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All Journal Jurnal Pilar Nusa Mandiri Communications in Science and Technology METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Teknologika Journal of Novel Engineering Science and Technology Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Saputra, Irwansyah
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Other

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COMPARATIVE PERFORMANCE OF TRANSFORMER AND LSTM MODELS FOR INDONESIAN INFORMATION RETRIEVAL WITH INDOBERT Sunendar, Nendi Sunendar; Saputra, Irwansyah
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6920

Abstract

Neural network-based Information Retrieval (IR), particularly with Transformer models, has gained prominence in information search technology. However, the application of this technology in Indonesian, a low-resource language, remains limited. This study aims to compare the performance of the LSTM model and IndoBERT for IR tasks in Indonesian. The dataset consists of 5,000 query–document pairs collected via scraping from three Indonesian news portals: CNN Indonesia, Kompas, and Detik. Evaluation was performed using MAP, MRR, Precision@5, and Recall@5 metrics. The results show that IndoBERT outperforms LSTM in all metrics with a MAP of 0.82 and MRR of 0.84, while LSTM only reached a MAP of 0.63 and MRR of 0.65. These findings confirm that Transformer models like IndoBERT are more effective at capturing semantic relevance between queries and documents, even with limited datasets.
PYTHON WEB SYSTEM TO RESTORE SQL SERVER DATABASE TO DRC WITH ADVANCED INFORMATION RETRIEVAL Rabertra, Devis; Saputra, Irwansyah
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 8 No 1 (2026): Maret 2026
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v8i1.559

Abstract

Disaster Recovery Centers (DRC) play a crucial role in ensuring the availability and continuity of database operations in enterprise environments. The process of restoring databases from production servers to DRCs is often performed manually, which can lead to errors such as selecting incorrect backups, corrupted files, and lengthy search times. The complexity increases with the growing number of databases and the variety of daily backup types.This study develops an automated system based on a Python Web Interface integrated with Advanced Information Retrieval (IR) to improve the accuracy and speed of finding relevant backups before restoration. The system employs Natural Language Processing (NLP) and multi-criteria relevance scoring, evaluating backup suitability based on fuzzy matching of database names, recency, semantic similarity, backup type, and file size.Testing was conducted using 28 backup records from 5 different databases. Results show that Advanced IR can accelerate backup searches in under 2 seconds, with relevance ranking ranging from 38% to 67%. Additionally, the automated restore process via Python achieved an average execution time of 7.49 seconds with a 100% success rate.
SMART CONTRACT-DRIVEN QUEUE MANAGEMENT FOR EFFICIENT ONLINE TICKET PURCHASING ON BLOCKCHAIN Puspitaningtyas, Mery Oktaviyanti; Ilmi, Happid Ridwan; Wardani, Yulita Ayu; Saputra, Irwansyah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.7367

Abstract

This study investigates how smart contract-driven queue management can be utilized to increase online ticket purchasing efficiency via blockchain technology. The system is designed to manage ticket purchase queues transparently and securely, using smart contracts written in the Solidity programming language and the Ionic UI framework. In addition, the system is connected with MetaMask as a transaction wallet, allowing users to purchase tickets directly and securely. Ganache serves as a testing environment for replenishing wallet balances without involving real transactions. The First In First Out (FIFO) approach is used to manage the transaction queue, with the first purchased ticket being processed first by the administrator. The administrator accepts each transaction, which is then confirmed by MetaMask. When the transaction is confirmed, the system automatically updates the ticket status. The implementation results show that this system effectively optimizes ticket transaction management transparently and securely. This work also makes a significant contribution to the application of blockchain technology for better management of online ticket purchasing systems, as well as minimizing the possibility of transaction errors and fraud.
Co-Authors Ade Christian ahmad yani Andra, Muhammad Bagus Asrul Fachri Amsury Fahdia, Muhammad Rizki Ida Ayu Putu Sri Widnyani Ika Kurniawati Ilmi, Happid Ridwan Irman Hermadi Karlena Indriani Marcelly, Frizca Fellicita nanang ruhyana Nur Arifin Akbar Nurmalasari Parulian, Onesinus Saut Puspitaningtyas, Mery Oktaviyanti R. Suharyadi Rabertra, Devis Riyadi, Andri Agung Rizki Fahdia, Muhammad RR. Ella Evrita Hestiandari ruhyana, nanang Sunendar, Nendi Sunendar Sutedja, Indrajani Wardani, Yulita Ayu Yandra Arkeman