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All Journal Jurnal Mandiri IT JEECS (Journal of Electrical Engineering and Computer Sciences) NERO (Networking Engineering Research Operation) INTER TECH
Hamidah, Mas Nurul
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Mathematics

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Quality of Service (QoS) Analysis using Wireshark on the LAN Network at An Najiyah High School Surabaya Hamidah, Mas Nurul; Tias, Rahmawati Febrifyaning; Zainal, Rifki Fahrial
Jurnal Mandiri IT Vol. 12 No. 4 (2024): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v12i4.273

Abstract

In the realm of information technology, Indonesia has entered the fourth generation, or 4G, which is a fast, widely available internet network that can be used to advance a variety of industries, including the agricultural, social, cultural, economic, and even educational ones. Additionally, from 2020 to the beginning of 2022, you will need to be connected to the internet in order to stay productive during the Covid-19 virus outbreak. This is especially true for the education sector, since online teaching and learning activities are essential for maintaining productivity. An Najiyah Surabaya High School needs reliable internet access in order to provide better support for its online learning students. An Najiyah High School Surabaya employs QOS (Quality of Service) to monitor network quality and data traffic transferred over the network. Three QoS parameters—packet loss, throughput, and delay—will be used in this research's analysis. concentrate on keeping an eye on the local area network (LAN); the value is then retrieved following the network's monitoring. When text data transmission on a LAN network was tested, the results indicated that the network quality at SMA Na Najiyah Surabaya was very good, with values of 2.6 Mbps for throughput, o% packet loss, and 0% and 0.12 ms delay.
A Comparative Analysis of K-Nearest Neighbors and Random Forest Methods for Recommendations on Selecting Islamic Boarding Schools Based on Student Interest Profiles (primary and middle school students at xxx) Hamidah, Mas Nurul; Tias, Rahmawati Febrifyaning; Zainal, Rifki Fahrial
NERO (Networking Engineering Research Operation) Vol 10, No 2 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i2.30548

Abstract

KNN and Random Forest are one of the classification methods, in this study will compare 2 methods in machine learning namely KNN and Random forest to recommend the type of Islamic boarding school based on student interests, the application of a comparison of 2 classification methods in the recommendation system for selecting the type of Islamic boarding school based on student interests at the Elementary and Middle School levels of Xxx, The types of Islamic boarding schools are salafi, khalafi and mixed, with attributes such as academic tendencies, religious interests, extracurricular involvement, and family background. application of machine learning methods to support decision making in selecting Islamic boarding schools that are in accordance with student character, which is still rarely found in Islamic educational institutions. Performance evaluation is carried out using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. The test results show that the Random Forest algorithm gives better results with an MAE of 0.23 and an RMSE of 0.57, compared to KNN which has an MAE of 0.6 and an RMSE of 0.96. Thus, Random Forest shown to be more effective in providing recommendations for selecting appropriate Islamic boarding schools, and can be used as a basis for developing a decision support system for Islamic boarding school-based schools.Keywords: KNN, Machine Learning, Random Forest, Islamic boarding schools
Sistem Pakar Hukum Darah Wanita Pada Masa Haid Dengan Menggunakan Metode Naïve Bayes Maqdisi, Ali; Hamidah, Mas Nurul; Arizal, Arif
INTER TECH Vol 3 No 2 (2025): INTER TECH
Publisher : Fakultas Teknik Universitas Bhayangkara Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/i.v3i2.1612

Abstract

Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem pakar yang dapat memberikan keputusan terkait hukum darah wanita selama masa haid berdasarkan metode Naïve Bayes. Metode Naïve Bayes dipilih karena kemampuannya dalam melakukan klasifikasi berbasis probabilitas, sehingga dapat memberikan hasil yang akurat berdasarkan data masukan pengguna. Sistem ini dirancang untuk membantu pengguna, khususnya wanita Muslim, dalam memahami hukum darah haid, istihadhah, dan nifas dengan lebih mudah dan cepat. Hasil pengujian menunjukkan bahwa sistem pakar ini memiliki tingkat akurasi yang tinggi dalam memberikan rekomendasi hukum yang relevan, sehingga dapat dijadikan alat bantu edukasi dan konsultasi.
Analysis of the Indonesian Tourist Destination Recommendation System Using User Profile-Based Collaborative Filtering Hamidah, Mas Nurul; Zainal, Rifki Fahrial; Tias, Rahmawati Febrifyaning; Ardiansyah, Tio Kukuh
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): JEECS (Journal of Electrical Engineering and Computer Sciences) - In press
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i1.6

Abstract

Tourism recommendation systems in Indonesia are challenged by highly heterogeneous user preferences and severe rating sparsity, which undermine the effectiveness of conventional collaborative filtering methods. However, prior studies predominantly rely on rating-based interactions and often utilize generic datasets, limiting their ability to capture the contextual and behavioural diversity of Indonesian tourism. Although user profile information is known to influence preferences, its integration with latent factor models is still fragmented and rarely evaluated in a unified, context-aware framework. Consequently, existing approaches often produce suboptimal accuracy and lack robustness in sparse and imbalanced data environments. This study proposes a unified user profile-enriched collaborative filtering framework that integrates Singular Value Decomposition (SVD), Jaccard similarity, and K-Nearest Neighbor (KNN) to jointly model latent preferences and contextual user characteristics. This integration constitutes the main novelty of this work, enabling simultaneous mitigation of sparsity and enhancement of personalization in a single pipeline. Experiments are conducted on an Indonesian tourism dataset, with performance evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and execution time. The results show that the proposed method consistently outperforms the rating-based baseline, achieving lower MAE (1.6994 vs. 1.7355) and RMSE (2.0653 vs. 2.1148), while maintaining comparable computational efficiency. Furthermore, the model demonstrates greater stability across varying neighbor sizes, indicating improved scalability and robustness. Practically, this approach provides a scalable and context-aware recommendation framework that can support more adaptive and personalized tourism services in Indonesia, particularly in real-world scenarios characterized by sparse and heterogeneous data.
Co-Authors Ardiansyah, Tio Kukuh Arif Arizal, Arif Maqdisi, Ali Rifki Fahrial Zainal Tias, Rahmawati Febrifyaning