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All Journal Prosiding National Conference for Community Service Project Journal of Applied Data Sciences
Davina Davina
Universitas Internasional Batam
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

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Sosialisasi Pemilu Pemula bagi Siswa SMA Negeri 8 Batam Bersama Bawaslu Kepri dan Bawaslu Kota Batam dengan Metode Pendidikan Masyarakat Jonathan Jonathan; Jervis William; Gracea Venice; Evi Yanti; Wendy Wendy; Julianto Julianto; Jesen Jeverlino; Vincent Vincent; Alviana Alviana; Davina Davina; Dessy Amelia; Joyslin Joyslin; Melvan Melvan; Jessica Christina; Valene Fortuna Lim; Fenky Fenky; Ratu Olivia; Moch Ihda Farhan Effendi; Caca Natasya; Suwarno Suwarno
National Conference for Community Service Project (NaCosPro) Vol 5 No 1 (2023): The 5th National Conference for Community Service Project 2023
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/nacospro.v5i1.8293

Abstract

Pemilihan Umum (Bawaslu) Kota Batam bekerja sama dengan Bawaslu Kepulauan Riau dan Universitas Internasional Batam untuk menyelenggarakan sosialisasi pengawasan partisipatif di SMA Negeri 8 Batam. Sosialisasi ini bertujuan untuk memberikan pemahaman kepada pemilih pemula tentang pentingnya hak suara, mekanisme Pemilu, dan peran aktif warga negara dalam proses demokrasi. Melalui sosialisasi ini, para pelajar diharapkan dapat memahami bahwa Pemilu adalah sarana bagi rakyat untuk memilih pemimpin sesuai aturan yang berlaku, dan dengan keterlibatan pemilih pemula dalam pengawasan partisipatif, proses Pemilu diharapkan dapat berjalan dengan jujur dan adil sesuai dengan prinsip-prinsip Pancasila dan Undang-Undang Dasar Negara Republik Indonesia tahun 1945.
Nutritionally Balanced Menu Optimization for a Healthy Lifestyle using Integer Linear Programming Suwarno Suwarno; Anderson Arvando; Davina Davina; Brain Gantoro; Hendi Sama; Deli Deli
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1141

Abstract

Unhealthy dietary patterns and limited access to personalized nutrition guidance contribute significantly to chronic diseases such as diabetes. These issues highlight the need for a reliable, data-driven approach capable of generating individualized dietary recommendations aligned with nutritional standards. This study aims to develop an Integer Linear Programming (ILP) approach integrated with nutritional datasets to generate personalized and nutritionally balanced meal plans. The goal is to determine whether ILP can effectively balance calorie and macronutrient distribution according to user-specific health profiles while ensuring compliance with dietary guidelines and disease-related restrictions. This study applied an ILP-based optimization framework to calculate total daily energy expenditure and macronutrient ratios, incorporating disease-specific constraints and balanced food distributions across meals. Using 244 standardized food items from clinical dietary data, the model’s performance was validated through comparisons with three AI models (ChatGPT, Gemini, DeepSeek) and a certified medical expert across three evaluation rounds. All AI models indicated that the generated meal plans adhered to macronutrient balance and health-specific requirements. Expert validation produced a mean score of 4.85 out of 5 on a Likert scale, reflecting strong agreement regarding the system’s nutritional adequacy, practicality, and safety. These outcomes confirm the ILP framework’s capability to produce balanced, individualized, and clinically sound meal plans. results demonstrate that ILP-based optimization can effectively generate scientifically sound and practical dietary recommendations, meeting both nutritional standards and user-specific needs. The findings highlight ILP’s potential as a computational decision-support tool that complements professional nutrition guidance. Future work should enhance the objective function by adding parameters that model individual preferences, allergy limitations, and cultural dietary norms, and should incorporate extensive clinical datasets to support adaptive recommendation mechanisms that consider chrononutrition, nutritional adequacy, and preparation methods, along with expert-driven adjustments to portion sizes and meal timing for more tailored dietary guidance.
Co-Authors Alviana Alviana Anderson Arvando Brain Gantoro Caca Natasya Deli Deli Dessy Amelia Evi Yanti Fenky Fenky Gracea Venice Jervis William Jesen Jeverlino Jessica Christina Jonathan Jonathan Joyslin Joyslin Julianto Julianto Melvan Melvan Moch Ihda Farhan Effendi Ratu Olivia Sama, Hendi Suwarno Liang Valene Fortuna Lim Vincent Vincent Wendy Wendy