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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Techno.Com: Jurnal Teknologi Informasi Journal of Computer and Information CommIT (Communication & Information Technology) JUSIFO : Jurnal Sistem Informasi Proceeding of the Electrical Engineering Computer Science and Informatics International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Syntax Literate: Jurnal Ilmiah Indonesia Communications in Science and Technology Jurnal ULTIMA InfoSys JURNAL PENDIDIKAN TAMBUSAI ComTech: Computer, Mathematics and Engineering Applications Journal of Information Systems and Informatics Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi Jurnal Teknologi Dan Sistem Informasi Bisnis Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Applied Data Sciences Innovative: Journal Of Social Science Research IJISCS (International Journal of Information System and Computer Science)
Benfano Soewito
Bina Nusantara University
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Injection Attack Detection on Internet of Things Device with Machine Learning Method Pohan, Mara Muda; Soewito, Benfano
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 1 (2023): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i1.556

Abstract

The Internet of Things (IoT) Industry is growing rapidly, security surrounding this Industry has to be upgraded. This study analyzes which machine learning performs the best in detecting Injection Attacks in IoT devices. The proposed machine learning methods includes Catboost, Decision Tree, Support Vector Machine (SVM), and Multilayer Perceptron (MLP). This study uses Edge-IIoTset dataset. The traffic data obtained in this dataset comes from 13 different types of IoT devices which contains 10 files with normal traffic and 14 files of attack traffics. This study takes normal traffic and injection attacks traffic from Edge-IIoTset. Results shows that Catboost machine learning model performs the best in terms of performance score with 0.95599 score in Accuracy, Precision, F1-Score, and recall score where as Decision Tree model performs the fastest with 0.09 seconds of runtime and achieving 0.93 score in the performance.
Impact of mobile subscribers dual stack IPv4/IPv6 deployment Pahlevi, Mohammad Dian; Soewito, Benfano
Communications in Science and Technology Vol 3 No 1 (2018)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (767.94 KB) | DOI: 10.21924/cst.3.1.2018.79

Abstract

The use of CGNAT at PT. ZYX as a mobile telecommunication service provider cannot be relied as the solution to solve addressing needs against subscriber growth in future technology. Meanwhile, native IPv6 deployment is currently application-driven, which requires maturity support in either subscriber user equipment, network, and application itself. IPv4/IPv6 dual stack deployment was selected by PT. ZYX as stepping stone towards native IPv6 deployment. This paper analyzes the impact of dual stack IPv4/IPv6 deployment for mobile subscribers at PT. ZYX. After selecting the dual stack approach and completing the deployment, test and measurements were performed to confirm the connectivity also against the performance and node utilization to conclude the impact. The test confirmed successful connectivity and the measurements showed that the deployment gives significant enhancement of routing table size and NAT table in node utilization and does not cause performance drop of hop count, throughput, and download time.
Information Security Evaluation Using Case Study Information Security Index on Licensing Portal Applications Wardhani, Widiastuti Kusumo; Soewito, Benfano; Zarlis, Muhammad
Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i4.563

Abstract

There's a lot of cyber attacks going on right now, so the Ministry of Public Works and People's Housing (PUPR) has to get serious about preventing them. One of the information system that contains critical public data need to be secured is Portal Perizinan. In order to maintain information security, an evaluation should be carried out to assess the level of readiness (comprehensiveness and consistency) of the implementation of information security based on the SNI ISO/IEC 27001:2013 criteria using the Information Security Index. (KAMI Index). The five areas assessed aim to determine the level of organization preparedness in the implementation of information security. Obtained a score of 31 for the level of organization dependence on electronic systems, with a high level of category. The presence of technology security is at level I through to level II and our index measures 351, which means that the level of maturity of the new ISMS is at the stage of Achievement of the Basic Framework. From the results of this case study it can be seen that the state of information security readiness in the Ministry of PUPR still needs to be improved to meet ISO 27001 standard.
Data Monetization Service Development Using Iterative Lifecycle Framework, Quality Assurance, and Open Web Application Security Project: A Case Study of a Utility Company in Indonesia Kusuma Atmaja, Wahyu Haris; Warnars, Harco Leslie Hendric Spits; Gaol, Ford Lumban; Soewito, Benfano
CommIT (Communication and Information Technology) Journal Vol. 18 No. 2 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i2.10293

Abstract

The research aims to provide Data Monetization (DM) services for an Indonesian utility company as a pilot to generate additional revenue beyond the primary operation. The service is built using an iterative development lifecycle framework and evaluated based on five Quality Goals (QGs), including application and security testing activities. The framework includes methods for processing and modeling electricity usage data, testing application quality, checking infrastructure quality, and ensuring access security for front-end and back-end applications using the Open Web Application Security Project (OWASP). For data modeling, Support Vector Regression (SVR) is used, and it outperforms Polynomial Regression (PR) and Multi-Layer Perception (MLP) Neural Networks. Furthermore, QG shows strong performance with an Relative Root Mean Squared Error (RRMSE) value < 10%, high forecasting ability with Mean Average Probability Error (MAPE) < 10%, and a near-zero average error rate (Mean Squared Error (MSE)) square using minimal data from four months. The services go through functional and integration test to ensure product quality and application performance, which results in a minimum of 95% service response in throughput, 0.128 seconds for processing 2,000 requests, stability at 300–500 in one second per hour, and 7–21 seconds during peak hours. Additionally, the service passes nine penetration tests and ten vulnerability assessments using the OWASP top 10:2021 category. Based on the comprehensive testing and evaluation results, both the application and the service are considered ready and secured for deployment.
Monitoring water quality parameters impacted by Indonesia’s weather using internet of things Riftiarrasyid, Mohammad Faisal; Soewito, Benfano
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1426-1436

Abstract

Increasing need for food resources, State of Indonesia to strive to maximize the output of food production. Not only in agriculture but also aquaculture results are also trying to be improved. This is also supported by the increase of Indonesia’s national fish consumption rate from 50.69 Kg per capita in 2018 to 55.37 Kg per capita in 2021. Recent aquaculture research only explored topics about monitoring the cultivation environment. But there have been no studies exploring how bad the impact of weather on the process of farming. Hence, this study aims to measure the influence of weather on freshwater aquaculture pond water quality and analyze its impact on fish growth namely Oreochromis Sp., using pH sensors and dissolved oxygen (DO). Then a weather simulation was carried out based on Indonesia’s tropical climate, which majorly consists of sunny and rainy weather. The experimental results indicate the instability of the pH value during the rainy period. DO values tend to decrease at the end of periods of sunny weather. Moreover, fish growth analysis showed that there was a decrease in food conversion ratio (FCR) by 0.956, specific growth rate (SGR) by 2.13% and survival rate (SR) by 5.715% during rainy weather.
Dispute on Security Framework Model of MFCC Mixed Methods in Speech Recognition System  Pratiwi, Heni Ispur; Kartowisastro, Iman Herwidiana; Soewito, Benfano; Budiharto, Widodo
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.689

Abstract

An audio recording system device has unprecedented activities of its authorized users which in a particular way cause vulnerability to the system. It starts to get into a fuzzy condition and deteriorate the system sensitivity in detecting unauthorized access to pass through, then the system inclination may occur. One case is when separate users picked speech voices with similar keywords to set their usernames or password. Moreover, when users are siblings or twins that could have merely similar voices.  Troublesome of this situation leads to a less sensitive manner of a security system, and in some situations, the system could operate blocking authorized users themselves to get access. This paper defines a proposed method to resolve the situation by combining Mel Frequency Cepstral Coefficient with other methodologies, which have been implemented for many other research’ specific objectives as well. This paper displays to prove its combination with an interval scoring in Fuzzy Relation complements a resolution to tackle the security of fuzzy issues mentioned. The Mel Scale has its capacity of delivering extractions output from audio input data, it is called as spectral centroids which refer to humans’ voices or an individual's voice features. Some spectral centroids get merely similar results due to those inclinations mentioned. This paper exposes Fuzzy Relation method to fit the need of verification procedures thorough its interval scale on any fuzzy features. The objective of verification procedure is to gain consistency measured scales, and security warrant remains valid. The inhouse experiments served to give user A of [0.49, 1.18] interval, user B of [0.76,1.07] interval, and user C of [0.44,0.95] interval, and those interval numbers are proposed to cap other login users accounts unto theirs.
Application Of XGBoost-Based Machine Learning Methods To Predict Stunting Anhar, Muhammad Fariz; Soewito, Benfano
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1542

Abstract

Child stunting remains a major public?health challenge across Asia, impairing growth, cognition, and lifelong productivity. Early risk identification is critical, yet conventional screening offers limited predictive power and scalability. This study evaluates machine?learning approaches for stunting prediction using routinely collected infant data, proposing XGBoost and benchmarking it against Logistic Regression and Random Forest. An Asian infant dataset was compiled, label encoding and standardization were applied, class imbalance was addressed with SMOTE, the three models were trained and hyperparameter tuning was performed within a reproducible pipeline. Performance was assessed using Area Under the ROC Curve (AUC) and confusion matrices. XGBoost with SMOTE achieved the highest AUC (0.85), exceeding Random Forest (0.83) and Logistic Regression (0.73). Confusion?matrix analysis indicates that XGBoost separates stunted from non?stunted cases more effectively. Models trained without SMOTE performed worse, underscoring the value of imbalance correction. These findings suggest that ML assisted screening can enable earlier, data?driven risk stratification and targeted interventions. Practical deployment, however, may be constrained by the need for a GPU enabled computer and an IDE based workflow, motivating external validation and implementation refinement.
STUDENT GRADUATION TIME PREDICTION USING LOGISTIC REGRESSION, DECISION TREE, SUPPORT VECTOR MACHINE, AND ADABOOST ENSEMBLE LEARNING Desfiandi, Ardhana; Soewito, Benfano
IJISCS (International Journal of Information System and Computer Science) Vol 7, No 3 (2023): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v7i2.1579

Abstract

Universities in Indonesia are working hard to improve the graduation rates of their students as it is considered a measure of success and quality in terms of accreditation. This study focuses on analyzing the effectiveness of machine learning algorithms, regression, Support Vector Machine (SVM) Decision Tree and ensemble learning, with AdaBoost wether the Computer Science students will graduate on time or not. The data used for this analysis consists of student records from 2015 to 2019. Includes 14 variables. To understand the relationships between these variables a two-dimensional visualization called a Heatmap was employed. The research findings indicate that the Support Vector Machine (SVM) and AdaBoost Decision Tree (DT) algorithm performs better than the other algorithms. The Decision Tree and AdaBoost (DT) model achieved an F1- score of 0,76 and 0,82. This research contributes towards enhancing education management by facilitating decision making to ensure timely graduation, for student
Pengembangan Aplikasi Aman Di Cloud Untuk Lean Startup Yuliartanto, Purnaresa; Soewito, Benfano
Innovative: Journal Of Social Science Research Vol. 4 No. 2 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i2.10253

Abstract

Intisari—Dua pertiga usaha kecil dan menengah atau startup mengalami serangan siber pada tahun 2018. Dari seluruh serangan tersebut, insiden siber pada aplikasi termasuk dalam tiga peristiwa paling merugikan. Hal ini menimbulkan pertanyaan tentang bagaimana startup mengembangkan aplikasinya. Saat mengembangkan suatu produk, startup selalu fokus pada peluncuran produk, dan mereka menggunakan komputasi awan untuk itu. Meskipun penyedia cloud bertanggung jawab atas infrastruktur keamanan cloud, aplikasi yang diterapkan oleh startup di cloud adalah tanggung jawab mereka untuk mengamankannya. Startup perlu mengamankan pengembangan aplikasi mereka. Namun, kerangka keamanan yang ada seperti NIST mencakup topik luas yang lebih cocok untuk perusahaan dibandingkan startup dengan tim dan portofolio produk yang lebih kecil. Dalam karya ini, kami menyajikan kerangka NIST SSDF yang dimodifikasi untuk startup untuk mengimplementasikan pengembangan aplikasi yang aman di cloud. Metodologi kami adalah menganalisis aspek keamanan apa yang penting bagi startup. Hilangkan praktik keamanan non prioritas dari kerangka awal. Kemudian memanfaatkan sebanyak mungkin kemampuan cloud publik untuk mengurangi risiko yang perlu dikelola oleh sebuah startup. Jadi startup bisa mendapatkan pengembangan aplikasi yang aman dengan upaya sesedikit mungkin agar mereka bisa fokus pada peluncuran produk.
Application of the Multi-Threading Method and Python Script for the Automate of Network Wicaksono, Deny; Soewito, Benfano
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v9i6.16345

Abstract

In recent times, there has been a noticeable surge in the inclination towards the implementation of network automation solutions. This trend is driven by the objective of optimizing network availability within the context of hybrid data center networks, which are becoming increasingly prevalent. Reliability, performance, scalability, and minimal resource overhead are crucial solution design characteristics that significantly impact the decision- making process regarding adoption. Recent research has shown that 90% of network outages happen because of human factors and 69% of respondents manage their network with manual method. That high human error of up to 90%, occurs when configuring network devices using manual method. These problems had a negative impact on the functioning of the organization and were harmful to the user. In this research, we investigate solutions to reduce network outage that caused human error using network automation using the Python programming language and multi-threading method. This network automation will reduce configuration time, eliminate human error, and significantly increase efficiency. The aim of this research is to investigate the efficacy of network automation using Python and multi-threading to reduce network outages.The method used in this research is a parallel or multi-tread execution process method using GNS3 as network simulator. Based on experimental results, the multi- thread automation approach is significantly faster than both the serial automation method (67 seconds) and the manual method (248 seconds), this method requiring only 41 seconds to configure all Cisco router devices. If you're looking for speed, look no farther than the multi- thread automation approach, which is 6 times quicker than the manual method and 3.7 times quicker than the serial automation approach. The findings of this study have substantial and immediate implications for the ability of network engineers to speed up the configuration of networks and lower the rate of human error in doing so.
Co-Authors Afdhani, Rizqi Anhar, Muhammad Fariz Berkah I. Santoso Cahyadi, Kevin Candra, Sevenpri Candra, Sevenpri Candra, Sevenpri Cosmas E. Suharyanto Deny Wicaksono, Deny Desfiandi, Ardhana Devia, Anne Diana Diana Diana, Diana Echo Wahana Marciano Simanjuntak, Echo Wahana Marciano Eka Yulisuyanti Erick, Erick Fergyanto E. Gunawan Fergyanto E. Gunawana Ford Lumban Gaol Ford Lumban Gaol, Ford Lumban Gunawana, Fergyanto E. Hapsara, Manik Hoa, Jeffrey Jason Timotius Purwoko Jeffrey Hoa Kartowisastro, Iman Herwidiana Kusuma Atmaja, Wahyu Haris Manik Hapsara Mansuan, Melki Sadekh Melki Sadekh Mansuan Muhammad Zarlis, Muhammad Oky Wijaya Pahlevi, Mohammad Dian Panggabean, Utama Hasiolan Patricia Citranegara Kusuma Pohan, Mara Muda Pratiwi, Heni Ispur Purnaresa Yuliartanto Putra, Ferlan Gisma Raymond Sunardi Oetama Riftiarrasyid, Mohammad Faisal Sani Muhamad Isa Sani Muhamad Isa, Sani Muhamad Sani Muhammad Isa, Sani Muhammad Satriawan, Marchand Sevenpri Candra Suharyanto, Cosmas E. Taurean Orlin Wingardi Wanandi, Krisantus Wardhani, Widiastuti Kusumo Warnars, Harco Leslie Hendric Spits Widodo Budiharto Wijaya, Oky wisnu, muhammad