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All Journal Jurnal Pekommas JOIV : International Journal on Informatics Visualization Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat JURNAL IT: Media Infromasi STMIK Handayani Makassar Jurnal Penelitian Enjiniring Fakultas Teknik Unhas JURNAL SIPISSANGNGI: Jurnal Pengabdian Kepada Masyarakat
Amil Ahmad Ilham
Departemen Teknik Elektro, Fakultas Teknik, Universitas Hasanuddin
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

 1 
Performance Improvement of Deep Convolutional Networks for Aerial Imagery Segmentation of Natural Disaster-Affected Areas Nugraha, Deny Wiria; Ilham, Amil Ahmad; Achmad, Andani; Arief, Ardiaty
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1383

Abstract

This study proposes a framework for improving performance and exploring the application of Deep Convolutional Networks (DCN) using the best parameters and criteria to accurately produce aerial imagery semantic segmentation of natural disaster-affected areas. This study utilizes two models: U-Net and Pyramid Scene Parsing Network (PSPNet). Extensive study results show that the Grid Search algorithm can improve the performance of the two models used, whereas previous research has not used the Grid Search algorithm to improve performance in aerial imagery segmentation of natural disaster-affected areas. The Grid Search algorithm performs parameter tuning on DCN, data augmentation criteria tuning, and dataset criteria tuning for pre-training. The most optimal DCN model is shown by PSPNet (152) (bpc), using the best parameters and criteria, with a mean Intersection over Union (mIoU) of 83.34%, a significant mIoU increase of 43.09% compared to using only the default parameters and criteria (baselines). The validation results using the k-fold cross-validation method on the most optimal DCN model produced an average accuracy of 99.04%. PSPNet(152) (bpc) can detect and identify various objects with irregular shapes and sizes, can detect and identify various important objects affected by natural disasters such as flooded buildings and roads, and can detect and identify objects with small shapes such as vehicles and pools, which are the most challenging task for semantic segmentation network models. This study also shows that increasing the network layers in the PSPNet-(18, 34, 50, 101, 152) model, which uses the best parameters and criteria, improves the model's performance. The results of this study indicate the need to utilize a special dataset from aerial imagery originating from the Unmanned Aerial Vehicle (UAV) during the pre-training stage for transfer learning to improve DCN performance for further research.
Optimization of Herbal Plant Classification Using Hybrid Method Particle Swarm Optimization With Support Vector Machine Amriana, Amriana; Ilham, Amil Ahmad; Achmad, Andani; Yusran, Yusran
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2576

Abstract

The classification process applied in this study helps identify the many kinds of herbal plants. Herbal plant leaf features are used based on color, shape, and texture. Particle Swarm Optimization and Support Vector Machine (PSO-SVM) hybridization are applied in the classification process to increase classification and identification accuracy. A well-liked metaheuristic approach for solving optimization issues is Particle Swarm Optimization (PSO). Particles look around the search area for the best responses.  A particle swarm is initially initialized randomly within the search area via the PSO algorithm. Every particle's mobility is determined by both its own experience and the experiences of the other particles in the swarm. Each particle keeps track of the best solution it has ever found and the swarm's most extraordinary remedy that has so far been discovered. The Hybrid approach concurrently selects features for the SVM and optimizes its parameters. The kernel function's gamma value non-linearly maps an input space to a high-dimensional feature space. At the same time, the C parameter determines the trade-off between fitting error minimization and model complexity. The Gaussian kernel parameter is set to determine the optimal parameter value of the RBF kernel function. Feature selection solves the issue by eliminating redundant, associated, and irrelevant features. A confusion matrix is utilized in the evaluation to gauge the system's performance. The results demonstrated an improvement in accuracy, with the hybrid PSO-SVM using test data achieving an accuracy of 98% compared to the SVM method, achieving a 91% accuracy.
Enhancing Relational Database Efficiency through Algorithmic Query Tuning in Virtual Memory Systems Yulis, Nurlina; Ilham, Amil Ahmad; Achmad, Andani; Samman, Faizal Arya
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.2850

Abstract

The rapid evolution of virtual memory-based relational database systems has significantly advanced data processing capabilities. However, the efficiency of these systems largely depends on query execution optimization, which can be enhanced through algorithmic query tuning techniques. This study investigates the impact of these techniques on enhancing query performance in virtual memory-based relational databases. Various algorithmic methods were analyzed to optimize query execution plans, with a focus on key performance indicators such as execution time, CPU and memory usage, disk I/O, and cache hit ratio. The systematic application of these methods revealed effective strategies for performance enhancement. Results show substantial improvements in execution time, resource utilization, and scalability. This work offers valuable insights for database administrators and system architects, highlighting the role of algorithmic query tuning in managing the growing demands for data processing. Future research endeavors should explore the realm of AI-driven automation, with a particular focus on enhancing query optimization techniques. Additionally, there is a pressing need to investigate advanced security measures that safeguard data integrity within expansive, large-scale systems. By adopting innovative approaches, we can ensure robust protection and efficient performance in an increasingly data-driven world.
Performance Analysis of Feature Mel Frequency Cepstral Coefficient and Short Time Fourier Transform Input for Lie Detection using Convolutional Neural Network Kusumawati, Dewi; Ilham, Amil Ahmad; Achmad, Andani; Nurtanio, Ingrid
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2062

Abstract

This study aims to determine which model is more effective in detecting lies between models with Mel Frequency Cepstral Coefficient (MFCC) and Short Time Fourier Transform (STFT) processes using Convolutional Neural Network (CNN). MFCC and STFT processes are based on digital voice data from video recordings that have been given lie or truth information regarding certain situations. Data is then pre-processed and trained on CNN. The results of model performance evaluation with hyper-tuning parameters and random search implementation show that using MFCC as Voice data processing provides better performance with higher accuracy than using the STFT process. The best parameters from MFCC are obtained with filter convolutional=64, kerneconvolutional1=5, filterconvolutional2=112, kernel convolutional2=3, filter convolutional3=32, kernelconvolutional3 =5, dense1=96, optimizer=RMSProp, learning rate=0.001 which achieves an accuracy of  97.13%, with an AUC value of 0.97. Using the STFT, the best parameters are obtained with filter convolutional1=96, kernel convolutional1=5, convolutional2 filters=48, convolutional2 kernels=5, convolutional3 filters=96, convolutional3 kernels=5, dense1=128, Optimizer=Adaddelta, learning rate=0.001, which achieves an accuracy of 95.39% with an AUC value of 0.95. Prosodics are used to compare the performance of MFCC and STFT. The result is that prosodic has a low accuracy of 68%. The analysis shows that using MFCC as the process of sound extraction with the CNN model produces the best performance for cases of lie detection using audio. It can be optimized for further research by combining CNN architectural models such as ResNet, AlexNet, and other architectures to obtain new models and improve lie detection accuracy.
Composition Model of Organic Waste Raw Materials Image-Based To Obtain Charcoal Briquette Energy Potential Saptadi, Norbertus Tri Suswanto; Suyuti, Ansar; Ilham, Amil Ahmad; Nurtanio, Ingrid
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1682

Abstract

Indonesia needs new renewable energy as an alternative to fuel oil. The existence of organic waste is an opportunity to replace oil because it is renewable and contains relatively less air-polluting sulfur. Previous research that has been widely carried out still utilizes coconut shell raw materials, which are increasingly limited in number, so other alternative raw materials are needed. A model is needed to make a formulation that can optimize the composition of organic waste raw materials as a basic ingredient for making briquettes. The research objective was to determine the best raw material composition based on digital image analysis in processing organic waste into briquettes. An artificial intelligence approach with a Convolutional Neural Network (CNN) architecture can predict an effective object detection model. The image analysis results have shown an effective model in the raw material composition of 60% coconut, 20% wood, and 20% adhesive to produce quality biomass briquettes. Briquettes with a higher percentage of coconut will perform better in composition tests than mixed briquettes. The energy obtained from burning briquettes is useful for meeting household fuel needs and meeting micro, small, and medium business industries.
Co-Authors ', Rahmawati A.Ais Prayogi Adnan - Adnan Adnan Adnan Adnan Ady Wahyudi P Ady Wahyudi Paundu Ahmad, Andani Amriana Amriana Andani Achmad Ansar Suyuti Anugrayani Bustamin Areni, Intan Sari Arief, Ardiaty Aswad, Iqra Aulia Darnilasari Batara U, A Sungkurawira Bustamin, Anugrayani Christoforus Yohanes Chudori, Paula C Deny Wiria Nugraha Dewi Kusumawati, Dewi Elly Warni Elly Warni Faizal Arya Samman Ifwan, Ichsanul Al Indrabayu Indrabayu Ingrid Nurtanio Intan Sari Areni Iqra Aswad Iqra Aswad Marindah Wardhani, Tyanita Puti Marindah, Tyanita Puti Muh. Alief Fahdal Imran Oemar Muhammad Awaluddin, Muhammad Muhammad Niswar Muhammad Nur Muhammad Rijal Mukarramah Yusuf Paundu, Ady Wahyudi Rahayu, Mesra Rieka Zalzabillah Putri Rijal, Muhammad Saadputra, Zulfiqry Wardi Wardi Yulis, Nurlina Yusran Yusran Yusri, Amiqatun Nasyati Yusuf, Mukarramah Zaenab - Zaenab Muslimin Zahir Zainuddin Zulkifli Tahir