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All Journal JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal Ilmiah Universitas Batanghari Jambi Premiere Educandum: Jurnal Pendidikan Dasar dan Pembelajaran Reswara: Jurnal Pengabdian Kepada Masyarakat Syntax: Journal of Software Engineering, Computer Science and Information Technology Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies
M. Arif Rahman
Rekayasa Perangkat Lunak / Universitas Dharmawangsa
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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PKM BUMDES JOHAR MANDIRI JAYA Umar Hamdan Nasution; Sahnan Rangkuti; Eddy Iskandar; Cut Zahri; Listya Devi Junaidi; Arif Rahman
RESWARA: Jurnal Pengabdian Kepada Masyarakat Vol 4, No 2 (2023)
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/rjpkm.v4i2.3270

Abstract

Bumdes Johar Mandiri Jaya merupakan Badan Usaha Milik Desa yang berada di Desa Pematang Johar. Adapun usaha yang dijalankan yaitu penyediaan ATK, foto copy, BRI LINK, Batik Sawah, unit usaha WIFI (HOTSPOT), layanan pembayaran Sistem Online dan wisata Sawah. Berbagai masalah yang terjadi pasca pandemic covid-19 lalu. Potensi desa yang ada tidak terkelola secara maksimal sehingga membuat masyarakat di desa Pematang Johar tidak sejahtera. Solusi yang tawarkan oleh tim pengabdi kepada Bumdes johar mandiri jaya yaitu memberikan pelatihan pengelolaan potensi desa, penyusunan laporan studi kelayakan bisnis, pemasaran produk desa, pengelolaan keuangan Bumdes, pengelolaan Manajemen BumDes dan Strategi pemasaran digital produk dan jasa. Program Pengabdian dilaksanakan pada hari Sabtu tanggal 08 April 2023 yang dilakukan via zoom meeting dengan peserta sebanyak 40 orang. Metode pelaksanaan yang dilakukan yaitu dengan memberikan pelatihan kepada peserta sesuai dengan materi pelatihan yang dibutuhkan oleh mitra. Hasil evaluasi dari pelatihan yang dilaksanakan yaitu meningkatnya pemahaman para peserta terhadap materi yang telah diberikan. Selanjutnya tim pengabdi akan melakukan pendampingan terhadap Bumdes dan UMKM yang ada di pematang Johar
Pengelompokkan Jenis Surat Masuk di Dinas Komunikasi dan Informatika Menggunakan Metode K-Means Clustering Sartika Siregar; Zulham Zulham; Arif Rahman
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.935

Abstract

Effective management of incoming mail administration is a crucial factor in improving performance and service delivery in government agencies. However, manual processing of incoming mail is often inefficient due to the ever-increasing volume of data and the diverse content, which can make archiving, data retrieval, and decision-making difficult. Therefore, a method capable of automatically grouping incoming mail data is needed. One data mining technique that can be used is K-Means clustering. This study aims to group incoming mail at the Medan City Communications and Informatics Office based on content similarity. The research process involved several stages: text preprocessing, including cleaning, tokenization, stopword removal, and stemming. Then, weighting was performed using the TF-IDF method, followed by clustering with the K-Means algorithm. Data processing was performed using the Python programming language on the Google Colaboratory (Google Colab) platform. The results showed that the incoming mail data could be grouped into three clusters. The first cluster, 3.9%, contains letters related to planning and strategic document preparation; the second cluster, 85.9%, is a group of personnel administration letters, specifically regarding the appointment to functional positions; and the third cluster, 10.2%, contains letters related to operational and routine agency activities. The results of this grouping indicate that most incoming letters are dominated by personnel administration. Thus, applying the K-Means Clustering method can help systematically group incoming letters and support more effective, efficient archive management.
Collaborative AI Governance for Digital Innovation and Knowledge Integration Performance Umar Hamdan Nasution; Cut Zahri; M. Arif Rahman
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.11

Abstract

 The rapid development of Artificial Intelligence (AI) has significantly transformed organizational strategies in managing knowledge resources, decision-making processes, and innovation activities within digital ecosystems. AI enables organizations to process large-scale data, generate predictive insights, and improve strategic responsiveness in increasingly dynamic environments. However, the successful implementation of AI does not rely solely on technological readiness, but also requires governance mechanisms that ensure transparency, accountability, fairness, and ethical use of data. Without appropriate governance structures, AI adoption may generate risks related to algorithmic bias, data misuse, lack of explainability, and reduced organizational trust. This study aims to develop an empirical model explaining the relationship between collaborative AI governance, knowledge integration capability, digital innovation capability, and innovation performance. The research is grounded in Resource-Based View (RBV) and Dynamic Capability Theory, which emphasize that organizational capabilities play an important role in creating sustainable competitive advantage. AI governance is conceptualized as a strategic capability that enables organizations to integrate knowledge resources and support innovation processes. Knowledge integration capability reflects the organizational ability to combine data, expertise, and digital resources into new value creation mechanisms, while digital innovation capability represents the ability to transform technological knowledge into innovative products, services, and business models. This study adopts a quantitative research approach using survey data collected from 210 respondents consisting of managers, IT professionals, and digital transformation specialists from organizations implementing AI-based technologies. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate measurement validity, reliability, and structural relationships among variables. The findings indicate that collaborative AI governance has a significant positive effect on knowledge integration capability, which subsequently enhances digital innovation capability and innovation performance. The results highlight the importance of governance mechanisms in ensuring responsible AI adoption and strengthening organizational innovation outcomes. This study contributes to the literature by integrating AI governance and knowledge integration capability into a unified empirical framework explaining innovation performance in digital ecosystems. The findings also provide practical implications for organizations seeking to improve innovation capability through responsible AI governance strategies 
Co-Authors Cut Zahri Eddy Iskandar Ega Evinda Putri Elyas, Ananda Hadi Hendra Alsa Fahmi Ibnu Rusydi Jovi Antares Listya Devi Junaidi Nita Sari Repelita Situmeang Nur Hidayah randika farike bania Rani Rahim Rijal Sabri Sabrina Hayuni Sahnan Rangkuti Sartika Siregar Suhariyanti Suhendri Tharina Ainaya Umar Hamdan Nasution Welnof Satria Zainal Arifin Zulham Zulham