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29,939 Article Results

Natural language processing based advanced method of unnecessary video detection

10.11591/ijece.v11i6.pp5411-5419
Nazmun Nessa Moon , Imrus Salehin , Masuma Parvin , Md. Mehedi Hasan , Iftakhar Mohammad Talha , Susanta Chandra Debnath , Fernaz Narin Nur , Mohd. Saifuzzaman
In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze and prepare the data set. We use both Naive Bayes and logistic regression classification algorithms in this detection system to determine the best accuracy for our system. In our research, our video MP4 data has converted to plain text data using the python advance library function. This brief study discusses the identification of unauthorized, unsocial, unnecessary, unfinished, and malicious videos when using oral video record data. By analyzing our data sets through this advanced model, we can decide which videos should be accepted or rejected for the further actions.
Volume: 11
Issue: 6
Page: 5411-5419
Publish at: 2021-12-01

Predictive ability of problem-solving efficacy sources on mathematics achievement

10.11591/ijere.v10i4.21416
Januard D. Dagdag , Noel A. Palapuz , Nikka A. Calimag
This study examined the relationship between mathematics achievement and mathematics problem-solving efficacy sources. A cluster sample of 123 first year prospective teachers of a Philippine higher education institution responded to a 30-item problem-solving efficacy scales and took the teacher-made tests in Mathematics in the Modern World course; namely, Non-Routine Problem Solving and Natures and Numbers Pattern Tracing (NRPS-NNPT), Math Language and Symbols (MLS), and Data Management (DM). The research data was analyzed using Descriptive statistics, Pearson-r and Standard Multiple Regression. On the average, the respondents had satisfactory mathematics achievement. They reported a high level of social persuasion and somatic response and a low level of vicarious experience and mastery experience in mathematics problem-solving. Vicarious experience was directly associated with mastery experience while social persuasion and mastery experience were both inversely related to somatic responses. Among the four problem-solving efficacy sources, only social persuasion significantly predicted mathematics achievement specifically in the areas of NRPS-NNPT, MLS, and DM. Thus, becoming a trusted voice of encouragement and designing a persuasive and optimistic learning environment are highly recommended roles of schools to facilitate students’ mathematics achievement.
Volume: 10
Issue: 4
Page: 1185-1191
Publish at: 2021-12-01

Local information pattern descriptor for corneal diseases diagnosis

10.11591/ijece.v11i6.pp4972-4981
Samer Kais Jameel , Sezgin Aydin , Nebras H. Ghaeb
Light penetrates the human eye through the cornea, which is the outer part of the eye, and then the cornea directs it to the pupil to determine the amount of light that reaches the lens of the eye. Accordingly, the human cornea must not be exposed to any damage or disease that may lead to human vision disturbances. Such damages can be revealed by topographic images used by ophthalmologists. Consequently, an important priority is the early and accurate diagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms, particularly, use of local feature extractions for the image. Accordingly, we suggest a new algorithm called local information pattern (LIP) descriptor to overcome the lack of local binary patterns that loss of information from the image and solve the problem of image rotation. The LIP based on utilizing the sub-image center intensity for estimating neighbors' weights that can use to calculate what so-called contrast based centre (CBC). On the other hand, calculating local pattern (LP) for each block image, to distinguish between two sub-images having the same CBC. LP is the sum of transitions of neighbors' weights, from sub-image center value to one and vice versa. Finally, creating histograms for both CBC and LP, then blending them to represent a robust local feature vector. Which can use for diagnosing, detecting.
Volume: 11
Issue: 6
Page: 4972-4981
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

Review of impedance source power converter for electrical applications

10.11591/ijaas.v10.i4.pp310-334
V. Saravanan , K. M. Venkatachalam , M. Arumugam , M. A. K. Borelessa , K. T. M. U. Hemapala
Power electronic converters have been actively researched and developed over the past decades. There is a growing need for new solutions and topography to increase the reliability and efficiency of alternatives with lower cost, size and weight. Resistor source converter is one of the most important power electronic converters that can be used for AC-DC, AC-AC, DC-DC and DC-DC converters which can be used for various applications such as photovoltaic systems, wind power systems, electricity. Vehicles and fuel cell applications. This article provides a comprehensive overview of Z-source converters and their implementation with new configurations with advanced features, emerging control strategies and applications.
Volume: 10
Issue: 4
Page: 310-334
Publish at: 2021-12-01

Solid waste classification using pyramid scene parsing network segmentation and combined features

10.12928/telkomnika.v19i6.18402
Khadijah; Universitas Diponegoro Khadijah , Sukmawati Nur; Universitas Diponegoro Endah , Retno; Universitas Diponegoro Kusumaningrum , Rismiyati; Universitas Diponegoro Rismiyati , Priyo Sidik; Universitas Diponegoro Sasongko , Iffa Zainan; Universitas Diponegoro Nisa
Solid waste problem become a serious issue for the countries around the world since the amount of generated solid waste increase annually. As an effort to reduce and reuse of solid waste, a classification of solid waste image is needed  to support automatic waste sorting. In the image classification task, image segmentation and feature extraction play important roles. This research applies recent deep leaning-based segmentation, namely pyramid scene parsing network (PSPNet). We also use various combination of image feature extraction (color, texture, and shape) to search for the best combination of features. As a comparison, we also perform experiment without using segmentation to see the effect of PSPNet. Then, support vector machine (SVM) is applied in the end as classification algorithm. Based on the result of experiment, it can be concluded that generally applying segmentation provide better source for feature extraction, especially in color and shape feature, hence increase the accuracy of classifier. It is also observed that the most important feature in this problem is color feature. However, the accuracy of classifier increase if additional features are introduced. The highest accuracy of 76.49% is achieved when PSPNet segmentation is applied and all combination of features are used.
Volume: 19
Issue: 6
Page: 1902-1912
Publish at: 2021-12-01

Impact of micro hydro power plants on transient stability for the micro grid 20 kV system

10.11591/ijeecs.v24.i3.pp1278-1287
Syarifuddin Nojeng , Syamsir Syamsir , Reny Murniati
Transient stability analysis is conducted to determine the ability of the electric power system in maintaining the operating stability after a major disturbance. The disturbance can be trigger an impact on the stability of the rotor angle, voltage, and system frequency which can cause loss of synchronization. In this paper, the impact of the interconnection of the Tombolo-Pao mini hydro power plant (MHPP) on the stability of the system was analyzed by several scenarios to determine the behavior of system parameters in a 20 kV system interconnection network. This research is an implementation of regulatory provisions relating to the study of the connection to the PLN distribution network through by regulator. Based on the result of simulation study, transient stability of generators at TomboloPao power plant about 0.1 second, will not occur with network configuration according to modeling activation of anti-islanding protection of Tombolo Pao Power Plant which is set by 2 second. The simulation results show that the location of the disturbance in the electric power system has been influenced by the behavior of the power plant (synchronous generator) which can lead to the instability of the micro-hydro connected to the micro-grid system 20 kV.
Volume: 24
Issue: 3
Page: 1278-1287
Publish at: 2021-12-01

Lifestyle breast cancer patients among Indonesian women: A nationwide survey

10.11591/ijphs.v10i4.20913
Solikhah Solikhah , Khairunnisaa Nuur Aliifah Setyawati , Monthida Sangruangake
Recently, cancer is a major health problem in the world. Lifestyle changes and growing urbanization likely led to increasing breast cancer incidence in such in Indonesia. Therefore, this study aimed to explore lifestyle breast cancer patients among Indonesian women. The investigation was a cross-sectional study distributed among 3,392 females drawn from 13 out of 27 provinces in Indonesia. Multiple binary logistic regressions were conducted to investigate breast cancer risk among Indonesian. A significance level of 0.05 was employed in all analysis. Of the 3,392 respondents included in the analysis, more than half (52.71%; n=1,788) was aged 40–49 years old. The most common marital status of the participants was married (98.20%; n=3,331), followed by no smoking (94.69%; n=3,212) and active exercise (62.12%; n=2,107). Education level was significantly associated with breast cancer (AdjOR_Junior high school=0.21; 95%CI=0.06 to 0.70; p<0.01 and AdjOR_senior high school=0.60; 95%CI=0.15 to 2.26; p<0.05). Education level was significantly related to breast cancer. Lifestyle such as smoking and physical activity was suspected to affect breast cancer indirectly.
Volume: 10
Issue: 4
Page: 730-734
Publish at: 2021-12-01

Disturbance observer-based controller for inverted pendulum with uncertainties: Linear matrix inequality approach

10.11591/ijece.v11i6.pp4907-4921
Van-Phong Vu , Minh-Tam Nguyen , Anh-Vu Nguyen , Vi-Do Tran , Tran Minh Nguyet Nguyen
A new approach based on linear matrix inequality (LMI) technique for stabilizing the inverted pendulum is developed in this article. The unknown states are estimated as well as the system is stabilized simultaneously by employing the observer-based controller. In addition, the impacts of the uncertainties are taken into consideration in this paper. Unlike the previous studies, the uncertainties in this study are unnecessary to satisfy the bounded constraints. These uncertainties will be converted into the unknown input disturbances, and then a disturbance observer-based controller will be synthesized to estimate the information of the unknown states, eliminate completely the effects of the uncertainties, and stabilize inverted pendulum system. With the support of lyapunov methodology, the conditions for constructing the observer and controller under the framework of linear matrix inequalities (LMIs) are derived in main theorems. Finally, the simulations for system with and without uncertainties are exhibited to show the merit and effectiveness of the proposed methods.
Volume: 11
Issue: 6
Page: 4907-4921
Publish at: 2021-12-01

Human activity recognition for static and dynamic activity using convolutional neural network

10.12928/telkomnika.v19i6.20994
Agus Eko; Universitas Muhammadiyah Malang Minarno , Wahyu Andhyka; Universitas Muhammadiyah Malang Kusuma , Yoga Anggi; Universitas Muhammadiyah Malang Kurniawan
Evaluated activity as a detail of the human physical movement has become a leading subject for researchers. Activity recognition application is utilized in several areas, such as living, health, game, medical, rehabilitation, and other smart home system applications. An accelerometer was popular sensors to recognize the activity, as well as a gyroscope, which can be embedded in a smartphone. Signal was generated from the accelerometer as a time-series data is an actual approach like a human actifvity pattern. Motion data have acquired in 30 volunteers. Dynamic actives (walking, walking upstairs, walking downstairs) as DA and static actives (laying, standing, sitting) as SA were collected from volunteers. SA and DA it's a challenging problem with the different signal patterns, SA signals coincide between activities but with a clear threshold, otherwise the DA signal is clearly distributed but with an adjacent upper threshold. The proposed network structure achieves a significant performance with the best overall accuracy of 97%. The result indicated the ability of the model for human activity recognition purposes.
Volume: 19
Issue: 6
Page: 1857-1864
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

A novel ontology framework supporting model-based tourism recommender

10.11591/ijai.v10.i4.pp1060-1068
Ho Quoc Dung , Lien Thi Quynh Le , Nguyen Huu Hoang Tho , Tri Quoc Truong , Cuong H. Nguyen-Dinh
In this paper, we present a tourism recommender framework based on the cooperation of ontological knowledge base and supervised learning models. Specifically, a new tourism ontology, which not only captures domain knowledge but also specifies knowledge entities in numerical vector space, is presented. The recommendation making process enables machine learning models to work directly with the ontological knowledge base from training step to deployment step. This knowledge base can work well with classification models (e.g., k-nearest neighbours, support vector machines, or naıve bayes). A prototype of the framework is developed and experimental results confirm the feasibility of the proposed framework.
Volume: 10
Issue: 4
Page: 1060-1068
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

A systematic literature review of machine learning methods in predicting court decisions

10.11591/ijai.v10.i4.pp1091-1102
Nur Aqilah Khadijah Rosili , Noor Hidayah Zakaria , Rohayanti Hassan , Shahreen Kasim , Farid Zamani Che Rose , Tole Sutikno
Envisaging legal cases’ outcomes can assist the judicial decision-making process. Prediction is possible in various cases, such as predicting the outcome of construction litigation, crime-related cases, parental rights, worker types, divorces, and tax law. The machine learning methods can function as support decision tools in the legal system with artificial intelligence’s advancement. This study aimed to impart a systematic literature review (SLR) of studies concerning the prediction of court decisions via machine learning methods. The review determines and analyses the machine learning methods used in predicting court decisions. This review utilised RepOrting Standards for Systematic Evidence Syntheses (ROSES) publication standard. Subsequently, 22 relevant studies that most commonly predicted the judgement results involving binary classification were chosen from significant databases: Scopus and Web of Sciences. According to the SLR’s outcomes, various machine learning methods can be used in predicting court decisions. Additionally, the performance is acceptable since most methods achieved more than 70% accuracy. Nevertheless, improvements can be made on the types of judicial decisions predicted using the existing machine learning methods.
Volume: 10
Issue: 4
Page: 1091-1102
Publish at: 2021-12-01

Spark plug failure detection using Z-freq and machine learning

10.12928/telkomnika.v19i6.22027
Nor Azazi; Universiti Teknikal Malaysia Melaka Ngatiman , Mohd Zaki; Universiti Kebangsaan Malaysia Nuawi , Azma; Universiti Teknikal Malaysia Melaka Putra , Isa; Bahrain Society of Engineers S. Qamber , Tole; Universitas Ahmad Dahlan Sutikno , Mohd Hatta; Universiti Teknikal Malaysia Melaka Jopri
Preprogrammed monitoring of engine failure due to spark plug misfire can be traced using a method called machine learning. Unluckily, a challenge to get a high-efficiency rate because of a massive volume of training data is required. During the study, these failure-generated were enhanced with a novel statistical signal-based analysis called Z-freq to improve the exploration. This study is an exploration of the time and frequency content attained from the engine after it goes under a specific situation. Throughout the trial, the misfire was formed by cutting the voltage supplied to simulate the actual outcome of the worn-out spark plug. The failure produced by fault signals from the spark plug misfire were collected using great sensitivity, space-saving and a robust piezo-based sensor named accelerometer. The achieved result and analysis indicated a significant pattern in the coefficient value and scattering of Z-freq data for spark plug misfire. Lastly, the simulation and experimental output were proved and endorsed in a series of performance metrics tests using accuracy, sensitivity, and specificity for prediction purposes. Finally, it confirmed that the proposed technique capably to make a diagnosis: fault detection, fault localization, and fault severity classification.
Volume: 19
Issue: 6
Page: 2020-2029
Publish at: 2021-12-01
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