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Deteksi Indikasi Kelelahan Menggunakan Deep Learning Fudholi, Dhomas Hatta; Nayoan, Royan Abida N; Suyuti, Maghfirah; Rahmadi, Ridho
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.292

Many students experience fatigue due to lack of sleep which can be caused by a psychological conditions or bad habits. Lack of sleep can affect student’s performance academically and causes many illnesses, stress and depression. Students with fatigue causes students to not study well, increasing risk of academic failure and will lead to having low GPA. In this research, fatigue detection is carried out to find out which students are experiencing fatigue. In this study, an annotated video dataset was used with a total of 18 subjects acted drowsy and alert. Fatigue detection is based on mouth movements, therefore mouth annotation is used. Mouth annotation has 2 categories, namely annotation 0 which indicates a closed mouth and annotation 1 which indicates the mouth is yawning. Previous study proves ResNet50 has better performance than other pre-trained models such as AlexNet, Clarifia, VGG-16, and GoogLeNet-19. We also applied image augmentation which is useful for providing new image variations to the model in each epoch by changing the rotation, random shift, and random zoom. ResNet50 model is used to perform binary classification which has two outputs, namely mouth stillness and yawning. The results of the frame classification are evaluated using precision, recall and f1-score. By using ResNet model, the results of the classification of frames labeled 0 or mouth stillness, obtained a precision of 0.72, a recall of 0.88, and an f1-score of 0.79. Meanwhile, the frame classification labeled 1 or yawning has a precision value of 0.85, a recall of 0.65, and an f1-score of 0.74.

Analisis Pola Nilai Akademik Siswa Ma Dengan Non-Boarding Di Pondok Pesantren Tradisional Dengan Menggunakan Formal Concept Analysis Supu, Nisfa Daud; Hidayat, Taufiq; Rahmadi, Ridho
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.296

The success or failure of learning objectives in the learning process is greatly influenced by how the learning process is experienced by students. One of the successes of students in education is shown by their academic achievement. The results of the evaluation of a learning process are usually expressed in quantitative form (numbers) which are often applied in evaluating learning scores or grades. This study focuses on analyzing the academic scores of general and religious subjects of students in Class IX Non-Boarding Madrasah Aliyah. The concept of analysis Formal Concept Analysis (FCA) is used as a data analysis method because it is able to represent data and model it into objects and attributes. In this study the FCA method was used to be able to analyze the academic excellence of students at boarding madrasah schools in general subjects or religious subjects. The research objective was to determine the pattern of success in student academic scores and to determine the model of student achievement academically specifically for general subjects. The results of the analysis carried out on the academic scores of 200 students of the Class IX Non-Boarding Madrasah Aliyah in general and religious subjects, it can be concluded that the average student has good and good enough scores, there are general subjects who also have good and quite good scores in the subject. religious lessons.

Analisis Pola Nilai Akademik Siswa MA Dengan Boarding Di Pondok Pesantren Tradisional Dengan Menggunakan Formal concept analysis Azizah, Nur; Hidayat, Taufiq; Rahmadi, Ridho
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 4, No 2 (2020): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v4i2.220

Traditional boarding schools still look very minimal in general (non-religious) learning. This can be caused by the absence of learning that focuses on these general lessons in the pesantren, there is not enough learning for general lessons to implement them. With the aforementioned causes, it will be carried out collecting and analyzing the academic value patterns of MA students with boarding conducted using the formal concept analysis method. The purpose of this study was to get an overview of the academic abilities of MA students in traditional huts, especially for general (non-religious) subjects. The results of the analysis can be used for policy improvement recommendations for boarding schools by increasing the ability of students in general subjects.

Deteksi Indikasi Kelelahan Menggunakan Deep Learning Fudholi, Dhomas Hatta; Nayoan, Royan Abida N; Suyuti, Maghfirah; Rahmadi, Ridho
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Many students experience fatigue due to lack of sleep which can be caused by a psychological conditions or bad habits. Lack of sleep can affect student’s performance academically and causes many illnesses, stress and depression. Students with fatigue causes students to not study well, increasing risk of academic failure and will lead to having low GPA. In this research, fatigue detection is carried out to find out which students are experiencing fatigue. In this study, an annotated video dataset was used with a total of 18 subjects acted drowsy and alert. Fatigue detection is based on mouth movements, therefore mouth annotation is used. Mouth annotation has 2 categories, namely annotation 0 which indicates a closed mouth and annotation 1 which indicates the mouth is yawning. Previous study proves ResNet50 has better performance than other pre-trained models such as AlexNet, Clarifia, VGG-16, and GoogLeNet-19. We also applied image augmentation which is useful for providing new image variations to the model in each epoch by changing the rotation, random shift, and random zoom. ResNet50 model is used to perform binary classification which has two outputs, namely mouth stillness and yawning. The results of the frame classification are evaluated using precision, recall and f1-score. By using ResNet model, the results of the classification of frames labeled 0 or mouth stillness, obtained a precision of 0.72, a recall of 0.88, and an f1-score of 0.79. Meanwhile, the frame classification labeled 1 or yawning has a precision value of 0.85, a recall of 0.65, and an f1-score of 0.74.

Analisis Pola Nilai Akademik Siswa Ma Dengan Non-Boarding Di Pondok Pesantren Tradisional Dengan Menggunakan Formal Concept Analysis Supu, Nisfa Daud; Hidayat, Taufiq; Rahmadi, Ridho
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.296

The success or failure of learning objectives in the learning process is greatly influenced by how the learning process is experienced by students. One of the successes of students in education is shown by their academic achievement. The results of the evaluation of a learning process are usually expressed in quantitative form (numbers) which are often applied in evaluating learning scores or grades. This study focuses on analyzing the academic scores of general and religious subjects of students in Class IX Non-Boarding Madrasah Aliyah. The concept of analysis Formal Concept Analysis (FCA) is used as a data analysis method because it is able to represent data and model it into objects and attributes. In this study the FCA method was used to be able to analyze the academic excellence of students at boarding madrasah schools in general subjects or religious subjects. The research objective was to determine the pattern of success in student academic scores and to determine the model of student achievement academically specifically for general subjects. The results of the analysis carried out on the academic scores of 200 students of the Class IX Non-Boarding Madrasah Aliyah in general and religious subjects, it can be concluded that the average student has good and good enough scores, there are general subjects who also have good and quite good scores in the subject. religious lessons.

Analisis Pola Nilai Akademik Siswa MA Dengan Boarding Di Pondok Pesantren Tradisional Dengan Menggunakan Formal concept analysis Azizah, Nur; Hidayat, Taufiq; Rahmadi, Ridho
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 4, No 2 (2020): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v4i2.220

Traditional boarding schools still look very minimal in general (non-religious) learning. This can be caused by the absence of learning that focuses on these general lessons in the pesantren, there is not enough learning for general lessons to implement them. With the aforementioned causes, it will be carried out collecting and analyzing the academic value patterns of MA students with boarding conducted using the formal concept analysis method. The purpose of this study was to get an overview of the academic abilities of MA students in traditional huts, especially for general (non-religious) subjects. The results of the analysis can be used for policy improvement recommendations for boarding schools by increasing the ability of students in general subjects.

Mi-Botway: a Deep Learning-based Intelligent University Enquiries Chatbot Windiatmoko, Yurio; Hidayatullah, Ahmad Fathan; Fudholi, Dhomas Hatta; Rahmadi, Ridho
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : Universitas Dharma Wacana

Intelligent systems for universities that are powered by artificial intelligence have been developed on a large scale to help people with various tasks. The chatbot concept is nothing new in today's society, which is developing with the latest technology. Students or prospective students often need actual information, such as asking customer service about the university, especially during the current pandemic, when it is difficult to hold a personal meeting in person. Chatbots utilized functionally as lecture schedule information, student grades information, also with some additional features for Muslim prayer schedules and weather forecast information. This conversation bot was developed with a deep learning model adopted by an artificial intelligence model that replicates human intelligence with a specific training scheme. The deep learning implemented is based on RNN which has a special memory storage scheme for deep learning models, in particular in this conversation bot using GRU which is integrated into RASA chatbot framework. GRU is also known as Gated Recurrent Unit, which effectively stores a portion of the memory that is needed, but removes the part that is not necessary. This chatbot is represented by a web application platform created by React JavaScript, and has 0.99 Average Precision Score.

Causal Relations of Factors Representing the Elderly Independence in Doing Activities of Daily Livings Using S3C-Latent Algorithm Tou, Nurhaeka; Rahmadi, Ridho; Effendy, Christantie
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

The growth of the elderly population in Indonesia from year to year has always increased, followed by the problem of decreasing physical strength and psychological health of the elderly. These problems can affect the increase in dependence and decrease the independence of the elderly in ADL. In previous studies, various factors affect independence in ADLs such as cognitive, psychological, economic, nutrition, and health. However, In general, these studies only focus on predictive analysis or correlation of variables, and no research has attempted to identify the casual relationship of the elderly independence factors. Therefore, this study aimed to determine the mechanism of the causal relationship of the factors that influence the independence of the elderly in ADLs using a casual method called the Stable Specification Search for Cross-Sectional Data With Latent Variables (S3C-Latent). In this research we found strong causal and associative relationships between factors.The causal relationship of elderly independence in ADLs was influenced by cognitive, psychological, nutritional and health factors and gender with Î± values respectively (0.61; 0.61;1.00, 0.65;0.70). Cognitive factors associated with psychological, economic, nutrition, and health with a value of Î± (0.77; 1.00; 1.00; 0.64). Furthermore, psychological factors associated with economy, nutrition, and health with a value of Î± (0.77; 0.95; 0.63). Bisides, economic factors are associated with nutrition and health with Î± values of ( 0.86; 0.75) and nutrition with health with Î± values of 0.64. The last association was found between nutritional factors and gender with a value of Î± 0.76. This research is expected to increase the independence of the elderly in carrying out daily activities.

A Mobile Deep Learning Model on Covid-19 CT-Scan Classification Susanto, Prastyo Eko; Kurniawardhan, Arrie; Fudholi, Dhomas Hatta; Rahmadi, Ridho
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : Universitas Dharma Wacana

COVID-19 pandemic is currently happening in the world. Previous studies have been done to diagnose COVID-19 by identifying CT-scan images through the development of the novel Joint Classification and Segmentation System models that work in real-time. In this study, the author focuses on a different motivation and innovation focused on the development of mobile deep learning. Mobile Net, a deep learning model as a method for classifying the disease COVID-19, is used as the base model. It has a good level of efficiency and reliability to be implemented on devices that have small memory and CPU specifications, such as mobile phones. The used data in this study is a CT-scan image of the lungs with a horizontal slice that has been classified as positive or negative for COVID-19. To give a broader analysis, the author compares and evaluates the model against other architectures, such as MobileNetV3 Large, MobileNetV3 Small, MobilenetV2, ResNet101, and EfficientNetB0. In terms of the developed mobile architecture model, the classification of COVID-19 using MobileNetV2 obtained the best result with 0.81 accuracy.

Pemodelan Kausal Faktor-Faktor Beban Keluarga dalam Merawat Pasien Kanker Menggunakan Algoritma S3C-Latent Surya, Rizki Surtiyan; Effendy, Christantie; Rahmadi, Ridho
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0814266

Pasien kanker memiliki kebutuhan yang kompleks mulai dari masalah fisik, psikologis, sosial dan spiritual. Keluarga yang merawat pasien kanker disebut family caregiver. Seorang family caregiver membantu mengatasi hampir semua permasalahan yang dialami pasien baik saat dirawat di rumah maupun di rumah sakit. Keluarga mengalami suka dan duka dalam merawat pasien. Dalam merawat pasien dengan penyakit kronis, bukan hanya pasien tetapi kesejahteraan dan kualitas hidup family caregiver juga penting. Oleh karena itu sangat penting untuk mengetahui bagaimana beban family caregiver dan faktor-faktor yang mempengaruhi beban keluarga dalam merawat pasien. Beban family caregiver dapat diukur menggunakan Caregiver Reaction Assesment (CRA), yang direpresentasikan oleh beberapa faktor. Dengan memahami hubungan kausal antara faktor-faktor beban keluarga, diharapkan dapat membantu untuk mengidentifikasi bagaimana beban caregiver bersumber dan berdampak. Untuk itu, penelitian ini bertujuan untuk mengidentifikasi hubungan kausal antara faktor-faktor yang berhubungan dengan beban family caregiver dalam merawat pasien. Penelitian ini menggunakan algoritma pemodelan kausal bernama Stable Specification Search for Cross-sectional Data with Latent Variable (S3C-Latent) untuk mendapatkan model kausal antara faktor-faktor beban family caregiver yang relevan. Dari hasil analisis pemodelan didapatkan ada 3 faktor yang memiliki hubungan kausal dan 2 faktor memiliki hubungan asosiasi. Gender memiliki hubungan kausal yang stabil terhadap kesiapan kesehatan dan kesiapan dalam merawat. Sedangkan faktor kesiapan merawat mempengaruhi faktor aktivitas family caregiver, selain itu faktor keuangan memiliki hubungan asosiasi yang kuat dengan faktor aktivitas dan hubungan keluarga. Pemodelan kausal ini dapat digunakan sebagai acuan bagi tenaga kesehatan dalam pelayanan kesehatan yang lebih tepat, efisien, dan efektif di dalam menangani permasalahan beban caregiver. AbstractCancer patients have complex needs ranging from physical, psychological, social, and spiritual problems. Families who take care for cancer patients are called family caregivers. A family caregiver helps to overcome almost all problems experienced by the patients both while being treated at home and in the hospital. Families experience joy and sorrow in caring for patients. In treating patients with chronic diseases, not only the patient but the family caregiver's well-being and quality of life are also important. Therefore, it is very important to know how the family caregiver's burden is and the factors that affect the family burden in caring for patients. Caregiver family burden can be measured using Caregiver Reaction Assessment (CRA), which is represented by several factors. By understanding the causal relationship between family burden factors, it is hoped that it can help to identify how the caregiver burden is sourced and impacted. Therefore, this study aims to identify the causal relationships between factors related to the burden on family caregivers in caring for patients. This study uses a causal modeling algorithm called Stable Specification Search for Cross-sectional Data with Latent Variable (S3C-Latent) to obtain a causal model between the relevant caregiver family load factors. The results of modeling analysis showed that there are 3 factors which have a causal relationship and 2 factors have an association relationship. Gender has a stable causal relationship to health readiness and readiness to care, Moreover, the caring readiness factor affects the family caregiver activity factor, and the financial factor has a strong association with the activity factor and family relationships. This causal modeling can be used as a reference for health workers so as to give health services which are precise, efficient, and effective in dealing with caregiver burden problems.

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