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All Journal Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal INFOTEL
Pratiwi, Anggy Eka
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology

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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.
Aspect-based Sentiment Analysis on Electric Motorcycles: Users’ Perspective Anwar, Muchamad Taufiq; Permana, Denny Rianditha Arief; Juniar, Ahmad; Pratiwi, Anggy Eka
Advance Sustainable Science, Engineering and Technology Vol 6, No 2 (2024): February - April
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i2.18129

Abstract

Electric Vehicles (EVs) adoption is emerging especially electric motorcycles due to their lower price. Research has shown that the majority of people have positive sentiments towards EVs but most of the sentiments were from people who did not already own or use EVs, but rather from people who reacted / commented towards a product that is recently being launched/announced. This research aims to evaluate users’ opinions regarding the positive and negative aspects of electric motorcycles they had purchased / used. This information will be beneficial for the manufacturers and marketers as an evaluation for their products; and it is also beneficial for prospective buyers as a buying consideration. This research uses Aspect-Based Sentiment Analysis applied on 844 electric motorcycles review data from www.bikewale.com website. Results showed that the notable positive sentiments are related to smooth riding experience and low maintenance. Whereas notable negative sentiments are related to poor build quality and product malfunctions. The other aspects of electric motorcycles received mixed sentiments such as related to vehicle speed and customer service. The research findings, limitations, and future research direction are discussed.
Automatic Complaints Categorization Using Random Forest and Gradient Boosting Anwar, Muchamad Taufiq; Pratiwi, Anggy Eka; Udhayana, Khadijah Febriana Rukhmanti
Advance Sustainable Science, Engineering and Technology Vol 3, No 1 (2021): November-April
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v3i1.8460

Abstract

Capturing and responding to complaints from the public is an important effort to develop a good city/country. This project aims to utilize Data Mining to automatize complaints categorization. More than 35,000 complaints in Bangalore city, India, were retrieved from the “I Change My City” website (https://www.ichangemycity.com). The vector space of the complaints was created using Term Frequency–Inverse Document Frequency (TF-IDF) and the multi-class text classifications were done using Random Forest (RF) and Gradient Boosting (GB). Results showed that both RF and GB have similar performance with an accuracy of 73% on the 10-classes multi-class classification task. Result also showed that the model is highly dependent on the word usage in the complaint's description. Future research directions to increase task performance are also suggested.
Co-Authors Ahmad Juniar Anwar, Muchamad Taufiq Denny Riandhita Arief Permana Feri Fahrianto Hendra Bayu Suseno Muhamad Azhari Shofi, Imam Marzuki Udhayana, Khadijah Febriana Rukhmanti Victor Amrizal Viva Arifin