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Journal of Informatics and Science Media (JISMEDIA)
Published by Yayasan Amanah Putra Mandiri
ISSN : 30641942     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Scope and Topics JISMEDIA publishes original research articles, review papers, and case studies that cover a broad range of topics, including but not limited to: Artificial Intelligence and Machine Learning Data Science and Big Data Analytics Software Engineering and Development Cybersecurity and Network Security Internet of Things (IoT) and Smart Systems Human-Computer Interaction Bioinformatics and Computational Biology Information Systems and Database Management Robotics and Automation Emerging Technologies and Innovations Aims and Objectives The journal aims to: Foster a comprehensive understanding of informatics and related scientific fields. Promote interdisciplinary research and collaboration between informatics and other scientific disciplines. Disseminate cutting-edge research that contributes to the development and application of innovative technologies. Provide a scholarly forum for the exchange of ideas, methodologies, and insights among researchers and practitioners.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 11 Documents
 1   2 
Trends and Visualization of Soft Computing Research in Deep Learning enggarani wahyu
Jurnal Informatika dan Sains Media (JISMA) Vol 1 No 1 (2024): Vol. 1 No. 1 (2024) July
Publisher : Yayasan Amanah Putra Mandiri

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Abstract

The background of this research is the significant increase in the number of scientific publications related to the use of soft computing in Deep Learning in recent years. As bibliometric data becomes increasingly complex, an effective tool is needed to map and analyze this data. This research aims to provide a step-by-step analysis to make it easier for novice users to follow how to use VOSViewer. This report enables and facilitates data analysis by utilizing mapping tools and provides an analysis of the development of research on digital learning media. The method used in this research is to conduct bibliometric analysis to produce network visualization of co-work maps and co-work density maps. The analysis was conducted using the number of publications obtained, related to the specified topic, totaling 500 documents in 2017-2021. As a practical example, we evaluate Soft Computing in Deep Learning. This research method involves Deep Learning. The results of this study show significant insights into research trends and developments in digital learning media. The conclusion shows that VOSviewer can be used to provide suggestions in data analysis results.
Bibliometrics Analysis Using Vosviewer and Publish or Perish: Trends and  Visualization of Soft Computing Research in Natural Language Processing (using Crossref data) Khoir, Mochamad Thoriq; Huda, Ednan Nauzal; Suhartono
Jurnal Informatika dan Sains Media (JISMA) Vol 1 No 2 (2024): Vol. 1 No. 2 (2024) December
Publisher : Yayasan Amanah Putra Mandiri

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Abstract

The background of this research is the significant increase in the number of scientific publications related to the use of soft computing in natural language processing (NLP) in recent years. As bibliometric data becomes increasingly complex, an effective tool is needed to map and analyze this data. This study aims to analyze and demonstrate the steps of bibliometric data analysis using VOSViewer comprehensively and systematically. The method used in this research involves conducting bibliometric analysis to generate visualizations of collaboration network  maps and collaboration density maps. The analysis was conducted on a total of 1000 documents obtained on the specified topic for the period 2019-2023. The results of this research can be visualized with a network map, it can be seen that the research development map in NLP is divided into 5 clusters. Cluster One consists of 8 topics, Cluster Two consists of 6 topics, Cluster Three consists of 4 topics, Cluster Four consists of 3 topics, and Cluster Five consists of 3 topics. The research conclusion shows that VOSViewer can be used to provide recommendations in data analysis results. This study provides a comprehensive and systematic guide for new users of VOSViewer to conduct bibliometric data analysis and effectively present the visualization of research developments in the field of NLP.    
Bibliometrik Menggunakan Vosviewer dengan Publish or Perish: Penelitian Neural Network dalam Klasifikasi Penyakit Tanaman Nova Rahma; Imamatul Khoiriyah; Suhartono
Jurnal Informatika dan Sains Media (JISMA) Vol 1 No 1 (2024): Vol. 1 No. 1 (2024) July
Publisher : Yayasan Amanah Putra Mandiri

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Abstract

Plant diseases pose a severe threat to global food security and agricultural sustainability. The impact of plant diseases causes economic losses for farmers and food shortages for the population. To overcome this problem, research in agriculture utilizes advanced technologies such as neural networks to detect plant diseases. This study aims to understand the development of research on using neural networks in plant disease classification. This method used the Neural Network method to identify plant diseases from images, with a bibliometric analysis of 72 articles published between 2019 and 2024. The results of this study map the network of knowledge and collaboration in the use of neural networks for plant disease classification, identifying three main clusters that reflect the research focus and application of this technology. It was concluded that this study successfully understood the research developments related to using neural networks in plant disease classification.
Mapping Soft Computing Concepts and Research Trends in Computer Vision through Bibliometric Analysis Using VOSviewer and Publish or Perish Aldiana Damayanti
Jurnal Informatika dan Sains Media (JISMA) Vol 1 No 1 (2024): Vol. 1 No. 1 (2024) July
Publisher : Yayasan Amanah Putra Mandiri

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Abstract

The background of this research is the significant increase in the number of scientific publications related to the use of soft computing in computer vision in recent years. As bibliometric data becomes increasingly complex, there is a need for effective tools to map and analyze such data. This study aims to analyze and demonstrate the steps of bibliometric data analysis using VOSViewer in a comprehensive and systematic manner. The method used in this research is to conduct bibliometric analysis to produce visualization of collaboration network maps and collaboration density maps. The analysis was carried out on a total of 200 documents obtained with predetermined topics for the period 2017-2021. Through network visualization, it can be seen that the research development map in the field of deep learning is divided into 4 clusters. Cluster One consists of 12 topics, Cluster Two consists of 12 topics, Cluster Three consists of 5 topics, and Cluster Four consists of 4 topics. The research conclusion show that VOSViewer can be used to provide recommendations in data analysis results. This research provides a comprehensive and systematic guide for new users of VOSViewer to conduct bibliometric data analysis and effectively present visualizations of research developments in the field of deep learning.
Bibliometric Analysis Using Publish or Perish and VOSviewer :Machine Learning in Relation to Cardiovascular Disease Akbar Sidqi
Jurnal Informatika dan Sains Media (JISMA) Vol 1 No 1 (2024): Vol. 1 No. 1 (2024) July
Publisher : Yayasan Amanah Putra Mandiri

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Abstract

Cardiovascular disease, commonly known as heart disease, is a condition where blood vessels narrow or become blocked, potentially leading to heart attacks, angina, or strokes. Disorders affecting the heart muscle, valves, or rhythm are also considered forms of heart disease. According to the American Heart Association (2017), Cardiovascular disease causes approximately 17.3 million deaths worldwide, with around 3 million of these occurring before the age of 60. Global statistics indicate that there are 9.4 million deaths annually due to cardiovascular disease, of which 45% are attributed to coronary heart disease. It is estimated that this number will increase to 23.3. million in 2030. This research is to identify patterns and trends and comprehensively evaluate data on cardiovascular disease. The method used is bibliometric analysis and VosViewer with a minimum number of term occurrences of 3 clusters with 96 relevant terms to visualize entity relationships, patterns and trends. In the analysis process, 50 articles were used to obtain scientific publications related to the topic of cardiovascular disease from the 2020-2024 period. with VosViewer you can produce the latest gaps and data analysis.  
Indonesian translation. Perbandingan kinerja Quicksort dan Mergesort dalam Flutter Framework Achmad Fahreza; Suhartono
Jurnal Informatika dan Sains Media (JISMA) Vol 1 No 1 (2024): Vol. 1 No. 1 (2024) July
Publisher : Yayasan Amanah Putra Mandiri

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Abstract

This study provides a comparative analysis between Quicksort and Mergesort sorting algorithms in the context of the Dart programming language environment. The results show that Quicksort dominates in terms of speed when dealing with small data, while Mergesort excels when data is large. Although Mergesort is more efficient in large data size situations, it requires significant additional memory allocation, whereas Quicksort makes more efficient use of memory space. Therefore, the choice between these two algorithms becomes highly dependent on the size of the data and the terms of use. For sorting data with small sizes (under 400 elements), Quicksort is a better choice in terms of performance. On the other hand, Mergesort is recommended for large data. In addition, when utilizing cache locality becomes a priority, Quicksort becomes a more optimal choice for all data sizes.  
Bibliometric Analysis Using VOSviewer with Publish or Perish (using Google Scholar data): Bibliometric Analysis in Heart Disease Detection Rizal Haris
Jurnal Informatika dan Sains Media (JISMA) Vol 1 No 1 (2024): Vol. 1 No. 1 (2024) July
Publisher : Yayasan Amanah Putra Mandiri

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Abstract

Heart disease detection is crucial for timely intervention and management to prevent adverse outcomes. This study uses bibliometric analysis with the tools VOSviewer and Publish or Perish, leveraging data from Google Scholar to explore research trends in heart disease detection. Bibliometric mapping, a valuable method for visualizing and analyzing research trends, collaboration, and knowledge structures across various fields, is used to gain insights into the research landscape of heart disease detection. VOSviewer, a widely used software tool, helps build and visualize bibliometric networks by analyzing co-authorship, co-occurrence, citation, bibliographic coupling, and co-citation data. This study aims to present detailed and systematic steps in the bibliometric data analysis related to heart disease detection research using VOSviewer, with the goal of observing the development of this research from 2019 to 2024. The two clusters identified are as follows: Cluster 1, colored red, consists of 3 words: hypertension, coronary, coronary heart disease. Cluster 2, colored green, consists of 2 words: heart disease, hypertension. Using bibliometric analysis, researchers identify influential sources, research themes, and key contributors in this field. By systematically analyzing publications and identifying major research areas, this study contributes to advancing knowledge in heart disease detection. These findings highlight the effectiveness of VOSviewer in mapping bibliometric data and its utility in providing comprehensive insights into scientific communication and collaboration in this important research area.
Analisis Bibliometrik Perkembangan Ekonomi Kreatif Menggunakan VOSviewer dan Publish or Perish Berdasarkan Data Scopus vinka
Jurnal Informatika dan Sains Media (JISMA) Vol 1 No 2 (2024): Vol. 1 No. 2 (2024) December
Publisher : Yayasan Amanah Putra Mandiri

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Abstract

This study uses bibliometric analysis tools, VOSviewer and Publish or Perish, to examine the development of creative economy research based on Scopus data from 2010 to 2024. By analyzing 100 articles, it identifies key thematic clusters, including globalization, digital transformation, sustainability, and regional economic impacts. The findings highlight the creative economy's pivotal role in fostering innovation, cultural preservation, and economic growth, while also addressing challenges such as digital infrastructure gaps and inequalities. Co-word density mapping provides insights into thematic relationships, offering valuable guidance for future research and policy development to ensure sustainable and inclusive growth within the creative economy.
Maturity Detection of Crystal Guava Fruit UsingConvolutional Neural Network Algorithm khoir, thoriq; suhartono
Jurnal Informatika dan Sains Media (JISMA) Vol 1 No 2 (2024): Vol. 1 No. 2 (2024) December
Publisher : Yayasan Amanah Putra Mandiri

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Abstract

Guava fruit in Latin is called Psidium Guajava L. is a tropical plant originating from Brazil, and distributed to Indonesia. One of thefruit commodities found in Indonesia which is a leading commodity and continues to increase in production is guava. Guava is aclimacteric fruit due to chemical changes, namely the activity of the enzyme pyruvate which causes an increase in the amount ofacetaldehyde and ethanol so that CO2 production increases and ethylene produced during fruit ripening will increase the respirationprocess. This research focuses on crystal guava fruit which is very commonly found in Indonesia, especially in East Java Province.Where at the time of harvesting guava fruit of course have a different maturity. From these problems, researchers used theConvolutional Neural Network (CNN) Algorithm to identify ripe guava fruit (Psidium Guajava L.) with image classification. Fromthis study, the CNN model using the VGG16 architecture that has been created in this study has an accuracy of 96% which isapproximately the same as the comparison of the accuracy of other CNN models and gets good performance on the testing model withan accuracy rate of 83%.
Sentiment Classification of Hate Speech Against Islam onTwitter Platform Using Multinomial Naïve Bayes Al Juhaeda, Zul Iflah; Muhammad Faisal; suhartono
Jurnal Informatika dan Sains Media (JISMA) Vol 1 No 2 (2024): Vol. 1 No. 2 (2024) December
Publisher : Yayasan Amanah Putra Mandiri

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Abstract

Twitter is widely used by public figures, politicians, celebrities, and organizations to communicate with the public. However, theplatform's freedom of speech is often misused, leading to conflicts such as hate speech, especially against Islam. This study aims todevelop a text classification system for detecting hate speech against Islam and to evaluate the performance of Multinomial Naïve Bayes(MNB) in this task. The data was obtained through Twitter data crawling and processed through several pre-processing steps: cleaning,case folding, tokenizing, stop words removal, and stemming. The processed data was then transformed using Bag of Words to computeword frequency, which was input into MNB. The first test compared the ratio of training to test data, adjusting the alpha hyperparameterwithin its minimum and maximum ranges. The second test involved k-fold cross-validation for model validation. The results showedthe highest accuracy of 85% at a 90:10 training-to-test data ratio with the maximum alpha value. Using 10-fold cross-validation, themodel achieved an average accuracy of 79.09%, with the highest accuracy of 85.05% in the 4th iteration. This study demonstrates thatthe training/test data ratio, alpha parameter, and cross-validation influence MNB's performance in classifying hate speech. 

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