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All Journal Jupiter Techno.Com: Jurnal Teknologi Informasi Jurnal TIMES Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Ilmiah FIFO Jurnal Sains dan Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) Jiko (Jurnal Informatika dan komputer) Jurnal Teknologi Sistem Informasi dan Aplikasi JSiI (Jurnal Sistem Informasi) Jurnal ICT : Information Communication & Technology International Journal of Advances in Data and Information Systems Infotech: Journal of Technology Information SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Pendidikan dan Teknologi Indonesia Jurnal Abdimas Sangkabira Jurnal Mahasiswa Sistem Informasi (JMSI) Kresna: Jurnal Riset dan Pengabdian Masyarakat Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Golden Ratio of Data in Summary Eduvest - Journal of Universal Studies OKTAL : Jurnal Ilmu Komputer dan Sains Artinara Radiant : Journal of Applied, Social, and Education Studies Jurnal Ilmu Komputer Journal of Computer Science Advancements
Ruwirohi, Jan Everhard
Universitas Budi Luhur
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Neuroscience Social Sciences Other

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INTRODUCTION OF LORA COMMUNICATION SYSTEM AND REMOTE CONTROL SYSTEM IN AGRICULTURAL AUTOMATION WITH INTERNET OF THINGS Prabowo, Yani; Riwurohi, Jan Everhard; Windihastuti, Wiwin; Hasan, Fuad
Journal of Computer Science Advancements Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i2.2230

Abstract

This research focuses on the integration of LoRa (Long Range) communication system and remote control system in agricultural automation with Internet of Things (IoT) using ESP32 microcontroller, Arduino nano and STM32 aims to improve the efficiency of intelligent agricultural management. LoRa is used as a long-range wireless communication protocol to collect data from sensors that are widely distributed in agricultural land, such as soil moisture sensors, temperature. The ESP32 microcontroller functions as the main controller that processes data from sensors and sends it in real-time to the control center via the LoRa network. Modbus is used as a standard serial communication protocol to connect sensors, actuators and other devices, thus ensuring compatibility between devices. In addition, Node-RED is used as a graphical interface (GUI) to manage data flow, control automation processes, and provide real-time data visualization to users. The results of this research are a stable integration system between sensor systems and communication systems. The novelty of this research is the integration of LoRa, ESP32, Modbus, and Node-RED to create a reliable and efficient agricultural automation system, enabling remote management of irrigation, fertilization, and environmental monitoring, thereby increasing agricultural productivity and optimizing resource use.
Implementasi Large Language Model dalam Multi-Domain Psikologi: Tinjauan Literatur Sistematis Ansor, Mohamad Zakaria; Ari Kusuma, Dyah Topan; Riwurohi, Jan Everhard
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 11 (2025): JPTI - November 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1105

Abstract

Implementasi large language models (LLM) dalam bidang psikologi menyajikan peluang signifikan untuk meningkatkan diagnosis, pengambilan keputusan klinis, dan penelitian medis. Studi ini melakukan tinjauan literatur sistematis untuk mengeksplorasi penelitian-penelitian terkini mengenai aplikasi LLM dalam bidang psikologi. Dengan mengikuti panduan PRISMA, pencarian literatur dilakukan pada database ScienceDirect. Kriteria inklusi dan eksklusi diterapkan untuk mengidentifikasi studi-studi yang relevan. Data yang diekstraksi mencakup tujuan penelitian, metodologi, bidang aplikasi, jenis data yang digunakan, key findings, dan hasil. Sebanyak 20 studi dimasukkan setelah proses seleksi. Review ini memberikan gambaran komprehensif mengenai aplikasi LLM dalam bidang psikologi, mengidentifikasi peluang, tantangan, dan arah penelitian masa depan yang bermanfaat bagi peneliti, praktisi, dan pembuat kebijakan. Temuan ini menunjukkan bahwa integrasi LLMs dalam praktik psikologi memiliki potensi transformatif untuk meningkatkan kualitas dan aksesibilitas layanan kesehatan mental, namun memerlukan pengembangan framework etis dan regulasi yang komprehensif untuk memastikan implementasi yang aman dan efektif.
ANALISIS KOMPARATIF EFISIENSI DAN KINERJA PROSESOR INTEL XEON 6 DAN AMD EPYC 9004 PADA LINGKUNGAN SERVER VIRTUALISASI Oktora, Andre; K, Irvan; K, Johanes H; Ridwan, Mohamad; Riwurohi, Jan Everhard
Jurnal TIMES Vol 14 No 2 (2025): Jurnal TIMES
Publisher : STMIK TIME

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

Abstract

Peningkatan konsumsi daya pada pusat data global menempatkan efisiensi energi (performance-per-watt) sebagai metrik krusial dalam pemilihan prosesor server modern, terutama dalam lingkungan komputasi awan dan virtualisasi berbasis container. Penelitian ini bertujuan untuk menganalisis komparatif kinerja (throughput relatif) dan efisiensi energi () antara prosesor Intel Xeon 6 (arsitektur hybrid) dan AMD EPYC 9004 (arsitektur Zen 4 dengan 96 core) di bawah skenario peningkatan beban kerja container. Studi ini menggunakan pendekatan kuantitatif simulatif berbasis data sekunder, mengimplementasikan model matematis yang mereplikasi degradasi kinerja dan peningkatan konsumsi daya seiring penambahan jumlah container (10 hingga 100). Hasil simulasi menunjukkan bahwa AMD EPYC 9004 unggul secara signifikan. Prosesor ini tidak hanya mempertahankan throughput absolut yang lebih tinggi di seluruh beban kerja ( hingga 463.30 pada 100 container), tetapi juga menunjukkan skalabilitas yang lebih baik (degradasi minimal dari ). Keunggulan kinerja ini menghasilkan Efisiensi Energi () yang superior (mencapai 2.47), yang membuktikan bahwa arsitektur berdensitas inti tinggi mampu mengkompensasi TDP yang sedikit lebih tinggi, memberikan rasio performance-per-watt yang lebih ekonomis. Disimpulkan bahwa AMD EPYC 9004 merupakan pilihan yang lebih optimal bagi pengelola data center yang mencari solusi kinerja tinggi yang stabil dan efisien energi untuk beban kerja virtualisasi yang intensif.
Performance Evaluation of Cloud-Init as Deployment Automation, Virtual Machine, and LXC Container on Proxmox VE for AI LLM Deployment Jody, Jody; Riandhito, Febry Aryo; Yusuf, Rika; Saputra, Anggi; Riwurohi, Jan Everhard
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2562

Abstract

As Artificial Intelligence (AI) is used more and more in digital certification systems, it is important to create stable and efficient environments for the use of Large Language Models (LLMs). AI-based chatbots are very helpful for people who are taking online tests at professional certification schools and for people who are giving tests. However, it is still not clear where the best place is to run AI inference workloads because virtualization can use different amounts of resources and cost different amounts. This study aims to identify the optimal deployment environment by assessing Cloud Init, Virtual Machine (VM), and Linux Container (LXC) within the Proxmox Virtual Environment (VE). This environment tested Ollama and FastAPI on the same hardware (4 vCPU, 16 GB RAM, 32 GB SSD, 80 Mbps) and the Phi3:3.8b model. The study also checked the important numbers like CPU and memory usage, disk and network throughput, latency, and response time. The tests showed that LXC had the fastest disk speed (2.45 MB/s) and network speed (3.33 MB/s). VM had the longest response time (15.64 s) and the longest latency (6.89 ms). Cloud Init had mixed results: it made automation easier but less effective. These results show that the best way to use Cloud Init and LXC together for big certification systems is through hybrid orchestration. This is the best way to get a good balance between AI deployment that is flexible and fast. The methodology section provides a clearer description of the experimental process, including benchmark tools (Hey CLI, Sysbench, Prometheus), the number of test repetitions (three sessions per environment), and comparative data analysis methods to ensure result validity. Moreover, the conclusion emphasizes the scientific implications by explaining how Cloud Init’s automation capabilities can be combined with LXC’s performance efficiency to improve AI inference deployments in scalable and institutional environments.
Integration of Yolov8 and OCR As E-KTP Data Extraction and Validation Solution for Digital Administration Automation Gumirang, Lalang; Riwurohi, Jan Everhard; Pramono, Agung
Eduvest - Journal of Universal Studies Vol. 5 No. 11 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i11.52365

Abstract

The exchange of personal data in Indonesia remains predominantly manual, involving form-filling and photocopying of electronic identity cards (e-KTP), despite the availability of embedded electronic chips designed for automated data processing. This study proposes an integrated data extraction and validation system combining YOLOv8 for precise region detection and Optical Character Recognition (OCR) with advanced preprocessing techniques for textual information extraction. Unlike previous approaches relying solely on OCR (e.g., Vision AI), this method employs YOLOv8 object detection to accurately localize key fields (NIK, Name, Address) before text extraction, followed by validation through the DUKCAPIL API. The system was evaluated using 20 e-KTP images captured under various conditions. Results demonstrate that the proposed approach achieves an average OCR accuracy of 98.7% with an Intersection over Union (IoU) of 0.975, significantly outperforming baseline Vision AI extraction by 15–20%. All extracted data successfully passed validation against the official DUKCAPIL database, confirming 100% authenticity verification. This system provides an economical and efficient solution for automating population data administration, particularly suitable for small non-governmental organizations with limited budgets. The integration of deep learning-based object detection and preprocessed OCR offers a robust framework for digital identity verification systems.
Implementation and Analysis of Distributed Cache Architecture Between Virtual Machines in VMware to Reduce Memory Access Latency Riwurohi, Jan Everhard; Syahrir, Muh.; Muslich, Muhammad Farid; Nurman, Indra; Adriansyah, A.
Golden Ratio of Data in Summary Vol. 6 No. 1 (2026): November - January
Publisher : Manunggal Halim Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52970/grdis.v6i1.1838

Abstract

Virtualization technology allows multiple virtual machines (VMs) to run on a single physical machine, improving efficiency and flexibility. However, virtualized systems often face performance problems such as high memory access latency and repeated data requests between VMs. To address this issue, this study implements a distributed caching system using Redis as an in-memory cache shared between virtual machines. The experiment was conducted on the VMware vSphere platform using two virtual machines: one VM acted as a Redis cache server, and the other as a client for testing. Both VMs were connected using a host-only network to ensure stable communication. Testing was performed in two scenarios: without cache and with Redis cache, each executed 10 times. The main metric measured was response time in seconds. The results show a clear performance improvement after using Redis. The average response time without cache was 0.0113 seconds, while with Redis cache it decreased to 0.00046 seconds. This indicates that Redis reduced memory access latency by approximately 97.6%. The system also remained stable during testing without any connection issues. In conclusion, implementing a distributed caching architecture using Redis effectively improves response time, reduces memory access latency, and enhances system performance in a VMware virtualized environment. This study can serve as a reference for developing more efficient and responsive virtualization systems in modern computing environments.
Hybrid Relevance and Sentiment Classification of Indonesian Gold Tweets Using Machine Learning for Market Risk Signal Extraction Kamalia, Antika Zahrotul; Indra, Indra; Wibowo, Arief; Riwurohi, Jan Everhard; Hassan, Shiza
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1517

Abstract

This study proposes a hybrid relevance–sentiment classification framework to analyze public opinion on physical Antam gold from Indonesian Twitter data and to support exploratory market-risk signal extraction. Tweets were collected during February–November 2025, after preprocessing and text-normalized deduplication, 1,271 unique tweets were retained. The approach combines weak supervision (rule-/lexicon-based silver labels) with TF-IDF-based machine learning in two stages: (1) relevance classification to separate tweets genuinely discussing physical Antam gold from non-relevant contexts (e.g., ANTM stock/capital-market discussions), and (2) two-class sentiment classification (positive vs negative) applied to relevance-filtered tweets. Random Forest achieved the strongest relevance performance (Accuracy = 0.984; macro-F1 = 0.943; 5-fold CV macro-F1 = 0.928 ± 0.033). For sentiment classification, performance was moderate and close across models; the most stable model under cross-validation (Logistic Regression/Naive Bayes) was used for downstream aggregation. Sentiment outputs were aggregated into a monthly sentiment index for descriptive comparison with gold prices; the observed association was weak, indicating that the index is better interpreted as a risk-perception proxy rather than a direct price predictor.
Tinjauan Literatur Sistematis dan Analisis Bibliometrik tentang Isu Etika dan Tata Kelola Kecerdasan Buatan dalam Aplikasi Militer dan Peperangan Bambang Suharjo; Dendi Sunardi; Jan Everhard
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 9 No. 1 (2026): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v9i1.58508

Abstract

The rapid advancement of Artificial Intelligence (AI) in military applications has raised a range of ethical and governance concerns, particularly regarding the use of Autonomous Weapon Systems (AWS) in making lethal decisions without direct human involvement. While these developments offer strategic advantages, they also introduce significant challenges in ensuring accountability, transparency, and compliance with international humanitarian law. This study aims to systematically examine and map the knowledge structure and global research trends related to ethical and governance issues of AI in the military domain. The research adopts a Systematic Literature Review (SLR) approach based on the PRISMA protocol, combined with bibliometric analysis of 469 articles published between 2020 and 2025. The analysis is conducted using VOSviewer to identify thematic clusters, relationships among research topics, and the overall density of scholarly discourse. The findings reveal seven major thematic clusters, including ethical foundations and human-centric approaches, operational systems and decision-making, robotics and autonomous systems, military applications and strategy, governance and regulatory frameworks, ethical principles and accountability, and technical foundations based on machine learning. Network visualization indicates that ethical issues are closely interconnected with governance as the central focus of the discourse, while density analysis shows that the terms “artificial intelligence,” “ethics,” and “application” dominate the research landscape. The study also highlights a gap between normative ethical frameworks and practical implementation in the development and deployment of AI in military contexts. Therefore, stronger governance frameworks are required to ensure accountability and compliance with international regulations. This research contributes by mapping current research directions and identifying future research opportunities, particularly in developing more adaptive and context-aware AI governance approaches.
DAMPAK ADOPSI KECERDASAN BUATAN TERHADAP KINERJA USAHA MIKRO, KECIL, DAN MENENGAH (UMKM) Sucipto Basuki; Riyanto Riyanto; I Ketut Sudaryana; Jan Everhard Riwurohi
Infotech: Journal of Technology Information Vol 12, No 1 (2026): JUNI (In Progress)
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v12i1.593

Abstract

The advancement of Artificial Intelligence (AI) has significantly accelerated digital transformation across various sectors, particularly Micro, Small, and Medium Enterprises (MSMEs). This Research aims to investigate the effects of AI integration on the operational efficiency of MSMEs in the Cibitung District of Bekasi Regency. Empirical data were gathered through a survey of MSME stakeholders, using a meticulously structured questionnaire, and subsequently analyzed using data-driven methodologies within the Orange Data Mining application. The analytical process encompassed data preprocessing and correlation analysis. The results reveal a positive correlation between AI integration and MSME operational performance. A correlation coefficient of 0.726 indicates a robust positive association between AI adoption and MSME sales performance, whereas an R² of 52.7% indicates that the model exhibits moderate to good predictive capability in explaining variations in MSME performance. These findings suggest that adopting artificial intelligence can enhance operational efficiency, boost business productivity, and expand MSMEs’ market reach. This study enriches the existing literature by proposing an analytical framework grounded in Orange Data Mining as a viable alternative to conventional analytical methodologies in MSME Research, while simultaneously underscoring the practical implications for digital transformation strategies and policy formulation aimed at facilitating AI adoption within the MSME sector.
Strategi Vertical Scaling dalam Meningkatkan Efisiensi Operasional Sistem Informasi Berbasis Database Jan Everhard Riwurohi; Arimaya Setyorini; Raden Bagus Dhana Pradana Adi; Tety Sapriani
Jurnal Sains dan Informatika Vol. 12 No. 1 (2026): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v12i1.1851

Abstract

Dalam sistem komputasi modern, optimasi penggunaan sumber daya seperti Central Processing Unit (CPU) dan memori manjadi faktor penting dalam mendukung performa aplikasi. Efisiensi operasional sistem informasi sangat bergantung pada kinerja infrastruktur teknologi yang mendukungnya, terutama server database yang menjadi pusat penyimpanan dan pemrosesan data. Pemanfaatan CPU dan memori secara efisien sangat dibutuhkan dalam pengelolaan server database. Kedua komponen ini berperan langsung dalam kecepatan pemrosesan query dan pengelolaan transaksi data. Beban kerja yang tinggi tanpa dukungan kapasitas hardware yang memadai dapat menyebabkan peningkatan latency, bottleneck proses, bahkan kegagalan sistem. Dengan demikian perlu dilakukan utilisasi pada CPU dan memori sehingga server dapat menangani permintaan data secara stabil. Salah satu strategi yang dapat diterapkan untuk meningkatkan performa sistem informasi adalah vertical scaling, yaitu penambahan sumber daya perangkat keras seperti CPU dan memori pada server yang ada. Vertical scaling menjadi pendekatan paling relevan dan efektif untuk meningkatkan kinerja server database, terutama dalam sistem dengan struktur monolitik dan membutuhkan pemrosesan intensif dalam satu node. Penelitian ini mengkaji dampak penerapan vertical scaling terhadap performa operasional sistem informasi berbasis database melalui studi kasus implementasi di perusahaan. Hasil observasi menunjukkan peningkatan performa I/O dan penurunan beban CPU secara signifikan, yang berdampak langsung pada kecepatan akses data dan stabilitas sistem. Pendekatan ini terbukti mendukung peningkatan efisiensi operasional tanpa harus mengubah arsitektur sistem secara keseluruhan.
Co-Authors Adelin, Adelin Adriansyah, A. Ady Wisma Putra Wardana Agnes Aryasanti Agung Pramono, Agung Ajar Rohmanu Akbar, Victor anggi saputra Anindya Putri Pradiptha Ansor, Mohamad Zakaria Antika Zahrotul Kamalia Arachman, Setyo Arief Ari Kusuma, Dyah Topan Arief Wibowo Arimaya Setyorini Arsanto Narendro Aryasanti, Agnes Bagus T Prabawa Bambang Suharjo Bima Cahya Putra Bonie Wijaya Budiarto, Despiyan Dwi Daffa Putra David Jefri Aruan Dendi Sunardi Devit Setiono Dhamma Nagara Dian Anubhakti Diana Juwi Megatarini Eka Hartati Fuad Hasan Gumirang, Lalang Hambali, Yusuf Hardjianto, Mardi Hari Soetanto Hassan, Shiza Hastomo, Mursid Dwi Hendarin, Hendarin Hendra Effendi Hendry Gunawan, Hendry I Ketut Sudaryana Indra Indra Jeremy Jonathan Jody, Jody Joko Christian K, Irvan K, Johanes H Kusumaningsih, Dewi M. Anif Maria Veronica Masad, Muhamad Masruin Maulana Malik Ibrahim Miftahudin . Mohamad Ridwan Mohammad Syafrullah Muhammad Fahrizal Muhrodi Muslich, Muhammad Farid Namin Namin Namora Novia Dewi Nugraha Abdullah, Indra nurhanudin nurhanudin Nurman, Indra Oktora, Andre Painem Prasasti Alam, Raden Gesit Presdianto, Eko Pudoli, Ahmad Purwanto Purwanto Putri Hayati Raden Bagus Dhana Pradana Adi Ramadhan, Ferry Muhamad Ratna Kusumawardani, Ratna Riandhito, Febry Aryo Riyanto Riyanto Roeswidiah, Ririt Rohmanu, Ajar Rusdah Rusdah Samidi Samidi Samsinar Samsinar Siswanto, Siswanto Sriyeni, Yesi Sucipto Basuki Sudija, Ija Suwasti Broto Syahrir, Muh. Tatang Wirawan Wisjhnuadji Tatang Wirawan Wisnuadji Tety Sapriani Tobias Duha Triana Anggraini Triana Anggraini Tutik Sri Susilowati Windihastuti, Wiwin Wisjnuadji TW Yani Prabowo Yulianawati Yusuf, Rika