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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) Teknotan: Jurnal Industri Teknologi Pertanian Bulletin of Electrical Engineering and Informatics PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic AGRIEKONOMIKA Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Kultivasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Agrotek Tropika Journal of Tropical Crop Science Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Community Services Cel International Journal of Applied Mathematics, Sciences, and Technology for National Defense Jurnal Pengabdian Masyarakat Nasional Journal Collabits
Rahmat Budiarto
Department Of Agronomy, Faculty Of Agriculture, Universitas Padjadjaran, Sumedang, 45363, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Public Health Social Sciences Other

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Detection of android malware with deep learning method using convolutional neural network model Maulana, Reza; Stiawan, Deris; Budiarto, Rahmat
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.
Machine learning model approach in cyber attack threat detection in security operation center Saputra, Muhammad Ajran; Stiawan, Deris; Budiarto, Rahmat
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.p80-90

Abstract

The evolution of technology roles attracted cyber security threats not only compromise stable technology but also cause significant financial loss for organizations and individuals. As a result, organizations must create and implement a comprehensive cybersecurity strategy to minimize further loss. The founding of a cybersecurity surveillance center is one of the optimal adopted strategies, known as security operation center (SOC). The strategy has become the forefront of digital systems protection. We propose strategy optimization to prevent or mitigate cyberattacks by analyzing and detecting log anomalies using machine learning models. This study employs two machine learning models: the naïve Bayes model with multinomial, Gaussian, and Bernoulli variants, and the support vector machine (SVM) model with radial basis function (RBF), linear, polynomial, and sigmoid kernel variants. The hyperparameters in both models are then optimized. The models with optimized hyperparameters are subsequently trained and tested. The experimental results indicate that the best performance is achieved by the RBF kernel SVM model, with an accuracy of 79.75%, precision of 80.8%, recall of 79.75%, and F1-score of 80.01%; and the Gaussian naïve Bayes model, with an accuracy of 70.0%, precision of 80.27%, recall of 70.0%, and F1-score of 70.66%. Overall, both models perform relatively well and are classified in the very good category (75%‒89%).
IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning Susanto; Stiawan, Deris; Santoso, Budi; Sidabutar, Alex Onesimus; Arifin, M. Agus Syamsul; Idris, Mohd Yazid; Budiarto, Rahmat
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.5871

Abstract

The rapid advancement of Internet of Things (IoT) technology has created potential for progress in various aspects of life. However, the increasing number of IoT devices also raises the risk of cyberattacks, particularly IoT botnets often exploited by attackers. This is largely due to the limitations of IoT devices, such as constraints in capacity, power, and memory, necessitating an efficient detection system. This study aims to develop a resource-efficient botnet detection system by using the Self-Organizing Feature Map (SOFM) dimensionality reduction method in combination with machine learning algorithms. The proposed method includes a feature engineering process using SOFM to address high-dimensional data, followed by classification with various machine learning algorithms. The experiments evaluate performance based on accuracy, sensitivity, specificity, False Positive Rate (FPR), and False Negative Rate (FNR). Results show that the Decision Tree algorithm achieved the highest accuracy rate of 97.24%, with a sensitivity of 0.9523, specificity of 0.9932, and a fast execution time of 100.66 seconds. The use of SOFM successfully reduced memory consumption from 3.08 GB to 923MB. Experimental results indicate that this approach is effective for enhancing IoT security in resource-constrained devices.
DATA SCIENTIST CERTIFICATION GUIDANCE FOR SENIOR HIGH SCHOOL AND UNIVERSITY STUDENTS Triana, Yaya Sudarya; Budiarto, Rahmat; Rahmad, Khozaeni Bin
Jurnal Pengabdian Masyarakat Nasional Vol 5, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/pemanas.v5i1.30343

Abstract

The importance of using Information and Communication Technology can increase our role in the Modern Era among academics, which is one of the driving factors to be able to compete in the digital world. The use of technology is not only for social media, but the use of technology has an important role in the world of work today. The increasingly rapid development of technology has created many changes and updates in every field. One of the applications most widely used and needed by society is Data Science. Data Science is a multi-disciplinary science that is very widely used in both exact and social fields. To make your job search easier, Data Scientist certification is required. This of course requires someone who is competent in their field. Today's young generations should be worthy of having these competencies. Based on the above, the lecturers at the Faculty of Computer Science, Universitas Mercu Buana contributed to providing a coaching understanding in the application of Information and Communication Technology to increase knowledge in the Modern Era among academics so that they can equip the younger generation to have certification in the field of information technology, especially Data Scientists. This activity or training is a form of concern and is also one of the duties as a lecturer at the Faculty of Computer Science who understands information technology, especially in the field of Data Science.
Shellcode classification analysis with binary classification-based machine learning Semendawai, Jaka Naufal; Stiawan, Deris; Anto Saputra, Iwan Pahendra; Shenify, Mohamed; Budiarto, Rahmat
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp923-932

Abstract

The internet enables people to connect through their devices. While it offers numerous benefits, it also has adverse effects. A prime example is malware, which can damage or even destroy a device or harm its users, highlighting the importance of cyber security. Various methods can be employed to prevent or detect malware, including machine learning techniques. The experiments are based on training and testing data from the UNSW_NB15 dataset. K-nearest neighbor (KNN), decision tree, and Naïve Bayes classifiers determine whether a record in the test data represents a Shellcode attack or a non-Shellcode attack. The KNN, decision tree, and Naïve Bayes classifiers reached accuracy rates of 96.26%, 97.19%, and 57.57%, respectively. This study's findings aim to offer valuable insights into the application of machine learning to detect or classify malware and other forms of cyberattacks.
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.
Melon Cultivation Guidance for Empowering Women in Pajagan Village, Sumedang Regency Budiarto, Rahmat; Sutari, Wawan; Farida; Soleh, Mochamad Arief; Nuraini, Anne; Mubarok, Syariful; Kusumiyati; Rasiska, Siska; Istifadah, Noor; Djaya, Luciana
Indonesian Journal of Community Services Cel Vol. 4 No. 2 (2025): Indonesian Journal of Community Services Cel
Publisher : Research and Social Study Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70110/ijcsc.v4i2.96

Abstract

Background: As one of popular fruit, melon is potentially to cultivate in homeyard by housewives.Aims: This community service is carried out in July 2025, for empowering women in Pajagan Village, Cisitu District, Sumedang Regency through melon cultivation guidance.Method: Thirty-five participants joined, mostly local women housewives aged 25–50 from the PKK organization, along with 15 students aged 20–22 conducting fieldwork. This work documents the initial stages of home melon cultivation through a participatory approach and provides hands-on experience in melon seedling cultivation.Results: Participants’ enthusiasm and confidence in applying the seeding techniques learned reflect the effectiveness and practicality of the training methods in supporting home-based melon cultivation. This work is hoped to empowers women in managing home gardens, contributing to both economic resilience and household food security.
Cybersecurity and AI as enablers of economic resilience: A framework for sustainable growth in developing countries Ahmed, Ali Siraj; Budiarto, Rahmat
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 3 No. 2 (2025): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v3i2.857

Abstract

Digital transformation offers unprecedented opportunities for sustainable economic growth in developing countries. However, these benefits are accompanied by increased cybersecurity risks and challenges in integrating emerging technologies like artificial intelligence (AI). This paper proposes a conceptual framework that positions cybersecurity and AI as dual enablers of economic resilience aligned with Sustainable Development Goal 8 (SDG 8). The framework comprises three interconnected pillars: cybersecurity infrastructure, AI-driven economic transformation, and AI-enhanced cybersecurity mechanisms. A case study of Sudan illustrates how tailored interventions can foster secure, inclusive, and resilient digital economies in low- and middle-income contexts. The study highlights the importance of ethical governance, multi-stakeholder collaboration, and capacity building to maximize the benefits of AI while mitigating risks. Future research directions include empirical validation and policy experimentation to refine the framework and accelerate digital-led economic resilience in the Global South.
The effect of melatonin and 6-Benzylaminopurine application on the post-harvest quality of cut roses (Rosa x alba) Putri, Azizah Tiara; Mubarok, Syariful; Budiarto, Rahmat
Kultivasi Vol 24, No 2 (2025): Jurnal Kultivasi
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/kultivasi.v24i2.62477

Abstract

Roses are known as a high-value commodity frequently used in various important events. However, they are susceptible to postharvest quality deterioration, which can affect their vase life and appearance. In this study, roses with a blooming stage of approximately 25–50% were immersed in melatonin and 6-benzylaminopurine (BAP) solutions at different concentrations. This research aims to analyze the effect of melatonin and BAP application on the freshness of cut roses. The parameters observed included flower vase life, flower wilting angle, increase in flower diameter, fresh weight, solution uptake, and chlorophyll content. The results showed that melatonin and BAP, applied individually or in combination, effectively extended the freshness of cut roses by up to eight days by maintaining solution uptake, flower quality, and chlorophyll content. This study provides new insights for farmers and researchers in improving the quality and longevity of cut roses through the use of plant hormones, particularly cytokinin.
Co-Authors Abdi Wahab Abdullakasim, Supatida Adi Hermansyah, Adi Aditya Pradana Afiyati, Afiyati Ahmad Heryanto, Ahmad Ahmed, Ali Siraj Al Aufa, Elfa Muhammad Ihsan Ali Firdaus Alshaflut, Ahmed ANDRIA AGUSTA Anne Nuraini Anni Yuniarti Anto Saputra, Iwan Pahendra Audrey, Berby Febriana Azka Ghafara Putra Agung Bambang Jokonowo Bedine Kerim, Bedine Bin Idris, Mohd Yazid Deris Stiawan Dikdik Kurnia Dwi Budi Santoso Dwinanda, Syahvan Rifqi Edi Santosa Efendi, Darda Efy Yosrita, Efy Envry Artanti Duidahayu Putri Erik Setiawan Ermatita - Erni Suminar Ezura, Hiroshi Fadlan Atalla Muhammad Fajri, Hauzan Ariq Musyaffa Fakhrudin, Zidan Al Buqhori Fakhrurroja, Hanif Farida Farida Farida Fauziah, Rossita Fiky Yulianto Wicaksono Firnando, Rici Firstina Iswari Ghorbanpour, Mansour Giyarto, Gunes Hadipurnawan Satria Hanifah, Nurul Afif Harjunadi Wicaksono, Harjunadi Haryanto, Yoyon Hauzan Ariq Musyaffa Fajri Hayane Adeline Warganegara, Hayane Adeline Helvi Yanfika Idris, Mohd Yazid Bin Iman Saladin B. Azhar Indah Listiana Indrianto Indrianto Iswari, Firstina Jajang Sauman Hamdani Jatmika, Muhammad O. Juli Rejito Kemahyanto Exaudi Komala, Mega Kus Hendarto, Kus Kusumadewi, Vira Kusumiyati Kusumiyati Luciana Djaya, Luciana M. Miftakul Amin Maolana, Adrian Mochamad Arief Soleh Mohamed Shenify Mohd Yazid Idris Mohd Yazid Idris Mohd. Yazid Idris Mugianto, Dwi Rizki Muhaimin Hasanudin Muhammad Afif Muhammad Rifqi Muhammad Rizki Muhammad, Fadlan Atalla Mutiara, Pipit Nisa, Kahirun Noor Istifadah Nursuhud Nursuhud Nuzulastri, Sari Osman, Mohd Azam Pakpahan, Hansel Arie Pertiwi, Hanna Prasetyo, Lindo Pratita, Dian Galuh Pratomo, Adji Prihandi, Ifan Putra Perdana Prasetyo, Aditya Putri, Azizah Tiara Putri, Dina Putri, Envry Artanti Duidahayu Rahma, Siti Auliya Rahmad, Khozaeni Bin Rahmat, Bayu Pradana Nur Ramadani, Selika Fitrian Reza Maulana Rika Meliansyah Roedhy Poerwanto Rofiq, Muhamad Abdul Rossita Fauziah Rufaidah, Fathi Ruminta Ruminta Salamah, Raisha Nur Samsuryadi Samsuryadi Saputra, Muhammad Ajran Sarmayanta Sembiring Semendawai, Jaka Naufal Setiawan, Deris Shadiq, Jafar Sidabutar, Alex Onesimus SIska Rasiska, SIska Siti Julaeha, Siti Susanto Susanto Syamsul Arifin, M. Agus Syariful Mubarok Varinto, Irvan Waluyo, Nurmalita Wawan Sutari Wibawa, Rangga Widyastuti, R.A.D. Yanyan Mochamad Yani, Yanyan Mochamad Yaya Sudarya Triana Yazid Idris, Mohd. Yudho Suprapto, Bhakti Yulianto, Fiky Yusti Yusti, Yusti Zulhipni Reno Saputra Els