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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IT JOURNAL RESEARCH AND DEVELOPMENT IJIS - Indonesian Journal On Information System JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Tekno Insentif Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Teknik Informatika (JUTIF) Journal of Dinda : Data Science, Information Technology, and Data Analytics Journal of Internet and Software Engineering Jurnal INFOTEL SmartComp Jurnal Informatika: Jurnal Pengembangan IT JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Agi Prasetiadi
Institut Teknologi Telkom Purwokerto
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
IDENTIFICATION OF MENTAL ILNESS FROM PATIENT DISEASES USING KNN AND LEVENSHTEIN DISTANCE ALGORITHM Yustika Rahma; Agi Prasetiadi; Merlinda Wibowo
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 5 (2022): JUTIF Volume 3, Number 5, October 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.5.371

Abstract

According to WHO, in 2017, the estimated number of people with mental disorders worldwide was around 450 million people, including schizophrenia. Globally, for the condition of Southeast Asia alone, the number of people affected by mental disorders is 13.5%. Meanwhile, 13.4% of cases in Indonesia are affected by mental illness. The Association of Mental Medicine Specialists (PDSKJ) during October 2020 noted that 5661 people who did self-examination through the PDSKJ website came from 31 provinces and found that 32% of the population had psychological problems and 68% had no psychological issues. Seeing that the level of mental illness in Indonesia is increasing, it is necessary to have a system to help the community with early prevention and treatment. With the growth of technology at its peak, Machine Learning technology can overcome the problem which is part of artificial intelligence. Furthermore, machine learning has an important role in improving the quality of health services because it is able to provide a medical diagnosis to predict disease. Therefore, the authors conducted a study to create a system to identify mental illness using the TF-IDF method. This method calculates the word weighting from a collection of complaints that the user gives. Then, these complaints will be classified using the KNN algorithm classification method and the Levenshtein Distance method to find the distance between the word inputted by the user and the word in the database and then calculate the number of differences between the two strings in the form of a matrix. The accuracy result of this machine learning classification is 0.928 or 93%, and will be visualized through web-based software using the Flask framework.
CLASSIFICATION OF CAT SOUNDS USING CONVOLUTIONAL NEURAL NETWORK (CNN) AND LONG SHORT-TERM MEMORY (LSTM) METHODS Fadhilah Gusti Safinatunnajah; Agi Prasetiadi; Merlinda Wibowo
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 5 (2022): JUTIF Volume 3, Number 5, October 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.5.373

Abstract

Cats become pets who are very close to humans, and they convey messages by producing identical sounds. Therefore, analysis of pet voices is important for a better relationship between cats and human. Animal communication through sound, especially in cats, depends on the situation or context in which the sound is made such as in a state of danger. Based on these problems, a classification method is needed to classify the similarity of characteristics in the resulting sound pattern. The classification methods used are Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) which can remember information for a long time and are used for a long time period. This study aimed to determine feelings or moods based on the sound produced into 4 categories: The Purr, The Meow, The Mating Call, and The Howl. The result of this study is that the best architectural model is to use 4 CNN convolution layers measuring 8-8-8-8 and 2 LSTM layers measuring 8-8. The precision value in this architecture is 0.68, the recall value is 1.00, the accurary value is 0.5625 and the f1-score value is 0.77. The small value of the confusion matrix is ​​caused by the lack of dataset duration in the training process, resulting in underfitting.
MAKHRAJ ‘AIN PRONUNCIATION ERROR DETECTION USING MEL FREQUENCY CEPSTRAL COEFFICIENT AND MODIFIED VGG-16 Ibnu Kasyful Haq; Agi Prasetiadi
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 1 (2023): JUTIF Volume 4, Number 1, February 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.1.419

Abstract

Based on research conducted by the Institute of Qur'anic Sciences (IIQ) as many as 65% of Muslims in Indonesia are illiterate in the Qur'an. In previous studies, research was conducted on the detection of Arabic word pronunciation errors against non-natives using the Mel Frequency Cepstral Coefficient (MFCC) and Support Vector Machine (SVM) methods with a test result of 54.6%. Due to the low accuracy results in previous studies, this study aims to design and build a system that can correct the accuracy of the pronunciation of makhraj letter ‘ain with the method used is a combination of MFCC and Convolutional Neural Network (CNN) with a vgg-16 structure that has been modified. The dataset used is 1,600 voice recordings divided into two categories of the correct pronunciation of the letter ‘ain and incorrect pronunciation of the letter ‘ain and four variations of pronunciation with different vowels with a total data of 800 records in each category. This study conducted several experiments on variations of the CNN kernel. The results of the training model that produced the best accuracy in all variations were the training model on kernels 16, 32, 64 with a final accuracy rate of 100% for all variations with 96% accuracy validation. In the fathah variation, the validation accuracy is 94%. In the variation of dhommah and the variation of kasrah obtained a validation accuracy of 97%. Therefore, this study succeeded in distinguishing the sound of the pronunciation of the letter ‘ain with different vowels and measuring the accuracy of the pronunciation of the letter ‘ain. Implementing the modified vgg-16 produces high accuracy and validation values for each speech variation during the model train process.
CLOTHING RECOMMENDATION AND FACE SWAP MODEL BASED ON VGG16, AUTOENCODER, AND FACIAL LANDMARK POINTS Ramadhanti, Imada; Prasetiadi, Agi; Kresna A, Iqsyahiro
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1016

Abstract

The selection of clothes in e-commerce sometimes contains doubts about the clothes that consumers choose because the clothes are not yet known to suit the consumer's body. So this research provides a solution through a clothing recommendation model according to the size and concept of clothing. Furthermore, there is a face exchange model whose job is to exchange faces between the consumer's face and the face on the recommended clothing. The dataset used in the classification model is clothing that is put into 8 classes with variations in size, clothing concept, and veiled or without headscarves, while making the autoencoder model requires source and target face datasets of 3,000 faces each. The method used to make clothing model recommendations is VGG16 and the face exchange model uses the autoencoder and facial landmark points methods. The results of the classification model with 2 different architectures obtain an accuracy of 97.01% and 94.49% respectively. Then the results of the autoencoder models for the 12 models produced the lowest loss values ​​with autoencoder I of 0.00012951 and in autoencoder II of 8.01e-05. The face landmark point method is used if the autoencoder method does not produce a good face swap.
Co-Authors Afrillebar Putra Pratama Amalia Beladinna Arifa Annisa Nugraheni Arum, Refiani Pintan Asti Dwi Sripamuji Athallah, Abiyan Condro Kartiko Diandra Chika Fransisca Dwi Sripamuji, Asti Eko Sanjaya Elfrida Ratnawati Elisa Usada Elisa Usada Fadhilah Gusti Safinatunnajah Ghani, Muhammad Hisyam habiba, adinda miftahul ilmi Haris Budi Widodo Harry Pratama Ramadhan Harry Pratama Ramadhan Ibnu Kasyful Haq Imada Ramadhanti Imada Ramadhanti Iqsyahiro Kresna A Jihan Rihadatul Aisya Julian Saputra Julian Saputra Lanyak, Andre Citro Febriliyan Lela Sari Kristina Merlinda Wibowo Merlinda Wibowo Olvy Diaz Annesa Pratama, Afrillebar Putra Ramadhan, Harry Pratama Ramadhani, Rima Dias Ramadhanti, Imada Ridho Ananda Risa Riski Amalia Riski Amalia, Risa Salma Pusriwijayanti Saputra, Yuris Mulya Utomo, Hanung Addi Chandra Wahyu Andi Saputra Yustika Rahma