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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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A Review on Alzheimer's Disease Classification Using Deep Learning Abdulqadir, Marwa M
The Indonesian Journal of Computer Science Vol. 13 No. 3 (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.v13i3.4031

Abstract

In recent years, there has been a substantial amount of research dedicated to using Deep Learning (DL) methods for the classification of Alzheimer's disease (AD) and other related tasks, specifically focusing on magnetic resonance imaging (MRI) data. According to a comprehensive analysis of recent studies, it seems that deep learning models, especially those that include the creation of different structures, have significant potential to improve the precision of identifying and classifying Alzheimer's disease at an early stage. This work aims to emphasize the importance of effective data preparation tactics and feature learning approaches, as well as the investigation of hybrid models using diverse deep learning technologies. This study primarily focuses on doing performance analysis of deep learning algorithms using the latest approaches. Finally, provide a concise overview and analysis of several methods that might enhance the effectiveness of identification and classification using deep learning.
OPTIMALISASI KINERJA KLASIFIKASI TEKS BERDASARKAN ANALISIS BERBASIS ASPEK DAN MODEL HYBRID DEEP LEARING Salsabila Rabbani; Agustin; Susandri; Rahmiati; M. Khairul Anam
The Indonesian Journal of Computer Science Vol. 13 No. 3 (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.v13i3.4034

Abstract

The conflict between Palestine and Israel has generated strong debates and reactions on social media, including in Indonesia. Public perception of various aspects is certainly important to identify issues in the Palestinian-Israeli conflict. However, the process of manually classifying aspects of the Palestinian-Israeli conflict requires human resources and considerable time. This research aims to explore the views of Indonesians on the Palestinian-Israeli conflict through sentiment analysis based on aspects of Territory, Religion, Politics, and History. Using deep learning technology, specifically a combination model of Convolutional Neural Networks with Long Short-Term Memory (CNN-LSTM), this research analyzes opinion and views data collected from X social media platform (Twitter). This research shows the results of the dataset obtained that the Political aspect dominates more than other aspects. The model evaluation results obtained an accuracy value of 96%, which indicates that the model's ability to classify X users' sentiments towards the Palestinian-Israeli conflict achieved a high level of success.
Machine Learning-Based Prediction of Thalassemia: A Review Abdulkarim, Dawlat; Abdulazeez , Adnan M.
The Indonesian Journal of Computer Science Vol. 13 No. 3 (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.v13i3.4035

Abstract

This article presents a comprehensive systematic review of recent advancements in machine learning (ML) applications for diagnosing Thalassemia, a genetic hematologic disorder. Focusing on studies from the last five years, this review highlighted significant technological advancements in ML, including the use of predictive modeling, image analysis, and deep learning algorithms, which have considerably improved the accuracy and efficiency of Thalassemia diagnosis. The review evaluates the application of various ML models in analyzing extensive biomedical data, which significantly enhances patient management and treatment outcomes. Key challenges such as data diversity, model transparency, and the need for robust training datasets are discussed, along with the integration of ML into existing clinical workflows. The potential transformative impact of ML in hematology is underscored, critically evaluating its effectiveness and ongoing developments in the field. This review aims to provide insights into the current research trends and future directions in the use of ML for the diagnosis and management of Thalassemia and other similar hematological disorders.
A Review on Deep Reinforcement Learning for Autonomous Driving Kamil, Zheen; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 3 (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.v13i3.4036

Abstract

Autonomous driving technology has gained significant attention, offering opportunities to modernize transportation systems worldwide. Deep reinforcement learning (DRL) has emerged as a robust approach to design smart driving policies for intricate and changeable environments. This paper provides a detailed investigation of state-of-the-art DRL methodologies that are effectively applied to autonomous driving. It begins by providing a clear explanation of the fundamental concepts of deep learning and reinforced learning, highlighting their application for control of self-driving vehicles. Consequently, the paper presents an overview of various DRL algorithms, including Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), and Actor-Critic methods, describing their structures, training approaches, and applications in autonomous driving situations. Recent advancements in DRL research, such as domain adaptation, imitation learning, and meta-learning, have also been addressed in the study, with an investigation of their potential implications for autonomous driving. Via a thorough assessment of current literature, key trends, challenges, and research directions have been identified for exploiting DRL in autonomous car development. This review intends to provide a comprehensive understanding of the current and future possibilities of DRL for self-driving vehicles to researchers, practitioners, and enthusiasts.
Face Recognition Based on Deep Learning: A Comprehensive Review Dakhil, Nasreen; M. Abdulazeez, Adnan
The Indonesian Journal of Computer Science Vol. 13 No. 3 (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.v13i3.4037

Abstract

Face recognition technology has undergone transformative changes with the advent of deep learning techniques. This review paper provides a comprehensive examination of the development and current state of face recognition techniques influenced by deep learning. We begin by discussing the fundamental deep learning models that have dramatically enhanced the accuracy and efficiency of face recognition, highlighting pivotal architectures such as convolutional neural networks (CNNs) and autoencoders. Subsequent sections delve into the application of these models in various environments and challenges, such as different lighting conditions, occlusion, and facial expressions. We also address the integration of deep learning with emerging technologies such as 3D facial reconstruction and multimodal biometrics. Furthermore, the review explores the ethical, privacy, and bias concerns inherent in facial recognition systems, focusing on the need for responsible and fair practices in AI. Finally, future directions are suggested, focusing on the need for robust, adaptable, and ethical face recognition systems. This paper aims to provide an important resource for researchers and practitioners in the field of computer vision, providing insight into the technological advances and ongoing challenges in deep learning-based face recognition.
Evaluasi Usability Aplikasi Mobile Banking Menggunakan Metode Retrospective Think Aloud dan Post-Study System Usability Questionnaire Naufal, Muhammad; Ahsyar, Tengku Khairil; Jazman, Muhammad; Permana, Inggih
The Indonesian Journal of Computer Science Vol. 13 No. 3 (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.v13i3.4039

Abstract

BRKS Mobile is a digital service provided by Bank Riau Kepri Syariah to facilitate its customers in conducting financial transactions via smartphones. Because this application is relatively new, there are problems when running the application. The results of user reviews on playstore comments and pre-surveys, the problem that often occurs is errors when making transactions. In this study, usability evaluation was carried out using the Retrospective Think Aloud (RTA) and Post-Study System Usability Quesionaire (PSSUQ) methods. The results of the usability measurement show that users experience little difficulty when running the transfer and purchase menus. This is reinforced by the results contained in the norms of the PSSUQ method where the results of the SyeUse variable value of 2.70 and InfoQual 2.95 are below the average which indicates that the usability of the system and the quality of information on BRKS Mobile are still lacking. For the InterQual value of 3.09, it is above average and overall the BRKS application is at 2.89 above average, which means that the application can be accepted by its users.
Implementation Of Naïve Bayes Classifier And Support Vector Machine For Stunting Classification Azani, Nilam Wahdiaz; M. Afdal
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4040

Abstract

Stunting is a condition when a child's physical growth and development are stunted or delayed due to a lack of adequate nutritional intake over a long period of time, especially during the early years of life. Indonesia still has a stunting prevalence rate above the WHO standard, which is at 21.6%. 2020 UN statistics recorded more than 149 million (22%) toddlers worldwide were stunted, of which 6.3 million were early childhood or stunted toddlers were Indonesian toddlers. This study aims to create a classification model using Data Mining Algorithms NBC and SVM to analyze and describe the class of a total of 2018 toddler nutritional status data in Lima Puluh Kota Regency. The results of this study are expected to be an evaluation of whether the stunting prevention program implemented has been successful, and can be the basis for creating the next program.
Perbandingan Algoritma XGBoost dan SVM Dalam Analisis Opini Publik Pemilihan Presiden 2024 Safitri, Dea; Susanti; Rahmaddeni; Fitri, Triyani Arita
The Indonesian Journal of Computer Science Vol. 13 No. 3 (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.v13i3.4041

Abstract

Pemilihan presiden dipengaruhi oleh berbagai faktor, termasuk latar belakang kandidat, masalah politik, dan preferensi ideologis, menjadikan pemilihan presiden sebagai subjek klasifikasi yang kompleks dan menarik. Menganalisis sentimen publik terhadap kandidat dan isu-isu politik memberikan wawasan penting tentang dinamika politik selama pemilihan. Penelitian ini berfokus pada pemilihan presiden dan membandingkan kinerja dua algoritma klasifikasi populer, XGBoost dan SVM, untuk menentukan metode mana yang lebih efektif. Setelah beberapa preprocessing teks dari 562 tweet, kami menemukan bahwa mayoritas pengguna Twitter cenderung memilih 347 tweet "Prabowo". Model Extreme Gradient Boosting (XGBoost) menunjukkan performa terbaik dengan presisi 78%, presisi 76%, recall 78%, dan skor f1 76%. Hasil ini menunjukkan bahwa XGBoost adalah model terbaik untuk mengklasifikasikan opini publik terkait pemilihan presiden 2024 dan memberikan kontribusi penting untuk memahami efektivitas metode klasifikasi dalam konteks pemilihan presiden.
Leukemia Detection and Classification Based on Machine Learning and CNN: A Review Rasheed, Hakar Hasan; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 3 (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.v13i3.4044

Abstract

Advancements in data mining methods have significantly improved disease diagnosis, particularly in the realm of leukemia detection. Leukemia, a complex cancer affecting white blood cells, poses significant challenges in diagnosis and management due to its diverse manifestations. Various machine learning algorithms, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), Random Forests (RF), Decision Trees (DTs), K-Nearest Neighbors (K-NN), Logistic regression (LR) and Naïve Bayes (NB) classifiers, have been employed to accurately classify leukemia cases based on diverse datasets and image analyses. This paper provides a comprehensive overview and comparison of these classification techniques, highlighting their effectiveness in diagnosing different leukemia subtypes. Additionally, the paper discusses the methodology and findings of several studies focusing on leukemia detection, emphasizing the significance of machine learning in enhancing diagnostic accuracy and treatment planning. Furthermore, it explores the challenges and future directions in leveraging machine learning for leukemia diagnosis, including the need for standardized datasets, algorithm refinement, and integration with clinical data for personalized treatment strategies.
Prostate Cancer: MRI Image Detection Based on Deep Learning: A Review Alhamzo, Jelan Salih Jasim; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 3 (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.v13i3.4045

Abstract

This comprehensive study delves into the transformative role of artificial intelligence (AI) and deep learning (DL) in the realm of prostate cancer care, an issue of paramount importance in men’s health worldwide. Prostate cancer, marked by the unchecked growth of cells in the prostate gland, poses risks of tumor formation and eventual metastasis. The crux of combating this disease lies in its early detection and precise diagnosis, for which traditional screening methodologies like Prostate-Specific Antigen (PSA) tests and multiparametric Magnetic Resonance Imaging (mp-MRI) are fundamental. The introduction of AI and DL into these diagnostic avenues has been nothing short of revolutionary, enhancing the precision of medical imaging and significantly reducing the rates of unnecessary biopsies. The advancements in DL, particularly through the use of convolutional neural networks (CNNs) and the application of multiparametric MRI, have been instrumental in improving the accuracy of diagnoses, foreseeing the progression of the disease, and tailoring individualized treatment regimens. This paper meticulously examines various DL models and their successful application in the detection, classification, and segmentation of prostate cancer, establishing their superiority over conventional diagnostic techniques. Despite the promising horizon these technologies offer, their implementation is not without challenges. The requisite for specialized expertise to handle these advanced tools and the ethical dilemmas they present, such as data privacy and potential biases, are significant hurdles. Nevertheless, the potential of AI and DL to inaugurate a new chapter in prostate cancer management is undeniable. The emphasis on interdisciplinary collaboration among scientists, clinicians, and technologists is crucial for pushing the boundaries of current research and clinical practice, ensuring the ethical deployment of AI and DL technologies. This collaborative effort is vital for realizing the full potential of these innovations in providing more accurate, efficient, and patient-centric care in the fight against prostate cancer, heralding a future where the burden of this disease is significantly mitigated.

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