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All Journal Jurnal Sains dan Teknologi Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Jurnal Pengabdian Kepada Masyarakat Teknologi Informasi dan Komunikasi
Jefri Junifer Pangaribuan
Universitas Bina Nusantara
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Transportation Veterinary

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Peningkatan Literasi AI melalui Pengenalan AI Agent bagi Mahasiswa Universitas Batuta Adli Abdillah Nababan; Jefri Junifer Pangaribuan; Ince Ahmad Zarqan; Dimas Yudistira Nugraha; Ignatius Edward Riantono; Ganda Tua Sitompul; Scherly Hansopaheluwakan; Fernando Sihotang; Irwansyah; Sutarman
Jurnal Pengabdian Kepada Masyarakat Teknologi Informasi dan Komunikasi Vol 3 No 1 (2026): Januari
Publisher : CV. ADMITECH SOLUTIONS

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Abstract

Perkembangan teknologi kecerdasan buatan (Artificial Intelligence atau AI) memberikan peluang besar dalam meningkatkan efisiensi dan produktivitas kerja di berbagai bidang. Salah satu bentuk pemanfaatan AI yang semakin berkembang adalah AI Agent, yaitu sistem cerdas yang mampu menjalankan tugas secara otomatis dan adaptif. Namun, pemanfaatan teknologi ini masih memerlukan pembekalan dan penguatan literasi AI agar dapat digunakan secara efektif oleh pengguna non-teknis, termasuk mahasiswa. Kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk memberikan sesi pengenalan dan penguatan pemahaman mengenai pemanfaatan AI, khususnya AI Agent, kepada mahasiswa Universitas Batuta. Kegiatan PKM dilaksanakan dengan melibatkan 30 mahasiswa sebagai peserta melalui beberapa tahapan, yaitu tahap persiapan, penyampaian materi, demonstrasi dan praktik, serta evaluasi. Materi yang diberikan mencakup konsep dasar Artificial Intelligence, AI Workflow Automation, AI Agent, dan Agentic AI, disertai dengan contoh penerapan sederhana yang relevan dengan aktivitas akademik dan kehidupan sehari-hari. Pendekatan edukatif dan aplikatif digunakan untuk mendorong pemahaman konseptual serta kemampuan awal peserta dalam mengidentifikasi peluang pemanfaatan AI. Hasil kegiatan menunjukkan bahwa mahasiswa mampu memahami konsep dasar AI dan AI Agent, membedakan AI Agent dan Agentic AI, serta mengidentifikasi ide penerapan AI untuk mendukung produktivitas dan efisiensi kerja. Selain itu, kegiatan ini berhasil meningkatkan literasi AI mahasiswa dan menumbuhkan kesadaran bahwa AI dapat dimanfaatkan sebagai alat bantu yang produktif dan kontekstual. Dengan demikian, kegiatan PKM ini memberikan kontribusi positif dalam mendukung kesiapan mahasiswa menghadapi perkembangan teknologi AI di era digital.
Model Data Mining untuk Penetapan Plafon Kredit dengan Algoritma C4.5 Frans Mikael Sinaga; Jefri Junifer Pangaribuan; Aulia Rizky Muhammad Hendrik Noor Asegaff; Wenripin Chandra; Riche Riche
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 6 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i6.6656

Abstract

Manual credit limit determination in distributor companies is often subjective and inconsistent, increasing the risk of bad debts. This research aims to design an objective data mining model to support customer credit limit decisions at CV. XYZ. The method used is the Decision Tree with the C4.5 algorithm, applied to 66 historical records of customer payment data. Data analysis was performed by calculating Entropy and Information Gain values to build the decision tree, which was then validated using RapidMiner Studio software. The research successfully built a valid and consistent classification model. The "Piutang" (receivables/transaction volume per invoice) attribute was identified as the main determinant (root node), followed by the "Pembayaran" (payment history) attribute as a branch node. This model generates three interpretable decision rules, including the discovery of a risky pattern where high-volume customers with poor payment histories are associated with large credit limits. The proposed model can be implemented as a decision support tool to standardize credit policies, reduce subjectivity, and minimize the company's financial risk.
Evaluasi Robustness dan Deployment Readiness Model XGBoost untuk Prediksi Risiko Gagal Jantung di Indonesia Triandes Sinaga; Ayumi Ayumi; Jefri Junifer Pangaribuan
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 6 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i6.7087

Abstract

Cardiovascular diseases, particularly heart failure, remain a leading cause of mortality in Indonesia, affecting an estimated 2.78 million individuals. This study aims to develop a heart failure risk prediction model using the XGBoost algorithm and to evaluate its performance through a comparative validation approach across two datasets with distinct characteristics. The primary model was trained on a large-scale Indonesian population dataset (N = 158,355; 28 features) representing the complexity of real-world clinical data, while the UCI Heart Disease dataset (N = 918; 12 features) was used as a benchmark under more controlled conditions. Experimental results show that the Indonesian model achieved a testing accuracy of 73.50% with a very small training–testing performance gap of 0.53% and an AUC-ROC value of 0.814, indicating strong stability and generalization capability. In contrast, the model trained on the UCI dataset obtained a higher accuracy of 88.59% but exhibited moderate overfitting, reflected by a larger performance gap of 4.60%. Feature importance analysis consistently identified a history of heart disease, hypertension, and smoking behavior as the most influential predictors across both datasets. These findings highlight that model stability and generalization on real-world data are more critical than raw accuracy derived from small, idealized datasets when assessing the clinical deployment readiness of medical artificial intelligence systems in Indonesia.
Digitalisasi Operasional UMKM Indekos: Implementasi Aplikasi Manajemen Penghuni, Pembayaran, dan Keluhan di Kota Medan Ali Akbar Lubis; Frans Mikael Sinaga; Riche Riche; Ade Sarah Huzaifah; Jefri Junifer Pangaribuan
ABDIKAN: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Vol. 4 No. 4 (2025): November 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdikan.v4i4.6655

Abstract

MSME 'Indekos' (boarding houses) in Medan City are generally still managed manually, causing partners to face fundamental problems in service and administration. The billing process, reliant on face-to-face meetings, makes tracking payments difficult, while tenants are often late paying due to a lack of reminders. Furthermore, the absence of a documented complaint channel means owners often forget to follow up on issues. This community service (PKM) activity aimed to overcome these problems through operational digitalization. The priority problems addressed were (1) the digitalization of billing and payments to provide real-time arrears information, and (2) the provision of a documented complaint channel with status tracking. The PKM method involved Training and Mentoring for 3 'indekos' owners in Medan City. The science-and-technology (ipteks) solution, a web-based management application, was developed using the adaptive Extreme Programming (XP) methodology, allowing iterative improvements based on partner feedback. Implementation included business process mapping (Planning), application installation (Small Releases), and guided testing (Testing/Feedback). The result of this activity is a ready-to-use application (PHP/MySQL) delivered to partners, along with operational SOPs and training modules. The implementation successfully improved the owners' administrative efficiency and service transparency for tenants.
Co-Authors Ade Sarah Huzaifah Adli Abdillah Nababan Ali Akbar Lubis Aulia Rizky Muhammad Hendrik Noor Asegaff Ayumi Ayumi Dimas Yudistira Nugraha Fernando Sihotang Frans Mikael Sinaga Ganda Tua Sitompul Ignatius Edward Riantono, Ignatius Edward Ince Ahmad Zarqan Irwansyah Riche Riche Scherly Hansopaheluwakan Sutarman Sutarman Triandes Sinaga Wenripin Chandra