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All Journal International Journal of Electrical and Computer Engineering International Journal of Informatics and Communication Technology (IJ-ICT) JOIV : International Journal on Informatics Visualization Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Teknik Informatika (JUTIF) Innovative: Journal Of Social Science Research
Kusuma, Gede Putra
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Public Health Other

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Recognition of music symbol notation using convolutional neural network Setyo, Ciara; Kusuma, Gede Putra
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2055-2067

Abstract

Musical notation is one thing that needs to be learned to play music. This notation has an important role in music because it can help in visualizing instructions for playing musical instruments and singing. Unfortunately, musical symbols that are commonly written in musical notation are difficult for beginners who have just started learning music. This research proposed a solution to create an optical music recognition (OMR) using a deep learning model to classify musical notes more accurately with some of the latest convolutional neural network (CNN) architectures. The research was carried out by implementing vision transformer (ViT), CoAtNet-0, and ConvNeXt-Tiny architecture. The training process was also combined with data augmentation to provide more information for the model to learn. Then the accuracy results of each model were compared to find out the best model for the OMR solution in this research. This experiment uses the Andrea dataset and Attwenger dataset which both get the best result by using the augmentation method and ConvNeXt-Tiny as the model. The best accuracy for the Andrea dataset is 98.15% and for the Attwenger dataset is 98.43%.
Predictive model for converting leads into repeat order customer using machine learning Maured, Deryan Everestha; Kusuma, Gede Putra
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp20-30

Abstract

In the competitive business landscape, customer relationship management (CRM) is pivotal for managing customer relationships. Lead generation and customer retention are critical aspects of CRM as they contribute to sustaining business growth and profitability. Also, identifying and converting leads into repeat customers is essential for optimizing revenue and minimizing promotional costs. This study focuses on developing a predictive model using machine learning techniques to convert leads into repeat order customers in conventional businesses. Leveraging data from a motorcycle distribution company in Jakarta and Tangerang, the study compares the performance of various models for predicting repeat orders. This includes individual models like DeepFM, random forest, and gradient boosting decision tree models. Additionally, it explores the effectiveness of stacking these models using logistic regression as a meta-learner. Furthermore, the study implements backward feature elimination for feature selection and hyperband for hyperparameter tuning to enhance model performance. The results indicate that Stacking model using base model default configuration stands out as the most robust, achieving the highest scores in accuracy (0.95), area under the curve receiver-operating characteristic curve (AUC-ROC) (0.67), log loss (0.19), weighted average precision (0.95), weighted average recall (0.95), and weighted average F1- score (0.92), effectively handling the imbalanced dataset.
Deep learning algorithms for breast cancer detection from ultrasound scans Lawysen, Lawysen; Kusuma, Gede Putra
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp427-437

Abstract

Breast cancer is a highly dangerous disease and the leading cause of cancer related deaths among women. Early detection of breast cancer is considered quite challenging but can offer significant benefits, as various treatment interventions can be initiated earlier. The focus of this research is to develop a model to detect breast cancer based on ultrasound results using deep learning algorithms. In the initial stages, several preprocessing processes, including image transformation and image augmentation were performed. Two types of models were developed: utilizing mask files and without using mask files. Two types of models were developed using four deep learning algorithms: residual network (ResNet)-50, VGG16, vision transformer (ViT), and data-efficient image transformer (DeiT). Various algorithms, such as optimization algorithms, loss functions, and hyperparameter tuning algorithms, were employed during the model training process. Accuracy used as the performance metric to measure the model’s effectiveness. The model developed with ResNet-50 became the best model, achieving an accuracy of 94% for the model using mask files. In comparison, the model developed with ResNet-50 and DeiT became the best model for the model without mask files, with an accuracy of 80%. Therefore, it can be concluded that using mask files is crucial for producing the best-performing model.
Brain Tumor Segmentation From MRI Images Using MLU-Net with Residual Connections Rompisa, Eric Timothy; Kusuma, Gede Putra
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4742

Abstract

Brain tumor segmentation plays an important role in medical imaging in assisting diagnosis and treatment planning. Although advances in deep learning such as Unet already perform image segmentation, many challenges exist in segmenting brain tumors with tumor spread boundaries. This paper proposes a model that combines CNN and MLP (MLU-Net) techniques enhanced by the addition of residual connections to improve segmentation accuracy called ResMLU-Net. This architecture combines 2D covolution layers, block MLP and residual connections to process MRI images with the dataset used is BraTS 2021. The residaul connection helps reduce gradient degradation which ensures smooth information flow and better feature learning. The performance of ResMLU-Net will be evaluated using Dice and IoU metrics and will also be compared with several models such as Unet, ResUnet and MLU-Net. The experimental scores obtained from ResMLU-Net for segmenting brain tumors are 83.43% for IoU and 89.94% for Dice. These results show that adding residual connections can improve the accuracy in segmenting brain tumors which can be seen that there is an increase in the Dice and Iou scores. The proposed ResMLU-Net model is a valuable contribution to medical imaging and health informatics. With its provision of a standard and computationally viable solution to brain tumor segmentation, it offers incorporation into Computer-Aided Diagnosis (CAD) systems and support to clinical decision-making protocols.
The Comparison of U-net and Deeplab V3 as Semantic Segmentation Models for Food Images Irwanto, Natanael Richie; Kusuma, Gede Putra
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 5, No 1 (2023): Edisi Desember
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v5i1.281

Abstract

The Semantic segmentation models have been used for many things, namely image classification, image detection, and other activities, including outdoor and object segmentation. Those models can either work with having a good result with a custom dataset and give up an excellent accurate process or a bad one. This research aimed to compare two models of semantic segmentation model, namely Unet and Deepvlab, for food images. The research procedure is to create an original food image dataset, process the dataset with two models, analyze the IoU of two models, and compare the mIoU between the models.  The research results show that U-net has a higher mIoU value of 0.01 than Deeplab V3 but has less processing time and some parameters. The research results also show that the completeness of performance details and the prediction segmentation results in the Deeplab v3 segmentation model are superior to this research. This research supports previous research findingsregarding the use of U-net and Deeplab v3 in semantic segmentation models. It enriches research on using these models in food image recognition. Further research is needed to evaluate other models in semantic segmentation for food images.
Analysis and Implementation of Website Improvement Proposals using Usability Testing Method Cimpago, Ibrahim Taufik; Kusuma, Gede Putra
Innovative: Journal Of Social Science Research Vol. 4 No. 1 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i1.8698

Abstract

Penelitian ini mengeksplorasi peningkatan pengalaman pengguna dan kegunaan melalui pendekatan desain berulang, dengan fokus khusus pada situs web IGreen iiAcademyi. Dengan menggunakan kuesioner Skala Kegunaan Sistem (SUS), penilaian kegunaan menyeluruh dilakukan, membandingkan desain saat ini dan desain yang diusulkan. Hasil kuesioner SUS menunjukkan peningkatan substansial dalam kemampuan belajar, efisiensi, daya ingat, manajemen kesalahan, dan kepuasan pengguna untuk desain yang diusulkan. Analisis terperinci terhadap skor SUS menunjukkan peningkatan signifikan secara keseluruhan pada Skor SUS Rata-rata, yang mencerminkan pengalaman pengguna yang lebih kaya. Studi ini menyoroti keefektifan desain yang berpusat pada pengguna dan evaluasi kegunaan dalam meningkatkan kegunaan situs web dan kepuasan pengguna. Temuan ini mendukung penerapan desain yang diusulkan dan menyarankan jalan untuk penyempurnaan lebih lanjut, meletakkan dasar untuk meningkatkan pengalaman dan keterlibatan pengguna di situs web iGreen iiAcademyi.
Handwritten Character Recognition using Deep Learning Algorithm with Machine Learning Classifier Liman, Muhamad Arief; Josef, Antonio; Kusuma, Gede Putra
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.8.1.1707

Abstract

Handwritten character recognition is a problem that has been worked on for many mainstream languages. Handwritten letter recognition has been proven to achieve promising results. Several studies using deep learning models have been conducted to achieve better accuracies. In this paper, the authors conducted two experiments on the EMNIST Letters dataset: Wavemix-Lite and CoAtNet. The Wavemix-Lite model uses Two-Dimensional Discrete Wavelet Transform Level 1 to reduce the parameters and speed up the runtime. The CoAtNet is a combined model of CNN and Visual Transformer where the image is broken down into fixed-size patches. The feature extraction part of the model is used to embed the input image into a feature vector. From those two models, the authors hooked the value of the features of the Global Average Pool layer using EMNIST Letters data. The features hooked from the training results of the two models, such as SVM, Random Forest, and XGBoost models, were used to train the machine learning classifier. The experiments conducted by the authors show that the best machine-learning model is the Random Forest, with 96.03% accuracy using the Wavemix-Lite model and 97.90% accuracy using the CoAtNet model. These results showcased the benefit of using a machine learning model for classifying image features that are extracted using a deep learning model.
Co-Authors Cimpago, Ibrahim Taufik Irwanto, Natanael Richie Josef, Antonio Lawysen, Lawysen Liman, Muhamad Arief Maured, Deryan Everestha Rompisa, Eric Timothy Setyo, Ciara