Lab-IS/filkom-research-papers huggingface dataset:
Penerapan Decision Tree Dan Random Forest Dalam Deteksi Tingkat Stres Manusia Berdasarkan Kondisi Tidur
Authors: Sza Sza Amulya Larasati, Elok Nuraida Kusuma Dewi, Brahma Hanif Farhansyah, Fitra Abdurrachman Bachtiar, Fajar Pradana
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer
Year: 2024
Topics: Machine Learning; Health Informatics
Abstract: Masalah kesehatan mental menjadi isu global yang sangat umum terjadi, termasuk perubahan suasana hati, perbedaan kepribadian, ketidakmampuan mengatasi masalah, serta mengisolasi diri dari keramaian. Berdasarkan data dari World Health Organization (WHO), gangguan kecemasan dan stres menjadi gangguan mental yang paling sering terjadi dari 970 juta kasus yang dilaporkan sepanjang tahun 2019. Stres telah banyak dikaitkan dengan tidur. Penelitian ini akan mengungkap hubungan kondisi tidur pada manusia dengan tingkat stres yang sedang diderita dengan 5 tingkatan: normal, stres ringan, stres sedang, stres tinggi, stres sangat tinggi. Data yang digunakan merupakan data kontinyu dengan 8 fitur: ‘sr’ (snoring rate), ‘rr’ (respiration rate), ‘t’ (body temperature), ‘lm’ (limb movement), ‘bo’ (blood oxygen), ‘rem’ (rapid eye movement), ‘sh’ (sleeping hours), dan ‘hr’ (heart rate). Setiap fitur memiliki rentang nilai yang tidak sama, sehingga dilakukan normalisasi untuk menyeragamkan rentang tersebut. Hyperparameter tuning dilakukan dengan teknik k-fold cross validation dan model dirancang dengan algoritma klasifikasi Decision Tree serta Random Forest. Hasilnya, 5 fitur: tingkat mendengkur, laju respirasi, pergerakan anggota tubuh termasuk bola mata, serta detak jantung saat tidur berbanding lurus dengan tingkat stres. Semakin tinggi nilai kelima fitur tersebut mengindikasikan tingkat stres yang lebih tinggi. Sedangkan dengan 3 fitur lainnya: suhu tubuh, kadar oksigen, dan waktu tidur memberikan hasil sebaliknya. Dengan kata lain, ketiga nilai tersebut berbanding terbalik dengan tingkat stres yang diderita. Model Decision …
Comparation of Federated and Centralized Learning for Image Classification
Authors: Farhanna Mar'i, Ahmad Afif Supianto, Fitra Abdurrachman Bachtiar
Journal: PIKSEL: Penelitian Ilmu Komputer Sistem Embedded and Logic
Year: 2023
Topics: Machine Learning; Federated Learning
Abstract: Federated Learning (FL) is a new approach in machine learning or it can also be called collaborative learning, which is a machine learning method that includes client devices to carry out the training process, so that clients do not need to send training data to the server but directly conduct training on their respective devices. respectively. The models generated from local training will be sent to the server for further global aggregation. Therefore, FL is referred to as machine learning which can maintain the privacy of the data owner, because the data is not submitted to the server and is still stored in each client's device. In this study, a performance comparison will be carried out to prove whether the latest approach which is Federated Learning, can produce the same accuracy performance as the traditional approach, that is Centralized Machine Learning, in the case of Image Classification. The comparison of two approaches would be conducted by using the Open Source Image Classification dataset, namely MNIST. The performance of two approaches would be presented by evaluation that is Accuracy. The result shows that Federated Learning almost overcome the performance of Centralized Learning in the case of Image Classification by provided Accuracy 76%.
Facial Palsy Detection Through Changes in Facial Muscle Functionality Using CNN Algorithm
Authors: Chindy Aulia Sari, Fitra Abdurrachman Bachtiar, Lailil Muflikhah, Aris Widayati
Year: 2023
Topics: Deep Learning; Computer Vision
Abstract: Facial palsy is a common condition in the medical field that results from facial nerve disorders, leading to weakened facial motor functions. Detecting facial palsy typically involves observing the functional ability of facial muscles, a process currently performed manually by healthcare professionals relying on visual cues. However, manual detection is prone to errors, this needs for early and automated facial palsy detection. Overall, our research aims to improve the detection of facial palsy by leveraging deep learning techniques. By automating the detection process, we hope to provide a more accurate and efficient approach for early identification of this condition. In this research, we aim to develop a facial palsy detection system using the Convolutional Neural Network (CNN) algorithm. We utilize three datasets, namely CK+, JAFFE, and YouTube Facial Palsy, which are organized into normal and dysfunctional …
Facial Expression Recognition Using Convolutional Neural Network with Attention Module
Authors: Habib Bahari Khoirullah, Novanto Yudistira, Fitra Abdurrachman Bachtiar
Journal: JOIV: International Journal on Informatics Visualization
Year: 2022
Topics: Deep Learning; Computer Vision
Abstract: Human Activity Recognition (HAR) is an introduction to human activities that refer to the movements performed by an individual on specific body parts. One branch of HAR is human emotion. Facial emotion is vital in human communication to help convey emotional states and intentions. Facial Expression Recognition (FER) is crucial to understanding how humans communicate. Misinterpreting Facial Expressions can lead to misunderstanding and difficulty reaching a common ground. Deep Learning can help in recognizing these facial expressions. To improve the probation of Facial Expressions Recognition, we propose ResNet attached with an Attention module to push the performance forward. This approach performs better than the standalone ResNet because the localization and sampling grid allows the model to learn how to perform spatial transformations on the input image. Consequently, it improves the model's geometric invariance and picks up the features of the expressions from the human face, resulting in better classification results. This study proves the proposed method with attention is better than without, with a test accuracy of 0.7789 on the FER dataset and 0.8327 on the FER+ dataset. It concludes that the Attention module is essential in recognizing Facial Expressions using a Convolutional Neural Network (CNN). Advice for further research first, add more datasets besides FER and FER+, and second, add a Scheduler to decrease the learning rate during the training data.
Klasifikasi Pola Pergerakan Bola Mata Menggunakan Metode Multilayer Backpropagation
Authors: Karina Amadea, Fitra A Bachtiar, Gusti Pangestu
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Year: 2022
Topics: Machine Learning; Neural Networks
Abstract: Salah satu organ terpenting yang ada pada tubuh manusia yaitu indera penglihatan. Manusia dapat memperoleh informasi sebanyak 80% hanya dengan melihat. Pada bagian mata, terutama iris, terdapat wilayah-wilayah yang merepresentasikan tiap bagian dari tubuh. Dengan adanya jaringan syaraf yang ada pada iris, dapat diketahui respons terhadap semua perubahan yang ada di dalam tubuh termasuk perubahan semangat hidup hingga karakter atau sifat seseorang. Pada penelitian ini dibuat sebuah sistem untuk mengenali pola pergerakan mata. Salah satu caranya yaitu melalui pendeteksian pupil. Data citra yang digunakan pada penelitian ini berjumlah 65 citra wajah berukuran 1280 x 720 pixel yang akan diklasifikasikan menjadi 5 label yaitu, mata menghadap atas, bawah, depan, kanan, dan kiri. Data citra akan disegmentasi menggunakan framework Deep-VOG untuk kemudian didapatkan 107 hasil dari ekstraksi fitur pupil menggunakan metode Sector Line Distance. Dari hasil ekstraksi tersebut, selanjutnya akan diklasifikasikan menggunakan metode Backpropagation. Arsitektur Backpropagation yang digunakan yaitu menggunakan 1 hidden layer dengan 11 neuron pada hidden layer. Sedangkan untuk parameter-parameter yang digunakan yaitu learning rate sebesar 0, 7 dan iterasi sebanyak 100 iterasi. Hasil dari klasifikasi pola pergerakan mata memperoleh tingkat akurasi sebesar 88, 24% pada saat pelatihan dan 80, 95% pada saat pengujian.
Pengembangan aplikasi pembelajaran dengan menerapkan model pembelajaran Teams-Games-Tournament (TGT)
Authors: Anita Rizky Agustina, Fajar Pradana, Fitra A Bachtiar
Journal: Jurnal Nasional Teknik Elektro dan Teknologi Informasi| Vol
Year: 2021
Topics: Education; E-Learning
Abstract: UPT SMP Negeri 6 Gresik is one of the few educational institutions that implement a Teams-Games-Tournament (TGT) learning model in their day-to-day class learning activities. The TGT learning model can assist students in understanding the learning materials by relying on their friends as age-equivalent tutors and via game elements. The current model that was being applied has several issues, where teachers formed the groups conventionally which could decrease the inclass study time. Conventional ways of forming a group leave room for an unfair knowledge distribution among groups, such as a group having only students who have high grades or low grades only. Other than that, the teachers will make a crossword puzzle conventionally. The playing board is made before a learning material is given to the students and the amount of “words” on the puzzle are determined on much material there is and how many groups are formed. The grouping feature is developed using kmeans clustering. The development process used the waterfall development process and Codeigniter framework. This application requirement analysis resulted in four actors, 37 functional requirements, and one non-functional requirement. Testing for this research was done by blackbox testing techniques and whitebox testing techniques.
Enhancement of Angry Birds Level Generation from Sketches Using Cycle-Consistent Adversarial Networks
Authors: Mury F Dewantoro, Febri Abdullah, Pujana Paliyawan, Ruck Thawonmas, Fitra A Bachtiar
Year: 2020
Topics: Deep Learning; Generative Adversarial Networks
Abstract: This paper presents our work to enhance a state-of-the-art level generator (Sketch-to-Level Generator) that generates levels for an Angry-Birds-like game from drawn sketches. To achieve this task, Cycle-Consistent Adversarial Networks (Cycle-GAN) are used. CycleGAN is trained using two datasets: sketch drawings and typical level-structures. The former are taken from Google's Quick, Draw! datasets, and the latter from the winning level generator at the 2017 and 2018 AIBIRDS level generation competitions. The output of the trained CycleGAN is used as the input of Sketch-to-Level Generator. Our results show that the proposed preprocessing technique using CycleGAN allows Sketch-to-Level Generator to more successfully generate levels from arbitrary sketch drawings.
Aspect Based Sentiment Analysis for Customer Reviews on Bakso President Malang.
Authors: Fitra Abdurrachman Bachtiar, Whita Parasati, Nanang Yudi Setiawan
Year: 2020
Topics: Natural Language Processing; Machine Learning
Abstract: Bakso President Malang highly appreciate the opinions of customers regarding the products and services they provide to increase customer satisfaction. However, Bakso President Malang does not have customer opinion data, nor does the technology in processing and analyzing data that can produce information about customer perspectives on customer satisfaction aspects. To get the customer perspective of Bakso President Malang on customer satisfaction aspects can be done through Sentiment Analysis at the aspect level. In this study, sentiment analysis is performed on customer review using algorithms namely, Multinomial Naïve Bayes Classifier and Linear Support Vector Machine. This study uses 2,252 customer review data from 2012 to 2019. Customer review data is obtained through Web Scraping techniques on the TripAdvisor and Google Review sites. Experimental results shows that Linear Support Vector Machine has better accuracy performance compared to Multinomial Naïve Bayes Classifier.
Optimasi Asymmetric City Tour di Kota Kediri Menggunakan Ant Colony System
Authors: Abidatul Izzah, Benni A Nugroho, Wayan F Mahmudy, Fitra A Bachtiar
Journal: J. Nas. Tek. Elektro dan Teknol. Inf
Year: 2020
Topics: Optimization; Swarm Intelligence
Abstract: Kediri City is a stopover/transit city and has many potentials in the fields of tourism, education, and industry. Thus, the City of Kediri became one of the cities that are very likely to develop and be crowded. Therefore, it is required to model city tours in several primary fields of Kediri City. In the literature, determining the optimum route can be approached as a traveling salesman problem. However, traveling salesman problem model cannot be used to determine the city tour path as the distance among the point may vary. In this study, we used the concept of asymmetric traveling salesman problem to solve the city tour path. Furthermore, we used the ant colony system algorithm to solve this problem. The cases resolved in this study are the location of the tourism center, industrial center, and education center in Kediri City. The results show the ant colony system is capable of providing optimum tour route solutions, namely the city tourist route 34.65 km, the industrial route 21.19 km, and the school route 28 km.
Application of Students Graduation Prediction Model Using Decision Tree C4. 5 Algorithm and Synthetic Minority Oversampling Technique (SMOTE)
Authors: I Putu Yoga Tunas Sugitha, Fitra Abdurrachman Bachtiar, Satrio Agung Wicaksono
Year: 2024
Topics: Machine Learning; Data Mining
Abstract: An important achievement for students and higher education institutions is timely graduation. Unfortunately, a large number of students fail to graduate on time, which can have a negative impact on the accreditation of higher education institutions. Academic authorities can improve the timely graduation rate by developing policies and regulations with the help of student graduation prediction. Using academic data, data mining is an efficient way to predict graduation. Consequently, the Decision Tree C4.5 approach is used in this study to predict student graduation. This study also uses the SMOTE oversampling technique to address class imbalance in minority data and the mean method to manage missing variables. According to the findings, the first-semester grade point average (GPA) has the greatest impact on graduation. Overall GPA, GPA from the fourth semester, and GPA from the second semester are additional significant factors. An accuracy of …
Deciphering Consumer Skin Care Needs: Analyzing Sentiments and Review Topics of Cetaphil Facial Wash Across Diverse Skin Types
Authors: Dinda Ockta Nooryawati, Fitra Abdurrachman Bachtiar, Achmad Ridok
Year: 2023
Topics: Natural Language Processing; Consumer Behavior
Abstract: This research aims to understand the usage and perception of skincare products, specifically focusing on the facial wash from the Cetaphil brand. A survey revealed that a significant number of women invest in skincare monthly, yet many still lack confidence in their skin's appearance. Despite the plethora of skincare products available, many users still face confusion in choosing the right product, leading to a trial and error approach. This study attempts to map the use of skincare products and identify specific points that can address various skin problems. The research employs sentiment analysis and topic classification to analyze user reviews, aiming to provide insights into user perspectives on skincare products. The sentiment analysis, using the IndoBERT method, achieved an average accuracy of 85% and an F1-score of 93%. Topic classification, on the other hand, achieved an accuracy of 93.49% and an F1 …
Penerapan Deep Convolutional Generative Adversarial Network Untuk Menciptakan Data Sintesis Perilaku Pengemudi Dalam Berkendara
Authors: Michael Stephen Lui, Fitra Abdurrachman Bachtiar, Novanto Yudistira
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer
Year: 2023
Topics: Deep Learning; Generative Adversarial Networks
Abstract: Kecelakaan kendaraan adalah salah satu penyebab kematian tertinggi di Indonesia. Salah satu solusi untuk mencegah kecelakaan adalah dengan menggunakan sensor eksternal untuk mendeteksi kondisi jalan. Namun, penyebab utama kecelakaan adalah kelalaian pengemudi ketika mengemudi yang tidak dapat terdeteksi oleh sensor eksternal. Sensor visual dapat mendeteksi perilaku pengemudi di dalam kendaraan. Penggunaan sensor visual memiliki performa yang lebih baik ketika menggunakan metode deep learning. Salah satu metode untuk meningkatkan performa metode deep learning adalah dengan menggunakan data sintesis hasil model generatif sebagai tambahan data. Deep Convolutional Generative Adversarial Network (DCGAN) adalah salah satu model generatif yang menggunakan lapisan konvolusi. DCGAN terdiri dari dua neural network bernama generator dan discriminator yang membentuk hubungan zero-sum game. Generator menerima masukan berupa gambar asli dengan tambahan noise sebagai input proses latih secara unsupervised, menghasilkan gambar sintesis, sedangkan discriminator menerima gambar asli dan gambar sintesis sebagai input dan menghitung keaslian gambar yang selanjutnya digunakan sebagai nilai loss dengan fungsi loss Binary Cross Entropy. Arsitektur DCGAN terdiri dari beberapa transposed convolutional layer dengan batch normalization dan fungsi aktivasi ReLU dan fungsi aktivasi Tanh sebagai output layer pada generator dan beberapa convolutional layer dengan batch normalization dan fungsi aktivasi Leaky ReLU dan fungsi aktivasi Sigmoid sebagai output layer pada …
Educational media development using guided discovery learning approach in chemical element subject
Authors: Ivenulut Rizki Diaz Renavitasari, Herman Tolle, Fitra A Bachtiar, Ahmad Afif Supianto
Journal: Journal of Information Technology and Computer Science
Year: 2023
Topics: Education; E-Learning
Abstract: A chemical element is one of the materials in high school for chemistry subject. Students have difficulties in studying chemical elements due to ample theories that must be memorized. In addition, conventional learning models that tend to be one-way create boredom feeling towards students. The use of interactive educational media can be a solution to the learning process. This study tries to provide a solution by developing mobile-based educational media combined with guided discovery learning models to help the learning process of chemical elements in high school. The use of appropriate educational media can be used as an independent learning tool. This study uses Research & Development (R&D) combined with SAM Model to develop this educational media. Testing is using an expert validation approach for the prototype of educational media. This study adapted the Computer System Usability Questionnaire (CSUQ) to assess an aspect of the usability of educational media. To assess the content of the material in this educational media, this study uses an evaluation questionnaire from the subject matter and media experts. The development process is carried out in three iterations, namely the preparation stage, iterative stage, and iterative development stage. The results of this study show that the assessment of chemical elements in educational media has a score of 86.8% on its system usability according to media experts and a score of 90.5% on educational media content according to material experts. So, this educational media is feasible to be applied to high school students. Overall, this educational media got a good score on the …
Classification Comparison of Activity Distraction Detection in Car Driving
Authors: Fitra A Bachtiar, Ferry Ardianto Rismawan, Erland Hilman Fuadi, Novanto Yudistira, Lailil Muflikhah
Year: 2022
Topics: Machine Learning; Automotive Safety
Abstract: Currently, development of safety in driving is advancing. Technology in safety in driving can be found in cars. A higher safety precautions is expected with lots of technology implemented in the car to minimize road accidents. However, most of the safety detection is built for the car and the external environment. Meanwhile, the human factor can also have determining role in safety driving. Safety driving to detect driver behavior is still not widely implemented. Therefore, a way is needed to be able to detect safe driving with the driver as the object. Furthermore, the development of an image-based safety driving system is a challenge so that the goal of driver safety can be achieved. Thus, there is a need to develop a system that could detect driver activity during driving. As an initial exploration, this study aims to compare four methods of machine learning and deep learning with the task of classifying activity distraction in …
Kansei Engineering Evaluation on Game Design Using Customer Reviews
Authors: Riza Setiawan Soetedjo, Fitra A Bachtiar
Year: 2022
Topics: Human-Computer Interaction; Kansei Engineering
Abstract: Video games are digital entertainment with several design elements that form the basic framework for making them and become one of the influences on player enjoyment. One of the media to assess the influence of their enjoyment is the video game’s review text because it contains customers’ impressions and emotions when playing the game. Kansei engineering is a product design methodology that can connect customer emotions and a product, which in this context is game design elements. Conventional Kansei engineering stages are taking customer surveys, analyzing the survey results, and getting relational rules between products and Kansei. However, the first stage requires time, effort, and money. Emotions from customers can be extracted through their review texts to overcome those problems. In this paper, we propose a method to determine the appropriate Kansei word for each game design element …
E-Course Information System Development for Web-Based Bisindo Learning
Authors: Muhammad Reihan, Fajar Pradana, Fitra Abdurrachman Bachtiar
Year: 2022
Topics: E-Learning; Information Systems
Abstract: Deaf are people with less than perfect hearing conditions so they are unable to hear clearly or unable to hear at all. According to data, there are 223,655 people who are deaf in Indonesia. To communicate, Deaf prefer to use Indonesian Sign Language (BISINDO). However, because BISINDO is not an official language, there is no official dictionary or guide for learning BISINDO. Making a web-based BISINDO learning system with E-course method can be the answer to this problem because it could enrich the media that can be chosen by Deaf and non-Deaf so that they can learn BISINDO without any barriers of time, distance or cost. This research begins by conducting a literature review. Then a needs analysis is carried out that adjusts to the needs analysis stage in the Waterfall method. The next stage is system design and implementation. The first step is to implement the database using MySQL, then implement …
Science Birds Gameplay With a Smile Interface to Promote the Spectator’s Emotion
Authors: Febri Abdullah, Mury F Dewantoro, Ruck Thawonmas, Fitra A Bachtiar
Year: 2022
Topics: Human-Computer Interaction; Gaming
Abstract: This demo paper presents a smile interface to promote the spectator’s emotion. Here, a smile interface is defined as a human-computer interaction system that detects the user’s smile. A past study suggested that watching Science Birds gameplay featuring Rube Goldberg Machine mechanisms with a domino effect can promote the spectator’s emotion. Science Birds is a clone version of Angry Birds for research purposes. Furthermore, other studies suggested that playing Science Birds with a smile interface has the benefit of promoting the player’s emotion. However, such benefit from the spectator’s perspective is yet to be investigated. The presented interface will be used in such investigation in the future.
A Study of Stressed Facial Recognition Based on Histogram Information
Authors: Barlian Henryranu Prasetio, Edita Rosana Widasari, Fitra Abdurrachman Bachtiar
Journal: Informatica
Year: 2022
Topics: Computer Vision; Image Processing
Abstract: Stress represents our subconscious emotions. The majority of the unconscious content is unacceptable or unpleasant such as pain, anxiety, or conflict. Most individuals do not realize that they are experiencing stress. Prolonged stressful experiences are likely to lead to health problems and affect one's facial appearance, specifically wrinkles shown in the face. This paper discussed the introduction of facial stress with histogram information. There are three stages in recognizing the stress pattern on the face of the registered image, feature extraction and classification. The registered image process takes three important parts of the face, ie the eyes, nose, and mouth. The feature extraction process was performed using the histogram method, ie Gabor filter and HOG feature. Each extracted feature was used as the model input to determine whether or not an individual is suffering from stress. Two classification methods were applied to learn stress patterns from the extracted feature. The classification process was performed using SVM with six kernel functions and a Tree algorithm with three numbers of split. Each model is trained using ten cross-fold validation strategies. The test results showed that the Gabor filter and HOG feature accuracy was 55% and 65%, respectively.
Desain Media Pembelajaran Dengan Model Problem-Based Learning Dan Gamifikasi Untuk Materi Bangun Ruang Tingkat Smk Berbasis Virtual Reality
Authors: Admi Rut Sinana, Herman Tolle, Fitra A Bachtiar
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Year: 2022
Topics: Educational Technology; Gamification
Abstract: Materi pembelajaran Bangun Ruang Matematika adalah salah satu materi dan pelajaran yang masuk ke dalam ujian Asesmen Kompetensi Minimum (AKM) pengganti Ujian Nasional (UN) pada Sekolah Menengah Kejuruan (SMK). Namun, Pelajaran Matematika menjadi salah satu pelajaran yang menjadi momok bagi kebanyakan siswa. Sehingga menjadikan siswa tertarik untuk mempelajari hal ini adalah suatu tantangan. Penelitian ini bertujuan untuk menggambarkan, menjelaskan dan menganalisis proses desain media pembelajaran dengan menggunakan pendekatan Human-Centered Design (HCD) yang digunakan dalam membangun aplikasi VR dengan menggunakan model PBL dan gamifikasi pada suatu Media Pembelajaran khususnya Bangun Ruang Matematika. Dalam penelitian ini terdapat salah satu model yang dapat diterapkan yaitu Problem-Based Learning. Problem-Based Learning merupakan model pembelajaran penyelesaian masalah, pembelajaran ini menjadikan masalah (problem) sebagai isu utamanya. Selain itu dalam penelitian ini juga digunakan model Gamifikasi (Gamification) yang merupakan pemanfaatan elemen-elemen di dalam game atau video game dengan tujuan menggugah, memotivasi para siswa dalam proses pembelajaran sehingga membantu membangun keterikatan (engagement) terhadap proses pembelajaran. Luaran yang dihasilkan dari penelitian ini adalah berupa desain model Problem Based Learning dan Gamifikasi dalam media pembelajaran materi Bangun Ruang tingkat SMK berbasis Virtual Reality yang bertujuan memotivasi siswa dalam pembelajaran sehingga membantu …
Review of technique and algorithm for educational data mining: trend and challenge in games design
Authors: Ulfatun Nadifa, Fitra Abdurracman Bachtiar, Ahmad Afif Supianto, Herman Tolle
Year: 2022
Topics: Educational Data Mining; Algorithms
Abstract: This study reviews techniques and algorithm models often used in the analysis of educational data mining. The review in this study is based on previous studies to provide researchers knowledge about trends and challenges analysis Educational data mining in game design meaningful. However, there is a lot of games design developed without analysis Educational data mining which then will not answer the student problem. The analysis needed periodic data and developing the game required actual student conditions, this is a combination inseparable. Determine Research questions, Search Terms, and filtering for the selection and analysis of the article review. There are some student problems on analysis review, namely prediction student performance, student behavior, student at-riks, and student dropout. The number of Articles in the study was 33 with 21 Articles of research and 12 of Article review. The number of studies 8 with percent 38% used techniques Confusion matric with 33% percent used algorithms Decision Tree in 7 of studies. The section in this study consists of techniques evaluation, model selection, outcome, subject, and algorithm method. Which are recommended techniques and algorithms for analysis Educational data mining and in ideal game design to further research.
Pelatihan Penggunaan Aplikasi Bimbingan Konseling bagi Guru SMA/SMK
Authors: Fajar Pradana, Fitra A Bachtiar, Denny Sagita Rusdianto, Achmad Arwan, Diva Fardiana Risa
Journal: DIMASLOKA: Jurnal Pengabdian Masyarakat Teknologi Informasi Dan Informatika
Year: 2022
Topics: Educational Technology; Teacher Training
Abstract: Bimbingan dan konseling (BK) adalah proses interaksi antara konselor dengan konseli baik secara langsung maupun tidak langsung dalam rangka untuk membantu konseli agar dapat mengembangkan potensi dirinya atau pun memecahkan permasalahan yang dialaminya. Selain itu, BK juga memiliki tugas untuk mengetahui kondisi psikologi dari anak didiknya. Dengan adanya BK, maka setiap murid bisa diobservasi dan juga dilakukan assessment lebih lanjut untuk mengetahui kondisi psikologis masing-masing muridnya. Banyak kasus dimana anak remaja mengalami kondisi stress yang dapat berdampak buruk. Terdapat 30,20% siswa SMA mengalami stres tingkat berat. Hal ini dikarenakan beberapa faktor diantaranya tekanan pada bidang akademik dan lingkungan lain, salah satunya adalah merasa tegang dan takut tidak lulus. Untuk saat ini, proses deteksi penyakit stres dapat dilakukan dengan berkonsultasi ke psikiater. Namun bagi siswa SMA yang memiliki permasalahan baik dibidang akademik maupun lainnya dapat melakukan konsultasi dengan guru Bimbingan Konseling (BK) yang terdapat di masing masing sekolah. SMAN 1 Pandaan dan SMA Maarif Pandaan merupakan mitra pengabdian yang memiliki permasalahan pada proses pembimbingan siswa yang dilakukan unit BK. Saat ini kegiatan konsultasi masih dilakukan dengan mencatat melalui logbook berupa buku catatan. Hal ini mengakibatkan sistem pencatatan yang tidak tertata dengan baik dan sulit untuk dilakukan monitoring kedepannya. Berdasarkan permasalahan tersebut, maka pada kegiatan pengabdian ini mengambil judul “Pelatihan Penggunaan Aplikasi …
Penerapan Antarmuka Adaptif Berbasis Perilaku Pemain pada E-Learning Bidang Pemrograman
Authors: Fajar Pradana, Fitra A Bachtiar, Retno Indah Rokhmawati
Journal: Jurnal Nasional Teknik Elektro dan Teknologi Informasi| Vol
Year: 2021
Topics: E-Learning; Adaptive Interfaces
Abstract: The pandemic has caused a significant impact on the educational sector’s implementation. The teaching and learning process that was previously carried out face-to-face now must be conducted online. Online learning utilizing e-learning is very beneficial for learners because it can be accessed online anytime, anywhere. Not only is it expected to be a medium to share material files that serve as learning supports, but e-learning is also required to replace the teachers’ or lecturers’ role in the classroom. In the teaching and learning process, it is essential to understand the students’ conditions and behaviors. This knowledge of students’ conditions and circumstances during the learning process can be used as resources for improving the quality and students’ learning process. Most of the existing e-learning has not been equipped with features to detect the students’ state while using the system. In this research, an adaptive interface was applied as a reflection of elearning that could adapt to the characteristics of user behavior. The performance testing results showed that the time required for the log data with 950 active users was 23, 35 ms. Meanwhile, based on the functionality test, the system succeeded in displaying the interface according to the cluster member.
Automatic Speech Recognition Bahasa Indonesia menggunakan Unidirectional Gated Recurrent Unit
Authors: Bayu Aji Firmansyah, Fitra Abdurrachman Bachtiar
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2021
Topics: Speech Recognition; Machine Learning
Abstract: Penelitian speech recognition dengan pendekatan Âend-to-end model berbasis bidirectional RNN masih memiliki kendala pada latency yang tinggi. Model speech recognition yang dibangun menggunakan end-to-endÂ model juga masih memiliki permasalahan pada kesalahan pengejaan. Selain itu, model speech recognition sangat sensitif terhadap dialek dan jenis alat perekam penutur. Penelitian ini akan meneliti perilaku jaringan dalam mempelajari fitur akustik berdasarkan gradient dan loss dengan penggunaan basis unidirectional GRU CTC yang mana memiliki cost computation yang lebih rendah dibandingkan basis bidirectional RNN CTC. Penelitian ini tidak menggunakan languge model dalam membantu model akustik dalam memetakan sinyal akusti. Dengan menggunakan data audio terjemahan Al-Quran dalam dialek dan berbahasa Indonesia, data diekstraksi menggunakan MFCC untuk mendapatkan fitur akustik. Batch Normalization juga digunakan pada jaringan GRU untuk menghindari covariate shift antar lapisan jaringan. Jaringan terdiri dari tiga layer jaringan MLP dengan fungsi aktivasi ReLU dan diteruskan dengan layer unidirectional GRU. Setelah melewati GRU, data akan diproses pada SLP dengan fungsi aktivasi softmax yang mana hasilnya akan menjadi masukan pada CTC. Jaringan dioptimasi menggunakan Adam optimizer dan menghasilkan 90.611% WER dari model terbaik yang diuji. Jaringan mengalami vanishing gradient dan mengakibatkan lambatnya proses belajar jaringan dalam merekognisi sinyal akustik. Penggunaan basis unidirectional GRU juga tidak memiliki signifikansi yang besar pada delay …
Feature Selection with Particle Swarm Optimization for Human Activity Recognition Using Learning Vector Quantization
Authors: Fitra A Bachtiar, Zainal Arifien, Adzanil Rachmadhi Putra, Pasca Imanuddin Akbar
Year: 2021
Topics: Machine Learning; Optimization
Abstract: As a social being, humans must do a variety of activities ranging from waking up in the morning to going back to sleep at night. Of the many activities, this research wants to find out how many activities humans do, for example how many times people walk, lie down, and so on in a day. To find out the number of these activities, this study uses Learning Vector Quantization (LVQ) method for recognizing human activities. This study uses 561 features from 1000 data to be used for the training process and 500 data to be used in the testing process. Data with a large number of features can affect the length of the calculation process. Because LVQ uses features as input data, we need a method that can reduce the number of features. One method that can be used is the Particle Swarm Optimization (PSO) method. The training process is carried out using the PSO selection feature with the parameters of the number of …
Klasifikasi Aktivitas Manusia Menggunakan Extreme Learning Machine dan Seleksi Fitur Information Gain
Authors: Fitra Bachtiar, Fajar Pradana, Issa Arwani
Journal: Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Year: 2021
Topics: Machine Learning; Feature Selection
Abstract: Human activity recognition has various benefits in daily lives. However, research in this area is still facing problems that is, unobtrusive data gathering, high dimensionality features, and the algorithm used to classify human activities. Those problems could impact in the result of the developed model. This research is a preliminary study in human activity recognition. Five common human activity will be recognized that is, walking, walking upstairs, walking downstairs, sitting, and standing. The dataset used in this study consist of 1500 data rows and 561 features. Feature selection is performed prior to the modeling step. Information Gain is used as the feature selection in which percentile method is used to subset the number of features in the dataset. The features are then normalized and will classified using ELM. Number of optimal hidden neuron will be searched to yield high predictive accuracy. The results show 240 feature subsets return the higher accuracy. A number of 100 hidden neuron results in highest predictive classification of human activity recognition. The classification results yield accuracy, precision, recall, and F1-score of 0.85.
Eyeball movement detection using sector division approach and extreme learning machine
Authors: Fitra A Bachtiar, Gusti Pangestu, Fajar Pradana, Issa Arwani, Dahnial Syauqy
Year: 2021
Topics: Computer Vision; Machine Learning
Abstract: Eyeball movement is being widely used for many purposes. A lot of research is trying to find the best approaches and methods to detect, track and recognize the movements. In this research, we propose an approach to detect the direction of eyeball movements using Sector Division Approach and Extreme Learning Machine (ELM). The extraction process of Sector Division is detecting facial image, detecting the eye location using subset points in the Facial Landmark. Selected eye location is segmented and through several processes such as image cropping, conversion into grayscale image, blurring process, and finally binary process. The final image in the binary process is divided into 9 (nine) sectors and extracted resulting in 9 feature vectors. ELM is used to classify the eyeball movement. The optimal number of hidden neurons identified first before the model is used in the testing step. A total of 50 data is used …
Application Of A Hybrid Method To Build A Mobile Device-based Event Recommendation System
Authors: Dio Saputra Kudori, Herman Tolle, Fitra A Bachtiar
Journal: Journal of Information Technology and Computer Science
Year: 2021
Topics: Recommendation Systems; Hybrid Methods
Abstract: In everyday life there are many events are held. These events use various ways in announcing the event for attracting people to participate come in the event. Because there are many events that are held in everyday life, an event recommendation system can be implemented to provide event recommendations that are appropriate for the user. In developing event recommendation systems, there are many methods that can be used, the one that frequently used is collaborative filtering. The event recommendation system has a unique character compared to other recommendation systems. This is because the event recommendation system does not use the classic scenario of a recommendation system. In this study we tried to use a hybrid method that combines collaborative filtering with sentiment analysis. The experiment show that the results of the event recommendations have an accuracy value of 82%. It shows that the hybrid method can be utilized for developing event recommendation systems.
Wound Classifications Of Breast Tissues with Electrical Impedance Spectroscopy (EIS): Comparison of LVQ and GA-LVQ
Authors: Yoke Kusuma Arbawa, R Adhitya Dwi Pisefty, Fitra A Bachtiar
Year: 2019
Topics: Medical Informatics; Machine Learning
Abstract: Deaths of people with breast diseases have reached 40% and are increasing every year. Breast Disease arises from wounds in breast tissue is breast cancer. Wounds of breast can be seen and discriminated the type of wound using Electrical Impedance Spectroscopy(EIS). The results of this EIS data can be used as an initial diagnosis before the doctor carries out treatment. This research tried to classify the types of wounds from breast tissue by using the Learning Vector Quantization (LVQ) method with the Genetic Algorithm (GA) to find out whether the solution provides the expected optimum results. The dataset we used for this study was very small which did not represent the existing class, with 106 data for 6 classes. The results of the comparison between LVQ and GA-LVQ obtained LVQ added by GA has higher accuracy values than LVQ with 73% accuracy and compared to LVQ, GA increased accuracy by ± 50%.
Effect of Angry Birds-like Live Streaming on Working Memory
Authors: Febri Abdullah, Changeun Yang, Pujana Paliyawan, Ruck Thawonmas, Tomohiro Harada, Fitra A Bachtiar
Year: 2019
Topics: Psychology; Gaming
Abstract: This paper investigates whether and to which degree watching Angry Birds-like live streaming could affect the spectator’s working memory (WM). We prepared Angry Birds-like gameplay on the Twitch streaming platform in which a domino effect based on Rube Goldberg Machine (RGM) mechanism is featured in each generated game level. The spectators’ working memory are measured using the N-back task, and our results show a possibility that watching the proposed live streaming can increase the WM performance.
Penilaian Penerimaan Teknologi E-Learning Pemrograman berbasis Gamification dengan Metode Technology Acceptance Model (TAM)
Authors: ACCEPTANCE MODEL TAM
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Year: 2019
Topics: E-Learning; Gamification
Abstract: E-learning telah berkembang tidak hanya sekedar berbagi materi pembelajaran melalui internet, namun telah berevolusi menjadi tempat untuk kolaborasi, sosialisasi, project based learning, reflective practise, dan pembelajaran dengan simulasi. Untuk mewujudkan hal tersebut telah dikembangkan sebuah sistem e-learning gamification khusus bidang pemrograman. Penelitian ini menerapkan Technology Acceptance Model (TAM) untuk memeriksa faktor-faktor apa saja yang berpengaruh terhadap penerimaan sebuah teknologi baru yang diperkenalkan atau digunakan. Faktor-faktor yang digunakan dalam penelitian ini adalah Perceived Usefullness
Management of Virtual Reality Application Usage: A Comparison of Usability and Immersiveness through Controller-Based and Hand Gesture Interaction Methods
Authors: Bianca Pingkan Nevista, Herman Tolle, Fitra A Bachtiar
Journal: Journal of Management Research and Studies
Year: 2025
Topics: Virtual Reality; Human-Computer Interaction
Abstract: Natural User Interface (NUI) is a latest development in the world of human-computer interaction can applied to immersive digital technology and realized in virtual reality. This virtual reality (VR) is usually accessed using a VR headset, so there are several companies that develop and improve VR headsets to facilitate interaction between humans and computers. One of the VR headsets available for beginners and professionals is the Meta Quest 2 which can be used with two interaction methods, namely, controllers and hand gesture. This study was conducted to compare the usability and immersivity of the two interaction methods on the Meta Quest 2 VR headset. This test was conducted at a Vocational High School in Malang, one of the city in Jawa Timur, in the Visual Communication Design Departement who had never used a VR headset before. There are two methods used, namely, Usability Testing to measure usability and the Igroup Presence Questionnaire (IPQ) method is used to measure immersivity. The applications used in this study are the Waltz of The Wizard Game, The Walking Dead Game: Saints & Sinners and Virtual Reality-Based Applications as Educational Media for Cultural Heritage Buildings in Malang City. The results of the usability and immersiveness tests show that in the Waltz of The Wizard game, it gets a high coefficient value on hand gestures, which is 0.136 and 0.096 on the controller. While in the game The Walking Dead: Saints & Sinners, it gets a high coefficient value on the controller, which is 0.398 and 0.171 on hand gestures. And in the Cultural Heritage Application, it gets a high coefficient value on hand …
The Exploration of Student Emotion Experience and Learning Experience in E-learning Platform
Authors: Fitra Bachtiar, Riza Setiawan Soetedjo, Joseph Ananda Sugihdarma, Retno Indah Rokhmawati, Lailil Muflikhah
Journal: Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Year: 2024
Topics: E-learning; Educational Technology
Abstract: Previous studies have shown that emotion is crucial in student learning. However, most studies in the e-learning environment have yet to consider emotion as part of learning that could lead to successful learning. Thus, this study explored the relationship between student emotion state, emotion sequences, and student learning experience. A preliminary data collection was conducted to explore the relationship between emotional experience and student learning experience, which involved 16 students. Students were asked to learn a programming subject in an e-learning environment. E-learning is designed to store the students' emotional experience and activity during learning. The sequential pattern mining technique was used to extract the data, exploratory data analysis was conducted to visualize the emotional trajectory during the learning process, and regression analysis was used to explain the relationship between students' emotional learning experiences. The results showed that emotional experience might affect student experience in learning. In one-sequence emotion, all emotion states contributed to the learning experience with p-values< 0.01 except for neutral and disgust with p-values< 0.05. The one-sequence emotion model shows R-squared= 0.585; Adj. R-squared= 0.734; F-statistic= 6.920; Prob (F-statistic)= 0.00702. Meanwhile, in two-sequence emotion, none of the emotion sequences contributed to the student learning experience. Lastly, three-sequence emotion models also showed that most sequences did not influence student learning experience. The only sequence of emotions that influenced the student learning …
Redesain Aplikasi Mobile Supervisi Observasi Kelas untuk Meningkatkan Tingkat Usability
Authors: Muhammad Ifa Amrillah, Retno Indah Rokhmawati, Fitra Abdurrachman Bachtiar
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer
Year: 2024
Topics: Mobile Applications; Usability
Abstract: Aplikasi LESON, yang kini berganti nama menjadi EDVISOR merupakan aplikasi berbasis mobile yang dikhususkan untuk merekam suasana pembelajaran dan mendukung kegiatan supervisi observasi kelas. Meskipun telah mengalami pembaharuan, pada penelitian sebelumnya mengungkapkan masih terdapat 24 permasalahan pada aplikasi yang perlu perbaikan dan pengembangan lebih lanjut. Metode Usability Testing dipilih untuk dapat mengidentifikasi permasalahan secara lebih mendalam dan memahami pengalaman pengguna dalam menggunakan aplikasi. Penelitian ini melibatkan 10 Mahasiswa dan 3 Dosen yang diberikan tugas khusus untuk menguji aplikasi. Pengujian dilakukan sebanyak 2 kali, yaitu sebelum dan setelah dilakukan perbaikan desain. Hasil observasi dan wawancara pada pengujian awal mengungkapkan adanya 42 permasalahan baru, yang kemudian menjadi dasar rekomendasi perbaikan desain. Perbaikan desain menghasilkan prototype yang mendekati hasil akhir dari aplikasi. Setelah perbaikan desain, prototype diuji kembali kepada pengguna yang sama dengan pengujian awal. Hasil pengujian menunjukkan peningkatan usability aplikasi, dengan peningkatan completion rate sebesar 13,19%, peningkatan efficiency sebesar 0,016 goals/sec, dan peningkatan satisfaction sebesar 1,21. Abstract The LESON application, now renamed EDVISOR, is a mobile-based application devoted to recording the learning atmosphere and supporting classroom observation supervision activities. Although it has undergone updates, previous research revealed that there are still 24 problems in the application that …