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All Journal INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Indonesian Journal of Electrical Engineering and Computer Science
Zakaria, Nur Haryani
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Computer Science & IT Decision Sciences, Operations Research & Management

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Performance evaluation of rank attack impact on routing protocol in low-power and lossy networks Al-Qaisi, Laila; Hassan, Suhaidi; Zakaria, Nur Haryani
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp242-251

Abstract

The internet of things (IoT) is a network of connected devices, enabling the exchange and collection of data from various environments. The routing protocol for low power and lossy networks (RPL) is a protocol for routing IPv6 over low-power wireless personal area networks, commonly used in IoT applications. However, RPL has several security and privacy issues that make it vulnerable to various attacks, including rank attacks (RA), which can lead to denial-of-service (DoS) scenarios. This research aims to address the impact of RA on RPL networks by conducting simulations using the Contiki/Cooja simulator with two topology types, random and grid, along with three RA scenarios and a normal network scenario. The study compares the performance of RPL network OF0 and MRHOF in terms of throughput, packet delivery ratio (PDR), hop count (HC) and delay. The results demonstrate that RA significantly degrades network performance and reduces network lifetime, thus draining its limited resources. Some possible solutions are also suggested to mitigate these attacks by focusing on core components of the network like objective function (OF) and node behavior. Future work will focus on studying security mechanisms for RPL against RA.
Machine Learning-Based Naïve Bayes Classification of Pineapple Productivity: A Case Study in North Sumatra Suendri, Suendri; Aprilia, Rima; Br. Rambe, Ramadiani; Zakaria, Nur Haryani
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24034

Abstract

Background: Pineapple is a major agricultural commodity in Indonesia, especially in North Sumatra, where increasing demand calls for improved productivity. Although machine learning has been widely applied in agriculture, most prior studies on pineapple focus on fruit quality assessment or employ complex, less interpretable models, leaving a gap in lightweight and practical approaches for productivity classification. Objective: This study aims to evaluate the novelty and effectiveness of the Naïve Bayes algorithm in classifying pineapple productivity based on agronomic characteristics, addressing the underexplored use of this method for productivity prediction in pineapple cultivation. Methods: A descriptive quantitative approach was applied using secondary data from the Labuhan Batu Agricultural Extension Center, consisting of 52 records with seven agronomic parameters. The dataset was divided into 31 training and 21 testing samples, and the Naïve Bayes model was implemented using RapidMiner 7.1, with performance measured by accuracy. The small dataset size is recognized as a limitation that may affect generalizability. Results: The Naïve Bayes model achieved an accuracy of 86.67%, effectively distinguishing between productive and unproductive pineapples and demonstrating its suitability for agricultural classification tasks even with limited data. Conclusion: This study highlights the novelty and practicality of applying Naïve Bayes for pineapple productivity classification, offering an interpretable and computationally efficient alternative to more complex models. Future work should address dataset limitations by incorporating larger and more diverse samples and exploring hybrid or ensemble approaches to further enhance performance and support precision agriculture.
Co-Authors Al-Qaisi, Laila Br. Rambe, Ramadiani Rima Aprilia Suendri Suendri, Suendri Suhaidi Hassan