Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control

Enhancing CNN Performance for Alzheimer’s Disease Classification Using Genetic Algorithm Optimization

Wildan Arif Maulana — 2026-04-26

The increasing global life expectancy has led to a rapidly growing elderly population, resulting in a higher prevalence of Alzheimer’s disease and a pressing need for effective diagnostic solutions. Despite advances in medical imaging, the early and accurate detection of Alzheimer’s disease remains a major challenge due to subtle differences in brain structures across disease stages. However, the interpretation of MRI images still depends heavily on the abilities of individual medical personnel, which risks introducing subjectivity and potential errors in the diagnostic process. In this context, particularly deep learning, emerges as an effective strategy to overcome these limitations by automating the analysis of medical images and reducing human bias. To address this issue, a custom Convolutional Neural Network (CNN) model was developed from scratch for Alzheimer’s disease classification using brain MRI images. To enhance data diversity and mitigate overfitting, a combination of Albumentations and CutMix data augmentation techniques was applied, yielding an initial classification accuracy of 90%. Model performance was further optimized using a Genetic Algorithm (GA), which efficiently explored the hyperparameter space and identified optimal configurations, boosting classification accuracy to 96%. The optimized model demonstrated robust generalization across all disease categories, confirming the effectiveness of the proposed approach. This research contributes to the development of a more reliable and adaptive deep learning framework for early-stage Alzheimer’s disease detection, offering potential support for clinical diagnostic systems

Performance Analysis of Cluster-based Multi-UAV Routing Protocol in Various Mobility Models using NS-3

Harry Darmawan — 2026-04-26

In this paper, the performance of a Cluster-Based Multi-UAV communication system is analyzed as a means to enhance network reliability and coordination in support of Search and Rescue (SAR) operations within disaster-affected area. The proposed approach is intended to address the challenges of maintaining connectivity, ensuring efficient data transmission, and facilitating effective collaboration among UAVs in critical environments. The system is designed with four layers architecture: Base Station (BS), Cluster Head (CH), Clustered Drone (CD), and User Equipment (UE). These layers are modeled and evaluated using Network Simulator 3 (NS-3). The three routing protocols, i.e OLSR, AODV, and DSDV have been evaluated through the three types of UAV mobility: Gauss-Markov, Random Waypoint (RWP). and Reference Point Group Mobility (RPGM). Quality of service parameter for wireless network, such as throughput, packed delivery ratio (PDR), delay, and packet loss has been analysed within several cluster-based UAV schemes. The simulation result shows that cluster-based multi-UAV model with OLSR routing protocol outperforms the best performance in RPGM mobility model with 67.57% average throughput, 87.47% PDR, 86 ms delay, and 12.53% packet loss, better than other routing protocols. OLSR routing protocol indicates the highest consistency with higher throughput and PDR value, smaller delay and packet loss comparing to AODV and DSDV protocols in the small to middle scale of node density. This research contributes in the development of UAV based cluster communication system, especially in efficiency, stabilization and adaptation towards the dynamic environment in the disaster area.

Modeling and Simulation of Heat and Airflow Control System in Fish Smoking Chamber using K-NN

Muhammad Edy Hidayat — 2026-04-26

This study presents the modeling and simulation of a heat and airflow control system in a fish smoking chamber using the K-Nearest Neighbors (K-NN) algorithm. Accurate control of temperature and airflow is crucial for ensuring consistent product quality, flavor, texture, and microbial safety in smoked fish. Traditional methods often face challenges in maintaining stable chamber conditions due to nonlinear interactions between heat sources, airflow distribution, and chamber geometry. The research was conducted through a structured methodology consisting of system modeling, K-NN algorithm development, simulation, and performance evaluation. The results show and demonstrate that the K-NN model achieved optimal performance at k = 5, with an overall prediction accuracy of 92.8%. The Root Mean Square Error (RMSE) was recorded at 1.85 °C for temperature prediction and 0.18 m/s for airflow, confirming the model’s robustness. Compared with conventional approaches, K-NN outperformed Linear Regression and achieved higher accuracy with less complexity than Artificial Neural Networks (ANN). The implications of these findings show that predictive modeling enables better process stability, reduces the risk of uneven smoking, and lowers energy consumption. The novelty of this research lies in the dual prediction of heat and airflow, providing a comprehensive framework for smart control in traditional food processing. While the study is limited to simulations, it offers valuable insights for future experimental implementation and integration into intelligent smoking chamber systems.

LITE-BoostTrack: A Hybrid RealTime MultiObject Tracking Architecture for Resource-Constrained Environments

Ruri Suko Basuki — 2026-04-26

Multi object tracking (MOT) is a crucial component of modern computer vision applications, ranging from intelligent surveillance to autonomous vehicles. The primary challenge in MOT lies in maintaining identity consistency under conditions of high density and frequent occlusion, while also ensuring computational efficiency for real time deployment on resource constrained devices. This paper introduces LITE BoostTrack, a hybrid architecture that combines the confidence scaling based association mechanism of BoostTrack with the lightweight feature extraction strategy of the Lightweight Integrated Tracking and Embedding (LITE) framework. By leveraging internal features from the YOLOv8 detector without relying on an external Re Identification module, the proposed approach reduces computational burden while preserving robustness in identity association. Experiments were conducted on the MOT20 benchmark using standard evaluation metrics, namely HOTA, MOTA, IDF1, IDSW, and FPS, to comprehensively assess both tracking accuracy and runtime efficiency. The results demonstrate that LITE BoostTrack achieves competitive accuracy, with a HOTA of 27.32 and an IDF1 of 37.49, which are nearly equivalent to the original BoostTrack. At the same time, it delivers a substantial improvement in runtime efficiency, reaching 13.23 FPS, almost twice the speed of standard BoostTrack. These findings confirm that efficiency optimization in MOT can be achieved through architectural reengineering that exploits detector internal features without the need for additional deep modules. LITE BoostTrack therefore represents a balanced and practical solution that combines accuracy with efficiency, making it well suited for real time applications in edge computing and resource constrained environments.

Performance Evaluation of Motion Estimation and Compensation Algorithms in SNR Scalable Video Encoding

Agus Purwadi Purwadi — 2026-04-26

Motion estimation is the sequential determination of the direction of motion of an object in a video. The movement of an object is denoted by the term motion vector. Between the current and reference frames, motion vectors can signify shift points. The SAD (Sum of Absolute Different) block matching technique is fundamentally dependent on the assessment of an object's motion. In this study proposes a hybrid approach that integrates the Three-Step Search (TSS) and Full Search (FS) algorithms. This integration aims to design a block matching algorithm that is applied to video encoding using signal-to-noise ratio (SNR) scalability. From this design, we hope to obtain the performance and evaluate the motion estimation process utilizes both the TSS and FS algorithms for performance comparison on SNR scalability video encoding to obtain video frame quality in relation to bit rate and PSNR, based on the average comparison of the two algorithms. Based on the experimental results, the FS algorithm achieved a total BD-PSNR of 0.22 dB with an efficiency rate of 12.45%, whereas the TSS algorithm achieved a total BD-PSNR of 0.18 dB and an efficiency rate of 7.6%. Therefore, the FS algorithm demonstrates superior performance compared to the proposed TSS algorithm in video transmission with SNR scalability.

An Adaptive Cross-Tied Interconnection for Shaded PV Arrays: A Mathematical Analysis for Efficiency Enhancement

Efendi S Wirateruna — 2026-04-26

This study investigates the Adaptive Cross-Tied Interconnection (ACTI) configuration to improve the power output efficiency of photovoltaic (PV) arrays operating under partial shade conditions. The objective of this study is to develop a mathematical formulation that describes the behavior of ACTI compared to the conventional Series-Parallel (SP) configuration. Mathematical modeling is used to analyze the current distribution, voltage relationship, and the effect of shading patterns on the total output power. Simulations are performed using MATLAB/Simulink to verify the theoretical analysis results. This adaptive configuration dynamically adjusts the cross-tied based on the illumination intensity data, thus balancing the current between the shaded and normal modules. The results show that ACTI successfully reduces current mismatch losses and increases the output power without increasing circuit complexity. In a 3x3 PV array, the ACTI configuration yields a power increase of up to 48% compared to the SP configuration. In a 5x5 array, the efficiency increase ranges from 2% to 6%, depending on the shading pattern. The adaptive switching strategy maintains the current flow stability and produces a smoother power-voltage curve, allowing faster and more accurate tracking of the global maximum power point. These results demonstrate that ACTI provides an efficient, economical, and mathematically sound solution for improving the performance of PV systems under non-uniform irradiation conditions

Comparison of Nutrient and pH Control in NFT Hydroponic Plants for Coupled and Decoupled Methods

Ina Rahmawati Putri — 2026-04-26

PH and TDS were critical parameters in hydroponic systems that directly influenced nutrient absorption and plant growth. This study developed an automatic nutrient solution control system for NFT hydroponics using a Proportional-Integral-Derivative (PID) controller with coupled and decoupled approaches. The system employed a DFRobot Gravity: Analog TDS sensor to measure TDS, an Electrode Probe pH-4502C to monitor pH, and an Arduino Uno microcontroller to regulate peristaltic pumps in real time. Lettuce was used as the test crop, requiring 550 ppm TDS and pH 6.5. System performance was evaluated through MATLAB Simulink simulations and hardware implementation based on rise time, settling time, overshoot, and steady-state error. The simulation results showed that the coupled method had slightly faster rise time and settling time compared to the decoupled method, whereas the decoupled method had less overshoot than the coupled. The hardware test showed that the decoupled method performed better, with a pH rise time of 8.34 s, a settling time of 11 s, an overshoot of 10%, and a steady-state error of 0.90%, as well as a TDS rise time of 30.7 s, a settling time of 36 s, an overshoot of 4.36%, and a steady-state error of 0.60%. In contrast, the coupled method exhibited slower responses, longer settling times, and higher steady-state errors. Overall, the decoupled method proved more effective and reliable in maintaining pH and TDS stability, showing strong potential to enhance the efficiency and robustness of NFT hydroponic control systems.

An Adaptive Swarm Clustering Algorithm for Game AI Based on Reinforcement Learning Godot and Particle Swarm Optimization (RLGPSO)

Trisna Gelar — 2026-04-26

Managing extensive agent swarms presents a significant difficulty in dynamic, real-time situations, especially in gaming artificial intelligence, such as real-time strategy. Traditional Particle Swarm Optimization (PSO) techniques, while effective for optimization tasks, often exhibit suboptimal convergence and inadequate flexibility in complex, demanding situations. This study introduces an innovative hybrid approach that integrates Reinforcement Learning (RL) with PSO to create an adaptive swarm clustering system. This approach employs a Deep Deterministic Policy Gradient (DDPG) agent to dynamically modify PSO parameters, enabling the swarm to adeptly maneuver and cluster within a procedurally generated 2D simulation environment featuring physical obstacles, in contrast to earlier studies that depend on static mathematical benchmarks. A rigorous quantitative analysis using Mixed Linear Model Regression (MLMR) demonstrates that this hybrid method significantly and statistically outperforms conventional, manually tuned PSO in terms of convergence time and diversity value. For example, the RLGPSO model achieved an 11.46% reduction in convergence time on high-complexity maps, a result confirmed as statistically significant with a p-value of 0.002 from the MLMR analysis. This study offers a pragmatic approach for the implementation of intelligent, self-organizing agent swarms, directly applicable to improving the realism and efficacy of present-day gaming AI.

Maleo Emotion Audio Dataset Indonesia For Emotion Classification

Ardi Mardiana — 2026-04-26

The limited availability of voice emotion datasets in Indonesian poses a challenge in the development of Speech Emotion Recognition (SER) systems, even though the need for such systems continues to grow in various sectors such as customer service, education, and human-computer interaction. To address this challenge, this study developed the Maleo Emotion Audio Dataset, a collection of three-second audio clips labeled with seven emotion categories: angry, neutral, disgusted, sad, happy, afraid, and surprised. The data was collected from the YouTube platform, and the Maleo Emotion Dataset is available at https://huggingface.co/datasets/maleo-ai/maleo-emotion. It was processed through preprocessing, feature extraction, and augmentation stages. The five main features extracted include Zero Crossing Rate, energy, Mel-Frequency Cepstral Coefficients (MFCC), spectral roll-off, and spectral flux. To enhance generalization, augmentation techniques such as pitch shifting, noise injection, and time stretching were applied. The classification model was built using a Convolutional Neural Network (CNN) architecture with TensorFlow-based implementation. Evaluation showed that the model achieved 94.48% accuracy on the test data, with balanced performance across all emotion categories. These results demonstrate that the developed dataset and model architecture have high capability in effectively recognizing emotions from Indonesian speech in a locally relevant context.

Analysis and Classification of Capital Assistance Recipients Kediri Trade and Industry Department Using Random Forest

Arika Norma Wahyu Dorroty — 2026-04-26

Capital assistance provided by the Kediri City Department of Trade and Industry often faces challenges related to the uncertainty of fund distribution, making it difficult to ensure the effectiveness of the assistance itself in improving business revenue. To address this, a prediction-based model is applied to evaluate the factors influencing the success of capital assistance in increasing recipients’ income. This study aims to classify recipients based on business revenue outcomes using the Random Forest algorithm. Furthermore, the model identifies key factors affecting the success of assistance and offers recommendations for optimizing future distribution through feature importance analysis. The results demonstrate that the Random Forest model achieves an accuracy of 75%, highlighting its potential as a reliable tool for predicting the success of capital assistance. The feature importance analysis further reveals that training contributes 49% and business type 43%, emphasizing their crucial role in enhancing the effectiveness of future assistance programs.