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All Journal Jurnal Pilar Nusa Mandiri Communications in Science and Technology METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Journal of Intelligent Computing and Health Informatics (JICHI) 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 Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Medicine & Pharmacology Public Health

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Applied of Classification Technique in Data Mining For Credit Scoring Heriyanto, Heriyanto; Kurniawati, Ika; Amsury, Fachri; Rizki Fahdia, Muhammad; Saputra, Irwansyah; Nanang Ruhyana; Asrul
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 12 No. 2 (2022): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (317.759 KB) | DOI: 10.35585/inspir.v12i2.17

Abstract

In the development of the banking business, credit issues remain interesting to study and uncover. Most of the problems occur not in the system implemented by the bank, but the problem occurs precisely in the human resources who manage credit, either in their relationship with consumers or in errors on the part of the bank which mispredicts in assessing consumers who apply for credit. Several studies in the computer field have been carried out to reduce credit risk which causes losses to the company. In this study, a comparison of the Naive Bayes, C4.5 and KNN algorithms was carried out which was applied to consumer data that received credit eligibility for good and bad customers. The best prediction results are nave Bayes with an accuracy of 95.95 % and an AUC of 0.974. The results of this classification are implemented in the form of a website-based application that can be used to facilitate related parties in the credit scoring system.
AniraBlock: A leap towards dynamic smart contracts in agriculture using blockchain based key-value format framework Saputra, Irwansyah; Arkeman, Yandra; Jaya, Indra; Hermadi, Irman; Akbar, Nur Arifin; Sutedja, Indrajani
Communications in Science and Technology Vol 8 No 2 (2023)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.8.2.2023.1240

Abstract

Blockchain technology offers data transparency and traceability, which is particularly useful in the agricultural sector, especially within the supply chains of commodities like coffee and fish. This sector often encounters issues such as quality degradation, unclear information, and socioeconomic injustice affecting stakeholders. The implementation of Static Smart Contracts (SSCs) on blockchains provides a structured method for executing agreements. However, this approach also has limitations, including a lack of flexibility and responsiveness to dynamic changes in the supply chain. Despite these challenges, blockchain remains a valuable tool for ensuring transaction transparency, traceability, and integrity, which are vital in agriculture. These limitations involve unchangeable parameters, rigid rules, and constraints on adaptability and scalability. This study aims to tackle these issues by designing a more dynamic and responsive smart contract system. We introduce AniraBlock, a revolutionary concept for the agricultural supply chain, particularly in the coffee and fish sectors, by implementing Dynamic Smart Contracts (DSCs) based on a key-value format framework. Unlike SSCs, DSCs offer enhanced adaptability and scalability, addressing the former's limitations. Our study adopts a mixed-method approach, utilizing both qualitative and quantitative data to validate AniraBlock's effectiveness. Preliminary results show significant improvements in data management and supply chain transparency. The proposed framework has the potential to influence the agricultural sector by boosting data integrity and operational efficiency.
Interplanetary File System for Custom Logging System Integrated with Smart Contract Parulian, Onesinus Saut; Saputra, Irwansyah
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp121-126

Abstract

The agricultural sector faces challenges in managing dynamic data during transactions, particularly price quotations between farmers and buyers. Traditional smart contract systems often lack the flexibility to handle real-time data changes. This research proposes a customized logging system that integrates smart contracts with the InterPlanetary File System (IPFS) within a web application. By storing data references (hashes) on the blockchain and actual logs on IPFS, the system ensures reliable data recording, flexibility in updating transaction logs, and improved storage efficiency. This integration enhances the system's ability to manage fluctuating agricultural transactions. The proposed method aims to create a robust framework for managing price quotations, which can be extended to other industries with similar requirements.
Hybrid Ensemble Retrieval-Augmented Generation for Indonesian Legal Consultation with Keyword Boosting Suharyadi; Saputra, Irwansyah
Journal of Novel Engineering Science and Technology Vol. 4 No. 02 (2025): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v4i02.1042

Abstract

This study presents the design and evaluation of a fully local, hybrid ensemble Retrieval-Augmented Generation (RAG) system tailored for Indonesian legal consultation. By integrating sparse (BM25), dense (FAISS), and keyword-aware retrieval mechanisms, the system balances lexical, semantic, and domain-specific relevance to retrieve high-quality legal context. A curated dataset of 8,450 legal consultation articles was scraped from a trusted legal platform, cleaned through multi-stage pre-processing, and indexed for efficient retrieval. Retrieved documents are formatted into structured prompts and fed into locally hosted large language models (LLMs) using Ollama, allowing for complete offline operation. Experiments comparing five retrieval configurations TF-IDF, BM25, FAISS, ensemble BM25+FAISS, and ensemble with keyword boosting demonstrate that the hybrid ensemble with keyword boosting yields the most relevant and grounded answers. Both quantitative (retrieval score analysis) and qualitative (manual relevance rating) evaluations were performed, confirming the effectiveness of the ensemble strategy in improving answer quality. Additionally, the system achieves practical response times (12–20 seconds) on consumer-grade hardware without reliance on cloud services. This work makes a novel contribution by demonstrating that a hybrid ensemble retrieval framework, specifically tuned to the linguistic characteristics and retrieval challenges of Indonesian legal texts, can significantly enhance the performance of local RAG-based legal QA systems. Future directions include real-time indexing, fine-tuning of legal-domain LLMs, and extending the system to support other legal domains such as statutory law, regulations, and court rulings.
EVALUATING PREPROCESSING EFFECTS IN NAME RETRIEVAL USING CLASSICAL IR AND CNN-BASED MODELS Marcelly, Frizca Fellicita; Saputra, Irwansyah; Andra, Muhammad Bagus
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.v21i2.6884

Abstract

Information Retrieval (IR) systems are pivotal for efficient data management, particularly in tasks involving name searches and entity identification. This study evaluates text preprocessing techniques, including case folding, phonetic normalization, and gender tagging, that affect the performance of classical (TF-IDF, LSI) and CNN-based retrieval models for multilingual name matching. Using a dataset of 365,468 globally diverse names, this study implements a preprocessing pipeline featuring: Double Metaphone phonetic preprocessing (92% validation accuracy), gender disambiguation for unisex names (92% accuracy), and optimized n-gram tokenization for short names. Evaluation metrics include precision, recall, F1-score, and our novel Name Similarity Score (NSS), combining orthographic and phonetic preprocessing. Results show our full pipeline improves recall to 1.00 and F1-score by 37% while reducing false negatives by 63%. Key findings reveal: TF-IDF achieves superior recall (0.98 vs CNN’s 0.85), LSI handles cultural variants effectively, and CNNs deliver the highest precision (0.91 vs TF-IDF’s 0.70), particularly for unisex names. This work contributes both a scalable multilingual preprocessing framework and the NSS evaluation metric for robust name retrieval systems.
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.
Evaluation of a Semantic Representation-Based Retrieval Model on a Text Dataset Generated from Image Transformation Firmansyah, Muhammad; Marutho, Dhendra; Ilham, Ahmad; Saputra, Irwansyah
Journal of Intelligent Computing & Health Informatics Vol 6, No 2 (2025): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v6i2.19240

Abstract

The increasing demand for efficient multimodal information retrieval has driven significant research into bridging visual and textual data. While sophisticated models like CLIP offer state-of-the-art semantic alignment, their substantial computational requirements present challenges for deployment in resource-constrained environments. This study introduces a lightweight retrieval framework that leverages the BLIP image captioning model to transform image data into rich textual descriptions, effectively reframing cross-modal retrieval as a text-to-text task. We systematically evaluated three retrieval models BM25, SBERT, and T5 on caption-transformed MSCOCO and Flickr30K datasets, utilizing both classical metrics (Recall@5, mAP) and semantic-aware metrics (SAR@5, Semantic mAP). Experimental results demonstrate that T5 achieves superior semantic performance (SAR@5 = 0.561, Semantic mAP = 0.524), surpassing SBERT (SAR@5 = 0.524) and outperforming the lexical BM25 baseline (SAR@5 = 0.312). Notably, the proposed BLIP+T5 pipeline attains 88% of CLIP’s semantic accuracy while reducing inference latency by approximately 60% and decreasing GPU memory consumption by over 60%. These findings underscore the potential of caption-based retrieval frameworks as scalable, cost-effective alternatives to computationally intensive multimodal systems, especially in latency-sensitive and resource-limited scenarios. Future work will explore fine-tuning strategies, domain-adapted semantic metrics, and robustness under real-world conditions to further advance retrieval effectiveness.
Co-Authors Andra, Muhammad Bagus Asrul Dhendra Marutho Fachri Amsury Ida Ayu Putu Sri Widnyani Ika Kurniawati Ilham, Ahmad Irman Hermadi Marcelly, Frizca Fellicita Muhammad Firmansyah, Muhammad nanang ruhyana Nur Arifin Akbar Parulian, Onesinus Saut R. Suharyadi Rizki Fahdia, Muhammad RR. Ella Evrita Hestiandari Sunendar, Nendi Sunendar Sutedja, Indrajani Yandra Arkeman