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All Journal Community: Jurnal Pengabdian Pada Masyarakat Journal of Management and Informatics
Yosep Aditya Wicaksono
Universitas Sains dan Teknologi Komputer, Semarang, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Health Professions Languange, Linguistic, Communication & Media

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Implementasi Program Peningkatan Literasi Digital Masyarakat Pedesaan melalui Workshop Komputer Berbasis Keterampilan Teknologi Informasi Silvia Faradila; Haikal Nur Rachmanrachim Achaqie; Sigit Umar Anggono; Yosep Aditya Wicaksono
Community : Jurnal Pengabdian Pada Masyarakat Vol. 6 No. 2 (2026): Juli: Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/gkfgwq66

Abstract

Perkembangan teknologi informasi menuntut masyarakat memiliki kemampuan literasi digital agar mampu beradaptasi dengan perubahan sosial dan ekonomi. Namun, masyarakat pedesaan masih menghadapi keterbatasan keterampilan dalam penggunaan komputer, aplikasi perkantoran, surat elektronik, serta pemanfaatan internet secara produktif. Kondisi tersebut mendorong pelaksanaan program pengabdian kepada masyarakat melalui workshop komputer berbasis keterampilan teknologi informasi di Desa Sukorejo, Kecamatan Bangsalsari, Kabupaten Jember, Jawa Timur. Kegiatan ini bertujuan meningkatkan pengetahuan dan keterampilan digital masyarakat pedesaan. Metode pelaksanaan menggunakan pendekatan partisipatif melalui observasi awal, pelatihan tatap muka, praktik langsung, pendampingan peserta, serta evaluasi menggunakan pre-test, post-test, lembar praktik, dan kuesioner kepuasan. Hasil kegiatan menunjukkan peningkatan nilai rata-rata peserta dari 48,7 pada pre-test menjadi 81,5 pada post-test. Sebanyak 82,9% peserta mampu membuat dokumen, 88,6% mampu menyimpan file, dan 85,7% mampu mencari informasi melalui internet. Tingkat kepuasan peserta terhadap manfaat program mencapai 94,3%. Program ini membuktikan bahwa pelatihan berbasis praktik efektif meningkatkan literasi digital masyarakat pedesaan dan layak dikembangkan sebagai model pemberdayaan masyarakat berkelanjutan.
Enhancing Decision Quality and Transparency via Machine Learning-Based Goodwill Impairment Estimation in Banks Gunawan Wibisono; Neilin Nikhlis; Yosep Aditya Wicaksono; Silvia Faradila
Journal of Management and Informatics Vol. 4 No. 3 (2025): December Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i3.233

Abstract

Goodwill impairment assessment remains a judgment-intensive process in banking institutions, where managerial discretion, information asymmetry, and regulatory complexity often challenge the quality of decisions and transparency. While prior studies have widely applied machine learning to financial risk assessment and credit analytics, they have paid limited attention to its role in improving managerial accountability in goodwill impairment decisions. This study aims to address this gap by developing and evaluating a machine-learning–based estimation framework to enhance the quality of decisions and transparency in bank-level goodwill impairment assessments. Using simulation-based analysis on synthetic financial statements, the proposed framework evaluates the performance of impairment estimation using quantitative metrics that capture predictive accuracy, decision consistency, and traceability. The findings demonstrate that ML-assisted estimation can systematically improve decision quality while strengthening transparency and accountability compared to traditional judgment-driven approaches. Beyond technical performance, the results indicate that machine learning can function as a governance-supporting mechanism by enabling more traceable and internally auditable impairment decisions. The study contributes theoretically by operationalizing transparency and accountability as measurable decision outcomes in corporate finance, and practically by offering banks a simulation-based tool for internal evaluation that does not rely on field experiments or sensitive proprietary data. Overall, the research highlights the potential of ML-enabled decision support systems to enhance both the quality and governance of goodwill impairment practices in the banking sector.
Co-Authors Gunawan Wibisono Haikal Nur Rachmanrachim Achaqie Neilin Nikhlis Sigit Umar Anggono Silvia Faradila