Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL INSTEK (Informatika Sains dan Teknologi)
Caturkusuma, Resha Meiranadi
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Predicting IT Incident Duration using Machine Learning: A Case Study in IT Service Management Caturkusuma, Resha Meiranadi; Alzami, Farrikh; Nurhindarto, Aris; Sulistiyono, MY Teguh; Irawan, Candra; Kusumawati, Yupie
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14310

Abstract

In the digital era, ensuring customer satisfaction with IT services is crucial for business success. However, the complexity of IT infrastructure makes it difficult to manage services, requiring companies to focus on improving efficiency and reducing operational costs. One of the strategies used is Information Technology Service Management (ITSM), the main component of which is incident management, which aims to minimize service disruptions. While various studies on ITSM exist, research focused on Machine Learning models for predicting incident resolution times is relatively limited. This research aims to develop an incident resolution duration prediction model using a Random Forest Regressor-based regression approach. The dataset used is an event log from the ServiceNow system containing data on 24,918 incidents. The model was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R2 metrics, where the model achieved a MAE of 14.33 hours, RMSE of 69.8 hours, and R2 of 0.98. These results show that the model can provide accurate predictions and support better decision-making in IT incident handling. Time-related features, such as sys_update_month and closed_month, proved to be the most influential factors in predicting incident resolution duration.
Implementation of YOLOv11 for Food Detection to Support Nutritional Information in Stunting Prevention Adji, Dian Restu; Lutfina, Erba; Caturkusuma, Resha Meiranadi; Galuh Wilujeng Saraswati; Mahmud, Wildan
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15553

Abstract

Stunting remains a persistent public health challenge in Indonesia, mainly due to chronic malnutrition and limited parental literacy regarding balanced diets. To address this issue, this study developed an integrated nutrition education system using YOLOv11 and Generative AI, structured based on the ADDIE framework. This system aims to bridge the literacy gap by automating food identification and transforming technical nutritional data into easy-to-understand insights for stunting prevention. The study used a dataset of 2,413 images, which was expanded to 4,687 through augmentation. Technical evaluation showed strong performance with a Mean Average Precision (mAP@0.5) of 97%, ensuring reliable detection of important protein sources such as eggs. In addition to accuracy, the system applies a heuristic nutritional assessment algorithm visualized through a ‘Traffic Light’ system to reduce the cognitive load on users. Qualitative evaluation with posyandu cadres showed a significant increase in nutritional understanding, with 90% of users able to explain appropriate dietary interventions based on AI recommendations. These results conclude that the integration of computer vision with structured educational design effectively transforms mobile devices into real-time decision support systems for stunting prevention initiatives at the community level.
YOLO11m-Based Vehicle Counting for Standardized CO₂ and CO Emission Estimation from Semarang CCTV Setiawan, Moch Adi; Saraswati, Galuh Wilujeng; Lutfina, Erba; Ratmana, Danny Oka; Caturkusuma, Resha Meiranadi
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 11 No 2 (2026): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v11i2.73033

Abstract

This study evaluated YOLO11m-based vehicle counting in Pantau Semar CCTV recordings and analyzed how class-specific counting differences affect standardized CO₂ and CO estimates. The dataset comprised 12,000 images: 9,120 for training, 2,280 for validation, and 600 for testing. Model development used main training and corrective fine-tuning with Ultralytics. A Python pipeline combined ByteTrack, class stabilization, and line-crossing counting. Automated counts were compared with manual reference counts across six daytime and nighttime recordings from three locations. The detector achieved precision of 0.904, recall of 0.895, mAP₅₀ of 0.947, and mAP₅₀–₉₅ of 0.833. The system counted 7,444 vehicles compared with 7,667 manually, a difference of −2.91%; daytime and nighttime differences were +0.88% and −6.55%, respectively. Using identical emission factors and a one-kilometer reference distance, system-derived estimates were 1.70–3.88% lower for CO₂ and 2.79–4.94% lower for CO across six scenarios. The study shows how class-specific counting errors and emission factors produce emission-estimate differences that cannot be inferred from total-count differences alone. Findings are limited to the evaluated recordings and assumptions; the results are standardized comparative estimates rather than measurements of actual emissions.
Co-Authors Adji, Dian Restu Alzami, Farrikh Candra Irawan Kusumawati, Yupie Lutfina, Erba Nurhindarto, Aris Ratmana, Danny Oka Saraswati, Galuh Wilujeng Setiawan, Moch Adi Sulistiyono, MY Teguh Wildan Mahmud