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All Journal TEKNIK Jurnal Simetris Jurnal Sains dan Teknologi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF Prosiding Semnastek Proceeding SENDI_U E-Dimas: Jurnal Pengabdian kepada Masyarakat SAINS TANAH - Journal of Soil Science and Agroclimatology Empowerment : Jurnal Pengabdian Masyarakat IKRA-ITH ABDIMAS Abdi Dosen : Jurnal Pengabdian Pada Masyarakat JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Mattawang: Jurnal Pengabdian Masyarakat International Journal of Engagement and Empowerment (IJE2) Seminar Nasional Hasil Riset dan Pengabdian (SNHRP) Empowerment: Jurnal Pengabdian Masyarakat Jurnal Lemhannas RI ABDINE Jurnal Pengabdian Masyarakat Educational Journal of Islamic Management (EJIM) J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Jurnal Elektro Kontrol (ELKON) Digitus : Journal of Computer Science Applications
Budi Gunawan
Universitas Muria Kudus
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Journal : Digitus : Journal of Computer Science Applications

 1 
Privacy-Preserving Machine Learning: Technological, Social, and Policy Perspectives Ramadhani, Indri Anugrah; Gunawan, Budi
Digitus : Journal of Computer Science Applications Vol. 3 No. 3 (2025): July 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i3.882

Abstract

As machine learning and data mining applications increasingly permeate sensitive domains, concerns over data privacy have intensified. This narrative review aims to synthesize current knowledge on privacy-preserving techniques in artificial intelligence, exploring the technological, socio-cultural, and economic-policy dimensions that shape their implementation. The review employed literature from databases including Scopus, IEEE Xplore, and PubMed, using keywords such as "privacy-preserving," "machine learning," and "differential privacy" to select peer-reviewed articles based on defined inclusion and exclusion criteria. The results reveal that differential privacy and federated learning are leading frameworks offering robust solutions for secure computation without compromising analytical performance. Deep learning models demonstrated strong accuracy, particularly when applied to complex datasets such as healthcare records. However, effectiveness is often impeded by systemic issues, including fragmented regulations and uneven infrastructural capacity. Moreover, socio-cultural factors like digital mistrust and limited awareness among users—especially older populations—pose additional barriers. Economic constraints and inconsistent international policy enforcement further complicate adoption across sectors. This review concludes that successful implementation of privacy-preserving technologies depends not only on algorithmic innovation but also on supportive regulatory, cultural, and financial ecosystems. It calls for integrated policy frameworks, targeted public education, and international cooperation to address existing barriers and advance the responsible use of AI in privacy-sensitive applications.
Enhancing Software Quality Through Automated Code Review Tools: An Empirical Synthesis Across CI/CD Pipelines Gunawan, Budi; Sitorus, Anwar T
Digitus : Journal of Computer Science Applications Vol. 3 No. 4 (2025): October 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i4.956

Abstract

Automated Code Review Tools (ACRT) have become increasingly integral to modern software development workflows, particularly within continuous integration and deployment (CI/CD) environments. This study aims to evaluate the effectiveness of ACRT in improving software quality, accelerating vulnerability remediation, and enhancing developer productivity. Using a combination of empirical analysis, industry case studies, and academic benchmarks, we examine how tools such as SonarQube, CodeQL, Copilot Autofix, and secret scanners impact key quality metrics including defect density, Mean Time to Repair (MTTR), and pull request (PR) throughput. A quasi experimental design was employed using Interrupted Time Series (ITS) and Regression Discontinuity Design (RDD) to measure longitudinal outcomes across six open source and enterprise projects. Results indicate that defect density decreased by 15–30% following ACRT adoption, accompanied by notable improvements in security MTTR. For example, Copilot Autofix reduced XSS remediation times from 180 minutes to just 22 minutes, underscoring the tool’s potential for accelerating vulnerability management. PR throughput also increased by up to 40%. However, this efficiency gain coincided with a 20–30% decline in human code review interactions, highlighting a trade-off between automation benefits and the reduced depth of manual oversight. We conclude that ACRT tools, when integrated thoughtfully into development pipelines, can deliver measurable improvements in software quality and responsiveness. However, sustained benefits require careful tuning, contextual alerting, and a hybrid review strategy that maintains human involvement to preserve long term maintainability.
Smart Farming Technologies for Global Food Security: A Review of Robotics and Automation Yuni T, Veronika; Saromah; Gunawan, Budi
Digitus : Journal of Computer Science Applications Vol. 3 No. 4 (2025): October 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i4.1076

Abstract

This narrative review explores the role of robotics and automation in precision agriculture, particularly in addressing global challenges such as food security, labor shortages, and environmental sustainability. A systematic literature search was conducted using Scopus, Web of Science, and other supplementary databases, focusing on studies from 2015 to 2025. Findings show that AI-based models and UAV monitoring can enhance crop yield by up to 20% and reduce water and fertilizer use by 30%. Smart irrigation, soft robotics, and autonomous systems also demonstrate effectiveness in specific applications like pruning, weeding, and aquaponics. Despite promising outcomes, adoption varies due to financial, infrastructural, and governance barriers, especially in developing regions. The review concludes that integrating robotics with AI, IoT, and UAVs has transformative potential for agriculture. Future research should prioritize system interoperability, dataset quality, and environmental impact assessments to support widespread, equitable implementation.
Co-Authors Achmad Ridwan Agus Margiwiyatno Ahmad Jazuli Ahmad Jazuli Ahmad Syahroni, Ahmad Arief Sudarmadji Arief Sudarmadji Arief Sudarmaji Arief Sudarmaji Arief Sudarmaji Arief Sudarmaji Arini, Nindya Budiman, Nita Adriyani Cornelius Siagian, Ruben Dedy Noor Ahwan Diah Ayu Susanti Diana Laily Fithri Djoko Purnomo Dyna Marisa Khairina F. Shoufika Hilyana Fajar Nugraha Fajar Nugraha Gunanjar Satrio Hadi Supriyo Hadi Supriyo Heliza Rahmania Hatta, Heliza Rahmania Hendro Juwono Hendy Hendro Hadi Sridjono Hendy Hendro, Hendy Henry Fernando Heru Saputro Imam Abdul Rozaq Kertati Sumekar Krisyarangga Krisyarangga Luqman Afian Noor Afandi Malvin Malvin Mamik Indaryani Maria Theresia Sri Budiastuti Marthalia, Lia Mirza, Hasyrul Moh Dahlan Moh Dahlan Moh Dahlan Moh. Dahlan Mohammad Dahlan, Mohammad Mohammad Iqbal muhammad alawy rosyid Muhammad Rivai Mukhamad Nurkamid Mukhammad Nurkhamid Nita Andriani Budiman Nita Andriyani Budiman NYD Setyaningsih R Rhoedy Setiawan Rajagukguk, Michael RAmadhani, Indri Anugrah Ratri Rahmawati Risnawati, Heni Rochmad Winarso Sa'adah, Miftakhus Salman Alfarisi saputro, heru Saromah Setyaningsih, Noor Yulita Sitorus, Anwar T Solekhan Solekhan Sri Mulyani Sri Mulyani Sugeng Slamet Sulistyowati Sulistyowati Supari Supari Tangguh Prakoso Taufik Taufik Untung Sudjianto Wenny Hizkia Aferdo Wijaya, Rachmawan Yelly Prawoto Yelly Prawoto Yely Prawoto Yely Prawoto, Yely Yuni T, Veronika Zed Nahdi Zed Nahdi