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All Journal Jurnal CoreIT Jurnal Riset Ilmu Teknik Jurnal INFOTEL
Victor Amrizal
UIN Syarif Hidayatullah
Computer Science & IT Electrical & Electronics Engineering Engineering

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IDENTIFIKASI MATAN HADITS MENGGUNAKAN NATURAL LANGUAGE PROCESSING DAN ALGORITMA KNUTH MORRIS PRATT BERBASIS WEB Munandar, Aris; Amrizal, Victor; Arini, A -
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 5, No 2 (2019): Desember 2019
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (353.13 KB) | DOI: 10.24014/coreit.v5i2.8477

Abstract

Proses penentuan kesahihan hadits adalah proses yang memerlukan masa yang panjang, karena proses tersebut dilakukan secara manual. Penyidik hadits perlu merujuk dari satu kitab ke kitab lain. Oleh karena itu, untuk memudahkan pencarian hadits dibutuhkan sistem identifikasi hadits. Penelitian ini menggunakan metode natural language processing melalui tahap tokenizing, filtering, dan analisis serta algoritma knuth morris pratt. Menggunakan natural language processing sistem mampu memahami teks hadits yang dimasukan oleh setiap pengguna. Dengan menggunakan algoritma knuth morris pratt, dapat memberikan kemudahan dalam pencarian teks matan hadits yang memiliki kemiripan. Sehingga sistem mampu menghasilkan hadits yang sesuai dengan harapan dan dapat mengetahui kevalidasian hadits tersebut.
Investigating Synthetic Traffic Generators for Zipf Distribution Simulation Accuracy Fahrianto, Feri; Arifin, Viva; Shofi, Imam Marzuki; Suseno, Hendra Bayu; Amrizal, Victor; Azhari, Muhamad; Pratiwi, Anggy Eka
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1359

Abstract

Accurate traffic generation is essential for realistic network simulations in systems such as Content Delivery Networks (CDNs), Information-Centric Networks (ICNs), and the Internet of Things (IoT). These environments handle various types of data traffic—ranging from web pages and videos to sensor data and software updates—making it critical to model traffic patterns effectively. A well-designed traffic generator enables researchers and engineers to simulate real-world workloads, test scalability, and evaluate the performance of caching, routing, and resource allocation strategies under realistic conditions. Each traffic class has unique characteristics, including object size distributions, access patterns, and temporal dynamics. Capturing these differences is key to producing meaningful simulation results. For instance, CDNs require simulation of content popularity and delivery latency, ICNs focus on content retrieval and caching efficiency, while IoT simulations demand modeling of device behavior and intermittent communication. To support such complex scenarios, a traffic generator must not only mimic real user behavior but also allow for flexible scaling, combination, and modification of traffic patterns. This paper presents a method for evaluating synthetic traffic generators by comparing their output to the statistical properties of the Zipf distribution. The focus is on assessing whether synthetic traffic accurately reflects the heavy-tailed nature of real-world traffic as modeled by Zipf’s law. By analyzing the frequency distribution of requests generated by the traffic model and comparing it to theoretical Zipf curves, the study provides insights into the fidelity of the traffic generator. We measure the discrepancy between the simulated network traffic and the theoretical model to evaluate the accuracy and realism of the traffic generation approach.
Implementation of Adaptive Neuro-Fuzzy Inference System and Image Processing for Design Applications Paper Age Prediction Cynthia Dewi, Valeria; Amrizal, Victor; Eka Muzayyana Agustin , Fenty
Jurnal Riset Ilmu Teknik Vol. 1 No. 1 (2023): May
Publisher : Lembaga Penelitian dan Ilmu Pengetahuan JEPIP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59976/jurit.v1i1.6

Abstract

The development of technology today is widely misused by some people who intend to forge paper on documents and books. One way to find out the authenticity of a paper is by knowing its age. The age of paper can be known in several ways: carbon dating, uranium dating, and potassium-argon dating. But these methods still have weaknesses, requiring sophisticated equipment at a high cost, long processes to get results and limited access. To solve this problem, researchers made an application that can identify the age range of a sheet of paper with a faster process, low cost and does not have to be used by laboratory employees alone. The application is a Paper Age Prediction Application made desktop-based, using the MATLAB programming language with the Anfis Sugeno (TSK) Gaussian membership function method. Image processing by taking the average values of C, M, Y, and K from 70 images used as a database and will be trained with ANFIS. The research method uses interviews, observations, and literature studies—the prototype application development method. The test results showed an application success rate in identifying 60 data that had been trained by 100% against 40 that had not been trained by 42.5%.  
Co-Authors Arini, A - Aris Munandar Cynthia Dewi, Valeria Eka Muzayyana Agustin , Fenty Hendra Bayu Suseno Muhamad Azhari Pratiwi, Anggy Eka Shofi, Imam Marzuki Viva Arifin