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Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika
Published by Universitas Muhammadiyah Surakarta
ISSN : 2621038X     EISSN : 2477698X     DOI : -
Core Subject : Science,
Computer Science & IT
Khazanah Informatika: Jurnal Ilmiah Komputer dan Informatika, an Indonesian national journal, publishes high quality research papers in the broad field of Informatics and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology.
Arjuna Subject : -
Articles 10 Documents
 1 
Search results for , issue "Vol. 7 No. 1 April 2021" : 10 Documents clear
Measurement Motoric System of Cerebral Palsy Disability using Gross Motor Function Measure (GMFM) Rakhmadi, Aris; Ariyanto, Ragil
Khazanah Informatika Vol. 7 No. 1 April 2021
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i1.11864

Abstract

Cerebral palsy (CP) is a mobility disorder, muscle tone or posture disruption caused by brain damage that appears during brain growth, and often occurs before birth. CP has an impact on the daily activities of the suffered patient. Gross Motor Function Measure (GMFM) is a type of clinical measurement to evaluate development progress in the motoric function of CP patients. The purpose of this research is to design software to monitor and evaluate motoric parameters of CP patients. The software implements the GMFM method. The development mechanism went through the process of Software Requirements Specification (SRS). The result shows that the software helps monitor and evaluate CP patients. Software application in the field assists in evaluating the initial examination of T-1 until the final examination of T-6. Records show the enhancement dimensions of lying and rolling by 13.3%, sitting 14.8%, crawling and kneeling by 15.7%, standing by 16.5%, and walking-running-jumping 17.4%. We conclude that the application supports recording and analyzing motoric cerebral palsy data.
Implementation of Hybrid Methods in the Application of Experimental Psychology for Analysis of Mental Endurance Conditions Nurhasan, Usman; Rahmantyo, Anugrah Nur; Maulidiyah, Aflah Rahman
Khazanah Informatika Vol. 7 No. 1 April 2021
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i1.11637

Abstract

Psychology is the study of the behavior and mental processes of a person. In psychology, psychological tests or psychological tests are often used as part of the selection to determine the maximum performance of prospective employees based on mental endurance conditions from the factors of speed, accuracy, and consistency. One of the psychological test tools is the Mirror Tracer Apparatus which is used to determine the condition of a person's mental endurance with visual coordination in responding to the inverted image of an object seen through a mirror. Because the use of the Mirror Tracer Apparatus only uses one pattern, and the cost is quite expensive, an Experimental Psychology Implementation Application is created which is implemented in an Android-based application to make it easier with many patterns. This application is designed using a combination of two methods (Hybrid Method). The Fuzzy Mamdani method is used to generate a mental health condition level score, and the Template Matching method is to match the pattern of the resulting images with the template. These two methods aim to apply the psychological knowledge base to the application. Data processing is carried out by giving weight ratings to the experimental tools carried out by the user. The results of this study have been tested on psychologists, resulting in a score of 83% agreeing that the application can be used as an alternative test tool, and 92% of prospective new employees stated that the application can determine the condition of mental endurance.
E-Prescription: Connecting Patients’ Prescriptions with Pharmacists and Cashiers Susilawati, Helfy; Wiharso, Tri Arif
Khazanah Informatika Vol. 7 No. 1 April 2021
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v6i2.12191

Abstract

The paper describes the development of an electronic prescription system. Electronic prescription or e-prescription is an innovation in the health sector that enables patients to read the types of medication they will receive along with its description and rational use. E-prescription shortens the waiting time to get a prescription, which is different from a manual prescription system. In conventional systems, patients must undergo several steps to get served. They have to give the prescriptions to the cashier and wait for the cashier to calculate the bill. They later submit the proof of payment to the pharmacists and wait for the pharmacist to produce the medicine. Using e-prescription, the patients only have to pay for prescription and wait for the pharmacists to bring the medicine. The waiting time may decrease from 4 complicated steps into 2 simple ones. The website-based e-prescription application enables physicians to electronically send prescriptions to pharmacy computers and send its bill to the cashier. The system allows patients to directly move to the pharmacy once they have paid the bill. The research adopts a quantitative method with a prototype research model and UAT (User Acceptance Test) model for testing.
Classification of Pandavas Figure in Shadow Puppet Images using Convolutional Neural Networks Supriyanti, Wiwit; Anggoro, Dimas Aryo
Khazanah Informatika Vol. 7 No. 1 April 2021
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i1.12484

Abstract

Indonesia is a nation with various ethnicities and rich cultural backgrounds that span from Sabang to Merauke. One of the cultural products of Indonesian society is shadow puppet. Shadow puppet has been internationally renowned as a masterpiece of cultural art and recognized by UNESCO. The development of Indonesian society is very dependent on technological sophistication and it may shift the existing traditional culture out from the memory of the nation. Practices of modern life and the busy activities of the people exacerbate the condition and may make the society to ignore traditional culture. This study seeks to preserve traditional Indonesian culture by making shadow puppets as the object of classification. We use a deep learning algorithm called convolutional neural network (CNN) to classify 430 puppet images into 4 classes. The proportion of training, validation and test data is 70 by 20 by 10. The experiments show that the most efficient model is obtained with 3 convolution layer. It reaches an accuracy rate of 0.93 and a drop out rate of 0.2
Speech Classification to Recognize Emotion Using Artificial Neural Network Helmiyah, Siti; Riadi, Imam; Umar, Rusydi; Hanif, Abdullah
Khazanah Informatika Vol. 7 No. 1 April 2021
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i1.11913

Abstract

This study seeks to identify human emotions using artificial neural networks. Emotions are difficult to understand and hard to measure quantitatively. Emotions may be reflected in facial expressions and voice tone. Voice contains unique physical properties for every speaker. Everyone has different timbres, pitch, tempo, and rhythm. The geographical living area may affect how someone pronounces words and reveals certain emotions. The identification of human emotions is useful in the field of human-computer interaction. It helps develop the interface of software that is applicable in community service centers, banks, education, and others. This research proceeds in three stages, namely data collection, feature extraction, and classification. We obtain data in the form of audio files from the Berlin Emo-DB database. The files contain human voices that express five sets of emotions: angry, bored, happy, neutral, and sad. Feature extraction applies to all audio files using the method of Mel Frequency Cepstrum Coefficient (MFCC). The classification uses Multi-Layer Perceptron (MLP), which is one of the artificial neural network methods. The MLP classification proceeds in two stages, namely the training and the testing phase. MLP classification results in good emotion recognition. Classification using 100 hidden layer nodes gives an average accuracy of 72.80%, an average precision of 68.64%, an average recall of 69.40%, and an average F1-score of 67.44%.This study seeks to identify human emotions using artificial neural networks. Emotions are difficult to understand and hard to measure quantitatively. Emotions may be reflected in facial expressions and voice tone. Voice contains unique physical properties for every speaker. Everyone has different timbres, pitch, tempo, and rhythm. The geographical living area may affect how someone pronounces words and reveals certain emotions. The identification of human emotions is useful in the field of human-computer interaction. It helps develop the interface of software that is applicable in community service centres, banks, and education and others. This research proceeds in three stages, namely data collection, feature extraction, and classification. We obtain data in the form of audio files from the Berlin Emo-DB database. The files contain human voices that express five sets of emotions: angry, bored, happy, neutral and sad. Feature extraction applies to all audio files using the method of Mel Frequency Cepstrum Coefficient (MFCC). The classification uses Multi-Layer Perceptron (MLP), which is one of the artificial neural network methods. The MLP classification proceeds in two stages, namely the training and the testing phase. MLP classification results in good emotion recognition. Classification using 100 hidden layer nodes gives an average accuracy of 72.80%, an average precision of 68.64%, an average recall of 69.40%, and an average F1-score of 67.44%.
Classification of Pandavas Figure in Shadow Puppet Images using Convolutional Neural Networks Wiwit Supriyanti; Dimas Aryo Anggoro
Khazanah Informatika Vol. 7 No. 1 April 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i1.12484

Abstract

Indonesia is a nation with various ethnicities and rich cultural backgrounds that span from Sabang to Merauke. One of the cultural products of Indonesian society is shadow puppet. Shadow puppet has been internationally renowned as a masterpiece of cultural art and recognized by UNESCO. The development of Indonesian society is very dependent on technological sophistication and it may shift the existing traditional culture out from the memory of the nation. Practices of modern life and the busy activities of the people exacerbate the condition and may make the society to ignore traditional culture. This study seeks to preserve traditional Indonesian culture by making shadow puppets as the object of classification. We use a deep learning algorithm called convolutional neural network (CNN) to classify 430 puppet images into 4 classes. The proportion of training, validation and test data is 70 by 20 by 10. The experiments show that the most efficient model is obtained with 3 convolution layer. It reaches an accuracy rate of 0.93 and a drop out rate of 0.2
Speech Classification to Recognize Emotion Using Artificial Neural Network Siti Helmiyah; Imam Riadi; Rusydi Umar; Abdullah Hanif
Khazanah Informatika Vol. 7 No. 1 April 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i1.11913

Abstract

This study seeks to identify human emotions using artificial neural networks. Emotions are difficult to understand and hard to measure quantitatively. Emotions may be reflected in facial expressions and voice tone. Voice contains unique physical properties for every speaker. Everyone has different timbres, pitch, tempo, and rhythm. The geographical living area may affect how someone pronounces words and reveals certain emotions. The identification of human emotions is useful in the field of human-computer interaction. It helps develop the interface of software that is applicable in community service centers, banks, education, and others. This research proceeds in three stages, namely data collection, feature extraction, and classification. We obtain data in the form of audio files from the Berlin Emo-DB database. The files contain human voices that express five sets of emotions: angry, bored, happy, neutral, and sad. Feature extraction applies to all audio files using the method of Mel Frequency Cepstrum Coefficient (MFCC). The classification uses Multi-Layer Perceptron (MLP), which is one of the artificial neural network methods. The MLP classification proceeds in two stages, namely the training and the testing phase. MLP classification results in good emotion recognition. Classification using 100 hidden layer nodes gives an average accuracy of 72.80%, an average precision of 68.64%, an average recall of 69.40%, and an average F1-score of 67.44%.This study seeks to identify human emotions using artificial neural networks. Emotions are difficult to understand and hard to measure quantitatively. Emotions may be reflected in facial expressions and voice tone. Voice contains unique physical properties for every speaker. Everyone has different timbres, pitch, tempo, and rhythm. The geographical living area may affect how someone pronounces words and reveals certain emotions. The identification of human emotions is useful in the field of human-computer interaction. It helps develop the interface of software that is applicable in community service centres, banks, and education and others. This research proceeds in three stages, namely data collection, feature extraction, and classification. We obtain data in the form of audio files from the Berlin Emo-DB database. The files contain human voices that express five sets of emotions: angry, bored, happy, neutral and sad. Feature extraction applies to all audio files using the method of Mel Frequency Cepstrum Coefficient (MFCC). The classification uses Multi-Layer Perceptron (MLP), which is one of the artificial neural network methods. The MLP classification proceeds in two stages, namely the training and the testing phase. MLP classification results in good emotion recognition. Classification using 100 hidden layer nodes gives an average accuracy of 72.80%, an average precision of 68.64%, an average recall of 69.40%, and an average F1-score of 67.44%.
Measurement Motoric System of Cerebral Palsy Disability using Gross Motor Function Measure (GMFM) Aris Rakhmadi; Ragil Ariyanto
Khazanah Informatika Vol. 7 No. 1 April 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i1.11864

Abstract

Cerebral palsy (CP) is a mobility disorder, muscle tone or posture disruption caused by brain damage that appears during brain growth, and often occurs before birth. CP has an impact on the daily activities of the suffered patient. Gross Motor Function Measure (GMFM) is a type of clinical measurement to evaluate development progress in the motoric function of CP patients. The purpose of this research is to design software to monitor and evaluate motoric parameters of CP patients. The software implements the GMFM method. The development mechanism went through the process of Software Requirements Specification (SRS). The result shows that the software helps monitor and evaluate CP patients. Software application in the field assists in evaluating the initial examination of T-1 until the final examination of T-6. Records show the enhancement dimensions of lying and rolling by 13.3%, sitting 14.8%, crawling and kneeling by 15.7%, standing by 16.5%, and walking-running-jumping 17.4%. We conclude that the application supports recording and analyzing motoric cerebral palsy data.
Implementation of Hybrid Methods in the Application of Experimental Psychology for Analysis of Mental Endurance Conditions Usman Nurhasan; Anugrah Nur Rahmantyo; Aflah Rahman Maulidiyah
Khazanah Informatika Vol. 7 No. 1 April 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i1.11637

Abstract

Psychology is the study of the behavior and mental processes of a person. In psychology, psychological tests or psychological tests are often used as part of the selection to determine the maximum performance of prospective employees based on mental endurance conditions from the factors of speed, accuracy, and consistency. One of the psychological test tools is the Mirror Tracer Apparatus which is used to determine the condition of a person's mental endurance with visual coordination in responding to the inverted image of an object seen through a mirror. Because the use of the Mirror Tracer Apparatus only uses one pattern, and the cost is quite expensive, an Experimental Psychology Implementation Application is created which is implemented in an Android-based application to make it easier with many patterns. This application is designed using a combination of two methods (Hybrid Method). The Fuzzy Mamdani method is used to generate a mental health condition level score, and the Template Matching method is to match the pattern of the resulting images with the template. These two methods aim to apply the psychological knowledge base to the application. Data processing is carried out by giving weight ratings to the experimental tools carried out by the user. The results of this study have been tested on psychologists, resulting in a score of 83% agreeing that the application can be used as an alternative test tool, and 92% of prospective new employees stated that the application can determine the condition of mental endurance.
E-Prescription: Connecting Patients’ Prescriptions with Pharmacists and Cashiers Helfy Susilawati; Tri Arif Wiharso
Khazanah Informatika Vol. 7 No. 1 April 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v6i2.12191

Abstract

The paper describes the development of an electronic prescription system. Electronic prescription or e-prescription is an innovation in the health sector that enables patients to read the types of medication they will receive along with its description and rational use. E-prescription shortens the waiting time to get a prescription, which is different from a manual prescription system. In conventional systems, patients must undergo several steps to get served. They have to give the prescriptions to the cashier and wait for the cashier to calculate the bill. They later submit the proof of payment to the pharmacists and wait for the pharmacist to produce the medicine. Using e-prescription, the patients only have to pay for prescription and wait for the pharmacists to bring the medicine. The waiting time may decrease from 4 complicated steps into 2 simple ones. The website-based e-prescription application enables physicians to electronically send prescriptions to pharmacy computers and send its bill to the cashier. The system allows patients to directly move to the pharmacy once they have paid the bill. The research adopts a quantitative method with a prototype research model and UAT (User Acceptance Test) model for testing.

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