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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) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab 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 Bantenese Jurnal Pengabdian Masyarakat JISA (Jurnal Informatika dan Sains) 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 Computer Journal 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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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
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.
PKM Pemberdayaan Kelompok Guru SMK Pgri Larangan Dalam Penerapan Learning Management System (LMS) Berdasis Digital Painem; Hari Soetanto; Anindya Putri Pradiptha; Joko Christian; Jan Everhard
KRESNA: Jurnal Riset dan Pengabdian Masyarakat Vol 6 No 1 (2026): Jurnal KRESNA Mei 2026
Publisher : DRPM Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/kresna.v6i1.303

Abstract

Program pengabdian kepada masyarakat di SMK PGRI Larangan bertujuan untuk memberdayakan guru dalam mengimplementasikan dan mengelola Learning Management System (LMS) berbasis digital. Hal ini penting untuk meningkatkan efektivitas dan efisiensi pembelajaran serta memastikan akses pendidikan yang luas. Program ini mencakup peningkatan kompetensi digital guru, pengembangan modul pembelajaran yang dapat diakses melalui LMS, dan implementasi LMS secara rutin dalam kegiatan pembelajaran. Diharapkan guru dapat lebih mandiri dalam mengintegrasikan teknologi dalam proses belajar mengajar, sehingga kualitas pendidikan yang disajikan kepada siswa meningkat. Hasil kegiatan menunjukkan peningkatan signifikan dalam kemampuan guru mengoperasikan LMS, serta peningkatan interaksi dan keterlibatan siswa dalam pembelajaran. Program ini juga berhasil mengidentifikasi tantangan seperti keterbatasan infrastruktur dan kebutuhan pelatihan lanjutan. Secara keseluruhan, program ini memberikan kontribusi positif terhadap peningkatan kualitas pembelajaran di SMK PGRI Larangan melalui pemanfaatan teknologi digital.
Classification of High-Risk Provinces for Fintech Lending Based on TWP90 from LPBBTI OJK Using Interpretable Model for Artificial Intelligence Ethical Transparency Adi Rizky Pratama; Ayu Ratna Juwita; Antika Zahrotul Kamalia; Jan Everhard Riwurohi
JISA(Jurnal Informatika dan Sains) Vol 9, No 1 (2026): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v9i1.2714

Abstract

The growth of the fintech lending industry (LPBBTI) in Indonesia has expanded access to financing but has also increased credit risk, as reflected in the 90-day Default Rate (TWP90). This condition demands proactive regional risk monitoring, while existing approaches are still dominated by historical descriptive analysis. This study aims to develop a high-risk provincial classification as a transparent and accountable early warning system. The research methodology uses multi-sheet data integration from the Financial Services Authority (OJK) LPBBTI Statistics to create a provincial-monthly panel dataset covering supply, demand, and transaction activity. The Logistic Regression model was used as the baseline model due to its interpretability and support for decision auditability. Model evaluation using a time-based split approach during the May–July 2025 test period demonstrated good performance, with an ROC-AUC of 0.8598, an accuracy of 0.8462, and a precision of 0.8000. The results of feature analysis indicate that scale and activity indicators, particularly the outstanding amount, the number of active borrowers, and the value of disbursed funds, contribute significantly to risk probability. Although detection sensitivity (recall: 0.5333) still needs improvement, this study provides a measurable, relevant regional risk-ranking framework for regulatory decision-making.
Application of Exponential Smoothing Method for Forecasting Spare Parts Inventory at Heavy Equipment Distributor Company Despiyan Dwi Budiarto; Miftahudin Miftahudin; Jan Everhard Riwurohi
Eduvest - Journal of Universal Studies Vol. 4 No. 3 (2024): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v4i3.1079

Abstract

PT. Kobexindo Tractors Tbk holds a significant spare parts inventory to meet their customers' needs. Over the period from 2016 to 2023, the company experienced an average annual loss of Rp. 1,176,438,113, due to the inadequate analysis of spare parts demand, which serves as a reference in the procurement process. To address this issue, this research focuses on developing a model that can generate accurate forecasts for spare parts inventory, particularly Jungheinrich parts, to support appropriate management decisions in the procurement process at the company. The Exponential Smoothing method is chosen for its ability to handle data with fluctuating patterns and trends. This study will compare the Simple Exponential Smoothing, Double Exponential Smoothing, and Triple Exponential Smoothing methods. The data ratio used in this research is 70% for training data and 30% for testing data. The prototype development is conducted using the Python programming language. The research results indicate that the Holts Winter Exponential Smoothing Model with Multiplicative Seasonality and Multiplicative Trend (Triple Exponential) is the best method among others, as follows: 1) Train RSME (7.082307), a low RSME value on training data indicates that this model has a small prediction error rate on the data used for training. 2) Test MAPE (6.343268), a low MAPE value on test data indicates that this model provides fairly accurate predictions in percentage terms of the actual values. 3) Test RSME Values (23.160521), a sufficiently low RSME value on test data indicates that this model also successfully generalizes well on unseen data.
The Role of Cache Memory In Enhancing Microprocessor Performance in PT. Srikandi Sinergi Sakti Hendarin Hendarin; Jan Everhard Riwurohi; Setyo Arief Arachman
Eduvest - Journal of Universal Studies Vol. 4 No. 12 (2024): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v4i12.43139

Abstract

Cache memory in microprocessors has an important role in improving computer system performance by reducing data access time. This research aims to test the hypothesis that increasing the size and level of cache memory can significantly improve microprocessor performance. The research methodology involves a literature study on the concept of cache memory and experimental simulations using computer architecture simulators, such as Gem5, to model scenarios with varying cache sizes and levels. In these simulations, performance parameters such as memory access latency, throughput, and Instructions Per Cycle (IPC) were measured and analyzed. The results show that increasing cache size and level generally contributes towards improving microprocessor performance by reducing data access time. Further statistical analysis supports the hypothesis that there is a positive correlation between cache size and level and system efficiency. These findings provide useful insights in future microprocessor architecture design and memory system optimization.
Smart Strategies in Hardware Provisioning for Ai Solutions in The Cloud Yusuf Hambali; Jan Everhard Riwurohi; Victor Akbar
Eduvest - Journal of Universal Studies Vol. 4 No. 12 (2024): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v4i12.43140

Abstract

Rapid developments in artificial intelligence (AI) have driven the need for more efficient and powerful computing infrastructure, especially in cloud environments. This research explores smart strategies in providing hardware for AI solutions in the cloud, focusing on the latest innovations in AI hardware such as neuromorphic chips, FPGAs, and ASICs. Through a comprehensive analysis of the current literature, performance benchmarks, and implementation case studies, the study identifies several key strategies. Key findings include the effectiveness of hybrid architectures that combine different types of AI hardware, the potential for resource disaggregators and composable architectures to improve flexibility and efficiency, and the importance of specific acceleration for different phases in the AI pipeline. The study also emphasizes the significance of performance optimization and energy efficiency, as well as the integration of security and data privacy features in AI hardware design. Challenges such as standardization, scalability, and complexity management are discussed along with future opportunities in green AI and computing-in-memory. In conclusion, implementing a smart strategy in the provision of AI hardware in the cloud requires a holistic approach that considers workload diversity, architectural flexibility, energy efficiency, and security aspects. This research provides valuable insights for cloud service providers, hardware manufacturers, and AI practitioners in optimizing infrastructure to support AI innovation in the cloud computing era.
Next-Generation CPU Architectures: A Study of the Influence of Nanometer Technology on Computer Performance Ija Sudija; Jan everhard riwurohi; Muhamad Masruin Masad
Eduvest - Journal of Universal Studies Vol. 4 No. 12 (2024): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v4i12.44711

Abstract

Nanometer technology has become one of the most significant innovations in the advancement of modern CPU architecture, enabling substantial improvements in computational performance, energy efficiency, and transistor density. This study examines the impact of 7nm, 5nm, and 3nm technology implementation on CPU performance under various workload scenarios, including multitasking, graphics rendering, and artificial intelligence-based applications. Based on a series of experimental tests, the findings indicate that reducing transistor size directly increases processor speed by up to 30% while reducing power consumption by 20%. However, challenges such as heat dissipation and power leakage become more pronounced with technology below 5nm. Several proposed solutions include the development of more advanced cooling systems and the use of alternative semiconductor materials, such as graphene, to mitigate power leakage. This research provides valuable insights into the future development of CPU architecture and its impact on the technology industry as a whole.
Integration of Yolov8 and OCR As E-KTP Data Extraction and Validation Solution for Digital Administration Automation Lalang Gumirang; Jan Everhard Riwurohi; Agung Pramono
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.
PENGEMBANGAN SISTEM AUTOMATIC WORKLOAD THROTTLING BERBASIS PYTHON UNTUK MITIGASI THERMAL THROTTLING CPU PADA PERANGKAT KOMPUTER Intan Oka Herdanis; Reza Pahlevi; Sunu Ilham Pradika; Hidayat Ramadhani; Jan Everhard Riwurohi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8063

Abstract

The increasing demand for modern computing drives processors to operate under heavy loads; however, this condition potentially elevates CPU operating temperatures and triggers thermal throttling. This study aims to develop the Python-based Yield Thermal Intelligent Handling & Automation (PYTHIA) system as a software-based CPU workload management mechanism to adaptively mitigate thermal throttling. The system utilizes real-time temperature monitoring from the Libre Hardware Monitor (LHM) Web Server and implements a duty-cycle worker control logic to adjust CPU workload based on predefined temperature thresholds. Testing was conducted on two processors with distinct characteristics, the AMD Ryzen 7 7730U and the Intel Core i7-10750H, through three experimental phases: pre-throttling, cooldown, and automatic throttling. The results indicate that PYTHIA successfully monitors processor temperature in real-time and reduces workload as temperatures rise. On the AMD Ryzen 7 7730U, the system effectively maintained stability following the cooldown and automatic throttling phases. Meanwhile, on the Intel Core i7-10750H, the system responded to temperature increases, although temperature fluctuations remained significant, occasionally approaching the 90–95°C range. It should be noted that the high temperature fluctuations on the Intel  Core i7-10750H processor indicate that the system still requires refinement in its adaptive control mechanism to achieve optimal thermal stability. Overall, PYTHIA is proven to assist in reducing thermal throttling risks through adaptive workload control, though the control mechanism requires further optimization for smoother duty-cycle transitions and improved temperature stability.
Co-Authors A. Adriansyah Adelin, Adelin Adi Rizky Pratama Adjie Nugroho Ady Wisma Putra Wardana Agnes Aryasanti Agung Permana Agung Pramono Ajar Rohmanu Angga Rizki Pratama Anindya Putri Pradiptha Ansor, Mohamad Zakaria Antika Zahrotul Kamalia Antika Zahrotul Kamalia Anwar Rifai Ari Kusuma, Dyah Topan Arief Wibowo Arimaya Setyorini Arsanto Narendro Aryasanti, Agnes Ayu Ratna Juwita Bagus T Prabawa Bambang Suharjo Bima Cahya Putra Bonie Wijaya Daffa Putra David Jefri Aruan Dendi Sunardi Despiyan Dwi Budiarto Devit Setiono Dhamma Nagara Dian Anubhakti Diana Juwi Megatarini Dion Setiawan Eka Hartati Farhani Ayu Amalina Fathan Nur Muhtadi Fuad Hasan Hardjianto, Mardi Hari Soetanto Hassan, Shiza Hastomo, Mursid Dwi Hendarin Hendarin Hendra Effendi Hendry Gunawan, Hendry Hidayat Ramadhani I Ketut Sudaryana Ija Sudija Indra Indra Indra Nurman Intan Oka Herdanis Jeremy Jonathan Joko Christian K, Irvan K, Johanes H Kusumaningsih, Dewi Lalang Gumirang M. Anif Maria Veronica Maulana Malik Ibrahim Miftahudin Miftahudin Mohamad Ridwan Mohammad Syafrullah Muh. Syahrir Muhamad Masruin Masad Muhammad Baso Adrian Ibrahim Muhammad Fahrizal Muhammad Faiz Burhanuddin Muhammad Farid Muslich Muhrodi Namin Namin Namora Novia Dewi Nugraha Abdullah, Indra nurhanudin nurhanudin 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 Reza Pahlevi Riyanto Riyanto Roeswidiah, Ririt Rohmanu, Ajar Rusdah Rusdah Ruth Hanseliani Samidi Samidi Samsinar Samsinar Setyo Arief Arachman Siswanto, Siswanto Sriyeni, Yesi Sucipto Basuki Sujono Sujono Sunu Ilham Pradika Suwasti Broto Tatang Wirawan Wisjhnuadji Tatang Wirawan Wisnuadji Tety Sapriani Tobias Duha Triana Anggraini Triana Anggraini Tutik Sri Susilowati Victor Akbar Wahyudin Wahyudin Wisjnuadji TW Wiwin Windihastuti Yani Prabowo Yulianawati Yusuf Hambali