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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) ComEngApp : Computer Engineering and Applications Journal Jurnal Pendidikan Karakter TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Bulletin of Electrical Engineering and Informatics Annual Research Seminar Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Pengabdian UNDIKMA Journal of Applied Data Sciences Journal of Innovation and Research in Primary Education Jurnal Indonesia Sosial Teknologi
Deris Stiawan
Universitas Sriwijaya
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Physics Public Health Social Sciences Other

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Innovative smart showcase design for indoors and eco-friendly hydroponics Exaudi, Kemahyanto; Sembiring, Sarmayanta; Putra Perdana Prasetyo, Aditya; Stiawan, Deris; Fakhrurroja, Hanif; Budiarto, Rahmat
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8353

Abstract

Hydroponics is a unique and fascinating farming technique for producing plants and vegetables. Without having to use a large area of land, people can easily apply the technique to produce fresh and hygienic vegetables. However, the technique cannot be used in apartment environment due to the limited sunlight. Thus, this study introduces an innovative hydroponic system, called as hydroponics smart showcase system that can be implemented indoors, even in the presence of minimal sunlight, and can be monitored online by users. The proposed system consists of a net pot of 4-5 hydroponics cups with a diameter of 50 mm, air temperature and humidity sensors, water level sensors, ultraviolet (UV) lights, indicator displays, and DC fans. Experimental results show that the development of innovative hydroponics using smart showcase has succeeded in stabilizing the air in the showcase according to the specified references. Moreover, UV light intensity settings for photosynthesis can be applied remotely with duration of 24 hours.
Revolutionizing internet of things intrusion detection using machine learning with unidirectional, bidirectional, and packet features Elsi, Zulhipni Reno Saputra; Stiawan, Deris; Yudho Suprapto, Bhakti; Syamsul Arifin, M. Agus; Yazid Idris, Mohd.; Budiarto, Rahmat
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3047-3062

Abstract

Detection of attacks on internet of things (IoT) networks is an important challenge that requires effective and efficient solutions. This study proposes the use of various machine learning (ML) techniques in classifying attacks using unidirectional, bidirectional, and packet features. The proposed methods that implement decision tree (DT), random forest (RF), extreme gradient boosting classifier (XGBC), AdaBoost (AB) and linear discriminant analysis (LDA) work perfectly with all kinds of datasets and includes. It also works very well with data type-based feature selection (DTBFS) and correlation-based feature selection (CBFS). The experiment results show a significant improvement compared to previous studies and reveals that unidirectional and bidirectional features provide higher accuracy compared to packet features. Furthermore, ML models, particularly DT, and RF, have faster computing times compared to more complex deep learning models. This analysis also shows potential overfitting in some models, which requires further validation with different datasets. Based on these findings, we recommend the use of RF and DT for scenarios with unidirectional and bidirectional features, while AB and LDA for packet features. The study concludes that using the right ML techniques along with features that work in both directions can make an intrusion detection system for IoT networks becomes very accurate.
New approach to measuring researcher expertise using cosine similarity algorithm and association rules Firdaus, Ali; Stiawan, Deris; Samsuryadi, Samsuryadi; Budiarto, Rahmat
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9506

Abstract

This study proposes a new method to assess researcher expertise using publication data. The quality of research publications is an important indicator in the ranking of universities that are undergoing diversification. Research publications have become an important indicator in the university ranking system and have a major impact on the reputation of universities as a lens for the study of expertise and prestige for human resources. Expertise is often difficult to verify objectively, as a result, many people claim to be experts or are considered experts without evidence and correct data. To ensure the expertise of researchers, it must be proven with valid data support through measurable and presentable expertise parameters. The model built uses the cosine similarity and association rule approaches. The publication variables attached to the researcher are formulated in the collaboration of the algorithm to assess the level of researcher expertise. Validation of important points of publications as parameters for measuring expertise has been identified as the main factor contributing to the measurement of researcher expertise and its impact on university reputation. The model built successfully validated researcher expertise up to 72% which is relevant to its support for university rankings up to 75%.
Personal Attributes As Indicators Of Students Leading Competence: A Conceptual Analysis In The Context Of Higher Education Mardhiyah, Sayang Ajeng; Stiawan, Deris; Meilinda, Meilinda; Pratiwi, Marisya; Iswari, Rosada Dwi
Jurnal Pendidikan Karakter Vol. 16 No. 2 (2025)
Publisher : Directorate of Research and Community Service, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jpka.v16i2.84768

Abstract

This research aims to formulate a personal attribute profile as an indicator of leading competence for Sriwijaya University students. This research uses a qualitative approach with content analysis and thematic analysis methods of academic literature, national policy documents, university vision and mission, and feedback from stakeholders. Based on the synthesis results, the four personal attribute profiles in this study are growth mindset, academic integrity, critical thinking disposition, and academic buoyancy. These profiles are expected to be integrated in the curriculum and various academic and non-academic activities, and become a strategic step to realize the vision of national higher education, achieve the goals and objectives of Sriwijaya University, and meet the competency demands of the world of work and the global community in the future.
Optimization of Cement Distribution Route Based on Hybrid Genetic-Firefly Algorithm (GAFA) Heryati, Agustina; Stiawan, Deris; Setiawan, Heri; Rini, Dian Palupi; Budiarto, Rahmat
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 4: December 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i4.6404

Abstract

This study focuses on optimizing the cement distribution route to improve efficiency, reduce costs, and minimize environmental impacts. A hybrid Genetic-Firefly Algorithm (GAFA) approach, integrating the Genetic Algorithm (GA) and the Firefly Algorithm (FA), is developed to solve the complex problem of determining the optimal distribution route to ensure timely, efficient, and sustainable delivery. The Data from PT Semen Baturaja includes three factory locations and 128 distributor points. Various parameter configurations are tested, including population size, mutation probability, total execution time, average execution time, standard deviation of execution time, best factory, and best distance to provide their impact on algorithm performance. The empirical results show that the optimal configuration produces the lowest total distance of 205.14 kilometers and high executiontime efficiency. The best route covers 128 strategic distribution points in the Sumatra region. These results prove the effectiveness of the hybrid GAFA algorithm in optimizing cement distribution routes, contributing significantly to operational efficiency and transportation cost savings. Thus, this approach offers a practical, efficient solution for optimizing cement distribution routes in the manufacturing industry.
Pengembangan Media Infografis Interaktif Berbasis Discovery Learning Pada Materi Energi Terbarukan Untuk Siswa SD Raini Marita; Deris Stiawan; Makmum Raharjo
Jurnal Penelitian Pendidikan IPA Vol 11 No 12 (2025): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i12.12689

Abstract

The limitations of visual learning media that are appropriate for the characteristics of elementary school students have an impact on the low level of understanding of abstract science material, such as renewable energy. In the context of the need for a learning approach that supports active engagement and visualization, this study aims to develop interactive infographic media based on Discovery Learning for renewable energy materials for sixth-grade elementary school students. This study employs the Research and Development (R&D) method using the ADDIE model, which consists of the analysis, design, development, implementation, and evaluation stages. Validation was conducted by experts in media, language, and content, while practicality and effectiveness were tested through limited trials and field tests involving teachers and students at SD Negeri 1 Pagar Kaya. The results of the study indicate that the developed media meet the criteria for high validity (average >85%), high practicality (average >90%), and effectiveness (N-Gain = 0.72) in enhancing students' understanding. The interactive infographic media developed facilitates the discovery-based learning process in a visual and engaging manner, encouraging students to actively explore concepts related to renewable energy. This study confirms that the integration of interactive visual design and the Discovery Learning approach can be an effective strategy for developing digitally based science learning media at the elementary level.
Shellcode Classification with Machine Learning Based on Binary Classification Jaka Naufal Semendawai; Deris Stiawan; Iwan Pahendra
Jurnal Indonesia Sosial Teknologi Vol. 6 No. 2 (2025): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v6i2.3233

Abstract

The Internet can link one person to another using their respective devices. The internet itself has both positive and negative impacts. One example of the internet's negative impact is malware that can disrupt or even kill a device or its users; that is why cyber security is required. Many methods can be used to prevent or detect malware. One of the efforts is to use machine learning techniques. The training and testing dataset for the experiments is derived from the UNSW_NB15 dataset. K-Nearest Neighbour (KNN), Decision Tree, and Naïve Bayes classifiers are implemented to classify whether a record in the testing data is Shellcode or non-Shellcode attack. The KNN, Decision Tree, and Naïve Bayes classifiers achieve accuracy levels of 96.82%, 97.08%, and 63.43%, respectively. The results of this research are expected to provide insight into the use of machine learning in detecting or classifying malware or other types of cyber attacks.
Optimizing K-Means Clustering Parameters for Mapping Smart Contract Transaction Characteristics: A Comparative Analysis of Evaluation Metrics in the IOTA Ecosystem Ubaya, Huda; Stiawan, Deris; Suprapto, Bhakti Yudho; Ekaputra, Rivaldi Febrian; Afifah, Nurul; Ningrum, Septiani Kusuma
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7741

Abstract

Smart contracts are already a major development in digital transaction automation thanks to blockchain technology, but their operational efficiency is still greatly impacted by resource consumption, transaction success rates, and gas cost dynamics. This study aims to optimize the K-Means Clustering algorithm's parameters in order to map the characteristics of smart contract transactions in the IOTA ecosystem and provide thorough insights into the efficiency of gas allocation. Using a massive dataset of 566,303 empirical transactions from the IOTA Tangle, three key metrics the Silhouette Coefficient, Davies-Bouldin Index, and Calinski-Harabasz Index were compared to verify the quality of the clustering. With a Silhouette Coefficient value of 0.9851, Davies-Bouldin Index of 0.4622, and Calinski-Harabasz Index of 741,423.92, quantitative evaluation results demonstrate that the 3- cluster structure performs better than two clusters. These results validate the 3-cluster model's ability to more accurately divide transactions into categories that are efficient, complex, and gas-inefficient. The results of this mapping can serve as the foundation for creating an automated recommendation system for optimizing transaction costs in decentralized networks. This study shows that the Gas Limit and Gas Consumed indicators are crucial predictors of transaction efficiency.
Machine learning-based anomaly detection for smart home networks under adversarial attack Juli Rejito; Deris Stiawan; Ahmed Alshaflut; Rahmat Budiarto
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p122-129

Abstract

As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detection of anomalies in smart home networks under adversarial attack. The proposed method uses network traffic data of the UNSW-NB15 and IoT-23 datasets to extract relevant features and trains a supervised classifier to differentiate between normal and abnormal behaviors. To assess the performance and reliability of the proposed method, four types of adversarial attack methods: evasion, poisoning, exploration, and exploitation are implemented. The results of extensive experiments demonstrate that the proposed method is highly accurate and reliable in detecting anomalies, as well as being resilient to a variety of types of attacks with average accuracy of 97.5% and recall of 96%.
Detection of Android malware with deep learning method using convolutional neural network model Reza Maulana; Deris Stiawan; Rahmat Budiarto
Computer Science and Information Technologies Vol 6, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i1.p68-79

Abstract

Android malware is an application that targets Android devices to steal crucial data, including money or confidential information from Android users. Recent years have seen a surge in research on Android malware, as its types continue to evolve, and cybersecurity requires periodic improvements. This research focuses on detecting Android malware attack patterns using deep learning and convolutional neural network (CNN) models, which classify and detect malware attack patterns on Android devices into two categories: malware and non-malware. This research contributes to understanding how effective the CNN models are by comparing the ratio of data used with several epochs. We effectively use CNN models to detect malware attack patterns. The results show that the deep learning method with the CNN model can manage unstructured data. The research results indicate that the CNN model demonstrates a minimal error rate during evaluation. The comparison of accuracy, precision, recall, F1 Score, and area under the curve (AUC) values demonstrates the recognition of malware attack patterns, reaching an average of 92% accuracy in data testing. This provides a holistic understanding of the model's performance and its practical utility in detecting Android malware.
Co-Authors Abd Rahim, Mohd Rozaini Abdul Hadi Fikri Abdul Hanan Abdullah Abdul Harris Adi Hermansyah Adi Hermansyah, Adi Adi Sutrisman Aditya Putra Perdana Prasetyo Aditya Putra Perdana Prasetyo Adji Pratomo Agung Juli Anda Agus Eko Minarno Ahmad Fali Oklilas Ahmad Firdaus Ahmad Ghiffari Ahmad Heryanto Ahmad Heryanto Ahmad Heryanto Ahmad Heryanto, Ahmad Ahmad Zarkasi Ahmad Zarkasi Ahmed Alshaflut Albertus Edward Mintaria Ali Bardadi Ali Firdaus Bedine Kerim Bedine Kerim Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bin Idris, Mohd Yazid Budiarto, Rahmat Darmawijoyo, Darmawijoyo Dasuki, Massolehin Dedy Hermanto Dendi Renaldo Permana Desak Putu Dewi Kasih Dewi Bunga Dian Palupi Rini Dwi Budi Santoso Edi Surya Negara Ekaputra, Rivaldi Febrian Eko Arip Winanto Endang Lestari Ruskan Ermatita - Erwin, Erwin Fachrudin Abdau Fakhrurroja, Hanif Ferdiansyah Ferdiansyah Fikri, Abdul Hadi Firdaus Firdaus Firdaus, Firdaus Firnando, Rici Firsandaya Malik, Reza Gonewaje gonewaje Habibullah, Nik Mohd Hadipurnawan Satria Harris, Abdul Heru Saputra Heryati, Agustina Huda Ubaya Huda Ubaya Huda Ubaya I Gede Yusa Idris, Mohd. Yazid Idris, Mohd. Yazid Imam Much Ibnu Subroto Indri Ramayanti Iswari, Rosada Dwi Iwan Pahendra Jaka Naufal Semendawai John Arthur Jupin Juli Rejito Kemahyanto Exaudi Kurniabudi, Kurniabudi Latius Hermawan Lelyzar Siregar Lina Handayani M. Miftakul Amin M. Ridwan Zalbina Majzoob K. Omer Mardhiyah, Sayang Ajeng Marisya Pratiwi Marita, Raini Massolehin Dasuki Mehdi Dadkhah Meilinda Meilinda Meilinda, Meilinda Mintaria, Albertus Edward Mohamed S. Adrees Mohamed Shenify Mohammad Davarpanah Jazi Mohammed Y. Alzahrani Mohd Arfian Ismail Mohd Azam Osman Mohd Faizal Ab Razak Mohd Rozaini Abd Rahim Mohd Saberi Mohamad Mohd Yazid bin Idris Mohd Yazid Bin Idris Mohd Yazid Idris Mohd Yazid Idris Mohd. Yazid Idris Mohd. Yazid Idris Mohd. Yazid Idris Muhammad Afif Muhammad Ajran Saputra Muhammad Fahmi Muhammad Fermi Pasha Muhammad Qurhanul Rizqie Muhammad Sulkhan Nurfatih Munawar A Riyadi Munawar Agus Riyadi Negara, Edi Surya Ni Ketut Supasti Dharmawan Nik Mohd Habibullah Ningrum, Septiani Kusuma Nur Sholihah Zaini Osama E. Sheta Osman, Mohd Azam Osvari Arsalan Pertiwi, Hanna Prabowo, Christian Primanita, Anggina Purnama, Benni Putra Perdana Prasetyo, Aditya Raharjo, Makmun Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Raini Marita Raja Zahilah Md Radzi Reza Firsandaya Malik Reza Maulana Rini, Dian Palupi Riyadi, Munawar A Rizki Kurniati Rizma Adlia Syakurah Rizqie, Muhammad Qurhanul Rossi Passarella Samsuryadi Samsuryadi Saparudin Saparudin Saparudin, Saparudin Sari Nuzulastri Sari Sandra Sarmayanta Sembiring Sarmayanta Sembiring Sasut A Valianta Sasut Analar Valianta Semendawai, Jaka Naufal Septiani Kusuma Ningrum Setiawan, Heri Shahreen Kasim Sharipuddin, Sharipuddin Sidabutar, Alex Onesimus Siti Hajar Othman Siti Nurmaini Sri Arttini Dwi Prasetyawati Sri Desy Siswanti Susanto Susanto Susanto Susanto Susanto, Susanto Sutarno Sutarno Syakurah, Rizma Adlia Syamsul Arifin, M. Agus tasmi salim Tasmi Salim Tole Sutikno Wan Isni Sofiah Wan Din Yaya Sudarya Triana Yazid Idris, Mohd. Yazid Idris, Mohd. Yesi Novaria Kunang Yoga Yuniadi Yudho Suprapto, Bhakti Yundari, Yundari Zulhipni Reno Saputra Elsi