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Rasenda, Rasenda
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The Role of Emotional Response in Mediating the Influence of Green Product and Green Promotion to Sales Performance, A Study on Dairy Products Marketed in Modern Market Channels Rizal, Moch; Zaenudin, Zaenudin; Rasenda, Rasenda; Ibarda, Aditya; Wiyana, Hari
International Journal of Integrative Sciences Vol. 3 No. 8 (2024): August 2024
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijis.v3i8.10540

Abstract

This study aims to analyze the role of emotional response in mediating the influence of green products and green promotion on sales performance. This quantitative research with a causality design uses a sample of dairy product customers sold in modern market channels and is analyzed using the Structural Equation Model. The results of the study show that green products and green promotion directly have a positive effect on emotional response and sales performance. Emotional response is proven to affect sales performance and can mediate the influence of green products and green promotion on sales performance. Empirical evidence shows that the implementation of green products and green promotions can increase consumer emotional response and have an impact on improving sales performance. This study implies that the green marketing approach can enhance the integration of environmental issues in all aspects of the company's activities, from strategy formulation, planning, and production to distribution, which positively impacts sales performance. This green marketing concept is very relevant in answering fierce business competition declining global economic conditions and helping companies improve customer service and satisfaction with going green  
OPTIMIZING HADITH CLASSIFICATION WITH NEURAL NETWORKS: A STUDY ON BUKHARI AND MUSLIM TEXTS Rasenda, Rasenda; Fabrianto, Luky; Faizah, Novianti Madhona
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8732

Abstract

The Bukhari and Muslim hadith collections encompass a total of 7008 hadith sentences, but it is not immediately clear which of these hadiths fall into the categories of prohibitions or orders. To enhance understanding and accessibility for readers, this study focuses on classifying these hadiths through a systematic process. The classification involves several key stages: Text Pre-processing, pre-processing the raw text data to clean and normalize (Stemming, Stopword Removal and Tokenization), Word vector features are extracted to capture the semantic relationships and contextual meanings of the words, then processed into a neural network model based on a multilayer perceptron (MLP) architecture (Model Architecture, Training and Optimization). The approach leverages the strength of neural networks, particularly through the use of multiple layers and feature extraction via word vectors, which significantly contributes to the accuracy of the classification process. The results of the study is very good, with a high accuracy rate of 97.72% achieved by employing a model with two layers and 256 neurons
Detection of Reconnaissance Attacks Using a Hybrid CNN–LSTM on IoT Network Susanto; Dermawan, Budi Arif; Rasenda, Rasenda
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2535

Abstract

The rapid expansion of the Internet of Things (IoT) has increased connectivity across various sectors but also exposed systems to new and evolving cybersecurity threats. One of the most critical threats is the reconnaissance phase, where attackers gather system information to prepare more sophisticated intrusions. Conventional intrusion detection systems often fail to detect reconnaissance due to similarities with benign traffic. To address this problem of ineffective reconnaissance detection, this study proposes a hybrid detection framework that combines autoencoder-based feature extraction with a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) classifier. The autoencoder, an unsupervised neural network that compresses input data and reconstructs it with minimal loss, is used to reduce data dimensionality and learn meaningful hidden features. The CNN captures spatial patterns and LSTM models temporal dependencies in network traffic. Experiments were conducted using the CICIoT2023 dataset, focusing exclusively on reconnaissance attacks. The evaluation metrics include accuracy, precision, recall, specificity, False Positive Rate (FPR), False Negative Rate (FNR), and F1-score. Results show that the proposed model achieves an overall accuracy of 99.79%, specificity of 0.9994, precision of 0.9948, recall of 0.9445, and F1-score of 0.9648. Class-level analysis demonstrates high performance across most attack types, though Ping Sweep exhibits a lower recall of 0.6853 despite achieving perfect precision. These results demonstrate that the hybrid CNN–LSTM model with autoencoder-based feature extraction can effectively detect reconnaissance attacks in IoT networks. The approach enhances detection accuracy, reduces false alarms, and provides a promising foundation for improving real-world IoT security monitoring systems.
Dynamic Modeling of Service Quality Improvement through Feedback-Based Governance Alrasyid, Wildan; A, Galih Prakoso Rizky; Dermawan, Budi Arif; Rasenda, Rasenda
International Journal of Enterprise Modelling Vol. 20 No. 1 (2026): January: Publishing Process (Inpress)
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/int.jo.emod.v20i1.144

Abstract

This study aims to develop a dynamic system model designed to improve service quality and reduce the gap between the expected and actual levels of community satisfaction. The study is motivated by the importance of understanding the interaction between key variables such as human resources (HR), information and communication technology (ICT), and evaluation mechanisms within a feedback-based service system. The system dynamics method is employed to construct causal loop and stock–flow models to simulate their impacts over a 12-month period. The simulation results show that strengthening HR, ICT, and evaluation aspects leads to a significant improvement in service quality, which directly increases community satisfaction. In addition, the gap between actual and targeted satisfaction shows a decreasing trend over time. Community feedback emerges as a crucial factor that accelerates performance improvement through reinforcing loops. Graphical and tabular representations support the positive dynamics of the main variables and demonstrate the system’s flexibility in responding to internal changes. This study demonstrates that dynamic system modeling is an effective approach to managing complex service systems. By illustrating nonlinear interactions among variables and considering time delays, decision-makers can simulate policy impacts before implementation. The study concludes that feedback-based models can serve as useful tools for simultaneously improving public services and reducing performance gaps.
DETEKSI SERANGAN SQL INJECTION MENGGUNAKAN ALGORITMA MACHINE LEARNING PADA JARINGAN IOT Rasenda, Rasenda; Susanto, Susanto; Dermawan, Budi Arif
JUTIM (Jurnal Teknik Informatika Musirawas) Vol 10 No 2 (2025): JUTIM (JURNAL TEKNIK INFORMATIKA MUSIRAWAS) DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v10i2.2795

Abstract

SQL Injection merupakan salah satu bentuk serangan siber yang paling berbahaya karena memungkinkan penyerang untuk mengakses, memodifikasi, atau menghapus data secara ilegal melalui manipulasi perintah SQL. Sistem deteksi berbasis aturan memiliki keterbatasan dalam menghadapi pola serangan baru yang bersifat dinamis dan sulit dikenali. Penelitian ini bertujuan mengembangkan model deteksi serangan SQL Injection dengan pendekatan machine learning menggunakan kombinasi Autoencoder dan Algoritma Machine Learning. Autoencoder digunakan untuk mengekstraksi fitur dan mendeteksi pola anomali pada data input, sedangkan Algoritma Machine Learning berperan sebagai model klasifikasi untuk membedakan antara permintaan normal dan serangan. Data yang digunakan terdiri atas payload berlabel yang mencakup input normal dan serangan SQL Injection, yang selanjutnya diproses melalui tahapan normalisasi, ekstraksi fitur, dan pelatihan model. Evaluasi dilakukan menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian diharapkan menghasilkan model deteksi yang adaptif, mampu mengenali pola serangan baru, serta memiliki tingkat kesalahan deteksi yang rendah pada sistem keamanan jaringan IOT.
Co-Authors A, Galih Prakoso Rizky Almarind, Marisa Alrasyid, Wildan Amenobelia, Amenobelia Anysa, Ega Asih, Afifah Tri Budi Arif Dermawan Darmayana, Zahra Diba, Adinda Farah Fabrianto, Luky Faizah, Novianti Madhona Hairani, Zahra Hani, Nadia Hengki Tamando Sihotang Ibarda, Aditya Jubaidah, Jubaidah Moch. Rizal Nova Rini Nurul Afifah Arifuddin Prihandayani, Tiwuk Wahyuli Putera, Adhitya Ibanda Rizky A, Galih Prakoso Saputri, Arum Dwi Sarina, Desi Subaedah, Sitti Supriatiningsih, Supriatiningsih Suryani, Ruthpani Susanto Susanto Susanto Susanto Syahputri, Nurlaily Thomas Thomas Wiyana, Hari Yolanda Yolanda, Yolanda Yusnadi, Yusnadi Zaenudin Zaenudin