pramesyaila Hendri Raden Mas Galih Pradityo Silvia Ariani Daulay Siti Laila Nurjannah Syahna Aulia Putri Vellisya Afifa Qonita Zafira A'idah Gunawan