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Digitalisasi Rekam Medis Klinik Apotek Bubulak Deki Satria; Putri Utami Rukmana; Tiara Rahmania Hadiningrum; Azizah Syazwina Amir; Hillel Faiz Atharwa; Muhammad Danish Abrisam
SOROT : Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 2 (2026): Juli
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/zj2wh884

Abstract

Kegiatan pengabdian kepada masyarakat dilaksanakan di Apotek dan Praktik Dokter Mandiri Bubulak, Kota Bogor, untuk mengatasi permasalahan pengelolaan rekam medis pasien yang masih dilakukan secara manual. Mitra mengalami kesulitan dalam penataan data pasien, pencarian riwayat kunjungan, penyusunan laporan pelayanan, serta tingginya risiko kehilangan dokumen karena pencatatan masih menggunakan buku besar. Sebagai solusi, tim pengabdian mengembangkan dan mengimplementasikan sistem Rekam Medis Elektronik (RME) berbasis web dengan pendekatan Agile. Kegiatan meliputi identifikasi kebutuhan melalui wawancara dan observasi, pengembangan sistem, pengujian bersama pengguna, implementasi, pelatihan bagi staf administrasi dan dokter, serta evaluasi penggunaan sistem. Sistem yang diterapkan menyediakan fitur pendaftaran pasien, pengelolaan rekam medis digital, riwayat kunjungan, pencatatan diagnosis dan terapi, serta administrasi pembayaran. Hasil kegiatan menunjukkan bahwa sistem membantu meningkatkan keteraturan dan akurasi pencatatan data pasien, mempercepat proses administrasi, memudahkan penelusuran riwayat rekam medis, serta mengurangi risiko kehilangan data. Pelatihan yang diberikan juga meningkatkan kemampuan pengguna dalam mengoperasikan sistem secara mandiri. Program ini menjadi fondasi awal bagi pengembangan layanan kesehatan digital yang berkelanjutan di lingkungan mitra.
Enhancing Customer Complaint Management through AI-Based Business Process Improvement Zain Ammar Falih; Deki Satria; Vandha Pradwiyasma Widartha Yasma
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5825

Abstract

The rapid advancement of digital technology has transformed business process management, particularly in the telecommunications sector, where manual customer complaint handling often causes inefficiencies such as delays, ticket backlog, and human error. The purpose of this study is to investigate how artificial intelligence can enhance the efficiency and effectiveness of customer complaint handling by redesigning workflows through process automation. This study employs a qualitative descriptive approach combined with business process analysis, with data collected through observations, in-depth interviews with 32 participants, and document reviews. NVivo software was used to code interview data, while Bizagi Modeler was used to visualize both the existing and proposed business processes. The results indicate several bottlenecks in the existing complaint handling process, including manual first call resolution activities, inefficient complaint classification, redundant coordination between units, and low customer confirmation rates. To address these issues, the proposed improved process introduces artificial intelligence–based solutions, such as automated first-call resolution, ticket classification using natural language processing, intelligent ticket routing, and automated customer confirmation systems. These improvements are projected to reduce complaint-handling time by 25–40 percent, minimize service-level agreement violations, and optimize resource allocation. This study concludes that integrating artificial intelligence into customer complaint handling processes significantly improves efficiency, accuracy, and service quality, while also supporting organizational digital transformation. Furthermore, the findings make theoretical contributions to the business process management literature and provide practical insights for implementing artificial intelligence–driven automation in large-scale telecommunications environments.