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
Teo Pei Kian
Unknown Affiliation
Computer Science & IT

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

Found 3 Documents
Search

 1 
ECG-Based Heart Rate Variability and KNN Classification for Early Detection of Baby Blues Syndrome in Postpartum Mothers Megawati, Citra Dewi; Asriningtias, salnan Ratih; Bima Romadhon Parada Dian; Teo Pei Kian; Sutawijaya, Bayu; Fransiska, Ratna Diana
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.14956

Abstract

Early detection of baby blues syndrome plays an important role in preventing postpartum emotional disturbances from developing into more serious mental health conditions. This study proposes a simple and non-invasive approach to identify early signs of baby blues in postpartum mothers by analyzing electrocardiogram (ECG) signals using the K-Nearest Neighbor (KNN) algorithm. The ECG data were gathered through wearable sensors and processed to extract heart rate variability (HRV) features such as RMSSD, SDNN, entropy, and energy. These features were then used to train and test a KNN classification model through a five-fold cross-validation process. KNN was chosen because it is easy to implement, does not assume any specific data pattern, and works well with small datasets like those commonly found in clinical settings. Its ability to group data based on similarity makes it suitable for recognizing subtle physiological changes linked to emotional stress. The model reached an accuracy of 87.5%, with strong precision and recall scores, showing its reliability in distinguishing mothers who show early symptoms of baby blues from those who do not. Among all features, RMSSD and SDNN had the highest impact, pointing to reduced parasympathetic activity in affected individuals. These findings suggest that combining HRV analysis with a straightforward machine learning approach like KNN offers a promising, low-cost solution for early emotional screening in maternal care, especially where resources are limited.
Integrating Agile Development and Content-Based Filtering for Personalized Digital Cultural Heritage Applications: A Case Study of Sri Ranggah Rajasa Sang Amurwabhumi Megawati, Citra Dewi; Asriningtias, salnan Ratih; Teo Pei Kian; Sutawijaya, Bayu
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.v10i1.15555

Abstract

The preservation of Indonesia’s cultural heritage increasingly requires digital innovation that not only archives historical material but also engages users through adaptive interaction. However, existing digital cultural platforms seldom provide personalized learning experiences and often lack iterative user-centered development, creating a clear gap in adaptive digital cultural heritage applications. This study aims to design and develop a cultural application titled Sri Ranggah Rajasa Sang Amurwabhumi using a hybrid framework that integrates the Agile Development Method with a Content-Based Filtering (CBF) approach. Agile was applied through iterative cycles of design, development, implementation, integration, and testing, enabling continuous enhancement based on user feedback. Meanwhile, the CBF algorithm was used to generate personalized cultural content recommendations by analyzing semantic similarities among historical items. The novelty of this research lies in the unified hybridization of Agile and CBF to support adaptive, personalized digital cultural learning centered on a specific Indonesian cultural figure. Data were gathered from 30 respondents, including students and cultural practitioners, through usability testing and structured questionnaires. Results indicate high performance across key aspects: functionality (91%), usability (90%), recommendation accuracy (88%), and user satisfaction (93%). These findings demonstrate that combining Agile and CBF strengthens technical reliability while improving engagement through adaptive content delivery. Agile supports iterative refinement of user interfaces and system responsiveness, whereas CBF enables intelligent personalization in cultural learning environments. Nevertheless, this study is limited by its modest sample size and its focus on a single cultural topic, which may reduce generalizability. Future work will expand the dataset, incorporate multimodal cultural content, and validate the hybrid framework across broader Indonesian cultural domains..
User Satisfaction in Moo Opinion App: Machine Learning for Cooperative Segmentation Megawati, Citra Dewi; Palevi, Bima Romadhon Parada Dian; Teo Pei Kian; Ramanda, Pramadika
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.v10i1.15589

Abstract

This study addresses the critical need to understand digital application user satisfaction within the agricultural cooperative sector, specifically for the Moo Opinion application at the Village Unit Dairy Cooperative (KUD). The study's primary novelty lies in the implementation of an integrated, sequential Machine Learning framework—combining Random Forest (RF), Principal Component Analysis (PCA), and K-Means Clustering—to provide a granular analysis of user behavior in a specialized dairy ecosystem. The methodology first utilized RF for key feature selection, followed by PCA for dimensionality reduction, and K-Means for precise user segmentation. Primary data was collected from 40 respondents (20 farmers, 20 customers). Key findings reveal that Service Quality (0.42) and Milk Quality (0.36) are the most significant drivers of satisfaction, considerably outweighing economic factors like Milk Price (0.08). PCA identified two core satisfaction dimensions: Quality-Service Synergy (explaining 56.7% variance) and Structural-Economic Factors (explaining 25.7% variance), confirming the dominance of non-economic aspects. K-Means Clustering successfully identified three segments: Highly Satisfied (45%), Moderately Satisfied (38%), and Low Satisfaction (17%), with high cluster validity (Silhouette Coefficient 0.71). A recognized limitation of this study is the small sample size (N=40), which may affect the generalizability of the findings to larger cooperative populations. However, the results offer significant practical implications, highlighting the need for KUD to prioritize digital service quality and product value over pricing strategies to enhance loyalty and prevent churn.
Co-Authors Bima Romadhon Parada Dian Megawati, Citra Dewi Palevi, Bima Romadhon Parada Dian Ramanda, Pramadika Ratna Diana Fransiska Salnan Ratih Asriningtias, Salnan Ratih Sutawijaya, Bayu