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JOIV : International Journal on Informatics Visualization
Published by Politeknik Negeri Padang
ISSN : 25499610     EISSN : 25499904     DOI : -
Core Subject : Science,
Computer Science & IT
JOIV : International Journal on Informatics Visualization is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of Computer Science, Computer Engineering, Information Technology and Visualization. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the JOIV follows the open access policy that allows the published articles freely available online without any subscription.
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Articles 45 Documents
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Search results for , issue "Vol 7, No 2 (2023)" : 45 Documents clear
Implementation of CRNN Method for Lung Cancer Detection based on Microarray Data Azka Khoirunnisa; - Adiwijaya; Didit Adytia
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1339

Abstract

Lung Cancer is one of the cancer types with the most significant mortality rate, mainly because of the disease's slow detection. Therefore, the early identification of this disease is crucial. However, the primary issue of microarray is the curse of dimensionality. This problem is related to the characteristic of microarray data, which has a small sample size yet many attributes. Moreover, this problem could lower the accuracy of cancer detection systems. Various machines and deep learning techniques have been researched to solve this problem. This paper implemented a deep learning method named Convolutional Recurrent Neural Network (CRNN) to build the Lung Cancer detection system. Convolutional neural networks (CNN) are used to extract features, and recurrent neural networks (RNN) are used to summarize the derived features. CNN and RNN methods are combined in CRNN to derive the advantages of each of the methods. Several previous research uses CRNN to build a Lung Cancer detection system using medical image biomarkers (MRI or CT scan). Thus, the researchers concluded that CRNN achieved higher accuracy than CNN and RNN independently. Moreover, CRNN was implemented in this research by using a microarray-based Lung Cancer dataset. Furthermore, different drop-out values are compared to determine the best drop-out value for the system. Thus, the result shows that CRNN gave a higher accuracy than CNN and RNN. The CRNN method achieved the highest accuracy of 91%, while the CNN and RNN methods achieved 83% and 71% accuracy, respectively.
Challenges and Best Practices Solution of Agile Project Management in Public Sector: A Systematic Literature Review Puja Putri Abdullah; Teguh Raharjo; Bob Hardian; Tiarma Simanungkalit
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1098

Abstract

Applying Agile methodologies in the public sector is nothing new. In recent years, governments worldwide have moved towards Agile development, especially with the Pandemic that requires governments to move and make decisions quickly. However, the difference between the government system and the private sector, such as holding the principle of a hierarchy of authority, still challenges Agile application. This study aims to explore challenges and provide solutions for applying Agile project management in the public sector by conducting a systematic literature review (SLR) using the PRISMA method. The literature used in the SLR was obtained from four paper databases, namely Scopus, IEEE Xplore, ACM, and Emerald Insight. Five hundred ninety-five papers were found, and 18 suitable papers were obtained, which were then analyzed and obtained a total of 43 challenging issues. Each of these issues is grouped based on eight project performance domains of PMBOK 7th edition, and the solution for each challenge is obtained from the mapping results from the SLR papers and PMBOK 7th edition Guide. The results showed that the most issues were in the Development Approach and Lifecycle and Project Work domain categories, with 8 issues each. Followed by Team with 7 issues, Stakeholder with 6 issues, Delivery with 5 issues, Measurement with 4 issues, Planning with 3 issues, and Uncertainty with 2 issues. This research can be useful for academics or practitioners as a reference in facing the challenges of implementing Agile project management in the public sector
Feature Selection Technique to Improve the Instances Classification Framework Performance for Quran Ontology Yuli Purwati; Fandy Setyo Utomo; Nikmah Trinarsih; Hanif Hidayatulloh
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1195

Abstract

The Al-Quran is the sacred book of Muslims, and it provides God's word in the form of orders, instructions, and guidelines for people to follow to have happy lives both here and in the afterlife. Several earlier research has used ontologies to store the knowledge found in the Quran. The previous study focused on extracting the relationship between classes and instances or the "is-a relation" by classifying instances based on the referenced class. Based on the performance testing of the instances classification framework, the test results show that Support Vector Machine (SVM) with Term Frequency-Inverse Document Frequency (TF-IDF) and stemming operation had dropped the accuracy value to 65.41% when the test data size was increased to 30%. Likewise, with BPNN with TF-IDF and stemming operations. In the Indonesian Quran translation dataset with a test data size of 30%, the accuracy value drops to 57.86%. Instances classification based on the thematic topics of the Qur'an aims to connect verses (instances) to topics (classes) to get an overall picture of the topic and provide a better understanding to users. This study aims to apply the feature selection technique to the instances classification framework for the Al-Quran ontology and to analyze the impact of applying the feature selection technique to the framework with a small dataset and training data. The instances classification framework in this study consists of several stages: text-preprocessing, feature extraction, feature selection, and instances classification. We applied Chiq-Square as a technique to perform feature selection. SVM and BPNN as a classifier. Based on the experiment results, it can be concluded that the feature selection implementation using Chi-Square increases the value of precision, f-measure, and accuracy on the test data size from 40% to 60% in all datasets. The feature selection using Chi-Square and SVM classifier provides the highest precision value with a test data size of 60% on the Tafsir Quran dataset from the Ministry of Religious Affairs Indonesia: 64.36%. Furthermore, the feature selection implementation and BPNN classifier also increase the highest accuracy value with a test data size of 60% in the Quranic Tafsir dataset from the Ministry of Religion of the Republic of Indonesia: 63.09%.
Determining the Rice Seeds Quality Using Convolutional Neural Network Sidiq Syamsul Hidayat; Dwi Rahmawati; Muhamad Cahyo Ardi Prabowo; Liliek Triyono; Farika Tono Putri
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1175

Abstract

Seed inspection is crucial for plant nurseries and farmers as it ensures seed quality when growing seedlings. It is traditionally accomplished by expert inspectors filtering samples manually, but there are some challenges, such as cost, accuracy, and large numbers. Speed and accuracy were the main conditions for increasing agricultural productivity. Machine learning is a sub-science of Artificial Intelligence that can be applied in research on the classification of rice seed quality. The pipeline of a machine learning system is dataset collection, training, validation, and testing. Model making begins with taking data on the characteristics of rice seeds based on physical parameters in the form of seed shape and color. The dataset used is two thousand images divided into two categories, namely superior seeds and non-superior seeds. Training and Validation was conducted using the Convolutional Neural Network (CNN) algorithm with the concept of cross-validation on Google Collaboratory notebooks. The ratio split of train data and validation data in modeling from a dataset is 80:20. The result of the model formed is a model with the development of a Deep Convolutional Neural Network (Deep CNN) that can classify the digital image data of rice seeds from the results of data calls uploaded into the system. The results of the experiment conducted on 30 test data can be analyzed so that the system can classify superior and non-superior seeds with a precision value of 93% and a recall of 95%.
Mining Opinions on a Prominent Health Insurance Provider from Social Media Microblog: Affective Model and Contextual Analysis Approach Ihda Rasyada; Ali Ridho Barakbah; Elizabeth Anggraeni Amalo
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1771

Abstract

Social media plays a significant role in enhancing communication among organizations, communities, and individuals. Besides being a mode of communication, the data generated from these interactions can also be leveraged to assess the performance of an institution or organization. People may evaluate public companies based on the opinions of their users. However, user-supplied information is brief and written in natural language. In addition to being brief, the process of sending messages or engaging in other social media interactions contains a great deal of context information. This multiplicity of context can be utilized to conduct a more in-depth analysis of user opinion. This study presents a new approach to opinion mining for social media microblogging data by applying an affective model and contextual analyses. The affective model is applied for sentiment analysis to measure the degree of each adjective from user opinion by evaluating adjectives according to their varying levels of pleasure and arousal. The contextual analysis in this paper is modeled based on topic, user, adjective, and personal characteristics. The contextual analysis has four main features: (1) Temporal keyword sentiment context, (2) Temporal user sentiment context, (3) User impression context, and (4) Temporal user character context. Our affective model outperformed 75.6% the accuracy and 74.98% of F1-score, rather than SVM. In the experiment, the contextual analysis performed graph visualization of output results for each query feature for future development. Feature one to four successfully processes the query to produce a visualization graph.

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