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An overview of information extraction techniques for legal document analysis and processing

10.11591/ijece.v11i6.pp5450-5457
Ashwini V. Zadgaonkar , Avinash J. Agrawal
In an Indian law system, different courts publish their legal proceedings every month for future reference of legal experts and common people. Extensive manual labor and time are required to analyze and process the information stored in these lengthy complex legal documents. Automatic legal document processing is the solution to overcome drawbacks of manual processing and will be very helpful to the common man for a better understanding of a legal domain. In this paper, we are exploring the recent advances in the field of legal text processing and provide a comparative analysis of approaches used for it. In this work, we have divided the approaches into three classes NLP based, deep learning-based and, KBP based approaches. We have put special emphasis on the KBP approach as we strongly believe that this approach can handle the complexities of the legal domain well. We finally discuss some of the possible future research directions for legal document analysis and processing.
Volume: 11
Issue: 6
Page: 5450-5457
Publish at: 2021-12-01

Awareness, perception and acceptability of digital physiotherapy intervention among Malaysian physiotherapist

10.11591/ijphs.v10i4.20876
Rajkumar Krishnan Vasanthi , Lee Cai Ling , Yughdtheswari Muniandy
Digital health intervention (DHI) can solve the patient's problem, such as geographical inaccessibility, delayed provision of care, low-level adherence to clinical protocols, and financial burden. DHI does not necessary to work as a substitution for a functioning health system but helps strengthen its function. Therefore, this study aimed to determine the awareness, perception, and acceptability of digital physiotherapy intervention (DPI) among Malaysian physiotherapists. A total of 209 practicing physiotherapists representing all the regions in Malaysia participated in this online self-reported questionnaire, including demographics profile, continuous professional development, awareness, perception and acceptability of DPI. Analyzed the collected data to determine the knowledge of DPI by using descriptive statistical methods. A 75.1% of the Malaysian physiotherapist aware of digital physiotherapy intervention, 69.38% perceived it reduces the cost for face to face, reduce the time for traveling 47.85%, improve adherence to exercises 42.58%, 78% of them agree with the DPI and 75.1% of them accepted to recommend the digital tools to their patient. Malaysian physiotherapists are aware, agree and recommend the digital physiotherapy intervention to their treatment plan. However, it should still raise awareness about digital physiotherapy intervention to lead them to the future. Developing new digital tools, utilization, and overcoming the various healthcare institutions' low acceptability considering the cost, conventional interventions, and time-consuming should be strategized in Malaysia.
Volume: 10
Issue: 4
Page: 778-784
Publish at: 2021-12-01

Research trend on TPACK through bibliometric analysis (2015-2019)

10.11591/ijere.v10i4.22062
Nadi Suprapto , Sukarmin Sukarmin , Rinie Pratiwi Puspitawati , Erman Erman , Dian Savitri , Chih-Hsiung Ku , Husni Mubarok
This paper aims to analyze the scientific trend of research on Technological Pedagogical Content Knowledge (TPACK) through bibliometric study and explore how the contribution of Indonesian researchers in the Scopus database from 2015 to 2019. The sample was composed of 2075 documents in total. The results revealed that scientific publication on TPACK has been increasing. United States contributed the most documents on TPACK as well as Singapore’s institutions dominated in this area. Meanwhile, Indonesia put its two representative’s institutions: Universitas Sebelas Maret and Universitas Pendidikan Indonesia, among the big ten institutions in the world. All Indonesian documents produced by teacher-producing universities and public universities. United States and Taiwan have also contributed to the most productive authors of TPACK. Then, the visualization of research trend on TPACK resulted in four major clusters: i) TPACK as a system; ii) TPACK in relating to its scale; iii) TPACK in connecting with quantitative parameters; and iv) TPACK under beliefs, intention, and technology acceptance. The research findings could aid related researchers to recognize the trend of TPACK research and recommend directions for further research.
Volume: 10
Issue: 4
Page: 1375-1385
Publish at: 2021-12-01

The degree of career polarization among educational leaders in the Jordanian Education Directorates

10.11591/ijere.v10i4.20993
Mohammad Hasan Hamadat , Mohammad Omar Al-Momani
The study aimed to identify the degree of career polarization among educational leaders in the Jordanian–education directorates of Ajloun and Jersah. The researchers adopted the descriptive -analytical approach for its suitability for such studies. The researchers used the questionnaire as the study instrument, which comprised 20 items. The researchers distributed the items in two domains, 10 items were for each domain as the study instrument. The sample of the study comprised 250 educational leaders for the first semester of the academic year 2019-2020. The study results showed that the degree of career polarization among educational leaders in the Jordanian Ministry of Education came with an average degree of rating in all its domains and for all items. The results also showed that there were no statistically significant differences at the level of statistical significance (α=0.05) attributed to the two study variables. Gender and the number of years of experience are the two study variables.
Volume: 10
Issue: 4
Page: 1282-1289
Publish at: 2021-12-01

Heart disease prediction model with k-nearest neighbor algorithm

10.11591/ijict.v10i3.pp225-230
Tssehay Admassu Assegie
In this study, the author proposed k-nearest neighbor (KNN) based heart disease prediction model. The author conducted an experiment to evaluate the performance of the proposed model. Moreover, the result of the experimental evaluation of the predictive performance of the proposed model is analyzed. To conduct the study, the author obtained heart disease data from Kaggle machine learning data repository. The dataset consists of 1025 observations of which 499 or 48.68% is heart disease negative and 526 or 51.32% is heart disease positive. Finally, the performance of KNN algorithm is analyzed on the test set. The result of performance analysis on the experimental results on the Kaggle heart disease data repository shows that the accuracy of the KNN is 91.99%
Volume: 10
Issue: 3
Page: 225-230
Publish at: 2021-12-01

Cloud computing adoption among state universities and colleges in the Philippines: Issues and challenges

10.11591/ijere.v10i4.21526
Catherine R. Alimboyong , Mardie E. Bucjan
The emergence of cloud computing (CC) adoption in higher education institutions (HEIs) is considered widespread today. Its growth comes with tremendous benefits and potential risks as well. This paper endeavors to investigate some issues and challenges that influence the adoption of cloud computing among state universities and colleges (SUCs) in the Philippines. A qualitative design was used in the study as it employed multiple case studies approach. Based on the results, this paper establishes two strong factors such as slow internet connection and lack of understanding or awareness of cloud computing. The findings revealed the impact of cloud computing to SUCs is found beneficial to the educational system amidst the global pandemic. Professors can easily upload lessons and teaching materials while students can easily access the materials online, though the challenge lies in the connectivity of internet in the country. Administrators can easily collaborate with the entire academic community and even to its stakeholder’s potential for collaboration even if not in face to face. It is a perfect avenue to be productive and efficient which allows all processes be made possible to all members of the entire academic community, may it be students, professors, staff and even other stakeholders.
Volume: 10
Issue: 4
Page: 1455-1461
Publish at: 2021-12-01

SAFEA application design on determining the optimal order quantity of chicken eggs based on fuzzy logic

10.11591/ijai.v10.i4.pp858-871
Sesar Husen Santosa , Agung Prayudha Hidayat , Ridwan Siskandar
The availability of stock in the chicken egg supply chain is influenced by the ability of egg Agents to determine the optimal orders to suppliers. The optimal number of orders is very important to manage for the Bogor City Egg Agent Indonesia because the stock capacity reaches 340 crates. The optimal number of orders for eggs at the Egg Agent is influenced by input variables, namely final stock (crate), selling price (crate), and consumer demand (crate) so that the inventory is under control. The three input variables have fuzzy values that must be processed using fuzzy logic to get the optimal number of orders to suppliers so that the egg stock in the warehouse is well maintained. The optimal order model for eggs in the smart application for egg agent (SAFEA) was developed using a fuzzy logic approach with the triangular and trapezoidal membership function. Based on the optimal order model in the SAFEA application, the optimal order to the supplier is 100-104 crates per day.
Volume: 10
Issue: 4
Page: 858-871
Publish at: 2021-12-01

Medical crisis during pandemic: Career preferences change in medical student

10.11591/ijere.v10i4.21897
Dian Natalia , Rizma Adlia Syakurah
The COVID-19 pandemic is a major threat to global education. Incidental emotions of fear and anxiety during pandemic have unconsciously influenced preference and outcome about their future career. This study aimed to assess the effect of the COVID-19 pandemic towards career preference change in medical students. A total of 1,027 responses from all over the medical students in Indonesia were collected from an online questionnaire which was broadcasted through social media from 14th July 2020–21st July 2020. This study was using Fear of COVID-19 Scale (FCV-19S) and Depression Anxiety Stress-Scale-21 (DASS-21) to assess fear of COVID-19, stress, anxiety, and depression. Out of 1,027 respondents, 44.6% had stressed, 47.8% had anxiety, and 18.5% had depression with an average FCV-19S score was 17.1. The result showed that the fear and anxiety of COVID-19 during the pandemic had associated significantly with the career decisions in medical students (p=<0.05). Indonesian policymakers had to keep in mind that the fear of the COVID-19 pandemic in medical students is due to the high mortality COVID-19 cases of health workers in Indonesia. Health workers need adequate working conditions and specific protection, this requires prompt attention from stakeholders.
Volume: 10
Issue: 4
Page: 1255-1261
Publish at: 2021-12-01

Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction

10.11591/ijece.v11i6.pp5467-5476
Surenthiran Krishnan , Pritheega Magalingam , Roslina Ibrahim
This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.
Volume: 11
Issue: 6
Page: 5467-5476
Publish at: 2021-12-01

Suport visual details of X-ray image with plain information

10.12928/telkomnika.v19i6.21592
Nashwan Jasim; University of Babylon Hussein , Sabah Khudhair; Techniques Imam Al-Kadhum University College Abbas
The objective of content-based image retrival (CBIR) is to retrieve relevant medical images from the medical database with reference to the query image in a shorter span of time. All the proposed approaches are different, yet the research goal is to attain better accuracy in a reasonable amount of time. The initial phase of this research presents a feature selection technique that aims to improvise the medical image diagnosis by selecting prominent features. The second phase of the research extracts features and the association rules are formed by the proposed classification based on highly strong association rules (CHiSAR). Finally, the rule subset classifier is employed to classify between the images. The last pert of our work extracts the features from the kidney images and the association rules are reduced for better performance. The image relevance inference is performed and finally, binary and the best first search classification is employed to classify between the images.
Volume: 19
Issue: 6
Page: 1975-1981
Publish at: 2021-12-01

Rimigs: the impact of gamification on students’ motivation and performance in programming class

10.11591/ijeecs.v24.i3.pp1789-1795
Ferdian Aditya Pratama , Riana Magdalena Silitonga , Yung-Tsan Jou
Gamification is described as the art of changing human activity to a game-like environment to gain user engagement. Rimigs is a developed gamification system discussed in this research and was built based on Octalysis framework. This research aims to design and identify the impact of using a gamification system on students’ motivation and performance, especially in programming classes. Through Rimigs, students’ engagement and performance can be enhanced by freely choosing tasks or quests according to the rank points, completing these tasks or quests, getting experience points, getting rewards, avoiding punishment at certain times, and competing with others friends. The proposed research was done by using the iterative methodologies and consists of four phases, starting from i) literature review, which identifies the Octalysis’ core, ii) design, which identifies the game elements used in the system and designing the unified modeling language (UML) diagram of the system, iii) testing, which involve the user in the using of the system, and iv) implementation, which is the analysis step for the impact of Rimigs in students’ performance and engagement. This research finds that Rimigs as a gamification system can positively impact both students’ motivation and performance.
Volume: 24
Issue: 3
Page: 1789-1795
Publish at: 2021-12-01

A performance evaluation of convolutional neural network architecture for classification of rice leaf disease

10.11591/ijai.v10.i4.pp1069-1078
Afis Julianto , Andi Sunyoto
Plant disease is a challenge in the agricultural sector, especially for rice production. Identifying diseases in rice leaves is the first step to wipe out and treat diseases to reduce crop failure. With the rapid development of the convolutional neural network (CNN), rice leaf disease can be recognized well without the help of an expert. In this research, the performance evaluation of CNN architecture will be carried out to analyze the classification of rice leaf disease images by classifying 5932 image data which are divided into 4 disease classes. The comparison of training data, validation, and testing are 60:20:20. Adam optimization with a learning rate of 0.0009 and softmax activation was used in this study. From the experimental results, the InceptionV3 and InceptionResnetV2 architectures got the best accuracy, namely 100%, ResNet50 and DenseNet201 got 99.83%, MobileNet 99.33%, and EfficientNetB3 90.14% accuracy.
Volume: 10
Issue: 4
Page: 1069-1078
Publish at: 2021-12-01

Indoor air quality in urban residential: Current status, regulation and future research for Indonesia

10.11591/ijphs.v10i4.20885
Ezra Ganesha Prihardanu , Haryoto Kusnoputranto , Herdis Herdiansyah
Modern society spends more time indoors, which has led to the hypothesis that indoor exposure can better represent the effects of air pollution at the individual level. Studies on the landscape of urban residential indoor air quality have never been carried out nationally in Indonesia. After 70 years of commitment to standardize the health aspects of the home in Indonesia, this study intends to make a chronological reflection on the Indonesian government's policy in residential indoor air quality. This study raised and analyzed several questions in the national and local context from the previous research. The government's various policies and regulations are chronologically listed to see the development and look for regulatory or implementation gaps. The indicators of insufficient ventilation and indoor air quality in much Indonesian urban housing have been shown in previous studies, encouraging this study to be necessary. This study used a chronological review using national and international journals. Previous studies have shown possibilities to estimate the landscape of indoor pollution exposure effectively using a socio-economic approach as a part. The latest national housing survey results can be used as references to discover the housing landscape status in Indonesia for further research suggestions.
Volume: 10
Issue: 4
Page: 824-833
Publish at: 2021-12-01

Perception of teaching performance in the virtual learning environment

10.11591/ijere.v10i4.22056
Guillermo Morales-Romero , Nicéforo Trinidad-Loli , Beatriz Caycho-Salas , Yanet Paucar-Manrique , César León-Velarde , Sofia Gamarra-Mendoza , Nestor Alvarado-Bravo , Almintor Torrez-Quiroz , Carlos Aliaga-Valdez , Florcita Aldana-Trejo
This article aims to analyze the perception of the students of the professional school of business administration about the teaching performance in the virtual learning environment during the COVID-19 pandemic. When developing the research, it was determined that there is a good teaching performance, according to the perception of the students. However, it was identified that the factors of domain of the topic and class session management, present a better perception in this context of virtual learning; while the didactic strategies factor is the one that presents a not so favorable perception. In addition, through a comparative analysis, it was determined that the didactic strategies and planning factors have suffered a negative variation with respect to the perception per teacher, when moving from face-to-face to virtual learning, since, of the total of 17 specialty teachers, seven of them one negative variation. These results are validated by the linear regression test, where an R2 value of 0.965 is calculated, with respect to the didactic strategies factor. It means that this factor influences 96.5% on the perception of students with the global factor and an R2 value of 0.921 for the planning factor, which indicates an influence of 92.1%.
Volume: 10
Issue: 4
Page: 1221-1228
Publish at: 2021-12-01

Power optimization of binary division based on FPGA

10.11591/ijeecs.v24.i3.pp1354-1366
Fadi T. Nasser , Ivan A. Hashim
In modern very large scale integrated (VLSI) digital systems, power consumption has become a critical concern of VLSI designers. As size shrinks and density increases in chips, it will be a challenge to design high performance and low-power digital systems. Therefore, VLSI designers are trying to reduce power dissipation in these systems by using power optimization techniques. Different mathematical operations can be found in the architectures of most digital systems. The focus of this paper is division. In comparison to other basic computational operations, division requires more iterations, takes a long time, covers a large area, and consumes more power from the digital system. As a result, the system's design requires high speed and a low-power divider in order to improve its overall performance. This paper focuses on dynamic power dissipation. In order to determine which design consumes the lowest dynamic power, different system designs of digit-recurrence division algorithms, such as restoring division and non-restoring division are suggested. An innovative power-optimization technique, the very hardware descriptions language (VHDL) technique, is utilized to the suggested system designs. The VHDL technique achieved the higher optimization in dynamic power, at 93.66% for non-restoring division with internal-loop iteration, than traditional approaches.
Volume: 24
Issue: 3
Page: 1354-1366
Publish at: 2021-12-01
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