The Indonesian Journal of Computer Science
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.
Articles
1,127 Documents
<![CDATA[Analisis Sentimen Aplikasi Inarisk Personal di Google Play Store Menggunakan Algoritma NLP ]]>
<![CDATA[Anugrah Putra]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i3.4021
<![CDATA[From a geographical perspective, Indonesia is recognized as a country that is prone to natural disasters, BNPB as one of the disaster management institutions in Indonesia has the Inarisk application as an application that provides information about disasters. To improve the effectiveness of services to users, sentiment analysis is carried out to find out user reviews on the InaRisk application. The purpose of this study is to explore user opinions on the InaRisk application on the Google Play Store platform. Researchers used a total of 597 reviews as data to be analyzed to determine polarity using NLP models. The results showed that as many as 34.7% of reviews had a positive sentiment, 52.6% of reviews had a neutral sentiment, and 12.7% of reviews had a negative sentiment. This shows that even though the application is good enough, it still needs some improvements in the application. In classifying predictions carried out using 6% of data as test data and 94% as training data where regression models were used and succeeded in producing an accuracy of 69.81%, this indicates that the logistic regression model used to classify predictions is good enough so that it can be used for sentiment analysis of other studies.]]>
<![CDATA[PENERAPAN MACHINE LEARNING PADA ANALISIS SENTIMEN APLIKASI MYTELKOMSEL MENGUNAKAN DATA ULASAN GOOGLE PLAYSTORE ]]>
<![CDATA[Fauzan, Farin Junita]]>;
<![CDATA[M Afdal]]>;
<![CDATA[Rice Novita]]>;
<![CDATA[Mustakim]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i3.4024
<![CDATA[The MyTelkomsel application is a digital access platform that provides telecommunications services. Therefore, sentiment analysis of MyTelkomsel application users is relevant for obtaining valuable insights for application development and management. This research aims to conduct sentiment analysis and compare methods on review data of the MyTelkomsel application. The dataset used is divided into two topics: service and user reviews. The labeling method in this research uses Lexicon Based and Indonesian Language Experts with three classes: positive, negative, and neutral. The labeled review dataset is then applied with SVM and Random Forest methods. The results obtained from applying two datasets with two labeling approaches indicate that the approach by experts tends to be more accurate compared to the lexicon-based approach because the highest accuracy of the lexicon-based approach is 79%, while the expert labeling achieves an accuracy of 83%. Additionally, in this study, the SVM algorithm demonstrates the highest accuracy, namely 83%, on the user dataset analyzed by Indonesian Language Experts.]]>
<![CDATA[Implementasi Association Rule Untuk Rekomendasi Strategi Up-Selling dan Cross-Selling Produk Menggunakan FP-Growth ]]>
<![CDATA[Nabiilah, Nabiilah]]>;
<![CDATA[M. Afdal]]>;
<![CDATA[Novita, Rice]]>;
<![CDATA[Mustakim]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i3.4025
<![CDATA[BC 4 HNI Pekanbaru is a subsidiary of PT. HNI-HPAI Indonesia offers a diverse range of items for sale. Insufficiently effective promotions, despite high transaction volumes, can result in certain items being less recognized and thus impractical. The purpose of employing the FP-Growth algorithm in data mining is to uncover product association patterns and produce rules for sales tactics using the CRM approach. Implementing CRM strategies that incorporate cross-selling and up-selling techniques can enhance sales. Cross-selling involves offering additional products or services connected to the items purchased, while up-selling involves encouraging customers to buy higher-value goods than initially intended, boosting sales of more expensive items. Among the 20 results obtained from analyzing transaction data from July 2023 to December 2023 using FP-Growth, only the rules with a minimum support value of 5% and a minimum confidence of 70% are considered for cross-selling strategies. Additionally, the rules with a minimum support value of 5% and a minimum confidence of 10% are considered for up-selling.]]>
<![CDATA[A Review on Decision Tree Algorithm in Healthcare Applications ]]>
<![CDATA[Abdulqader, Hozan Akram]]>;
<![CDATA[Abdulazeez , Adnan M.]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i3.4026
<![CDATA[Decision tree algorithms have emerged as a pivotal tool in healthcare, offering substantial benefits in diagnostics, prognosis, and health monitoring. This paper provides a comprehensive review of decision tree applications in medical settings, highlighting their ability to simplify complex decision-making processes and improve accuracy in disease diagnosis and outcome prediction. By dissecting various research studies and clinical implementations, we demonstrate the versatility of decision trees in handling diverse datasets—from genetic markers to electronic health records and real-time patient data. This review also explores the integration of decision trees with machine learning techniques to enhance diagnostic procedures and prognostic evaluations, underscoring the significant role of these algorithms in advancing personalized medicine and public health strategies. Challenges such as data sensitivity, privacy concerns, and the need for large annotated datasets are discussed to provide a balanced perspective on the capabilities and limitations of decision tree algorithms in healthcare. Through this analysis, we aim to illuminate the transformative potential of decision trees in improving patient care and streamlining healthcare operations.]]>
<![CDATA[A Review of Text Classification Based on ML & Data Mining Algorithms ]]>
<![CDATA[Mustafa, Ashraf Atam]]>;
<![CDATA[Mohsin Abdulazeez, Adnan]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i3.4027
<![CDATA[In the digital era, the field of text classification has experienced transformative growth through the application of Machine Learning (ML) and Data Mining (DM) algorithms. This review traces the evolution from traditional data mining methods to sophisticated ML strategies that significantly enhance the analysis and categorization of textual data. We discuss pivotal technologies including Bayesian classifiers, Support Vector Machines (SVM), and contemporary advances such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The integration of Natural Language Processing (NLP) techniques is highlighted for their critical role in enriching semantic analysis capabilities, a necessity for effective text classification. Additionally, the paper addresses challenges like handling high-dimensional data, dealing with imbalanced datasets, and confronting ethical issues such as bias and privacy in automated systems. By synthesizing the latest research, this review identifies current gaps, proposes practical solutions, and forecasts future trends in text classification to support ongoing research and application across various sectors.]]>
<![CDATA[Review paper Deep and Machine Learning Algorithms for Diagnosing Brain Cancer and Tumors ]]>
<![CDATA[Rebar, Zhehat]]>;
<![CDATA[Mohsin Abdulazeez, Adnan]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i3.4028
<![CDATA[In the rapidly evolving field of medical diagnostics, the integration of deep learning (DL) and machine learning (ML) technologies has dramatically advanced the accuracy and efficiency of brain cancer and tumor diagnosis using magnetic resonance imaging (MRI). This review explores the transformative impact of these technologies, highlighting their role in enhancing tumor detection, classification, and early diagnosis interventions. DL and ML algorithms have significantly improved the analysis of complex imaging data, enabling more precise and faster diagnostic decisions, which are crucial for effective patient management and treatment planning. This review encompasses a broad spectrum of studies that illustrate the capabilities of these computational techniques in handling large datasets, learning intricate patterns, and achieving a high diagnostic performance. By delving into various algorithmic approaches and their clinical implications, this study underscores the importance of continued advancements and the integration of AI technologies in the field of oncology, aiming to foster better patient outcomes through innovative diagnostic tools.]]>
<![CDATA[CNN-Based Segmentation and Detection of Brain Tumors MRI Images: A Review ]]>
<![CDATA[Sedeeq, Nihaya]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i3.4029
<![CDATA[In order to provide effective therapy and enhance patient outcomes, it is essential to diagnose brain tumors as early as possible and with high accuracy. Due to the fact that it provides very specific anatomical information, magnetic resonance imaging (MRI) is an extremely important tool for diagnosing brain tumors. The manual segmentation and identification of brain tumors from MRI images, on the other hand, would take a significant amount of time and are prone to human mistake. The use of convolutional neural networks, often known as CNNs, has become more popular as a resource for automating certain activities. The purpose of this review article is to investigate the current developments in CNN-based methods for the segmentation and identification of brain tumors in magnetic resonance imaging (MRI) images. We describe the most significant difficulties that are connected with the analysis of brain tumors using MRI, investigate the different CNN designs that are used for this purpose, and evaluate the performance metrics of these architectures. The purpose of this study is to offer a complete overview of the present state-of-the-art in CNN-based brain tumor analysis of MRI data, emphasizing both the promise and limits of this method.]]>
<![CDATA[A Review on Alzheimer's Disease Classification Using Deep Learning ]]>
<![CDATA[Abdulqadir, Marwa M]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i3.4031
<![CDATA[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.]]>
<![CDATA[OPTIMALISASI KINERJA KLASIFIKASI TEKS BERDASARKAN ANALISIS BERBASIS ASPEK DAN MODEL HYBRID DEEP LEARING ]]>
<![CDATA[Salsabila Rabbani]]>;
<![CDATA[Agustin]]>;
<![CDATA[Susandri]]>;
<![CDATA[Rahmiati]]>;
<![CDATA[M. Khairul Anam]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i3.4034
<![CDATA[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.]]>
<![CDATA[Machine Learning-Based Prediction of Thalassemia: A Review ]]>
<![CDATA[Abdulkarim, Dawlat]]>;
<![CDATA[Abdulazeez , Adnan M.]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS) ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v13i3.4035
<![CDATA[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.]]>
