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All Journal Mandalika Mathematics and Educations Journal Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan
Lingga Gita Dwikasari
Universitas Mataram
Religion Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Other

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PELATIHAN PENULISAN KARYA ILMIAH MAHASISWA BERBANTUAN ARTIFICIAL INTELLIGENCE (AI) Dilla Afriansyah; Firman Fajar Perdhana; Made Gendis Putri Pertiwi; Lingga Gita Dwikasari
Jurnal Pengabdian Masyarakat: Pemberdayaan, Inovasi dan Perubahan Vol 6, No 1 (2026): JPM: Pemberdayaan, Inovasi dan Perubahan
Publisher : Penerbit Widina, Widina Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59818/jpm.v6i1.3012

Abstract

Artificial Intelligence (AI) offers new opportunities to support academic writing in higher education. This community service program aimed to improve students’ competencies in utilizing AI, prompt engineering, and digital reference management for scientific writing. The program involved 24 students from Universitas Mataram and was conducted through workshops, guided practice, and evaluation activities. The training covered the use of ChatGPT for academic writing, AI-assisted literature searching, and Mendeley for citation and reference management. The results showed an average increase of 52.4% in participants’ understanding. Furthermore, 87.5% of participants successfully created research projects using ChatGPT, while more than 90% were able to manage references and generate citations automatically using Mendeley. These findings indicate that integrating AI, prompt engineering, and digital reference management can enhance the effectiveness and quality of students’ academic writing. ABSTRAKArtificial Intelligence (AI) menawarkan peluang baru dalam mendukung penulisan karya ilmiah di perguruan tinggi. Kegiatan pengabdian ini bertujuan meningkatkan kompetensi mahasiswa dalam memanfaatkan AI, prompt engineering, dan manajemen referensi digital untuk mendukung penulisan karya ilmiah. Kegiatan diikuti oleh 24 mahasiswa Universitas Mataram melalui workshop, praktik terbimbing, dan evaluasi. Materi pelatihan meliputi penggunaan ChatGPT untuk penulisan akademik, pencarian referensi berbantuan AI, serta penggunaan Mendeley untuk pengelolaan sitasi dan daftar pustaka. Hasil evaluasi menunjukkan peningkatan rata-rata pemahaman peserta sebesar 52,4%. Selain itu, 87,5% peserta berhasil membuat project penelitian menggunakan ChatGPT dan lebih dari 90% peserta mampu mengelola referensi serta menyusun sitasi secara otomatis menggunakan Mendeley. Kegiatan ini menunjukkan bahwa integrasi AI, prompt engineering, dan manajemen referensi digital dapat meningkatkan efektivitas dan kualitas penulisan karya ilmiah mahasiswa.
Optimization of Coffee Inventory and Replenishment Planning under Demand Uncertainty: A Linear Programming Approach Lingga Gita Dwikasari; Dilla Afriansyah
Mandalika Mathematics and Educations Journal Vol 8 No 2 (2026): Edisi Juni
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jm.v8i2.12437

Abstract

This study develops a multi-period linear programming model to optimize coffee inventory and replenishment planning under demand uncertainty. The model integrates inventory balance, replenishment capacity, storage capacity, and safety stock constraints to determine cost-efficient replenishment quantities and ending inventory levels for three coffee products: Robusta, Arabica, and Blend. Simulated data over six planning periods were analyzed under low, medium, and high demand scenarios using PuLP in Python. The results show that optimal solutions were obtained under low and medium demand conditions, with total inventory costs of Rp 286,836,000 and Rp 480,466,000, respectively. Under low demand, inventory was maintained exactly at safety stock levels, reflecting a just-in-time strategy. Under medium demand, the model temporarily increased Robusta inventory to anticipate future demand. However, the high-demand scenario was infeasible, indicating insufficient replenishment capacity. The model provides a practical decision support tool for cost-efficient and resilient coffee inventory management.
Co-Authors Dilla Afriansyah Firman Fajar Perdhana Made Gendis Putri Pertiwi