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Rancang Bangun Sistem Informasi Penggajian Karyawan Berbasis Web Menggunakan Metode Waterfall Al Kaafi, Ahmad; Rizaq Rifatulloh, Muhammad; Rachmi, Hilda
JAIS - Journal of Accounting Information System Vol. 5 No. 02 (2025): Desember
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jais.v5i02.11053

Abstract

Pengelolaan data penggajian pada organisasi sering kali menghadapi kendala akibat fragmentasi data yang tersebar dalam berbagai berkas spreadsheet terpisah, sehingga meningkatkan risiko kesalahan perhitungan dan inefisiensi administratif. Penelitian ini bertujuan untuk merancang dan membangun sistem informasi penggajian berbasis web yang mengintegrasikan data absensi, jabatan, dan pinjaman karyawan secara otomatis. Metode pengembangan sistem yang digunakan adalah Waterfall, yang meliputi tahapan analisis kebutuhan, desain sistem, implementasi menggunakan framework Laravel 8, hingga pengujian Black-Box. Kebaruan penelitian ini terletak pada penerapan mekanisme integrasi data multi-modul tiga arah, di mana setiap perubahan pada data presensi dan status pinjaman secara otomatis memengaruhi hasil kalkulasi gaji bersih pada periode terkait tanpa memerlukan input manual ulang. Hasil penelitian menunjukkan bahwa sistem yang dibangun berhasil mengotomatisasi proses penggajian dan meminimalisir risiko kesalahan manusia melalui sinkronisasi data yang konsisten. Selain itu, fitur presensi berbasis lokasi memperkuat validitas data kehadiran serta meningkatkan efisiensi waktu pemrosesan laporan bulanan. Implementasi sistem ini membuktikan bahwa integrasi basis data terpusat dapat mereduksi beban kerja administratif serta meningkatkan transparansi informasi bagi manajemen dan karyawan.
Developing an NLP-Powered Chatbot Application for MSME Legal Literacy Hidayatulloh, Syarif; Utami, Lilyani Asri; Rachmi, Hilda
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 1 (2026): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.111460

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in the national economy but often face legal challenges due to limited literacy regarding regulations and lack of access to information. This study aims to develop a legal literacy chatbot application for MSMEs based on Natural Language Processing (NLP) using the Rapid Application Development (RAD) method. The development process was carried out iteratively by involving users to ensure the system meets their needs. System evaluation included Black Box testing, usability testing using the System Usability Scale, relevance testing, and performance testing. The Black Box results showed that all functions ran 100% successfully. Usability testing involving 24 respondents obtained an average SUS score of 71.98, which exceeded the standard threshold of 68, indicating that the application is acceptable and easy to use. Relevance testing showed a high level of answer suitability, while performance testing with GTmetrix produced a Performance score of 87%, Structure score of 92%, a fully loaded time of 2.1 seconds, and a total page size of 0.98 MB. These findings highlight that the chatbot application can provide legal information quickly, accurately, and practically, as well as has the potential to improve the legal literacy of MSME actors.
A Hybrid TF-IDF and Knowledge Graph-Enhanced Retrieval-Augmented Generation Framework with Large Language Models for Domain-Aware Question Answering Utami, Lilyani Asri; Rachmi, Hilda; Hidayatulloh, Syarif
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1136

Abstract

This study aims to develop a domain-aware legal Question-Answering (QA) system tailored for Indonesia’s Micro, Small, and Medium Enterprises (MSMEs) by proposing a hybrid Retrieval-Augmented Generation (RAG) framework that integrates Term Frequency–Inverse Document Frequency (TF-IDF), Knowledge Graph (KG), and Large Language Model (LLM) components. In this framework, TF-IDF contributes by performing lexical-level retrieval to identify the most relevant documents based on keyword weighting; the KG enriches this retrieval by providing semantic relationships among legal entities, enabling deeper contextual understanding; and the LLM generates coherent responses conditioned on both lexical and semantically grounded evidence. Together, these components work synergistically to strengthen factual grounding during retrieval and improve contextual reasoning during generation. Methodologically, the system processes a curated dataset of 1,400 legal question–answer pairs collected from national legal repositories, including legislation, government regulations, and MSME digitalization guidelines. The process includes text preprocessing, keyword extraction using TF-IDF, semantic enrichment through a KG that maps legal entities and their relationships, and answer generation via an LLM powered by the RAG pipeline. The system was evaluated using Precision, Recall, F1-Score, Bilingual Evaluation Understudy (BLEU), and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, validated by five legal experts. Results show an accuracy improvement from 76.5% to 83.5% after integrating KG, with Precision of 0.853, Recall of 0.877, and F1-Score of 0.865. The generative evaluation yielded a BLEU score of 0.9276 and ROUGE-L of 0.9301, indicating strong linguistic and semantic alignment between system outputs and expert-authored references. The study concludes that this approach offers a practical foundation for building AI-based legal assistance tools and highlights future opportunities for expansion to other legal domains and multilingual RAG applications.