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All Journal EKSAKTA : Jurnal Penelitian dan Pembelajaran MIPA Journal on Education KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Antivirus : Jurnal Ilmiah Teknik Informatika Journal of Information Systems and Informatics Jurnal ABDINUS : Jurnal Pengabdian Nusantara Indonesian Journal of Electrical Engineering and Computer Science Journal of Intelligent Decision Support System (IDSS) Jurnal Bumigora Information Technology (BITe) Jurnal Mandiri IT Smart City Enrichment: Journal of Multidisciplinary Research and Development VISA: Journal of Vision and Ideas Asian Journal of Social and Humanities SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
H. A. Danang Rimbawa
Universitas Pertahanan Republik Indonesia
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences

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IMPLEMENTASI HUMAN FIREWALL UNTUK MENCEGAH ANCAMAN SOCIAL ENGINEERING DALAM MEWUJUDKAN KEAMANAN SIBER Faidin, Firman; Rimbawa, H.A. Danang; Saputro, J.W.
Eksakta : Jurnal Penelitian dan Pembelajaran MIPA Vol 10, No 1 (2025): Eksakta : Jurnal Penelitian dan Pembelajaran MIPA
Publisher : Fakultas Keguruan Dan Ilmu Pendidikan, UM-Tapsel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/eksakta.v10i1.62-70

Abstract

Perkembangan teknologi informasi dan komunikasi yang begitu cepat dan telah memberikan banyak kemudahan dalam berbagai aspek kehidupan. Namun, di sisi lain, hal ini juga membawa ancaman keamanan siber yang semakin meningkat, salah satunya adalah ancaman social engineering. Oleh karena itu, penelitian ini bertujuan untuk menganalisis implementasi human firewall untuk mencegah ancaman social engineering dalam mewujudkan keamanan siber. Metode penelitian yang digunakan adalah metode penelitian kualitatif dengan wawancara mendalam kepada beberapa key informan diantaranya manajer keamanan siber, karyawan yang terlibat dalam program pelatihan, dan pakar keamanan siber di PT. Mekar Armada Jaya Magelang. Dari hasil penelitian yang telah dilakukan, dapat disimpulkan bahwa implementasi human firewall efektif dalam mencegah ancaman social engineering dan meningkatkan keamanan siber. Integrasi teknologi yang memungkinkan dengan pelatihan manusia memperkuat pertahanan perusahaan, sementara kebijakan keamanan yang jelas dan dukungan manajemen puncak membantu mengatasi tantangan partisipasi dan perubahan budaya organisasi. Namun, tantangan seperti rendahnya partisipasi karyawan dan resistensi terhadap perubahan budaya tetap ada. Agar lebih berhasil, pendekatan pelatihan harus relevan dengan peran karyawan, menggunakan teknologi untuk meningkatkan keterlibatan, dan melibatkan karyawan dalam pengembangan materi pelatihan. Studi kasus keberhasilan dan kegagalan yang ditemukan menunjukkan bahwa strategi ini dapat diadaptasi dan ditingkatkan secara terus menerus.
Analysis and design of an inset-feed microstrip antenna for a LEO satellite IoT ground station at 921 MHz Taqwa, Rangga; Rimbawa, H.A. Danang; Miptahudin, Apip; Hasibuan, Bayu Nuar Khadapi; Sastradinata, Aria Kusumah; Bangun, Abbas Madani
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.464

Abstract

The evolution of the Internet of Things (IoT) demands global connectivity that terrestrial networks alone cannot provide1. Low Earth Orbit (LEO) satellites equipped with Long Range (LoRa) communication technology offer a promising solution to bridge this connectivity gap2. This paper presents a specific case study calculation for a LoRa-based IoT satellite mission, defining the system's operational constraints based on selected hardware3. This analysis is framed by the RFM95W LoRa transceiver for the ground station and the Satlab Polaris receiver for the satellite4. The datasheet specifications of these components establish the critical link parameters that dictate performance: a maximum Transmit Power (Pt) ) of 20 dBm from the RFM95W 5and a Receiver Sensitivity threshold of -130 dBm for the Satlab Polaris6. The objectives are: (1) to conduct a comprehensive link budget analysis to validate the communication viability between a LEO satellite and a ground station 77, and (2) to design and predict the performance of an inset-feed microstrip antenna operating in the 920-925 MHz Indonesian LoRa frequency band using an FR-4 substrate. The detailed link budget analysis, performed for an uplink to a 500 km orbit 9, reveals that these specific parameters create a stringent performance requirement: while a reliable link margin of $+7.8 \text{ dB}$ is achieved at a 90°  elevation (best case) 10101010, the system reaches its theoretical critical threshold (0.0 dB margin) at 19.1° and enters link failure with a -2.8 dB margin at the target 10°  elevation. This failure is directly linked to the preliminary simulation of the initial antenna design, which shows a suboptimal return loss (S11) of -9.41 dB. This paper concludes that the system's target for low-elevation communication has not been met. The performance gap, defined by the hardware constraints, confirms that the initial antenna design is insufficient15. Therefore, systematic optimization of the antenna design is identified as the crucial next step to achieve a positive link margin at the 10° target elevation and ensure a robust communication link across all operational scenarios.
Improving Large Language Model’s Ability to Find the Words Relationship Alam, Sirojul; Abdul Jabar, Jaka; Abdurrachman, Fauzi; Suharjo, Bambang; Rimbawa, H.A Danang
Jurnal Bumigora Information Technology (BITe) Vol. 6 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i2.4127

Abstract

Background: It is still possible to enhance the capabilities of popular and widely used large language models (LLMs) such as Generative Pre-trained Transformer (GPT). Using the Retrieval-Augmented Generation (RAG) architecture is one method of achieving enhancement. This architectural approach incorporates outside data into the model to improve LLM capabilities. Objective: The aim of this research is to prove that the RAG can help LLMs respond with greater precision and rationale. Method: The method used in this work is utilizing Huggingface Application Programming Interface (API) for word embedding, store and find the relationship of the words. Result: The results show how well RAG performs, as the attractively rendered graph makes clear. The knowledge that has been obtained is logical and understandable, such as the word Logistic Regression that related to accuracy, F1 score, and defined as a simple and the best model compared to Naïve Bayes and Support Vector Machine (SVM) model. Conclusion: The conclusion is RAG helps LLMs to improve its capability well.
Implementasi Extended Detection and Response pada Security Operation Center dan Computer Security Independent Response Team dalam Peningkatan Sistem Keamanan Informasi Guna Meningkatkan Sistem Pertahanan Informasi Luqman, Fathan; Rimbawa, H.A. Danang; Sunarta, Sunarta; Wibisono, Nugroho
VISA: Journal of Vision and Ideas Vol. 5 No. 3 (2025): Journal of Vision and Ideas (VISA)
Publisher : IAI Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/visa.v5i3.9145

Abstract

The rapid development of digital technology increases the complexity of cyber threats, which are now increasingly sophisticated and organised, targeting individuals, enterprises and critical infrastructure. Therefore, an information security system capable of automatically detecting and responding to threats is an urgent need. This research aims to examine the implementation of Extended Detection and Response (XDR) in the Security Operation Centre (SOC) and Computer Security Incident Response Team (CSIRT) to improve the effectiveness of information security systems. The method used is experimental with testing in a controlled environment using Wazuh as the XDR platform. This study analyses how XDR collects, analyses and responds to log data in real-time to detect threats more accurately. The results show that XDR is able to improve threat detection by integrating logs from multiple sources, including endpoints, networks, and cloud services, and automating incident mitigation for faster response. The integration of Machine Learning in XDR is also proven to improve attack detection accuracy, reduce false positives, and speed up incident analysis. In conclusion, XDR is a comprehensive solution for modern information security systems, especially in the context of SOC and CSIRT, with its capabilities in detection-based analytics, multi-source data correlation, and automated response to threats. Based on this test, the efficiency of XDR in detecting and mitigating malware attacks is 98.3% using up to 60 malware and respons time under 10 second. Therefore, the implementation of XDR is recommended for organisations looking to enhance their security systems in a more adaptive and proactive manner in the face of evolving cyber threats.
Peningkatan Peran Usaha Kesehatan Sekolah di Sekolah Dasar Negeri Gisik Cemandi Sidoarjo Rustam, Muh Zul Azhri; Susanti, Ari; Amalia, Nuke; Suhardiningsih, A.V. Sri; Riestiyowati, Maya Ayu; Amalin, Atika Mima; Rimbawa, H. A. Danang
Jurnal ABDINUS : Jurnal Pengabdian Nusantara Vol 7 No 2 (2023): Volume 7 Nomor 2 Tahun 2023
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/ja.v7i2.19008

Abstract

Student health center as a forum for providing Health Education for schoolchildren is expected to help improve the quality of education and achievement reflected in a healthy lifestyle and environment. Based on the observations made by the community service team, it is known that the implementation of the Student health center at SDN Gisik Cemandi is still straightforward due to limited funds, existing facilities, and infrastructure, and there is no form for recording and reporting Student health center activities. The purpose of this community service activity is to evaluate the activities of the student health center role at SDN Gisik Cemandi, Sedati, in Sidoarjo Regency. This community service activity was carried out on November 13-14th through small group discussions with the headmaster, teachers, and Student health center administrators at SDN Gisik Cemandi to identify needs in implementing the Student health center program. Based on the results of the cases found during the community service that had been carried out, the community service team made some products, namely: the form of a student health center service flow in the form of roller banners and videos as well as a student health center register book. The product is expected to improve the quality of Student health center services at SDN Gisik Cemandi.
Strategy for preventing human trafficking through verification of online job vacancies in Indonesia: English Passu Beta, Arga Husein; Rimbawa, H.A. Danang; Heikhmakhtiar, Aulia Khamas
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 4 (2025): December: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i4.324

Abstract

This study addresses the rise of online job ads used to recruit victims of human trafficking (TPPO). We propose a practical screening approach that combines automated checks with human moderation. The goal is not to prove crimes, but to prioritize high-risk ads for fast review and referral. Using a public dataset of 500 job postings (fake_job_postings_500), we clean the text and basic metadata, extract simple text features (TF–IDF), and add light verification signals (e.g., contact and firm consistency). We then train two models in a leakage-safe pipeline: calibrated Logistic Regression (LR-cal) and Random Forest (RF). Performance is evaluated with standard accuracy measures ROC-AUC, PR-AUC, F1 plus calibration (how well risk scores match reality) and triage metrics that reflect real operations: precision for the highest-risk group, recall for all medium-and-above risk, and the share of ads moderators must review. Results show LR-cal is accurate and well-calibrated (5-fold means: ROC-AUC 0.993, PR-AUC 0.986, F1 0.934). In triage with thresholds T_high = 0.80 and T_med = 0.50, LR-cal yields Precision@High = 1.00 and Recall@≥Med=0.925 with ~34% of ads needing review. RF reaches near-ceiling accuracy (1.00/1.00 at ~35.3% workload) but requires careful calibration and leakage auditing. Practical contribution: AI-assisted, risk-based gatekeeping can reduce exposure to Human Trafficking or TPPO at the source. We recommend: (1) adopting calibrated models with adjustable thresholds; (2) standard operating procedures (SOPs) for cross-platform verification, including Know Your Customer (KYC) and Open-Source Intelligence (OSINT) checks; and (3) direct integration with official reporting channels to escalate flagged ads swiftly.
Invisible watermarking as an additional forensic feature of e-meterai Rimbawa, H.A Danang; Alam, Sirojul; Saputro, Joko W.; Mantoro, Teddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp344-356

Abstract

The e-meterai is an official digital product of the Indonesian government issued by the Directorate General of Taxation (DGT). Its usage has become increasingly widespread as conventional documentation transitions to digital formats, serving the same function as its printed counterpart. This product features a quick-response code embedded with unique Indonesian codes and offers overt, covert, and forensic features. This study aims to experiment with adding a forensic feature in the form of an invisible watermark. We employed two watermark embedding techniques, discrete Fourier transform (DFT) and scale-invariant feature transform (SIFT), to determine which is more suitable for this application. After embedding the watermark, we also simulate various attacks including gaussian noise, salt and pepper noise, averaging filter, rotation, translation, and speckle noise. For each attack, we calculated with normalized-cross correlation (NCC) values, obtaining 0.863 and 0.976 for the gaussian noise attack, 0.929 and 0.984 for the salt and pepper attack, 0.975 and 0.984 for the averaging filter attack, 0.173 and 0.097 for rotation attacks, 0.172 and 0.032 for translation attack, and 0.972 and 0.996 for speckle noise attack, using DFT and SIFT techniques, respectively.
Empowering Sustainable Household Waste Management through Rubbin: App-Based Transactions using Google Maps API and QR Code Recognition Rimbawa, H.A. Danang; Arghanie, Muhammad Abditya; Renoult, Muhammad Rey; Ananda, Dea Dwi
Smart City Vol. 4, No. 2
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Digitization of environmentally friendly technology must be applied to advance smart cities. Waste management carried out conventionally often causes irregularities in the classification data collection, difficulties in accessing information related to garbage collection schedules, and a lack of transaction history information, which causes a decrease in the quality of waste collection. The development of functional application features is urgently needed as a container for proper waste management. The Rubbin App has been created, which implements Google Maps API for route optimization and waste mapping, QR code recognition using Enhanced Adaptive Median Filter, and private chat for clients and collectors as additional features. The purpose of designing this research is to overcome the impacts of climate change, significantly reducing gas emissions, by proposing a digital waste management system by providing iOS and Android multiplatform applications with attractive and easy-to-use UI/UX interfaces. The Rubbin application utilizes Google Maps API technology to enable waste tracking and route optimization while employing QR codes to streamline the transaction process between clients and collectors, ensuring efficient waste management operations. The Rubbin application results show a change in the community's waste management habits, forming a culture of caring for the environment.
Data Mining Applications to Prediction Stock Prices Using Decision Trees and Neural Networks Dadan Shavkat Riswantoro; Harry Pratomo Bagaskoro; Bambang Suharjo; Danang Rimbawa
Asian Journal of Social and Humanities Vol. 4 No. 10 (2026): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v4i10.749

Abstract

Stock price prediction remains a significant challenge in financial markets due to the high volatility and complexity of influencing factors. This study explores the application of hybrid models combining Decision Tree (DT) and Neural Network (NN) methodologies to enhance stock price prediction accuracy. The research utilizes extensive historical market data as the foundational input for training both models individually. The Decision Tree model is employed for its interpretability and ability to handle non-linear relationships, while the Neural Network model capitalizes on its capacity to learn complex patterns through its layered architecture. After training and evaluating each model separately, a hybrid approach is introduced, which averages the predictions from both the DT and NN models. Performance is quantitatively assessed using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results indicate that the hybrid model consistently outperforms individual models, achieving an MAE of 5.6 and RMSE of 7.94, with an overall accuracy of 91.5%. This fusion of methodologies demonstrates improved accuracy and significantly reduces error margins, showcasing the complementary strengths of both algorithms. The findings suggest that leveraging hybrid models can effectively mitigate risks associated with market fluctuations and enhance investment strategies. This research contributes to the field of financial forecasting by providing investors with more robust tools for making informed decisions, and offers recommendations for future research directions in integrating machine learning techniques for financial prediction.
Co-Authors Abdillah, Abdillah Imam Julianto Abdul Jabar, Jaka Abdurrachman, Fauzi Alam, Sirojul AlFatrah, M. Ilham Amalia, Nuke Ananda, Dea Dwi Andri Purwoko Arghanie, Muhammad Abditya Ari Susanti Atika Mima Amalin Atturoybi, Abdurrosyid Aulia Khamas H Bambang Suhardjo Bambang Suharjo Bambang Suharjo Bangun, Abbas Madani Bisyron Wahyudi Bisyron Wahyudi Budi Rahardjo Budi Raharjo Chadafa Zulti Noorta Dadan Shavkat Riswantoro Dananjaya Ariateja Fadhil Muhammad Hadini Faidin, Firman Gilang Prakoso Harry Pratomo Bagaskoro Hasibuan, Bayu Nuar Khadapi Heikhmakhtiar, Aulia Khamas Heri Azhari Noor Hery Sudaryanto I Made Wiryana I Made Wiryana Kencana, Lisdi Inu Luqman, Fathan M. Fachrurrozy M. Ilham AlFatrah Madramsyah, Adam Maya Ayu Riestiyowati Miptahudin, Apip Muhamad Zein Satria Ni Putu Ayu Astriyani Noorta, Chadafa Zulti Nurrahman, Muhammad Irsyaad Onky Prilianda Putra Passu Beta, Arga Husein Puri Ratna Larasati Puringgar Prasadha Putra, Rayasa Rais, M. Fazil Randi Agustio Ra’idah Naufaliana Dewi Refino Maulana Hansbullah Subarkah Renoult, Muhammad Rey Richardus Eko Indrajit Riswantoro, Dadan Shavkat Ruby Alamsyah Rustam, Muh Zul Azhri Saputro, J.W. Saputro, Joko W. Saragih, Gabriel Winandika Sastradinata, Aria Kusumah Sembali, Tryas Putranto Setyowati, Danny Suhardiningsih, A.V. Sri Suharjo, Bambang Sunarta Sunarta Supriyadi, Devi Tangang Qisthina Handayani Zatadini Taqwa, Rangga Teddy Mantoro Tidar, R Haryo Tutun Juhana Uvi Desi Fatmawati Wahyudi, Bisyron Wibisono, Nugroho Yosef Prihanto Yudistira Asnar