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Tri A. Sundara
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,127 Documents
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Leveraging of Gradient Boosting Algorithm in Misuse Intrusion Detection using KDD Cup 99 Dataset Sulaiman , Sulaiman Muhammed; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3720

Abstract

This study addresses the persistent challenge of intrusion detection as a long-term cybersecurity issue. Investigating the efficacy of machine learning algorithms in anomaly and misuse detection. Research employs supervised learning for misuse detection and explain anomaly detection. Focused on adaptability and continual evolution the study explores the application of ensemble learning models AdaBoost, LightGBM, and XGBoost. Applying these algorithms in the context of intrusion detection. Utilizing the KDD Cup 99 dataset as a benchmark the paper assesses and compares the performance of these models. Besides, illuminating their effectiveness particularly in identifying smurf attacks within the cybersecurity landscape.
A Hybrid Bird Mating Optimizer for Welded Beam Design Optimization Problem: Design Optimization Ibrahem, Ali Hikmat; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3721

Abstract

This study introduces a hybridization of the Bird Mating Optimizer (BMO) with Differential Evolution (DE). The Bird Mating Optimizer exhibits certain limitations, such as a slow convergence rate and a tendency to become trapped in local optima. To address these issues, a new method, BMO-DE, is proposed to enhance the performance of the BMO swarm intelligence algorithm. BMO-DE is a versatile swarm intelligence algorithm applicable to various engineering problems. In this research, it is specifically employed in the optimization of welded beam design, a type of problem characterized by numerous constraints. The penalty function approach is used to handle the constraints associated with welded beam design. Comparative analysis indicates that the proposed BMO-DE method outperforms other swarm intelligence algorithms in tackling this category of problems. Notably, the method demonstrates efficacy in finding optimal solutions with a low number of objective function evaluations, making it a potent and promising approach for addressing such problems.
Comparative Analysis of Machine Learning and Deep Learning Models for Bitcoin Price Prediction Ahmed Al-Zakhali, Omar; Abdulazeez, Adnan M.
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3722

Abstract

This research endeavors to forecast Bitcoin prices by employing a suite of machine learning and deep learning models. Five distinct models were employed: Random Forest, Linear Regression, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), each evaluated based on their R-squared scores. Notably, the models showcased diverse performances, with the ensemble learning approach of Random Forest exhibiting near-perfect accuracy, closely followed by GRU and SVM. The deep learning architectures, LSTM and GRU, demonstrated remarkable predictive capabilities, showcasing their adeptness in capturing intricate temporal patterns within the cryptocurrency price data. This study sheds light on the comparative performance of these models, emphasizing their strengths and limitations in predicting Bitcoin prices.
Implementasi Algoritma EfficientDet-D0 dan SSD-MobileNet-V2 FPNLite untuk Sistem Deteksi Gulma Nailul Muna; Norma Ningsih; Nanang Syahroni; Abd. Malik Syamlan; Vina Larasati; Karimatun Nisa’
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3723

Abstract

Gulma merupakan tumbuhan yang tumbuh disekitar tanaman dan dapat merugikan tanaman yang dibudidayakan. Pengendalian gulma menjadi faktor penting yang dapat mempengaruhi produktivitas tanaman. Pengendalian gulma dapat ditanggulangi dengan melakukan penyemprotan pestisida pada gulma. Cakupan penyemprotan yang tepat sasaran dapat dilakukan untuk mencegah timbulnya masalah limbah. Sistem pertanian cerdas sangat dibutuhkan untuk mengatasi permasalahan tersebut, seperti deteksi gulma yang memanfaatkan teknik deep learning. Pada penelitian ini membangun sistem deteksi gulma yamg mengimplementasikan EfficientDet-D0 dan SSD-MobileNet-V2 FPNLite. Dataset yang digunakan berjumlah 941 citra gulma yang kemudian dilakukan pelabelan untuk data latih dan data uji. Sistem menunjukkan kinerja yang baik untuk mendeteksi gulma dengan accuracy berturut-turut dari EfficientDet-D0 dan SSD-MobileNet-V2 FPNLite mencapai 95,69% dan 99,138%. Hasil tersebut menunjukkan EfficientDet-D0 dan SSD-MobileNet-V2 FPNLite dapat mendukung dalam pengendalian gulma.
Image Denoising Techniques Using Unsupervised Machine Learning and Deep Learning Algorithms: A Review Ferzo, Barwar; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3724

Abstract

The continuous evolution of imaging technologies has accentuated the demand for robust and efficient image denoising techniques. Unsupervised machine learning algorithms have emerged as promising tools for addressing this challenge. This review scrutinizes the efficacy, versatility, and limitations of various unsupervised machine learning approaches in the area of image denoising. The paper commences with a clarification of the foundational concepts of image denoising and the pivotal role unsupervised machine learning plays in enhancing its efficacy. Traditional denoising methods, encompassing filters and transforms, are briefly outlined, highlighting their insufficiencies in handling complicated noise patterns prevalent in modern imaging systems. Subsequently, the review delves into an exploration of unsupervised machine learning techniques tailored for image denoising. This includes an in-depth analysis of methodologies such as clustering deep learning. Each technique is surveyed for its architectural variation, adaptability, and performance in denoising diverse image datasets. Additionally, the review encompasses an evaluation of prevalent metrics used for quantifying denoising performance, discussing their relevance and applicability across varying noise types and image characteristics. Furthermore, it delineates the challenges faced by unsupervised techniques in this domain and charts prospective avenues for future research, emphasizing the fusion of unsupervised methods with other learning paradigms for heightened denoising efficacy. This review merges empirical insights, critical analysis, and future perspectives, serving as a roadmap for researchers and practitioners navigating the landscape of image denoising through unsupervised machine learning methodologies.
Bitcoin Price Prediction Using Hybrid LSTM-GRU Models Hussein, Nashwan; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3725

Abstract

Cryptocurrency price prediction is a challenging task due to the inherent volatility and complexity of the market. In this research, we propose a hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network model for predicting Bitcoin prices. The model is implemented using the TensorFlow and Keras libraries and is evaluated on historical Bitcoin price data obtained from Yahoo Finance. Our approach aims to leverage the strengths of both LSTM and GRU architectures to enhance the accuracy of price predictions. The results suggest that the proposed hybrid LSTM-GRU model holds promise for effectively capturing the complex dynamics of cryptocurrency markets, addressing the challenges associated with traditional time-series analysis techniques.
Classification of Diabetic Retinopathy Images through Deep Learning Models - Color Channel Approach: A Review Salih, Sardar; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3726

Abstract

On a global scale diabetic retinopathy, or DR, is the most common cause of vision loss. Blindness can be prevented with prompt treatment and early identification with retinal screening. Automated analysis of fundus imagery is growing prominently as a means of increasing screening efficiency, thanks to the development of deep learning. This work focuses on deep learning methods for automatic DR severity grading using color channel information. First, we give some basic information on the etiology and color features of DR lesions. Next, a novel support for deep learning technique that use unprocessed color photos as input for comprehensive feature learning. A review is mentioned on color space encodings, data augmentation methods. A summary of the evaluation parameters and public databases that were utilized to benchmark DR techniques are provided. The objective of how color channel information in retinal pictures can be efficiently utilized by deep learning models for automated DR screening has been discussed with statistical support.
Implementasi E-Learning Berbasis Moodle Pada Mata Pelajaran Informatika Akmalul Ahsan, Rivaldo
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3728

Abstract

Kemajuan teknologi yang semakin pesat mengakibatkan perkembangan di segala bidang, khususnya di bidang pembelajaran dan pendidikan. Teknik belajar mengajar konvensional kurang efektif untuk meningkatkan antusiasme siswa dalam belajar. Oleh karena itu, penerapan e-learning di sekolah dapat meningkatkan motivasi belajar siswa. Fungsionalitas Moodle sering digunakan dalam pengembangan perangkat lunak e-learning. Tahapan penelitian ini menggunakan teknik pengembangan ADDIE, yang terdiri dari lima langkah: analisis, pembuatan desain, pengembangan, implementasi, dan penilaian. Data dikumpulkan melalui penyebaran kuesioner yang mencakup lima dimensi pengembangan e-learning berbasis model di bidang pendidikan informatika. Subjek penelitian adalah siswa kelas 7 dari SMK N 1 Warungasem, yang terbagi dalam satu kelas yang terdiri dari 30 orang. Temuan penelitian menunjukkan bahwa penggunaan e-learning berbasis Moodle dalam pendidikan informatika dapat meningkatkan motivasi belajar siswa.
OCT Images Diagnosis Based on Deep Learning – A Review Abdi, Abdo; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3731

Abstract

The recent advancements in deep learning technology have significantly transformed the field of medical imaging, namely in the diagnosis of ocular illnesses. The progress made in this field has improved the capacity to extract and evaluate intricate characteristics in images, with Optical Coherence Tomography (OCT) playing a crucial role. OCT has become known for its safe qualities and its high level of detail, rendering it an essential instrument in the diagnosis of eye diseases. The interesting improvement in research is centered around the integration of deep learning with OCT for the purpose of automating the detection of eye diseases. We conducted a comprehensive study that explores several diagnostic methods and the wide-ranging uses of OCT. Additionally, it addresses the accessibility of publicly available datasets that are specifically tailored to optical coherence tomography (OCT). The paper provides a detailed review of the most recent advancements in computer-assisted diagnostic methods for diseases of the eye, such as age-related macular degeneration, glaucoma, and diabetic macular edema, with a particular focus on the effective use of OCT. Moreover, the paper systematically analyzes the primary challenges that deep learning faces in OCT image interpretation, emphasizing the intricate nature and possibilities of this field.
Developing Digital Interactive Exploration of Historical Places with Blending BIM and Virtual Reality Suhari, Ketut Tomy; Purwanto, Hery; Andinisari, Ratri
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3732

Abstract

The advancement of technology has opened new possibilities for exploring and experiencing historical places. This research aims to develop a digital interactive exploration platform that blends BIM and VR to provide an immersive and informative experience of historical sites. The study focuses on Candi Kidal, located in Kecamatan Tumpang, Kabupaten Malang, as the primary study area. The proposed method involves the integration of BIM and VR technologies to create a detailed and interactive virtual representation of Candi Kidal. The BIM models are the foundation for capturing and integrating various data sources. These models are then transformed into a VR environment, allowing users to explore the site virtually, interact with objects, and access relevant historical information. Data collection methods include site surveys, and historical research in the field. The BIM models are developed using software tools such as Autodesk Revit, while the VR environment is created using platforms like Unity3D. The development of the digital interactive exploration platform involves programming and scripting languages such as C#. The results demonstrate the effectiveness of developed platform in providing an immersive and informative experience for Candi Kidal. Users can navigate the virtual environment, view detailed architectural elements, and access historical information through interactive interfaces. The significance of this research lies in its potential to enhance the preservation, promotion, and accessibility of historical places. By blending BIM and VR technologies, the digital interactive exploration platform offers a unique and engaging experience that can attract a wider audience and foster a deeper understanding of cultural heritage.

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