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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 1,170 Documents
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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
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
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
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
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
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
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.
An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier Almufti, Saman M.; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3735

Abstract

Gesture-based systems have emerged as a prominent breakthrough in the field of smart access control, effectively integrating security measures with user comfort. This study presents a novel gesture detection framework for smart entry systems that combines the computational capabilities of a Random Forest Classifier with the practicality of Arduino-based hardware. Central to methodology is the utilization of MediaPipe, an advanced computer vision library, to extract hand motion landmarks from live video streams. The selected landmarks function as a comprehensive dataset for training a Random Forest Classifier, which has been specifically chosen due to its high level of accuracy and efficiency in managing intricate classification jobs. The model exhibits outstanding competence in the categorization of gestures in real-time, attaining high levels of accuracy that are crucial for ensuring dependable entrance control. The Arduino microcontroller plays a vital role in the execution of the entry mechanism as it serves as the intermediary between the gesture detection software and the tangible entry control hardware. The incorporation of gesture recognition technology facilitates a cohesive and prompt user experience, wherein identified motions are directly converted into input commands. The system's practical use is demonstrated through a series of detailed tests, which highlight its dependability and efficiency across diverse climatic circumstances. The findings underscore the system's capacity as a flexible and safe solution for contactless access in many environments, including both private homes and highly protected establishments. Furthermore, the study makes a substantial contribution to the larger domain of human-computer interaction by showcasing the practicality of advanced gesture detection systems in many everyday contexts. The suggested framework presents a novel approach to smart entry systems and also paves the way for further investigation in the domains of smart home automation and interactive systems. In these areas, gesture-based interfaces have the potential to deliver user experiences that are both intuitive and efficient.
Analisis Keamanan Web Menggunakan Open Web Application Security Web (OWASP) Victor Ilyas Sugara; I Wayan Sriyasa
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3736

Abstract

Aplikasi web telah menjadi bagian integral dari kehidupan sehari-hari, memberikan layanan yang diperlukan untuk berkomunikasi, berbelanja, bertransaksi, dan berbagai aktivitas lainnya. Permasalahan yang muncul terkait keamanan web adalah ketidakmampuan sistem aplikasi untuk melindungi informasi sensitif dari ancaman siber. Top 10 OWASP adalah daftar yang diperbarui secara berkala yang memuat sepuluh kerentanan keamanan aplikasi web yang paling umum terjadi Level risiko yang bersifat medium memiliki confidence level yang sama, yakni 11.1%, dengan total level risiko Medium adalah 33.3%. Seluruh celah kemanan yang ditemukan hampir semuanya berkaitan dengan A05 Kesalahan Konfigurasi Keamanan & A06 Komponen yang Rentan dan Kedaluwarsa pada OWASP Top 10:2021.Rekomendasi perbaikan terhadap temuan sudah diberikan, dan diantaranya bersifat perbaikan didalam source code dan konfigurasi pada application server/web server yang diprioritaskan kepada temuan yang bersifat High, Medium dan Low.
Perancangan Antarmuka Aplikasi Edukasi Bisnis dengan Pendekatan Design Thinking Humaira Fauziah, Myeisha; Andrian, Rian; Venica, Liptia
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3737

Abstract

Di era digital sekarang teknologi sangat berperan aktif dalam hampir semua kegiatan manusia. Termasuk kegiatan bisnis, sekarang seorang pengusaha dituntut untuk bisa memanfaatkan teknologi untuk mempertahankan bisnisnya. Salah satu tipe usaha yang paling membutuhkan perubahan digitalisasi adalah UMKM (Usaha Mikro Kecil dan Menengah). Karena masih banyak UMKM tidak bisa beralih ke digitalisasi, salah satu cara untuk membantu meningkatkan perubahan UMKM ke dunia digital adalah dengan mendorong pelaku UMKM untuk mengikuti pelatihan bisnis atau kelas bisnis agar mereka mendapatkan pengetahuan untuk mengembangkan bisnisnya ke arah dunia digital. Maka dari itu dirancanglah antarmuka aplikasi edukasi bisnis Entrevo untuk memahami lebih baik permasalahan dan kebutuhan pengguna. Penelitian ini menggunakan metode design thinking dan Single Ease Question (SEQ) didapatkan hasil akhir yaitu 9 (dari 10 poin) yang menunjukkan bahwa desain aplikasi sudah baik dan user-friendly.
Predictions of Early Hospitalization of Diabetes Patients Based on Deep Learning: A Review: Machine Learning Al-Atroshi, Chiai; Adnan Mohsin Abdulazeez
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3738

Abstract

Unmanaged diabetes can result in a number of complications that need to be hospitalised. Diabetes is a chronic disorder. With preventive treatment, outcomes may be improved through early prediction of diabetes-related hospitalisation using data-driven algorithms. Here, we examine recent advances in deep learning methods for anticipating readmissions and unexpected hospital stays in adult patients with diabetes. Firstly, we present an overview of the main factors that indicate the need for hospitalisation due to diabetic complications. The research on hospitalisation risk prediction using structured health data, such as demographics, prescriptions, test results, etc., using conventional machine learning techniques is then summarised. Using data from insurance claims and electronic health records, we then examine current research that has used deep learning models. It is emphasised that longitudinal data can be included using recurrent neural networks. Model architectures, training methods, and important data modalities are covered. The assessment also addresses deployment difficulty and the model's performance assessment on real-world datasets. Ultimately, potential paths forward include hybrid models that integrate data diversity, explainable predictions, and clinical knowledge. In order to provide evidence-based predictions of the risk of hospitalisation and readmission for diabetes patients, we examine the potential and constraints of recently developed deep learning algorithms in this review.

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