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All Journal EKSAKTA : Jurnal Penelitian dan Pembelajaran MIPA Pendas : Jurnah Ilmiah Pendidikan Dasar 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 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 Other

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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.
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.
RANCANG BANGUN SISTEM SILENT COMMUNICATION MENGGUNAKAN CAHAYA LASER BERBASIS ARDUINO NANO UNTUK MENDUKUNG KOMUNIKASI TAKTIS PADA LINGKUNGAN MILITER Legowo, Danang; Rimbawa, H.A Danang; Hutapea, Herwin Melyanus
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 03 (2026): Volume 11 No. 03, September 2026 Release
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i03.62128

Abstract

The advancement of military communication technology requires communication systems that are secure, difficult to detect, and resistant to Radio Frequency (RF) jamming. One promising alternative is Optical Wireless Communication (OWC) using laser light, which operates in the optical spectrum and is therefore immune to RF interference. This study aims to design and evaluate a laser-based Silent Communication system using an Arduino Nano for military communication applications. An experimental method was employed through the design, implementation, and testing of a prototype consisting of an Arduino Nano, a laser diode as the transmitter, and a light sensor as the receiver. Data transmission was performed using On-Off Keying (OOK) modulation over a Line-of-Sight (LOS) communication link. Experimental results show that the proposed system maintains a transmission rate of 15 bps. The system achieved a 100% Success Rate with a 0% Bit Error Rate (BER) at transmission distances of 5–200 m. At 300 m and 400 m, the Success Rate decreased to 93% and 86%, with BER values of 6.67% and 13.33%, respectively, due to reduced optical signal intensity. In addition, the system showed no significant performance degradation under RF interference because communication was conducted entirely through the optical spectrum. These results demonstrate that the proposed system was successfully implemented and provides Low Probability of Intercept (LPI) and Low Probability of Detection (LPD) characteristics, making it a promising alternative tactical communication solution for modern military operations.
Implementasi Teknologi Mediapipe Menggunakan Metode CNN Berbasis Website Untuk Pengamanan VVIP Dalam Mobil M. Ilham AlFatrah; Hery Sudaryanto; H. A. Danang Rimbawa
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

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

Abstract

Penelitian ini bertujuan mengembangkan sistem deteksi gestur tangan berbasis MediaPipe dan Convolutional Neural Network (CNN) guna meningkatkan efektivitas pengamanan VVIP. Mengingat ancaman modern yang semakin kompleks, sistem ini dirancang untuk mendeteksi gestur darurat secara real-time dan memungkinkan respons cepat. Metodologi yang digunakan meliputi pengumpulan dataset gestur tangan, anotasi data menggunakan MediaPipe, dan pelatihan model CNN di Google Colab. Kinerja model dievaluasi dengan metrik akurasi, presisi, recall, dan F1-score. Pengujian juga dilakukan dalam berbagai kondisi, seperti pencahayaan rendah dan gerakan cepat, untuk menilai ketangguhan sistem di dunia nyata. Hasilnya, sistem ini berhasil mendeteksi gestur tangan darurat dengan akurasi tinggi dan kecepatan kurang dari satu detik. Kinerja optimal, dengan akurasi mendekati 100%, tercapai pada kondisi pencahayaan yang baik. Meskipun akurasi sedikit menurun pada kondisi ekstrem, integrasi sistem pada platform website memungkinkan pengawasan dan pengambilan keputusan cepat di pusat komando. Penelitian ini membuktikan bahwa kombinasi MediaPipe dan CNN adalah solusi inovatif, namun optimasi lebih lanjut tetap dibutuhkan.
Addressing SIM Card and IMEI Security Vulnerabilities in Preventing Illegal Online Activities by Using Elliptic Curve Cryptography Danny Setyowati; Danang Rimbawa
Enrichment: Journal of Multidisciplinary Research and Development Vol. 3 No. 4 (2025): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v3i4.453

Abstract

The telecommunication system in Indonesia faces serious challenges due to the misuse of SIM card data and devices with illegal IMEIs, as seen in the case of mass registration of prepaid cards using fake or stolen identities in 2018. This problem is further exacerbated by the circulation of black market (BM) phones that use unregistered IMEI to avoid network blocking. As a result, these security loopholes are used to support illegal activities such as account registration on online gambling platforms. This study proposes the application of Elliptic Curve Cryptography (ECC) as a solution to improve SIM card data security and IMEI validation. ECC provides efficient and secure encryption methods to protect data, verify device authentication, and block illegal activities through telecommunication systems. The main contribution of this research is the development of ECC-based systems that can prevent the misuse of SIM card and device data, support the validation of legitimate devices, and tighten control over network access. The evaluation shows that ECC technology can be applied effectively in improving telecommunication security in Indonesia.
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 Danny Setyowati Fadhil Muhammad Hadini Faidin, Firman Gilang Prakoso Harry Pratomo Bagaskoro Hasibuan, Bayu Nuar Khadapi Heikhmakhtiar, Aulia Khamas Heri Azhari Noor Hery Sudaryanto Hery Sudaryanto Hutapea, Herwin Melyanus I Made Wiryana I Made Wiryana Kencana, Lisdi Inu Legowo, Danang Luqman, Fathan M. Fachrurrozy M. Ilham AlFatrah 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. Saragih, Gabriel Winandika Sastradinata, Aria Kusumah Sembali, Tryas Putranto Suhardiningsih, A.V. Sri Suharjo, Bambang Sunarta Sunarta Supriyadi, Devi Tangang Qisthina Handayani Zatadini Taqwa, Rangga Tidar, R Haryo TUTUN JUHANA Uvi Desi Fatmawati Wahyudi, Bisyron Wibisono, Nugroho Yosef Prihanto Yudistira Asnar