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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 AGRIEKONOMIKA Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Kultivasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SAINS TANAH - Journal of Soil Science and Agroclimatology 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 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 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 Public Health Social Sciences Other

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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.
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.
Effect of preharvest paclobutrazol and nitrogen fertilizers on the sprouting performance of ‘median’ potato seed G0 tuber Hamdani, Jajang Sauman; Budiarto, Rahmat; Nuraini, Anne; Ramadani, Selika Fitrian
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.v24i2.64652

Abstract

Various intensifications of potato cultivation, such as the provision of paclobutrazol and nitrogen (N) fertilizer, are thought to impact the quality of the seeds produced. This study aims to evaluate the effects of different N fertilizer doses and paclobutrazol concentrations applied in the preharvest period on the sprouting performance of G0 potato tuber seeds after storage. Nine treatment combinations, each repeated three times, were tested, using 50%, 100%, and 150% of the recommended N dose and paclobutrazol concentrations of 50, 100, and 150 part per million (ppm), applied at 30 and 45 days after planting, respectively. The interaction effect between N fertilizer and paclobutrazol concentration was not significantly affected on all observed variables. Preharvest application of 100% N fertilizer produced the largest seedlings, indicated by the highest shoot length at 56 and 74 days after storage. Preharvest application of 150 ppm paclobutrazol produced the highest shoot length, shoot emergence rate, and seedling dry weight than other treatments. The present study implied the importance of preharvest N and paclobutrazol for improving the sprouting performance of G0 potato seed tuber.
Security and Performance Evaluation of PPTP-Based VPN with AES Encryption in Enterprise Network Environments Heryanto, Ahmad; Setiawan, Deris; Audrey, Berby Febriana; Hermansyah, Adi; Afifah, Nurul; Azhar, Iman Saladin B.; Idris, Mohd Yazid Bin; Budiarto, Rahmat
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4818

Abstract

In the context of the current digital era, Virtual Private Networks (VPNs) serve a critical function in ensuring the confidentiality and integrity of data transmitted across public networks, particularly within corporate environments. This study presents a comprehensive analysis of VPN security and performance, with a specific focus on the Point-to-Point Tunneling Protocol (PPTP) and the implementation of encryption algorithms such as AES-128 and AES-256. Despite the widespread adoption of PPTP due to its simplicity and broad compatibility, it exhibits significant security vulnerabilities, primarily stemming from its reliance on the outdated RC4-based Microsoft Point-to-Point Encryption (MPPE) and the susceptible MS-CHAP authentication protocol, which is highly vulnerable to brute-force and dictionary attacks. Empirical findings indicate that, although AES-128 and AES-256 introduce minor performance trade-offs compared to unencrypted configurations, AES-256 demonstrates markedly enhanced security, achieving a 98.9% authentication success rate and a threat detection time of 122 milliseconds. Nevertheless, increased user load adversely impacts network performance, with throughput declining from 95 Mbps to 40 Mbps as the user count rises from 5 to 50, accompanied by elevated latency and packet loss. Comparative analysis across three encryption scenarios AES-128, AES-256, and MPPE-PPTP reveals a consistent degradation in network performance as user load increases, with AES-256 offering the strongest security at the cost of slightly reduced throughput and increased latency under high-load conditions. MPPE-PPTP, while providing better throughput, lacks adequate security, making it unsuitable for high-risk environments. Based on these observations, this study recommends the implementation of AES-256 encryption in enterprise networks requiring high security, supported by continuous performance monitoring and strategic capacity planning. Furthermore, the adoption of a secure site-to-site VPN architecture is proposed to facilitate reliable and secure communication between geographically distributed office locations.
PHENYLALANINE-INDUCED MODULATION OF CALLUS CHARACTERISTICS AND SECONDARY METABOLITE ACCUMULATION IN Ocimum basilicum L. UNDER IN VITRO CONDITIONS Suminar, Erni; Mubarok, Syariful; Budiarto, Rahmat; Yulianto, Fiky; Nuraini, Anne; Yuniarti, Anni; Kusumadewi, Vira; Meliansyah, Rika; Kurnia, Dikdik; Julaeha, Siti
Jurnal Agrotek Tropika Vol. 13 No. 3 (2025): JURNAL AGROTEK TROPIKA VOL 13, AGUSTUS 2025
Publisher : Departement of Agrotechnology, Agriculture Faculty, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jat.v13i3.11044

Abstract

Basil (Ocimum basilicum L.) is widely used in traditional medicine due to its rich content of phenolic and flavonoid compounds. However, the natural production of these metabolites is limited. Callus culture offers a controlled method to enhance their accumulation, with medium composition, particularly precursor supplementation, playing a critical role. Phenylalanine, an aromatic amino acid, is a key precursor in the biosynthesis of phenolics and flavonoids via the shikimate pathway. This study aimed to evaluate the effect of phenylalanine on callus growth and the accumulation of total phenolic and flavonoid compounds in basil. The experiment was conducted at the Plant Tissue Culture Laboratory, Faculty of Agriculture, Universitas Padjadjaran, using Murashige and Skoog (MS) medium with phenylalanine concentrations of 0, 1.3, 1.6, and 2 g.L-1, each replicated six times. Results showed that phenylalanine treatments caused brown coloration, compact callus texture, and inhibited growth, as indicated by reduced fresh and dry weights. The 1.6 g.L-1 treatment produced the highest total phenolic content, while the 2 g.L-1 treatment yielded the highest flavonoid content. These findings suggest that phenylalanine can enhance secondary metabolite accumulation in basil callus, although it may suppress biomass growth.
Co-Authors Abdullakasim, Supatida Adi Hermansyah, Adi Aditya Pradana Ahmad Heryanto, Ahmad Ahmed Alshaflut Al Aufa, Elfa Muhammad Ihsan Ali Firdaus ANDRIA AGUSTA Anindita, Sastrika Anne Nuraini Anni Yuniarti Anto Saputra, Iwan Pahendra Audrey, Berby Febriana Azka Ghafara Putra Agung Bedine Kerim, Bedine Bin Idris, Mohd Yazid Deris Stiawan Dikdik Kurnia Dwi Budi Santoso Dwinanda, Syahvan Rifqi Eddy Renaldi Edi Santosa Efendi, Darda Emma Trinurani Sofyan Envry Artanti Duidahayu Putri Erik Setiawan Ermatita - Erni Suminar Eso Solihin Ezura, Hiroshi Fadlan Atalla Muhammad Fajri, Hauzan Ariq Musyaffa Fakhrurroja, Hanif Farida Farida Farida Fauziah, Rossita Fiky Yulianto Wicaksono Firnando, Rici Firstina Iswari Ghorbanpour, Mansour Giyarto, Gunes Hadipurnawan Satria Haryanto, Yoyon Hauzan Ariq Musyaffa Fajri Hayane Adeline Warganegara, Hayane Adeline Helvi Yanfika Idris, Mohd Yazid Bin Iman Saladin B. Azhar Indah Listiana Indra Permana, Indra Iswari, Firstina Jajang Sauman Hamdani Jatmika, Muhammad O. Juli Rejito Kemahyanto Exaudi Khuong, Nguyen Quoc 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 Muhamad Kadapi Muhammad Afif Muhammad Rizki Muhammad, Fadlan Atalla Murgayanti Murgayanti Mutiara, Pipit Nisa, Kahirun Noor Istifadah Nursuhud Nursuhud Nuzulastri, Sari Osman, Mohd Azam Pakpahan, Hansel Arie Pertiwi, Hanna Pratita, Dian Galuh Pratomo, Adji 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 Rohman, Saefur Rossita Fauziah Rufaidah, Fathi Ruminta Ruminta Samsuryadi Samsuryadi Sari, Stefina Liana Sarmayanta Sembiring Semendawai, Jaka Naufal Setiawan, Deris Sidabutar, Alex Onesimus SIska Rasiska, SIska Sistyananda, Firstian Naufal 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 Yaya Sudarya Triana Yazid Idris, Mohd. Yudho Suprapto, Bhakti Yulianto, Fiky Yusti Yusti, Yusti Zulhipni Reno Saputra Els