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All Journal Jurnal Teknik Informatika C.I.T. Medicom International Journal of Basic and Applied Science Jurnal Bakti Nusantara
Fristi Riandari
Politeknik Negeri Medan, Medan, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Medicine & Pharmacology Physics Other

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Pelatihan Pemanfaatan Website terhadap Pemahaman Pemilihan PTN Siswa SMK Negeri 14 Medan Ramadhanu Ginting; Fristi Riandari; Afrisawati Afrisawati; Indri Sulistianingsih; Ade Rizka; Virdyra Tasril
Jurnal Bakti Nusantara Vol. 3 No. 3 (2026): Jurnal Bakti Nusantara
Publisher : Pustaka Media Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63763/jutira.v3i3.150

Abstract

Pemilihan perguruan tinggi negeri (PTN) merupakan tahap krusial bagi siswa sekolah menengah kejuruan (SMK), namun masih ditemukan keterbatasan pemahaman siswa dalam mengakses dan memanfaatkan informasi digital terkait jalur seleksi serta pemilihan program studi. Kondisi ini menunjukkan adanya kesenjangan literasi digital yang berpotensi mempengaruhi ketepatan pengambilan keputusan pendidikan. Kegiatan pengabdian ini bertujuan untuk meningkatkan pemahaman siswa melalui pelatihan pemanfaatan website sebagai media informasi pemilihan PTN di SMK Negeri 14 Medan. Metode yang digunakan adalah pendekatan kuantitatif dengan desain pre-test dan post-test yang melibatkan 16 siswa. Tahapan kegiatan meliputi pemberian pre-test, sosialisasi, pelatihan pemanfaatan website, serta evaluasi melalui post-test. Hasil menunjukkan adanya peningkatan rata-rata nilai siswa dari 56,25 pada pre-test menjadi 83 pada post-test dengan persentase peningkatan sebesar 47,1%. Temuan ini mengindikasikan bahwa pelatihan pemanfaatan website efektif dalam meningkatkan pemahaman siswa terkait pemilihan PTN. Dengan demikian, pemanfaatan website sebagai media edukasi dapat menjadi solusi dalam meningkatkan literasi digital serta mendukung pengambilan keputusan pendidikan yang lebih tepat bagi siswa SMK.
Distribution cost optimization: Comparison of NWC, MODI, and Stepping Stone methods in transportation problems Fristi Riandari; Hengki Tamando Sihotang
International Journal of Basic and Applied Science Vol. 14 No. 2 (2025): Optimization and Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i2.688

Abstract

Solving transportation problems is essential in minimizing distribution costs in logistics and supply chains. Three classical methods North West Corner (NWC), Modified Distribution Method (MODI), and Stepping Stone are frequently used, but few studies offer a comprehensive comparison. This study fills this gap by evaluating their performance using simulated data representing real-world distribution scenarios. This study applies a structured comparative framework to analyze NWC (a cost-agnostic initial allocation technique), MODI (a dual-variable-based optimization approach), and Stepping Stone (a closed-loop path evaluation method). Each method was tested on a simulated cost matrix using Python. Evaluation metrics included total distribution cost, number of iterations, and computation time. The NWC method yielded a feasible but suboptimal solution with a cost of 540 units. Optimization using MODI reduced the cost to 425, while Stepping Stone further minimized it to 410 after three iterations. MODI showed greater computational efficiency, while Stepping Stone offered visual traceability of cost reductions. This study contributes methodologically by combining heuristic and iterative optimization techniques in one analytical framework. Practically, it provides decision-makers with insights into selecting appropriate solution methods based on trade-offs between simplicity, efficiency, and cost minimization.
Machine Learning Integration in DEA Models: Current Developments and Future Challenges Hengki Tamando Sihotang; Fristi Riandari; Rasenda Rasenda; Wildan Alrasyid
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 2 (2026): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The increasing availability of large and complex datasets has created new opportunities for enhancing Data Envelopment Analysis (DEA) through the integration of Machine Learning (ML) techniques. This study reviews current developments in the integration of ML and DEA models and identifies key challenges, trends, and future research opportunities. A systematic literature review was conducted by examining recent studies that combine DEA with various machine learning algorithms across multiple application domains, including healthcare, banking and finance, manufacturing, supply chain management, energy, agriculture, and higher education. The findings indicate that Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests, Gradient Boosting methods, and Deep Learning models are among the most frequently employed techniques in DEA-ML frameworks. Despite these advantages, several challenges remain, including data quality issues, model interpretability, computational complexity, limited generalizability, and the lack of standardized integration frameworks. The review concludes that the integration of ML and DEA offers substantial potential for advancing efficiency analysis and organizational performance evaluation. Future research should focus on developing explainable artificial intelligence (XAI) solutions, real-time efficiency analytics, federated learning approaches, and standardized hybrid DEA-ML frameworks to improve transparency, scalability, and practical applicability across diverse operational environments.
Co-Authors Ade Rizka Afrisawati Afrisawati Hengki Tamando Sihotang Hengki Tamando Sihotang Indri Sulistianingsih Ramadhanu Ginting Rasenda Rasenda Virdyra Tasril Wildan Alrasyid