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Sinkron : Jurnal dan Penelitian Teknik Informatika
Published by Politeknik Ganesha Medan
ISSN : 2541044X     EISSN : 25412019     DOI : 10.33395/sinkron.v8i3.12656
Core Subject : Science,
Computer Science & IT
Scope of SinkrOns Scientific Discussion 1. Machine Learning 2. Cryptography 3. Steganography 4. Digital Image Processing 5. Networking 6. Security 7. Algorithm and Programming 8. Computer Vision 9. Troubleshooting 10. Internet and E-Commerce 11. Artificial Intelligence 12. Data Mining 13. Artificial Neural Network 14. Fuzzy Logic 15. Robotic
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 1,401 Documents
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Monitoring of Irrigation Channel Discharge Based on IoT and LoRaWAN Communication with Long Short-Term Memory (LSTM) Predictive Analysis Toga Aldila Cinderatama; Afta Ramadhan Zayn; Rinanza Zulmy Alhamri; Yoppy Yunhasnawa; Kenneth Pinandhito
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16612

Abstract

This study presents the design and implementation of an Internet of Things (IoT)-based irrigation canal monitoring system that integrates Long Range Wide Area Network (LoRaWAN) communication with a Long Short-Term Memory (LSTM) prediction model. The system is designed to provide real-time monitoring and prediction of water discharge in irrigation canals to support efficient water resource management on agricultural land. The proposed system consists of two IoT sensor nodes, each equipped with a YF-B5 flow sensor, an HC-SR04 ultrasonic sensor, and a DS18B20 temperature sensor, all connected to an ESP32 microcontroller. The collected data are transmitted via the LoRaWAN protocol and stored in Firebase Realtime Database, where they are visualized through an Android application. The predictive component employs an LSTM algorithm to forecast future water discharge based on historical time-series data. Experimental results indicate that the LSTM model achieved a Mean Absolute Error (MAE) of 13.1640 and a Root Mean Squared Error (RMSE) of 23.0630, demonstrating high accuracy and stability in predicting water discharge fluctuations. The integration of IoT, LoRaWAN, and LSTM-based prediction enables real-time monitoring and predictive analysis for smart irrigation management.
Development of Deep Learning Based Augmented Reality Learning to Improve Student Motivation and Skills Muhammad Ridwan Lubis; Eka Irawan
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16238

Abstract

This research aims to develop Augmented Reality (AR) learning media based on Deep Learning to overcome the visualization barriers of abstract material in higher education. The main problem addressed is the low motivation and practical skills of students due to conventional static methods. Through the Research and Development (R&D) method with the ADDIE model, this research integrates Object Detection algorithms with YOLO into the AR system to intelligently recognize environmental objects and provide adaptive feedback. The results show that this application is highly feasible with object detection accuracy above 90%. Implementation in the classroom proves a significant increase in learning motivation (based on the ARCS model) and students' technical skills, where smart AR users can complete practicum tasks 25-35% faster than conventional methods. In conclusion, Deep Learning integration transforms AR into an effective active learning assistant that precisely enhances cognitive engagement and mastery of practical skills of students.
Comparative Evaluation of Random Forest and XGBoost for Bearing Capacity Prediction On Slopes Rahmathiyah Amanda; Lindung Zalbuin Mase; Aidil Fitriansyah; Rena Misliniyati; Muharram Nur Fikri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16417

Abstract

Predicting the ultimate bearing capacity of shallow foundations is crucial in geotechnical planning because it is directly related to the safety and stability of the structure. This is especially true for foundations located at the edge of a slope. However, conventional analytical methods often fail to capture the complex and nonlinear relationships between soil and foundation parameters. This study evaluates the performance of Random Forest (RF) and Extreme Gradient Boosting (XGBoost) ensemble machine learning models for predicting the ultimate bearing capacity (q_ult) of shallow foundations in non-cohesive soils on slopes, with emphasis on methodological rigor and physical model interpretability. The dataset consists of 393 secondary data points obtained from numerical modeling based on the Finite Element Method (FEM) in a previous study. Input variables include slope angle (sin β), foundation width (B), foundation depth (Df), and soil angle of internal friction (tan φ), while ultimate bearing capacity (qult) is used as the output variable. Model performance was evaluated using the coefficient of determination (R²), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the A20 index.  Both models achieved a test R² of 0.97. XGBoost demonstrated superior generalization with a test RMSE of 7.130 kN/m² and MAPE of 8.833%, while Random Forest achieved a higher A20 index of 88.136% compared to 86.44% for XGBoost.
LLM-Assisted Annotation in Comparative Sentiment and Trend Analysis of Indonesian K-12 Education on X Abdurrohhim S. Wahyudi; Rachmat Ramadhiansyah; Indra Budi; Prabu Kresna Putra; Aris Budi Santoso
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16433

Abstract

Indonesia was ranked 69th out of 81 participating nations in the 2022 PISA test results, reflecting persistent challenges in K-12 education quality. Public discourse on these challenges has grown substantially on social media, yet automated sentiment analysis of Indonesian education-related text remains difficult due to informal language and implicit sentiment expression. Prior work on Indonesian education sentiment has focused on short observation windows or a single policy, without comparing transformer-based models directly against traditional classifiers or scaling annotation with large language models. This study aims to compare traditional machine learning classifiers and transformer-based models for sentiment analysis of Indonesian K-12 education discourse on X, as well as the trend of sentiments longitudinally and key terms associated with different sentiments. The dataset comprises 38,957 tweets in the Indonesian language collected between January 2022 and October 2025 that were annotated via a multi-stage, large language model-assisted few-shot classification pipeline with self-consistency sampling tiebreaker, reaching inter-annotator agreement of 82.01% (Cohen’s kappa = 0.672). This study evaluated the accuracy and macro F1 score of four traditional classifiers, including Logistic Regression, Support Vector Machine, Naïve Bayes, and Random Forest, alongside three transformer-based models, namely IndoBERTweet, IndoBERT-base-p1, and IndoBERT-large-p1. Transformer-based models performed better than traditional classifiers based on all metrics. IndoBERTweet reached the best performance with 0.884 accuracy and 0.859 macro F1, compared to Logistic Regression as the best traditional classifier achieved 0.768 and 0.714, respectively. Negative sentiment dominated public discourse, with “kurikulum merdeka” as the most frequent term across all sentiment categories.
IoT-Based Server Room Environmental Monitoring Using ASHRAE TC 9.9 and RoC Alarm Rachmat Farizky; Sahirul Alam
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16456

Abstract

Server infrastructure is a vital asset for PT Hino Motors Manufacturing Indonesia (HMMI), whose performance is significantly influenced by the stability of temperature and humidity. Previously, environmental monitoring in the server room was still conducted manually and conventionally, leading to risks where hardware failures due to temperature anomalies were often detected too late. This research aims to design and implement a centralized IoT based monitoring system with the integration of ASHRAE TC 9.9 standards and the Rate of Change Alarm algorithm to overcome manual supervision constraints and minimize the potential for hardware damage. The system is built using ESP32 and ESP8266 microcontrollers as sensor nodes, the MQTT protocol for data transmission, and an interactive Next.js-based web dashboard. Frontend implementation includes real-time data visualization via WebSocket and dynamic sensor location maps. Sensor accuracy testing resulted in a temperature MAE of 0.22°C and a relative humidity MAE of 8.1 percentage points. These results indicate that the DHT11 provided adequate temperature measurements for the proposed monitoring application, although its relatively high humidity deviation remains a limitation of the current implementation. QoS testing using Wireshark proves the high reliability of the communication channel, yielding an average latency of 20.19 ms for the ESP32 and 50.85 ms for the ESP8266, packet loss ratio of 0%, and efficient bandwidth usage ranging from 200-450 bytes/s. The system has been successfully deployed on-premise at HMMI’s local server, providing a responsive monitoring solution for environmental anomalies and enhancing the IT team’s operational efficiency through systematic historical data reporting features.
Robust Multi-Sensor Machine Learning Models for Goat Barn Microclimate Automation Citra Dewi Megawati; Bima Romadhon Parada Dian Palevi; Teo Pei Kian
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16481

Abstract

Conventional goat barn management faces critical productivity drawbacks due to unmonitored microclimatic fluctuations, where environmental heat stress and toxic gas accumulations suppress livestock welfare. This study proposes "CapraFarm," a proactive thermal control architecture that integrates ambient temperature, relative humidity, and ammonia gas monitoring into a multi-criteria predictive automation pipeline. To stress-test algorithmic resilience against physical hardware degradation, a 5,000-entry dataset derived from sensor calibration error margins was injected with 5% to 10% Additive Gaussian White Noise. Six machine learning algorithms—including Random Forest, Extra Trees, LightGBM, XGBoost, Support Vector Machines, and Gaussian Naive Bayes—alongside conventional and hysteresis-enhanced rule-based baselines were evaluated using Stratified 10-Fold Cross-Validation. Experimental results revealed that Random Forest achieved the highest predictive performance, maintaining a classification accuracy of 97.12% ± 0.89%, a macro F1-score of 0.9354, and an Area Under Curve of 0.9941. This performance significantly outperformed the hysteresis-enhanced rule-based baseline (94.58% accuracy) under McNemar’s statistical test (). Feature importance attribution using SHapley Additive exPlanations confirmed that ammonia gas spikes exert critical non-linear overrides for instant hazard mitigation. Hardware-in-the-Loop profiling established that while Random Forest is optimal for centralized cloud optimization, Gaussian Naive Bayes excels in edge microcontroller environments with a minimal memory footprint of 45 Kilobytes and an execution latency of 1.94 milliseconds. This study solidifies a dual-layer Hybrid Edge-Cloud Paradigm for continuous climate control, laying a validated computational foundation for future long-term in-vivo physical field deployments in smart livestock facilities.
Clustering Pospay User Reviews Using Rule-Based Hybrid K-Means and TF-IDF Suci Tamaro Siahaan; Christian Dwi Suhendra; Lion Ferdinand Marini
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16556

Abstract

Pospay is a digital financial services application created by PT Pos Indonesia, which offers a variety of financial transaction services. The huge volume of unstructured data and the exponential growth of the number of users’ assessments in Google Play Store makes it impossible to be analyzed manually. This research aims to classify Pospay customer evaluation to identify the main problems experienced by the user and provide recommendation in upgrading application services. This work adopts the Knowledge Discovery in Databases (KDD) technique. The inputs include 11000 user evaluations, of which 10943 are maintained after pre-processing. The input text has been vectorized using TF-IDF Bigram and the ideal number of clusters has been calculated using the Elbow Method and Silhouette Score. Then, a Rule-Based Hybrid K-Means technique was used, which integrated K-Means++ clustering with rule-based refinement to enhance the interpretability of the clusters. The findings produced five primary clusters that are related to verification and identity, system and error, login and account, transaction and balance, and positive reviews. Authentication and identification and transaction and balance were the most talked-about issues among users from these countries, accounting for 31.3% and 26.8% of discussions respectively. PCA visualization and word cloud analysis further supported the interpretation of each cluster. Overall, the proposed approach effectively grouped user reviews into meaningful topics and can assist developers in identifying service priorities to improve the quality and reliability of the Pospay application.
Implementation of Integrity Zone Document Classification Using IndoBERT Model and Logistic Regression Adidtya Perdana; Nurul Ain Farhana; Didi Febrian
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16585

Abstract

The Indonesian government's Integrity Zone program mandates systematic classification of administrative documents into hierarchical compliance codes, yet manual categorization remains labor-intensive, inconsistent, and unscalable for higher education institutions. This study aims to develop and evaluate an automated multi-label classification pipeline that maps Indonesian bureaucratic documents to hierarchical compliance codes while maintaining computational efficiency for institutional deployment. A curated dataset of 330 Integrity Zone documents from the Faculty of Mathematics and Natural Sciences, Universitas Negeri Medan, annotated across 82 hierarchical codes, was processed using a frozen IndoBERT encoder to extract 768-dimensional contextual embeddings. These features were classified using a balanced One-vs-Rest Logistic Regression model, with decision thresholds optimized via grid search on a held-out validation set to balance precision and recall. The complete pipeline was deployed as a REST microservice integrated into an existing PHP-based document management system. On a held-out test set of 66 documents not used for training, validation, or threshold selection, the pipeline achieved a Macro F1-score of 0.872, Micro F1-score of 0.894, Hamming Loss of 0.082, and a samples-averaged accuracy of 0.917 (pooled label-wise accuracy 0.918; subset accuracy 0.412). The optimized decision threshold of 0.38 favored recall over the conventional 0.50 cutoff, consistent with the higher institutional cost of missing a relevant compliance code. End-to-end inference latency averaged 2.36 seconds on the deployment server. The proposed pipeline shows practical viability as a decision-support tool for resource-constrained public institutions operating under mandatory human verification; the present evaluation is nonetheless limited by a small held-out test set, and several methodological details are reported in full in the Method section to support reproducibility and to rule out validation leakage.
Fairness and QoS Comparison of Equal, Max-Min, and Demand-Proportional Allocation in 5G Network Slicing Sroor Habeeb Mahmood; Ali AL-ALLAWEE
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16613

Abstract

Network slicing is one of the main mechanisms used in 5G systems to let several service types operate over the same physical infrastructure. In this work, the shared system includes eMBB, URLLC, mMTC, and Best Effort slices, all competing for limited bandwidth and CPU capacity. Because the resource-allocation rule can change both fairness and service quality, this paper studies three policies under the same traffic conditions: Jain-based egalitarian allocation (JF), Max-Min Fairness (MMF), and Proportional Fairness (PF). A Python simulator is used to apply each policy separately to identical demand samples, after which the resulting allocations are evaluated using Jain’s Fairness Index (JFI) and the Gini coefficient. The traffic model follows a Poisson process with low, medium, and high load levels, and each case is repeated for 100 independent Monte-Carlo runs. In addition to fairness, the evaluation reports throughput, packet loss ratio, jitter, delay, bandwidth and CPU utilization, SLA satisfaction, and one-way ANOVA tests. The results show that JF gives perfect equality at all loads (JFI = 1.0, Gini = 0), although this equality reduces performance when the offered load is high. MMF is the strongest demand-aware fairness policy and provides the most balanced behavior under medium and high load. PF gives the lowest high-load jitter, but it also records the weakest SLA satisfaction (0.12). The ANOVA results indicate statistically significant differences (p < 0.05) among the policies for most metrics in the medium- and high-load scenarios.
Implementation of a Hybrid CNN-BiLSTM-Attention Model for Detecting Respiratory Sound Abnormalities Fikky Aprico; Kristiawan Nugroho
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16621

Abstract

Early detection of respiratory disorders generally relies on digital stethoscope devices which are expensive and have limited availability. Although the use of smartphones offers a highly practical alternative for independent screening purposes in the community, respiratory sounds recorded directly through built-in microphones are highly susceptible to environmental noise and acoustic characteristic differences (domain shift). Consequently, artificial intelligence (AI) models trained exclusively on public medical datasets often fail to adapt in real-world clinical scenarios, yielding an initial accuracy rate of only 57.78%. To address this critical limitation, this study proposes the application of a Transfer Learning technique with a Full Fine-Tuning approach on a hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) architecture. Furthermore, an Attention Mechanism is specifically integrated so the model can focus its computational weight on very short-duration spectral anomalies that are often masked by background noise. Following the pre-training phase, the model was comprehensively calibrated using 45 primary respiratory sound samples from smartphones. The testing results demonstrated a substantial performance leap, with the overall accuracy rapidly increasing to 93.33%. The proposed system successfully identified all abnormal pulmonary cases without a single miss, achieving 100% Sensitivity. This finding Indicates that the integration of CNN-BiLSTM and Attention Mechanism is promising in handling acoustic distortion. Thus, the model is highly viable to be implemented as an early clinical decision support system with a showing high sensitivity on the evaluated localized cohort predictions.

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