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All Journal Mandalika Mathematics and Educations Journal Griya Journal of Mathematics Education and Application Jurnal Kolaboratif Sains Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Teknologi dan Mutu Pangan
Lingga Gita Dwikasari
Universitas Mataram
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing 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.
Analisis Antioksidan dan Mutu Organoleptik Snack Bar dari Kacang Gude dan Ubi Jalar Kuning: Antioxidant Analysis and Organoleptic Quality of Snack Bars From Pigeon Pea and Yellow Sweet Potato Husnul Khotimah; Lingga Gita Dwikasari; Eko Basuki; Siska Cicilia; Riezka Zuhriatika Rasyda; Satrijo Saloko; Dody Handito; Setyaning Pawestri
Jurnal Teknologi dan Mutu Pangan Vol. 4 No. 2 (2026): JTMP: Jurnal Teknologi dan Mutu Pangan
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/jtmp.v4i2.5360

Abstract

Snack bars were an easy‑to‑consume foods shaped like bars. Snack bars that circulated in the market at that time largely used imported raw materials and highlighted their value as sources of carbohydrates, protein, and fiber. But, snack bars as antioxidant sources had not existed up to that point. Local ingredients such as pigeon pea and sweet potato were used as alternative substitutes for imported ingredients due to their high antioxidant potential. This study aimed to determine the rasio effect of pigeon pea and sweet potato on the antioxidant activity and organoleptics of snack bars. The parameters tested were antioxidant activity, total phenolic content, and organoleptics (color, aroma, texture, and taste), assessed using scoring and hedonic methods. The results showed the best treatment  was the snack bar with 60% pigeon pea and 40% sweet potato ratio, which resulted 78.225% antioxidant activity, 11.063 total phenol content, a balanced distribution of black and yellow colors, a slightly beany aroma, a slightly crunchy texture, and a neutral taste.
Optimization of Coffee Production and Distribution under Multi-Demand Scenarios: A Linear Programming and Dual Analysis Approach Dilla Afriansyah; Lingga Gita Dwikasari
Griya Journal of Mathematics Education and Application Vol. 6 No. 2 (2026): Juni 2026
Publisher : Pendidikan Matematika FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/griya.v6i2.1209

Abstract

Efficient production and distribution planning is essential for improving profitability and operational performance in coffee supply chains. This study develops a linear programming model to optimize coffee production and distribution decisions under multiple demand scenarios. The model considers three coffee products, namely Robusta, Arabica, and Blend coffee, distributed to four regional markets. The objective is to maximize total net profit while satisfying production capacity, demand, distribution capacity, service-level, and minimum production constraints. Three demand scenarios—low, medium, and high demand—were evaluated to examine the impact of varying market conditions on optimal production and distribution strategies. The results indicate that the optimal profit increased from Rp 56.585 million under the low-demand scenario to Rp 80.375 million under the medium-demand scenario and Rp 94.350 million under the high-demand scenario. The optimization model consistently prioritized Arabica and Blend products because of their higher profitability, while Robusta was utilized primarily to satisfy capacity and demand requirements under higher-demand conditions. Shadow price analysis identified Arabica production capacity and regional distribution capacities as the most critical resources affecting profitability. In addition, reduced cost analysis revealed distribution routes that were not economically competitive under current operating conditions. The findings demonstrate that the proposed linear programming framework provides an effective decision-support tool for optimizing coffee production and distribution planning. The integration of scenario analysis and dual analysis offers valuable managerial insights for improving resource allocation, operational efficiency, and profitability in coffee-based food enterprises.
Comparative Mathematical Modeling of Coffee Shelf-Life Using Linear Regression and Ensemble Learning under Simulated Storage Conditions Lingga Gita Dwikasari; Dilla Afriansyah
Griya Journal of Mathematics Education and Application Vol. 6 No. 2 (2026): Juni 2026
Publisher : Pendidikan Matematika FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/griya.v6i2.1276

Abstract

management. However, comparative studies evaluating interpretable statistical models and ensemble learning algorithms for coffee shelf-life prediction remain limited, particularly using simulation-based datasets. This study compared the predictive performance of Multiple Linear Regression (MLR), Random Forest Regression (RFR), and Gradient Boosting Regression (GBR) using a simulation-based dataset of 400 observations representing realistic storage conditions. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). MLR achieved the best performance with the lowest MAE (10.01 days), the lowest RMSE (12.55 days), and the highest R² (0.8649), outperforming both ensemble learning models. Feature importance analysis consistently identified storage temperature as the most influential predictor of coffee shelf-life. These findings demonstrate that increased model complexity does not necessarily improve predictive accuracy and support the use of simulation-based datasets for developing predictive models prior to validation with experimental data.
Pemodelan Matematika dalam Optimasi Laba Produksi Olahan Rumput Laut di UD Harkat Makmur: Mathematical Modeling in Production Profit Optimization of Seaweed Products at UD Harkat Makmur Lingga Gita Dwikasari; Setyaning Pawestri; Riezka Zuhriatika Rasyda
Jurnal Kolaboratif Sains Vol. 7 No. 6: Juni 2024 - Jurnal Kolaboratif Sains (JKS)
Publisher : Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/jks.v7i6.5371

Abstract

Penelitian ini membahas tentang penerapan pemodelan matematika dalam optimasi laba menggunakan pemrograman linier. Tujuan dari penelitian ini adalah untuk menentukan model matematika yang sesuai dan menentukan penyelesaian atas model tersebut untuk mengoptimalkan produksi olahan rumput laut di UD Harkat Makmur, sehingga diperoleh laba maksimum. Metode analisis data yang digunakan dalam penelitian ini adalah metode kuantitatif deskriptif dengan melakukan pengumpulan data melalui wawancara dengan pemilik dan/ atau pengelola UD Harkat Makmur. Data yang telah diperoleh tersebut menjadi acuan dalam pembuatan model matematika. Selanjutnya, model matematika tersebut dicari solusinya dengan menggunakan metode simpleks. Sebelum model matematika dibentuk, diasumsikan bahwa setiap pak produk yang dijual berisi 100 gram produk. Hasil dari penelitian ini adalah model matematika berupa model pemrograman linier untuk 4 kuintal bahan baku utama berupa rumput laut, dengan fungsi tujuan memaksimumkan fungsi laba dan 16 kendala berupa keterbatasan bahan baku dan permintaan atas setiap produk. Penyelesaian atas model tersebut adalah untuk 4 kuintal bahan baku utama berupa rumput laut, UD Harkat Makmur perlu memproduksi sebanyak 5.031 pak manisan rumput laut, 2.791 pak jeli rumput laut, 4.950 pak dodol rumput laut, 1.200 pak tepung rumput laut, dan 600 pak tepung karagenan. Laba yang diperoleh UD Harkat Makmur dari 4 kuintal rumput laut dengan jumlah produksi tersebut adalah Rp. 123.898.900.
Pasta Udang dan Cemaran Logam Berat : Pasar Global, Risiko Kesehatan dan Regulasi Keamanan Setyaning; Lingga Gita Dwikasari; Zuhdiyah Matienatul Iemaaniah; Lalu Unsunnidhal
Jurnal Kolaboratif Sains (Special Issue) Jurnal Kolaboratif Sains (JKS) - July 2026
Publisher : Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/jks.v9i7.11370

Abstract

Pasta udang merupakan produk fermentasi tradisional dari udang atau krill yang bernilai ekonomi tinggi di pasar global, dengan proyeksi mencapai USD 500 juta pada tahun 2025. Namun, di balik peluang pasar tersebut, terdapat tantangan serius terkait cemaran logam berat pada produk akhir diakibatkan bahan utama yang digunakan. Spesies udang seperti Penaeus vannamei dan Penaeus monodon diketahui mengakumulasi logam berat seperti kadmium (Cd), timbal (Pb), arsenik (As), dan merkuri (Hg), sehingga terasi dapat menjadi jalur paparan bagi manusia. Kajian literatur ini menggunakan basis data ilmiah (Sinta, ResearchGate, PubMed, Wiley, ScienceDirect) dengan kata kunci terpilih. Hasil kajian menunjukkan bahwa konsentrasi logam berat dalam jaringan udang sering melebihi kadar lingkungan, dengan variasi distribusi menurut jenis logam dan jaringan. Paparan logam berat menimbulkan risiko kesehatan kronis seperti nefrotoksisitas, neurotoksisitas, dan karsinogenisitas. Regulasi internasional (Codex, EU, FDA, MHLW Jepang) serta regulasi nasional (BPOM, SNI) menetapkan ambang batas logam berat untuk menjamin keamanan pangan. Kajian ini pentingnya pengawasan, kontrol kualitas, dan penerapan regulasi ketat untuk mempertahankan daya saing produk pasta udang di perdagangan global.
Co-Authors Dilla Afriansyah Dody Handito Eko Basuki Firman Fajar Perdhana Husnul Khotimah Lalu Unsunnidhal Made Gendis Putri Pertiwi Riezka Zuhriatika Rasyda Satrijo Saloko Setyaning Setyaning Pawestri Siska Cicilia Zuhdiyah Matienatul Iemaaniah