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All Journal Jupiter Jurnal Simetris Elkom: Jurnal Elektronika dan Komputer Jurnal Informatika Upgris Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal SOLMA Jurnal DISPROTEK Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Sekolah Dasar JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal SITECH : Sistem Informasi dan Teknologi bit-Tech Jurnal Abdi: Media Pengabdian Kepada Masyarakat Bakti Cendana: Jurnal Pengabdian Masyarakat Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Indonesian Journal of Technology, Informatics and Science (IJTIS) DEVICE Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Dialektika Informatika (Detika) Jurnal Info Sains : Informatika dan Sains INOVTEK Polbeng - Seri Informatika
Esti Wijayanti
Universitas Muria Kudus
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Nursing Social Sciences Other

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Pengembangan Sistem Pengelolaan Sampah Berbasis Cloud Computing SAAS untuk Optimalisasi Pengelolaan Lingkungan Berkelanjutan Muhammad Arifin; Eko Darmnato; Esti Wijayanti; Heru Sabputro
Jurnal SITECH : Sistem Informasi dan Teknologi Vol. 8 No. 1 (2025): JURNAL SITECH VOLUME 8 NO 1 TAHUN 2025
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/sitech.v8i1.15502

Abstract

Waste management problems continue to increase along with population growth and urbanization, which have negative impacts on the environment and public health. Waste management is a major challenge in creating a sustainable environment. Digital-based technologies, such as Cloud Computing with a Multi-tenant SaaS (Software as a Service) model, offer solutions to overcome challenges in waste management efficiently and integrated. This technology enables real-time waste management with higher efficiency, and can be accessed by various parties, including the government, managers and the community. This study aims to develop a digital-based waste management system that utilizes Cloud Computing SaaS Multi-tenant technology for the waste management process. The research method used is the Agile-based system development method, where the research stages include needs analysis, system design, implementation, and evaluation. The results of this study include increased operational efficiency of waste management, cost savings, and increased community participation in waste management programs through an easily accessible digital platform. In addition, this research also supports sustainable development (SDGs), particularly in efforts to maintain environmental sustainability.
PERANCANGAN SISTEM PEMESANAN MANDIRI BERBASIS WEBSITE PADA CAFFE MLATEA UNTUK OPTIMALISASI LAYANAN Fika Kamalul Wafi; Esti Wijayanti; Ahmad Abdul Chamid
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.v10i4.7241

Abstract

Penelitian ini didasarkan pada pesatnya perkembangan industri kuliner serta berbagai tantangan dalam pelayanan yang dihadapi oleh Caffe Mlatea Kudus, seperti antrean panjang, kesalahan pencatatan pesanan, dan keterbatasan dalam melayani lonjakan pelanggan. Untuk mengatasi permasalahan tersebut, dikembangkan sistem pemesanan mandiri berbasis website dengan akses melalui QR Code yang mengintegrasikan fitur pemesanan dan pembayaran digital. Pengembangan sistem menggunakan metode waterfall dengan tahapan analisis kebutuhan, perancangan sistem menggunakan use case diagram dan class diagram, implementasi, serta pengujian. Hasil pengujian black box testing menunjukkan seluruh fungsionalitas sistem berjalan sesuai spesifikasi, sementara pengujian System Usability Scale (SUS) menghasilkan skor 74.2 yang menunjukkan sistem berada pada kategori acceptable dengan grade C dan tingkat Good. Implementasi sistem berhasil mengoptimalkan proses pelayanan dengan menyederhanakan pemesanan, mengurangi kesalahan pencatatan, serta meningkatkan efisiensi operasional melalui integrasi pembayaran digital dan fitur analisis data pelanggan untuk pengambilan keputusan strategis.
INDOLAW: Sistem Klasifikasi Struktur Putusan Hukum dengan Deep Learning Berbasis Indobert Dan Low-Rank Adaptation (LoRA) Muhammad Wafa Zain; Ahmad Jazuli; Esti Wijayanti
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 11, No 2 (2026): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v11i2.962

Abstract

Court decision documents in Indonesia are generally lengthy in structure and not yet digitally organized, making the automatic extraction of information a challenging task. Furthermore, the application of deep learning models to legal documents is often constrained by high computational demands. Therefore, this study aims to develop an efficient classification system for legal decision structures (IndoLaw) using the IndoBERT model with the Low-Rank Adaptation (LoRA) method. The study utilizes the IndoLaw dataset consisting of 111,108 rows of first-level decision text. Data splitting was performed using a document-level split with an 80:10:10 ratio to prevent data leakage across documents. The LoRA method was applied to the attention layers of IndoBERT by inserting low-dimensional matrices, making the fine-tuning process more efficient. The model was then integrated into a Streamlit-based application to support interactive text classification and PDF document extraction. Test results show that the IndoBERT + LoRA model achieved an Accuracy and F1-Score of 0.9944, outperforming the Full Fine-Tuning method which recorded an Accuracy of 0.9939 and an F1-Score of 0.9938. In addition, the use of LoRA reduced trainable parameters by up to 99.74%, decreased GPU VRAM usage by 44% (from 2.5 GB to 1.4 GB), and accelerated computation time per epoch by 36%. Based on these results, the IndoBERT + LoRA approach is proven capable of producing accurate and efficient legal document classification with lower computational requirements.
Co-Authors Abraham Wahyu Wicaksono Aditya Akbar Riadi Ahmad Abdul Chamid Ahmad Jazuli Ahmad Jazuli Alif Catur Murti, Alif Catur Anastasya Latubessy Arfiyan khusnul Umam Dhevie Novita Sari Diana Ermawati Dimas Satrio Oktavianto Eko Darmnato Evanita Evanita, Evanita Fika Kamalul Wafi Hanik Hidayati Heru Sabputro Heru Saputro ilham dwi setiawan Lovika Ardana Riswari Muhammad Arifin Muhammad Wafa Zain Naufal Meidhana Kenmahdy Ricky Novian Riyadi Rina Fiati Rizkysari Mei Maharani Rizkysari Meimaharani Rizkysari Meimaharani Savitri Wanabuliandari Sekar Dwi Ardianti