Garuda - Garba Rujukan Digital

Analisis Manipulasi Splicing pada Citra Digital Menggunakan Metode Deteksi Tepi Block JPEG Terkompresi Efendi, Muhamad Masjun; Fadli, Sofiansyah Fadli; Wathan, M. Hizbul Wathan Hizbul
Jurnal Explore Vol 8, No 2 (2018)
Publisher : STMIK Mataram

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

Citra digital semakin mudah untuk dimanipulasi dan diedit. Sering kali sebelum citra tersebut dipublikasi dilakukan proses manipulasi. Salah satu bentuk manipulasi citra adalah splicing. Manipulasi ini dilakukan dengan menduplikasi bagian tertentu dari satu citra atau lebih dan meletakkannya pada bagian tertentu di citra target (copy-move pada citra yang berbeda). Tujuan dari manipulasi splicing ini adalah untuk menambah objek dalam citra, contohnya meletakkan suatu objek pada citra target yang seolah-olah objek tersebut berada disana.Pada penelitian ini manipulasi citra jenis ini dideteksi menggunakan metode deteksi tepi block JPEG terkompresi. Metode ini mampu mendeteksi objek citra yang dimanipulasi dengan baik dan akurat

COMPARISON OF HOP COUNT ON WIRELESS MESH NETWORK Eliza Staviana; Hizbul Wathan
International Journal of Informatics and Computation Vol 2 No 2 (2020): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v2i2.29

Wireless Mesh Network (MWN) is a self-configured and self-organized network that can typically be implemented on 802.11 hardware. It consists of several nodes that make up the network backbone in a multi-story and sealed room, in contrast to building a hall or a place without bulkheads. This experiment uses an odd and even number scheme with a maximum number of routers of 8 pieces. In a sealed room, the performance of the method of installation of the number of strange Hops is better than the number of even Hops, with throughput calculation of 2665.19 KB, delay 0.25 s, data lost 0.60 %, and jitter 0.01 s and the best scheme that is with the number of Hops as much as five pieces, with the calculation of the number of throughput 7001.88 KB, delay 0.51s, data lost 0.47%, and jitter 0.002 s. In the free spaces, it can produce the better performance of the even hop count calculation scheme than the odd hop count by building throughput 16709.8 KB, delay 0.2 s, data lost 0.08 %, and jitter 0.03 s. and the best scheme that is with the number of throughput 68975,2 KB, wait for 0.0148 s, data lost 0 %, and jitter 0.0014 s. WMN performance in unshared space is more maximized than the version in a sealed area, with throughput values of 11786.82 kbps, delay of 2.08 ms, and data lost by 0.08 %, and jitter 0.03 s.it can produce the better performance of the even hop count calculation scheme than the odd hop count by producing throughput 16709.8 KB, delay 0.2 s, data lost 0.08 %, and jitter 0.03 s. and the best scheme that is with the number of throughput 68975,2 KB, wait for 0.0148 s, data lost 0 %, and jitter 0.0014 s. WMN performance in unshared space is more maximized than the version in sealed space, with throughput values of 11786.82 kbps, delay of 2.08 ms, and data lost by 0.08 %, and jitter 0.03 s. and data lost by 0.08%, and jitter 0.03s.

Smart City Infrastruktur: Perancangan Integrasi Sistem Melalui Jaringan Fiber Optic di Kota Yogyakarta Tea Qaula Ferbia
CESS (Journal of Computer Engineering, System and Science) Vol 4, No 1 (2019): Januari 2019
Publisher : Universitas Negeri Medan

Abstrak— Smart Infrastruktur adalah salah satu bagian dari komponen-komponen pada Smart City. Bagian ini adalah bagian yang pertama direalisasikan karena merupakan bagian yang cukup penting yang berfungsi mengintegrasikan data dan mambuat fasilitas-fasilitas Sistem informasi didalam keberlangsungan Smart City menjadi berjalan. Kasus yang diambil adalah di Kota Yogyakarta, Indonesia adalah salah satu negara berkembang Asia Tenggara. Kota ini sudah mulai mengikuti perkembangan zaman modern dengan mengimplementasikan Smart City juga. Namun setelah dilihat pada infrastrukturnya, masih belum terjamah untuk mengintegrasikan data jaringan internet melalui media Fiber Optic. Hal tersebut terjadi karena belum adanya perancangan untuk mengintegrasikan data secara terpusat dan secara ringkas dalam bidang system informasi. Pada tulisan ini, penulis membuat perancangan jaringan dan system informasi untuk membantu pemerintah Kota Yogyakarta dalam menyelesaikan bidang Smart Infrastruktur tersebut. Terutama dibagian jaringan internet yang menggunakan Fiber Optic dan Sistem informasi.Keywords— Smart Infrastruktur, Fiber Optic, Sistem Informasi, Perancangan Jaringan.

PERENCANAAN STRATEGIS SISTEM INFORMASI UNTUK MENINGKATKAN KEUNGGULAN KOMPETIFIF PADA LEMBAGA KURSUS BAHASA INGGRIS PARE Rani Laple Satria Putra; M Hizbul Wathan; Muhamad Masjun Efendi
CESS (Journal of Computer Engineering, System and Science) Vol 3, No 2 (2018): Juli 2018
Publisher : Universitas Negeri Medan

Pemanfaatan pelayanan menggunakan teknologi informasi dibutuhkan sebuah lembaga harus memiliki perencanaan strategis SI/TI. Manfaat dari perencanaan strategis SI/TI yaitu terciptanya kegiatan yang lebih efektif, efisien dan transparan. Lembaga kursus bahasa inggris pare telah menerapkan SI dalam melaksanakan kegiatannya, namun belum sepenuhnya digunakan serta masih banyak kegiatan yang dikerjakan secara manual akibatnya membuat kegiatan operasional tidak berjalan maksimal. Tulisan ini akan membahas tentang-tentang langkah-langkah perencanaan strategis SI/TI pada Lembaga Kursus Bahasa Inggris Pare dengan menggunakan kerangka Ward and Peppard serta analisis dengan metode Analisis SWOT, Analisis Value Chain dan Mc Farlan Grid.

Analisis Manipulasi Splicing pada Citra Digital Menggunakan Metode Deteksi Tepi Block JPEG Terkompresi Muhamad Masjun Efendi; Sofiansyah Fadli Fadli; M. Hizbul Wathan Hizbul Wathan
Jurnal Explore Vol 8, No 2 (2018)
Publisher : Universitas Teknologi Mataram

Citra digital semakin mudah untuk dimanipulasi dan diedit. Sering kali sebelum citra tersebut dipublikasi dilakukan proses manipulasi. Salah satu bentuk manipulasi citra adalah splicing. Manipulasi ini dilakukan dengan menduplikasi bagian tertentu dari satu citra atau lebih dan meletakkannya pada bagian tertentu di citra target (copy-move pada citra yang berbeda). Tujuan dari manipulasi splicing ini adalah untuk menambah objek dalam citra, contohnya meletakkan suatu objek pada citra target yang seolah-olah objek tersebut berada disana.Pada penelitian ini manipulasi citra jenis ini dideteksi menggunakan metode deteksi tepi block JPEG terkompresi. Metode ini mampu mendeteksi objek citra yang dimanipulasi dengan baik dan akurat

Studi Perbandingan: Algoritma Random Forest, Naive Bayes Dan Support Vector Machine Dalam Analisis Sentimen Pada Aplikasi Capcut Di Google Play Store irawan, Indra; Wardianto, Wardianto; Wathan, M.Hizbul; Prayogi, M. Bagus
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 5 No. 4 (2024): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v5i4.1959

CapCut, a highly popular video editing tool, boasts millions of users worldwide across various age groups. Posting reviews on the Google Play Store can provide valuable insights into this application. This study aims to evaluate the effectiveness of three classification algorithms Random Forest, Naïve Bayes, and Support Vector Machine in performing sentiment analysis on Google Play Store reviews of the CapCut application. User reviews are identified and categorized into positive, negative, and neutral labels using sentiment analysis methods. A total of three thousand user review datasets were employed in this investigation. The research procedure involved data preprocessing, feature extraction, and model training. The results show that the Random Forest classification method achieved 83% accuracy, the Naïve Bayes method 70% accuracy, and the Support Vector Machine method 86% accuracy, indicating user sentiment towards the CapCut application. With an accuracy of 0.86, the SVM algorithm is found to yield the best results

ConFruit: Effective Fruit Classification Using CNN Algorithm Rani Laple Satria; M Hizbul Wathan
International Journal of Informatics and Computation Vol. 5 No. 1 (2023): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v5i1.44

Fruit is one type of food containing nutrients, vitamins, and minerals that are generally very good for daily consumption. However, various fruit choices make consumers confused about choosing and buying fruit. Many papers have proposed fruit classification to deal with this problem in recent years. Therefore, this study offers a new recommendation model using type to dissect fruit so that buyers can more easily recognize fruit. We collected the primary dataset from Cagle to 3000 fruit images. Based on experiments, our research achieved good accuracy results using the CNN algorithm to classify fruit so that consumers can distinguish between types of fruit. Experimentally demonstrated, we harvested the promised results with better accuracy and small losses than the general fruit classification study.

Establising CNN for Network Intrusion Detection: A Comparative Approach M. Hizbul Wathan; Moh. Aziz
International Journal of Informatics and Computation Vol. 6 No. 1 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i1.69

Intrusion detection plays an important role in protecting systems from various threats. However, as intrusion techniques become more sophisticated, traditional detection methods have shown limitations in identifying new attacks. This research addresses the pressing issue of improving intrusion detection by utilizing Convolutional Neural Networks (CNN) algorithms, compared to various other machine learning techniques such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Decision Trees, and Gradient Boosting (GBoost). The main objective is to evaluate and compare the performance of these algorithms using a comprehensive dataset sourced from Kaggle, which includes 25,192 data and 42 features. Using metrics such as accuracy, precision, recall, and F1-score, the results show a complex pattern in the strengths and weaknesses of each. Surprisingly, CNN achieved exceptional accuracy, raising questions that require further investigation. Notably, KNN stands out as the best-performing machine learning algorithm. Contextualized within existing research, this study advances the understanding of the role of machine learning in intrusion detection, providing valuable insights for practical implementation. The findings reinforce the relevance of adapting to the evolving network threat landscape while raising interesting questions for future research. In conclusion, this research provides a comparative analysis of intrusion detection techniques, offering a basis for making informed decisions to improve network security and mitigate evolving threats.

Vehicle Theft Detection Using YOLO Based on License Plates and Vehicle Ownership Bradika Almandin Wisesa; M. Hizbul Wathan; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Silvia Agustin; Better Swengky
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.105

Detection of vehicle theft requires innovative approaches to address an increasing number of cases in Indonesia. This study presents a YOLOv11-based system for detecting vehicle theft by combining real-time object detection with a vehicle ownership database. The proposed system identifies license plates, detects vehicle owners using facial recognition, and analyzes suspicious activity to determine theft occurrences. The proposed method can produce model effectiveness with an accuracy = 70%. Key improvements in architecture, including enhanced feature fusion and dynamic anchor assignment, contribute to the object’s detection in complex environments. This research can be a potential technique to provide efficient, scalable, and real-time security solutions in dynamic surveillance applications.

Implementasi Convolutional Neural Network (CNN) dalam Diagnosa Penyakit Daun Padi Berdasarkan Citra Digital Irawan, Indra; Wathan, M.Hizbul; Swengky, Better; Ramadani, Ardi
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 3 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i3.2756

This study investigates the implementation of Convolutional Neural Network (CNN) in classifying rice leaf diseases based on digital images. The model classifies three types of diseases: Bacterial Leaf Blight, Rice Blast, and Rice Tungro Virus. A dataset of 240 images was obtained from Kaggle, with 80 images per class. Four training scenarios were applied using 25, 50, 75, and 100 epochs. Preprocessing steps included resizing all images to 150x150 pixels and normalizing pixel values. Evaluation results show that classification accuracy increases with the number of training epochs. The best model was achieved at 100 epochs, yielding a validation accuracy of 91.67% and testing accuracy of 92%. These results demonstrate that CNN is effective in diagnosing rice leaf diseases and can support early detection efforts to strengthen national food security.

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