Article Per Year (5 Year)
p-Index From 2021 - 2026
3.484
P-Index
This Author published in this journals
All Journal JURNAL HUTAN LESTARI Media Penelitian Pendidikan : Jurnal Penelitian dalam Bidang Pendidikan dan Pengajaran JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Infomedia INTECOMS: Journal of Information Technology and Computer Science Computer Based Information System Journal JURNAL PENDIDIKAN TAMBUSAI Jurnal Mantik Jurnal Teknologi Dan Sistem Informasi Bisnis Zonasi: Jurnal Sistem Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Applied Data Sciences Telcomatics Journal of General Education and Humanities Malcom: Indonesian Journal of Machine Learning and Computer Science IJOEM: Indonesian Journal of Elearning and Multimedia Conference on Business, Social Sciences and Technology (CoNeScINTech) Blantika : Multidisciplinary Journal Jurnal Medika: Medika JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Deli, Deli
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 23 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 23 Documents
Search

 1   2   3 
Yolov12N: Implementation and Measuring an Ingredient-Detection Recipe App for Household Food-Waste Reduction Suwarno, Suwarno; Jackson, Jackson; Deli, Deli
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16494

Abstract

Household food waste remains pervasive, driven by suboptimal meal planning and the underuse of available ingredients, with particularly acute impacts in Indonesia. This study presents a mobile application using YOLOv12n for multi-ingredient detection that translates recognized items into actionable recipes, and it evaluates user acceptance through the Technology Acceptance Model. On this implementation, the detection module attains mAP at 0.5 of 0.579 and mAP from 0.5 to 0.95 of 0.331. This study implements a Flutter application with YOLOv12n multi-ingredient detection integrated with TheMealDB and observes 219 users. The instrument validity is established and reliability is strong. While, Structural Equation Modelling supports 3 hypotheses, meanwhile Perceived Usefulness to Behavior Intention is not significant, indicating an indirect pathway via attitude. In conclusion, the solution is feasible for daily use, and strengthening perceived usefulness and ease of use appears to be a promising route to increase adoption and help reduce household waste.
Development of Dolly Technique Videos (In, Out, Sideways, Chasing, Establishing) and Analysis of Their Application in Cinematography Learning Ningsih, Vivian Febri; Pratama, Jimmy; Deli, Deli
Journal of General Education and Humanities Vol. 5 No. 1 (2026): February
Publisher : MASI Mandiri Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58421/gehu.v5i1.1054

Abstract

This study addresses the lack of structured, empirically tested learning media for dolly camera movement techniques in formal cinematography education, where students often understand theory but struggle to translate it into practical and emotionally expressive camera work. The research aims to develop an instructional video demonstrating five key dolly movements (Dolly In, Dolly Out, Dolly Sideways, Dolly Chasing, and Dolly Establishing) using the Multimedia Development Life Cycle (MDLC) framework and to evaluate its effectiveness in improving students’ conceptual understanding, functional application, emotional interpretation, and self-efficacy. A quasi-experimental one-group pretest–posttest design was applied to 100 purposively selected cinematography and multimedia students, using a 20-item Likert-scale questionnaire that had been validated (item–total correlations 0.38–0.78) and shown to be reliable (Cronbach’s Alpha 0.81). The instructional video (duration 2 minutes 5 seconds, distributed via YouTube) was designed according to cognitive load and multimedia learning principles, integrating visual demonstrations, narration, and on-screen cues within the MDLC stages of concept, design, material collection, assembly, testing, and distribution. Results show substantial learning gains, with mean scores increasing from 2.80 (pretest) to 3.89 (posttest), representing a 62.7% improvement across all measured competency domains, and both the paired sample t-test and Wilcoxon signed-rank test indicated statistically significant differences between pretest and posttest scores (p < 0.001), confirming that the MDLC-based instructional video significantly enhances students’ conceptual comprehension, practical readiness, and confidence in applying dolly camera movements.
Nutritionally Balanced Menu Optimization for a Healthy Lifestyle using Integer Linear Programming Suwarno, Suwarno; Arvando, Anderson; Davina, Davina; Gantoro, Brain; Sama, Hendi; 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 Adnas, Diny Anggraini Anaztasya, Azra Putri Andi Andi Andik Yulianto Angraini, Deffy Arvando, Anderson Christian, Marvin Christine Davina, Davina Dicky Wahyudi Gantoro, Brain Gunawan, Arifin Faqih Hanita, Hanita Harahap, Andre Jonathan Hasan Ashari Oramahi Ikhlas, Junior Jackson Jackson, Jackson Jhon Carlie Juhari Juhari, Juhari Julyance, Zoey Kennedy, Kalon Kho, Jensen Leonardo, Kevin Lim, Julianto Wijaya Akoi M Idham, M Marbun, Ricky Yohannes Marlon, Richie Ningsih, Vivian Febri Oktavirgianti, Alifah Pratama, Jimmy Sama, Hendi Saputra, M. Abdilah Shunhaji, Akhmad Sibarani, Melda Siregar, Dian Maya Sari Suhardi Suhardi Suryadana, Anly Suwandi Suwandi Suwarno Suwarno Suwarno Syaidah, Khasnah Tony Wibowo, Tony Winda Agustina Yuniati Yuniati Zeng, Louis