Lab-IS/filkom-research-papers huggingface dataset:
Learning Analytics on YouTube Video Learning Content Using Clustering Method
Authors: Fitra Abdurrachman Bachtiar, Retno Indah Rokhmawati, Farhanna Mar’i, Jenita Ekka Istyadi, Luvena Cornelia, Mentari TF Tarigan
Year: 2024
Topics: Learning Analytics; Clustering
Abstract: Learning resources are one of the important things that support student learning. Learning resources could be used as an amplification outside the class to the learning material delivered by the teacher. Learning videos are often used as additional learning resources However, students may have difficulties choosing learning resources since various learning resources are available. In addition, not all of the learning resources are beneficial to the students. In addition, learning resources must be directed to ensure students learn the right material and support the student’s learning success. Therefore, there is a need to understand what video would be beneficial to student learning. This study proposes an analysis of comments in a learning video to understand the student impression of the learning material. The comments are obtained using the scrapping method. Basic preprocessing is performed such as case folding …
ANALYZING EEG SIGNALS FOR STRESS DETECTION USING RANDOM FOREST ALGORITHM
Authors: Fi Imanur Sifaunnufus Ms, Fitra Abdurrachman Bachtiar, Barlian Henryranu Prasetio
Journal: Jurnal Neutrino: Jurnal Fisika dan Aplikasinya
Year: 2024
Topics: Machine Learning; Stress Detection
Abstract: Detection of stress using EEG signals has gained much interest because of monitoring and early intervention. As for the contribution of this research, a reliable method for stress identification has been suggested, using a random forest model to categorize stress levels from EEG signals. Data were filtered using a bandpass filter, Independent Component Analysis, and more so using the Z-score to remove outliers and poor signals. Data that has been cleaned from noise and outliers will go through a feature extraction process using Power Spectral Density (PSD). The result of PSD is the power of each frequency of the EEG signal. The number of features used is 20. Random Forest was chosen due to its high accuracy and robustness in handling complex, high-dimensional data, which is common in EEG analysis. Thus, the model obtained an accuracy level of 0.8571, thereby approving the tool’s efficiency in distinguishing between different degrees of stress. The computational efficiency of the model, with a classification time of 0.2762 seconds, demonstrates its feasibility for practical applications. Based on these findings, it can be concluded that the Random Forest algorithm can be used to integrate wearable technology and for offering suggestions and timely interventions for better mental health.
Harnessing Residual Attention Networks for Stress Level Classification Using EEG Spectrograms
Authors: Sza Sza Amulya Larasati, Fitra Abdurrachman Bachtiar
Year: 2024
Topics: Deep Learning; Stress Detection
Abstract: Stress detection is becoming one of the important applications in neuroscience and mental health. Conventional measurement of stress levels is often done by self-reported methods such as through filling out questionnaires, which rely heavily on individual perceptions and tend to be subjective because they are influenced by personal biases. To overcome this problem, a more objective approach is proposed by analyzing Electroencephalogram (EEG) signals that capture brain activity. Increasing detection accuracy is now supported by many developments in machine learning. This study focuses on stress level classification using spectrogram images from EEG signals. The main objective of this study is to develop a detection model that can identify stress levels based on EEG data. In this study, EEG signals were processed through several steps, including segmentation, filtering, and conversion into spectrograms …
Stress Detection Using EEG Signals: A SVM-Based Classification and Feature Selection Approach
Authors: Fi Imanur Sifaunnufus, Fitra Abdurrachman Bachtiar, Barlian Henryranu Prasetio
Year: 2024
Topics: Machine Learning; Stress Detection
Abstract: Stress is a problem with various adverse effects, such as mental disorders and suicide. With these problems arising, a stress detection system is needed as an initial examination of the level of stress experienced. Generally, stress detection is done with a stress test at a hospital. However, medical examinations done in the hospital require high costs. Another alternative to examine stress levels is by using a biosignal electroencephalogram (EEG), which provides more objective and accurate results. In addition, there is a need to develop a portable device that can be used to detect stress. This study tries to detect stress levels using EEG Signals. The data used in this study is primary data that simulates several stress-level activities. In total, 55 subjects participated in this stress detection research. The received signal goes through preprocessing to clean it from noise and artifacts. The data will be extracted using Power …
Comparison of Q&A Classification Using BiLSTM Based on Closed Domain Knowledge
Authors: Alifi Lazuardi Gunawan, Fitra Abdurrachman Bachtiar, Budi Darma Setiawan
Year: 2024
Topics: Natural Language Processing; Deep Learning
Abstract: The Faculty of Computer Science, Universitas Brawijaya (Filkom UB) is committed to providing quality services for the users especially internal and external stakeholders, one of which is through the HaloFilkom service. HaloFilkom services have limitations in terms of time. HaloFilkom services are not available 24 hours due to limited working hours. Questions asked by users are not answered directly. This weakness in the HaloFilkom system can be overcome by using a chatbot system. Chatbot is an interactive system that works with natural human language and can work 24 hours. Thus, the current study explores the basic chatbot model by classifying the Q&A in the closed domain knowledge. The dataset in this research is in the form of pairs of questions and answers regarding various topics at the Filkom UB. The knowledge is preprocessed using text preprocessing which includes case folding, tokenization …
Engagement Level Detection Using Facial Extraction and Multi-Stacked Convolutional Neural Network in e-Learning Settings
Authors: Fitra Abdurrachman Bachtiar, Al'Ravie Mutiar Mahesa, Eric Wallace Cooper
Year: 2024
Topics: Computer Vision; Deep Learning
Abstract: Engagement in learning is crucial in maintaining student eagerness as fuel for student learning to achieve learning success. In e-Learning settings, engagement also plays an im-portant role as a factor influencing student learning success as students are regulating themselves during learning one-on-one using computer devices. Failing to detect student engagement may result in unsuccessful learning. In extreme cases, students may disengage and learning purpose cannot be achieved. This research aims to detect user engagement in e-Learning settings by using a deep learning approach with a feature extraction method. The dataset used in this study is the DAiSEE dataset. The dataset is in the form of videos and is extracted into frames and specific facial features. The facial area was extracted using the Mediapipe Library. The features include 1434 3D landmark features, 52 action unit features, one depth …
Facial Emotion Analysis for Evaluating the Suitability of Online Learning Modules: A Case Study on a MERN-Based Platform
Authors: Utara Setya Wardaya, Fitra Abdurrachman Bachtiar, Fajar Pradana, Daniel Moritz Marutschke
Year: 2024
Topics: Computer Vision; E-learning
Abstract: Learning is a process that involves the progressive transfer of knowledge, encompassing both the physical and psychological components of the learner. Emotions play a crucial role in the success of learning, yet they are often overlooked in the context of online education. Facial emotion detection technology and emotion analysis offer a solution for effectively recognizing and accommodating students' emotions in digital learning environments. The use of a Learning Management System (LMS) based on the MERN architecture (MongoDB, Express.js, React.js, Node.js) is also critical in supporting the flexibility and responsiveness of online learning platforms. This research employed the Agile model, which was systematically planned and organized. The Agile model is an approach that allows for continuous adjustments during development. The aim of this study is to integrate artificial intelligence into an e-learning …
A Conceptual Framework for Multidisciplinary Data Science Education Based on IoT Projects
Authors: Daniel Moritz Marutschke, Atanas Kirjakovski, Fitra A Bachtiar, Valentinus Roby Hananto, Mate Kovacs, Patricia Brockmann, Victor V Kryssanov
Year: 2024
Topics: Data Science; Internet of Things
Abstract: The necessity for professionals to make predictions using digital data has been recognized for many decades. With its roots in statistical analysis and later implemented as data analytics, Data Science emerged as a widely accepted discipline. The data science life cycle begins by setting a goal and ends with reporting/reflecting on the gained results. It incorporates known analytical techniques, including modern artificial intelligence and machine learning techniques. While often considered an independent discipline, both engineering and liberal arts colleges have recognized the need for an understanding of data science in the general workforce. The literature still suggests a disconnect between the study fields and how the Internet of Things could be leveraged in Project-Based Learning (PBL) style data science education. In this paper, the authors propose a conceptual framework to teach multidisciplinary data …
Comparative Study of Facial Expression Recognition Using Facial Landmark in e-Learning System
Authors: Fitra Abdurrachman Bachtiar, Cesilia Natasya Nainggolan, Riza Setiawan Soetedjo, Retno Indah Rokhmawati, Lailil Muflikhah
Year: 2024
Topics: Computer Vision; E-learning
Abstract: The e-Iearning system or distance learning system has become important, especially during the COVID-19 pan-demic. Several tertiary institutions have made the e-Iearning system an alternative teaching and learning activity. Research on the use of e-Iearning needs to be explored to obtain innovative, creative, and fun learning. One aspect that can be examined in e-learning is students' emotional response to the learning activities. The emotional state of students directly or indirectly affects the learning process. The student's emotional responses can be measured by their facial expressions. In this study, an exploration was carried out on the facial expressions shown by students in HSS Learning, an e-Iearning for learning web programming. The student's facial expressions were extracted using 68 pairs of facial landmarks and additional 6 facial feature vectors. The total number of features used is 142. Machine …
Facial Expression Classification Using Extreme Learning Machine and Genetic Algorithm
Authors: Fitra Abdurrachman Bachtiar, Mufidatun Nuha
Year: 2024
Topics: Machine Learning; Optimization
Abstract: Feature extraction is a critical component of facial expression recognition, requiring careful selection to ensure optimal performance. Traditional template-based models often struggle with computational efficiency and are sensitive to factors such as illumination, color variation, and image quality. Although geometry-based models have demonstrated robustness in feature extraction, further optimization remains needed to enhance the recognition accuracy. In this study, the CK+ dataset is utilized, and employ facial landmarks as a geometry-based feature extraction method. The extracted features are then processed using an Extreme Learning Machine combined with Genetic Algorithms (ELM-GA) to select a subset of features and classify facial expressions. The proposed model effectively reduces the number of features used in the classification model by more than half, improving computational efficiency. Additionally …
Deteksi Pertanyaan Tidak Tulus pada Quora dengan Metode Bidirectional Long Short Term Memory (LSTM) dan Self-Attention
Authors: Siska Novianti, Novanto Yudistira, Fitra Abdurrachman Bachtiar
Journal: Jurnal Teknologi dan Sistem Komputer
Year: 2024
Topics: Natural Language Processing; Deep Learning
Abstract: Quora adalah salah satu situs tanya jawab terbesar dengan ribuan pertanyaan setiap harinya. Berbagai macam topik pertanyaan dan jawaban disampaikan pengguna untuk bertukar informasi. Demi memberikan kenyamanan pengguna, Quora ingin mendeteksi pertanyaan-pertanyaan yang tidak tulus yang muncul pada platform mereka. Pertanyaan tidak tulus tersebut adalah pertanyaan yang secara sengaja disampaikan pengguna dimana sebenarnya pertanyaan tersebut tidak membutuhkan jawaban, namun hanya untuk membuat sebuah pernyataan yang memprovokasi pengguna lain. Pada penelitian ini diusulkan metode Bidirectional LSTM dan Self Attention untuk membuat model terbaik yang dapat memprediksi pertanyaan-pertanyaan di Quora. Pada akhir pelatihan, didapatkan model terbaik dengan nilai akurasi sebesar 0, 9668, nilai pressisi sebesar 0, 9880, nilai sensitifitas (recall) sebesar 0, 9769 …
PGV: An Edge Operator Based Random Point Localization Algorithm for Eye Tracking System
Authors: Hamdani Al-Ikhsan, Fitra Abdurrachman Bachtiar
Year: 2024
Topics: Computer Vision; Algorithms
Abstract: Eye tracking is an important variable and offers benefits in Human-computer interaction (HCI) which is often complex and impractical in practice. Eye tracking implementation usually requires specialized devices, technology, or setup to work. On top of that, challenges like face obstructions like glasses or masks, differences in lighting, distance, and angles add another layer to the implementation difficulty. A novel random point detector algorithm called Pixel Gradient Value (PGV) is proposed which is an interest point detector-like algorithm to be used for eye-tracking purposes, which works by calculating the gradient value of a pixel in a given neighborhood by using an edge detection operator like Sobel edge detector and Canny edge detector as its basis. PGV is image-agnostic, it is capable of covering a wide amount of eye-tracking scenarios while running on a minimum setup. PGV is then used in a series of Fully …
Klasifikasi Aktivitas Manusia Menggunakan Metode Long Short-Term Memory
Authors: Latansa Nurry Izza Afida, Fitra Abdurrachman Bachtiar, Imam Cholissodin
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer
Year: 2024
Topics: Deep Learning; Long Short-Term Memory
Abstract: Klasifikasi aktivitas manusia merupakan salah satu topik penelitian yang penting karena dapat diterapkan pada berbagai bidang. Penelitian mengenai klasifikasi aktivitas manusia sebelumnya telah banyak dikembangkan dengan menerapkan dataset public HAR Repository yang telah tersedia. Namun dataset tersebut memiliki hasil dari ekstraksi fitur keluaran nilai sensor memiliki dimensi yang tinggi. Tingginya dimensi fitur dapat menyebabkan penurunan akurasi, untuk itu pada penelitian ini diusulkan penerapan dataset primer tanpa ekstraksi fitur. Selain tingginya dimensi, pada penelitian sebelumnya, banyaknya jumlah label dengan menerapkan machine learning tradisional tidak mampu melebihi akurasi 88%. Sehingga pada penelitian ini menerapkan dataset primer dengan menggunakan label kelas sebanyak 16 sehingga diusulkan metode deep learning Long Short Term Memory (LSTM). Proses penelitian dimulai dari pengambilan data, preprocessing data, modelling dan perbandingan algoritma deep learning LSTM dan machine learning KNN. Berdasarkan hasil pengujian perbandingan kedua algoritma tersebut dengan implementasi dataset yang sama, algoritma terbaik yaitu LSTM dengan nilai akurasi sebesar 0.94 dan KNN dengan nilai akurasi sebesar 0.71.
Evaluation of Usability in LetsCode Digital Learning Media Using the Usability Testing Method and Use Questionnaire
Authors: Rahajeng Mufti Nadifa, Fajar Pradana, Fitra Abdurrachman Bachtiar
Journal: Journal of Information Technology and Computer Science
Year: 2024
Topics: Usability Testing; Human-Computer Interaction
Abstract: Basic Programming is one of the compulsory courses at the lecture level in the field of computer science. The problem that is often encountered in these courses is the low understanding of students towards the programming material provided. Digital learning media, namely the LetsCode website, was developed to become a learning support medium and increase students' understanding of basic programming. To find out how far learning media can meet user needs, a usability test is carried out to determine the usability level of the system. The research was conducted using the usability testing method using a Use Questionnaire for LetsCode website users. The criteria for the test respondents were students of the Information Technology Education Study Program, Brawijaya University, with a total of 30 respondents. From the results of the questionnaire test using the F test, it shows that the variables of expediency, ease of use, and ease of learning have a simultaneous effect on satisfaction. For the feasibility of getting a result of 88.43% of the overall usability aspect which shows the LetsCode website is in the very feasible category. The research was limited to the analysis of questionnaire data and did not enter the system development stage.
The Development of Augmented Reality to Support Geomorphology Learning In Observing Landforms
Authors: Fachrizal Azzuri, Lazuardy Syahrul Darfiansa, Fitra Abdurrachman Bachtiar, Herman Tolle
Year: 2024
Topics: Augmented Reality; Educational Technology
Abstract: The application of technology is very important in the world of education, especially in the field of geography, which studies geomorphology and the shape of the earth. This is influenced by the limited number of teaching aids and the breadth of analytical understanding. Therefore, this research aims to develop and analyze Augmented Reality-based mobile learning for learning landforms to support learning in the Department of Land, Faculty of Agriculture, Brawijaya University. This research used a quantitative approach, and the participants were 18 students. Model design and development use the ADDIE method with the following stages: Analysis will be carried out to identify problems and research needs, Design will design the map layout as a marker, create 2D and 3D object model assets, Development includes all steps and elements which is planned to be implemented to produce the final product …
Classification Driver Emotion with Deep Learning Method for Driver Safety Detection
Authors: Kurniawan Indra Jaya, Fitra Abdurrachman Bachtiar
Year: 2023
Topics: Deep Learning; Computer Vision
Abstract: The main thing that car drivers care about is driving safety. At present, deaths and material losses due to car driving accidents commonly happen. According to several studies, human emotions also influence the level of risky driver behavior, for example, angry emotions show more frequent risky driving behavior more often. Based on this problem, researchers propose the classification of driver’s emotions using deep learning methods. By using deep learning methods, drivers can understand their emotions and help to obtain information of driver’s emotion state. The emotion detection is usually used for driving safety systems as one of the module to detect human factors in driving. In this study, KMU-FED dataset used for the emotion classification process. In addition, preprocessing step is conducted to prepare the dataset. The dataset image is resized to . Two deep learning model used for driver emotion …
Distraction Detection in Driving Using Pose Extraction and Machine Learning Techniques
Authors: Fitra Abdurrachman Bachtiar, Issa Arwani, Ridwan Aji Budi Prasetyo, Gusti Pangestu, Riza Setiawan Soetedjo, Al Ravíe Muthiar Mahesa
Year: 2023
Topics: Machine Learning; Computer Vision
Abstract: There are a lot of technologies have been implemented in the modern cars these days, ranging from preventive braking system to driving assistance. It is believe that equipping car with modern technology would enhance safety. However, there are other aspects need to be considered in safety driving, that is human factor. Human factors are believed have high contribution in road accidents. Human factors such as emotional control and distraction may leads to dangerous driving behaviours. This study investigates the driver pose distraction. The distracted driver pose detected through pose detection using landmark feature extraction. The dataset used in this study is State Farm Distracted Driving Detection which is a secondary dataset. The extracted driver pose are in numeric form and used as the input for the classification model to detect ten kinds of driving distraction ranging from safety driving to talking to …
Engagement Classification in E-Learning Environments Through Eye Features Using Scaled Dot-Product Attention Convolutional Neural Network
Authors: Joseph A Sugihdharma, Fitra A Bachtiar
Year: 2023
Topics: Deep Learning; Computer Vision
Abstract: Engagement can be defined as how individuals are involved and interact with a task that requires their attention and emotional state. Analyzing a student’s emotional reactions to four affective states, namely boredom, engagement, confusion, and frustration in e-learning environment can identify their learning behavior. By measuring student engagement and implementing the right prevention, such as rearranging the learning design or building an effective feedback system, students’ learning intake can be increased. A person’s actions and intentions can be identified using eye movement without knowing what the person is looking at. Therefore, this study utilized eye landmarks and gaze features extracted with OpenFace as the input to a Convolutional Neural Network (CNN)-based classifier that enhances the state-of-the-art model with the Scaled Dot-Product Attention mechanism to capture important part of the …
Mutual Information for Learning Context Representation on RNN-Attention Based Models in Open Domain Generative Chatbot
Authors: Alfirsa Damasyifa Fauzulhaq, Fitra Abdurrachman Bachtiar
Year: 2023
Topics: Deep Learning; Natural Language Processing
Abstract: Chatbot is an example of the application of Artificial Intelligence that can receive and answer questions automatically. Chatbots are widely used in various fields such as health, customer service, entertainment, education and others. There are two approaches to chatbot development, rule-based and generative. Rule-based chatbot has the advantage of being easy to develop and produces good answers but requires predefined rules that are defined manually. Generative chatbot can provide dynamic and natural answers and does not require predefined rules. However, the drawback of generative chatbot lies in the weak representation of sentence information and information bottleneck which results in loss of information or context. The main objective of this research is to get the best model for open domain generative chatbot in a predefined scenario and improve the performance of the model in terms of word …
Comparative Analysis of Deep Learning and Machine Learning Techniques for Question Classification in Bloom's Taxonomy
Authors: Lazuardy Syahrul Darfiansa, Fachrizal Azzuri, Fitra Abdurrachman Bachtiar, Dian Eka Ratnawati
Year: 2023
Topics: Deep Learning; Machine Learning
Abstract: In the current digital era, automation of question classification based on cognitive level has become important, especially with the increasing adoption of digital learning methods. This study investigated and compared the capabilities of various Deep Learning (DL) and Machine Learning (ML) techniques in question classification according to Bloom's Taxonomy. The four models considered in this analysis include Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Random Forest (RF), and K-Nearest Neighbor (KNN). The research methodology involves developing and training each model with a data set of questions pre-categorized based on Bloom's Taxonomy levels. After the training phase, each model is evaluated based on its accuracy in classifying questions into appropriate categories. The results show that the LSTM model provides the best performance with an accuracy of 83%, followed …
Comparative Analysis of Term Weighting Methods for Question Classification in Bloom Taxonomy Using Machine Learning Approach
Authors: Lazuardy Syahrul Darfiansa, Fitra Abdurrachman Bachtiar
Year: 2023
Topics: Machine Learning; Natural Language Processing
Abstract: In higher education institutions, examinations are crucial in assessing students' cognitive levels. Educators manually categorize exam questions according to Bloom's taxonomy cognitive domain. Recognizing educators' challenges, previous research has explored automated question classification using machine learning methods. Feature selection, feature extraction, and term weighting are common strategies to enhance classification accuracy. Unsupervised term weighting methods like TF and TF-IDF have been widely used among these. This paper aims to delve into the effectiveness of TF-IDF, Bag-of-Words (BoW), and Binary term weighting techniques. Three distinct classifiers were used to scrutinize these techniques: Support Vector Machine (SVM), Naïve Bayes (NB), and Random Forest (RF). The research seeks to identify the most suitable term weighting technique and classifier for automating question …
Modeling User Profile in Gamification Based e-Learning Using k-Means Clustering
Authors: Fitra Abdurrachman Bachtiar, Fajar Pradana, Bayu Priyambadha
Year: 2023
Topics: Machine Learning; Clustering
Abstract: The current development of e-Learning is still in the form of simple e-Learning as a place to share materials and do the exercises given by lecturers. This should be a problem since such e-Learning activities are not different from conventional learning and do not facilitate a personalized learning process. Personalized learning is important to address students’ different comprehension abilities in the learning process. Personalization can be done by characterizing student profiles. Therefore, we need a system to determine profiles by modeling user profiles in e-Learning. In this study, personalization was done by recognizing the users’ characteristics obtained from internal factors including personality, achievement emotion, and student activity logs when engaging in the e-Learning process. Student activity logs were automatically recorded by the system. To find out the profile of existing user, grouping was done using …
Classification of Mobile Usage Car Driving Activities Using Convolutional Neural Network
Authors: Ferry Ardianto Rismawan, Fitra Abdurrachman Bachtiar
Year: 2022
Topics: Deep Learning; Computer Vision
Abstract: A traffic accident is an incident on the road that is unexpected and unintentional involving a vehicle with or without other road users that results in human casualties and loss of property value loss. The lack of awareness of the driver is a critical behavior in driving safety to deal with all possibilities that can occur while driving to maintain the safety of the driver and passengers. Several factors cause factors causing traffic accidents in Indonesia. The human factor is the most significant, with one example being using cell phones while driving. Previously, research on the introduction of human activities was conducted using several methods and approaches, but there are still several issues, such as a high false detection rate, inefficient architecture, and unsuitable data and feature use. As a result, the Convolutional Neural Network (CNN) algorithm was used in this study to classify human activities in images of car drivers …
Comparing and Analysis of Geospatial Interpolation Prediction Algorithm: Case Study The Quality of Education of Malang and Batu City, Indonesia
Authors: Erik Yohan Kartiko, Fatwa Ramdani, Fitra Abdurrachman Bachtiar
Journal: Journal of Information Technology and Computer Science
Year: 2022
Topics: Geospatial Analysis; Data Interpolation
Abstract: The number of schools in Indonesia continues to grow. This must also be balanced with improving the quality of education in accordance with the objectives of the 4 SDGs, which as a whole are to improve the quality of education that is inclusive, equitable and provides lifelong learning opportunities. However, until now it is very difficult to determine differences in the quality of education in an area. From the problem of education quality and education equity, it is necessary to have a regional analysis of the quality of education. This analysis can be performed using various geospatial interpolation methods. Geospatial Interpolation is a technique to find the value of a missing variable in a known data range in an area. The data used for the Geospatial interpolation process in this study are School Quality data taken through research questionnaires, as well as school accreditation data at the junior high school level. The geospatial interpolation method used in this study is the Inverse Distance Weighted, Spline, Kriging and Natural Neighbor methods. The use of different interpolation methods can indicate the best method for this research case study. Measurement validation results from each geospatial interpolation method using RMSE. From the results of this accuracy validation, the most accurate method will be obtained in determining the quality of education contained in an area.
Penerapan Algoritme Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance (LMKHNCN) Untuk Klasifikasi Hasil Kinerja Pegawai Negeri Sipil
Authors: Adam Syarif Hidayatullah, Fitra Abdurrachman Bachtiar, Imam Cholissodin
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer
Year: 2021
Topics: Machine Learning; Clustering
Abstract: Keberhasilan sebuah perusahaan terjadi karena dapat mengelola sumber daya manusianya dengan baik begitu juga sebaliknya. Salah satu instansi yang mengelola sumber daya manusia menggunakan Manajemen Talenta adalah Badan Kepegawaian Daerah (BKD) kota Malang, dengan mengevaluasi pegawainya setiap tahunnya setelah pekerjaan selesai dilakukan. Hal ini menyebabkan hasil pekerjaan yang telah dilakukan tidak optimal, sehingga perlu identifikasi dini pegawai yang memiliki kinerja dibawah rata – rata sehingga dapat dievaluasi dan meminimalisir hasil pekerjaan yang tidak optimal dengan menggunakan teknik klasifikasi. Penelitian ini menggunakan teknik klasifikasi Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance (LMKHNCN). Metode ini merupakan metode modifikasi dari metode K-Nearest Neighbor (KNN) dan dibuktikan memiliki …
Detection and Classification of Embung Land Cover using Support Vector Machine
Authors: Ahmad Syarif Hidayat, Fatwa Ramdani, Fitra Bachtiar
Year: 2021
Topics: Machine Learning; Remote Sensing
Abstract: The agricultural sector is the mainstay sector in the economy of Malang Regency. However, Malang Regency has experienced a decrease in rice harvested area caused by drought. One of the Government's efforts to overcome this is by carrying out embung for agriculture. The use of remote sensing technology is one of the practical tools to monitor the phenomenon of change that occurs continuously and in a large area, in this case, the reservoir. This study aims to determine and analyze the use of SVM classification in satellite imagery to detect embung in Malang Regency. This research uses PlanetScope satellite imagery and Support Vector Machine (SVM) to classify land cover types. This research consists of three main tasks: satellite image preprocessing, satellite image classification, and land cover detection. The results showed that the increase in the number of sample areas in the SVM algorithm impacted …
BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews
Authors: Kuncahyo Setyo Nugroho, Anantha Yullian Sukmadewa, Haftittah Wuswilahaken DW, Fitra Abdurrachman Bachtiar, Novanto Yudistira
Journal: arXiv e-prints
Year: 2021
Topics: Natural Language Processing; Deep Learning
Abstract: User reviews have an essential role in the success of the developed mobile apps. User reviews in the textual form are unstructured data, creating a very high complexity when processed for sentiment analysis. Previous approaches that have been used often ignore the context of reviews. In addition, the relatively small data makes the model overfitting. A new approach, BERT, has been introduced as a transfer learning model with a pre-trained model that has previously been trained to have a better context representation. This study examines the effectiveness of fine-tuning BERT for sentiment analysis using two different pre-trained models. Besides the multilingual pre-trained model, we use the pre-trained model that only has been trained in Indonesian. The dataset used is Indonesian user reviews of the ten best apps in 2020 in Google Play sites. We also perform hyper-parameter tuning to find the optimum trained …
Analisis Kinerja Algoritme TCP Congestion Control Berdasarkan Single dan Multiple Flow pada Multi-Path Routing
Authors: Bayu Sutawijaya, Achmad Basuki, Fitra Abdurrachman Bachtiar
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Year: 2020
Topics: Networking; Algorithm Analysis
Abstract: Teknik multi-path routing merupakan solusi efektif untuk menambah kapasitas bandwidth jaringan. Namun, TCP menggunakan multiple paths sama dengan di single path. Penelitian ini melakukan analisis kinerja algoritme TCP congestion control Reno, BIC, CUBIC, dan BBR pada multi-path routing dengan setiap multiple paths menggunakan cost yang sama. Analisis yang dilakukan meliputi perbandingan antara single path routing dengan multi-path routing, single flow, dan multiple flow. Analisis single flow meliputi link delay dan loss rate. Sedangkan analisis multiple flow meliputi inter TCP protocol fairness dan fairness antara TCP dengan UDP. Semua evaluasi dilakukan berdasarkan emulasi pada VirtualBox. Berdasarkan hasil emulasi, multi-path routing dapat berdampak pada packet reordering, tetapi tidak mengakibatkan penurunan rata-rata throughput yang signifikan. Pada single flow, BBR merupakan algoritme TCP congestion control terbaik pada multi-path routing. Namun, pada multiple flow, CUBIC merupakan algoritme TCP congestion control terbaik pada multi-path routing. Pada evaluasi link delay, rata-rata RTT BBR lebih rendah hingga 58 ms dibandingkan dengan Reno, BIC, dan CUBIC. Sedangkan pada evaluasi loss rate, rata-rata throughput BBR lebih tinggi hingga 12 Mbps dibandingkan dengan Reno, BIC, dan CUBIC. Pada evaluasi inter TCP protocol fairness dan fairness antara TCP dengan UDP, fairness CUBIC paling mendekati nilai 1 dibandingkan dengan Reno, BIC, dan BBR.
Prediksi Penerimaan Driver terhadap rute yang ditawarkan pada Layanan Gosend Sameday menggunakan Algoritme Deep Neural Network dengan Metode Latih Back-Propagation
Authors: Baharudin Yusuf Widiyanto, Fitra Abdurrachman Bachtiar
Year: 2020
Topics: Deep Learning; Neural Networks
Abstract: Gojek adalah aplikasi transportasi dalam jaringan (daring) buatan PT Aplikasi Karya Anak Bangsa. Salah satu layanan dari aplikasi Gojek adalah layanan pengantaran barang yang disebut GoSend Sameday. Layanan GoSend Sameday merupakan layanan antar-jemput lebih dari satu barang dalam satu kali perjalanan melalui seorang driver dengan waktu antar maksimal satu hari. Namun, waktu penyelesaian pesanan menjadi lebih lama bila banyak driver menolak tawaran. Penelitian ini bertujuan memecahkan permasalahan tersebut dengan membuat model yang mampu memprediksi apakah driver akan menerima atau menolak rute yang ditawarkan. Penelitian ini melakukan ekstraksi fitur terhadap data mentah menggunakan library Pandas dan Google S2, kemudian membuat suatu model prediksi penerimaan rute menggunakan algoritme Deep Neural Network (DNN) dengan metode back-propagation. Pengujian dilakukan menggunakan k-fold cross validation dengan nilai k sebanyak 5. Model DNN yang dibuat menghasilkan accuracy rata-rata 0,833 dengan parameter optimal yaitu epoch sebanyak 12, fungsi aktivasi ReLU, 100 ukuran batch, 2 hidden layer, 50 neuron pada hidden layer, 0,001 weight decay, 0,9 momentum, dan learning rate 0,1. Rata-rata f-measure yang didapatkan yaitu 0,83 dan rata-rata precision yaitu 0,79. Berdasarkan hasil pengujian tersebut, dapat disimpulkan bahwa model Deep Neural Network dengan metode back-propagation mampu memprediksi dengan baik apakah seorang driver akan menerima atau menolak rute yang ditawarkan.
Asymmetric City Tour Optimization in Kediri Using Ant Colony System
Authors: Abidatul Izzah, Benni A Nugroho, Wayan F Mahmudy, Fitra A Bachtiar
Journal: Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Year: 2020
Topics: Optimization; Algorithm
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.
Initial Centroid Optimization in K-Means Algorithm for Educational Data Mining.
Authors: Raga SH Istanto, Arinda D Listikowati, Bekti P Wibowo, Fitra Abdurrachman Bachtiar
Year: 2020
Topics: Machine Learning; Clustering
Abstract: Senior High School is a formal educational institution at the level of secondary education that aims to develop students’ potential in various disciplines. In the process of development, students are directed to certain interests. In the traditional method, the process of selecting interests is carried out by the teacher which turns out to be difficult, especially in identifying and finding information that is useful to determine student areas of interest. To overcome these problems, the Educational Data Mining (EDM) approach is used. K-Means is one method that can be used in EDM. However, the K-Means method has a disadvantage in choosing initial centroids, because it is slow and less accurate. This study proposed 3 methods for conducting initial centroid selection. Evaluation results obtained in the study using 245 student data showed that the initial centroid using average calculations on data that had been normalized using min-max normalization obtained better results.
INDEKS NAMA PENULIS
Authors: Aditya Rachmadi, Admaja Dwi Herlambang, Agus Murnomo, Agus Suryanto, Ahmad Abdul Chamid, Alif Catur Murti, Eko Budi Cahyono Aminudin, Andrari Grahitandaru, Andy Hidayat Jatmika, Chastine Fatichah, Cleoputri Yusainy, Dania Ayu Wulandari, Dhika Rizki Anbiya, Ema Utami, Emha Taufiq Luthfi, Farhanna Mar'i, Fauzan Natsir, Fitra A Bachtiar, Fitri Rahmah, Fitria Hidayanti, Hari Wibawanto, Imam Mahfudl Nasrulloh, Imam Riadi, Indra K Syahputra, Kartika Utami, Lukman Hakim, Mochammad Gamal Rindarjono, Mutma Innah, Nadiyasari Agitha
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Year: 2019
Topics: Indexing
Abstract: Tempatkan abstrak berbahasa Indonesia pada bagian ini. Abstrak memberikan gambaran umum tentang isi makalah dan harus ditulis dengan Times New Roman 10 dalam format satu kolom. Panjang ideal sebuah abstrak adalah 150 sampai 250 kata. Jika terdapat istilah-istilah asing yang belum dibakukan ditulis italic.(1 baris kosong, 10pt)
Risk Identification For Excise Tape Ordering Service With Fuzzy AHP Approach
Authors: Fitra Abdurrachman Bachtiar
Year: 2019
Topics: Decision Making; Fuzzy Logic
Abstract: The application of information technology (IT) in business processes in various types of organizations generally aims to improve performance, achieve organizational goals and objectives, and competitive advantage. However, not all organizations that utilize IT can achieve this goal properly. The Directorate General of Customs and Excise has centralized data based on information technology. In the process of implementing the Customs Application System (SAC) it is necessary to conduct periodic evaluations to mitigate the risks that have an impact on the use of the system. Researchers intend to identify risks to the Excise Application System using Fuzzy AHP. Expected results, namely the value of cracking from a number of high IT risks can then be a priority in dealing with problems so that the system becomes better in accordance with applicable business processes and objectives. Based on the results, People …
Kualitas Daya Tarik Dusun Sumberwangi dalam Pengenmbgan UB Forest sebagai Destinasi Ekowisata
Authors: Fitra Bachtiar
Year: 2019
Topics: Tourism; Environmental Science
Abstract: Dusun Sumberwangi merupakan salah satu dusun yang terdapat didalam UB Forest, Kabupaten Malang. Dusun Sumberwangi memiliki potensi daya tarik yang melimpah meliputi daya tarik alam dan buatan. Potensi daya tarik yang ada saat ini perlu dikaji ulang sehubung adanya pengembangan UB Forest sebagai destinasi ekowisata, Penelitian ini bertujuan untuk mengetahui kulitas daya tarik Dusun Sumberwangi yang nantinya dapat mendukung pengembangan ekowisata UB Forest. Penilaian kualitas daya tarik didasarkan pada persepsi masyarakat yang didukung dengan kajian teoritik kondisi lapangan. Hasil dari penelitian ini didapatkan aspek – aspek daya tarik yang perlu dikembangkan sehingga dapat menjadi acuan dalam pengembangan Ekowisata UB Forest.
INDEKS NAMA PENULIS
Authors: Ade Iriani, Adyan Nur Alfiyatin, Agung Wahyu Setiawan, Ahmad Habbie Thias, Andhika Iman Rizqy, Ardian Prima Atmaja, Bayu Priyambadha, Benny Adiwijaya, Candra Fajri Ananda, Chastine Fatichah, Danny Manongga, Dyah Ayu Irawati, Dyna Marisa Khairina, Eko Wahyu Tyas Darmaningrat, Eric Megah Perdana, Esti Rahmawati Agustina, Faisal Rahutomo, Faizal Achmad, Fajar Pradana, Famylia Puspa Rani, Fitra Abdurrachman Bachtiar, Fredy Susanto, Gembong Edhi Setyawan, Hanim Maria Astuti, Heliza Rahmania Hatta, Hurriyatul Fitriyah, Husnul Khatimi, Immanuel Saragih, Jenny Doloksaribu, Kennardi Dewanto Tiono, Lavita Nur'aviana Rizalputri
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Year: 2019
Topics: Indexing
Abstract: Tempatkan abstrak berbahasa Indonesia pada bagian ini. Abstrak memberikan gambaran umum tentang isi makalah dan harus ditulis dengan Times New Roman 10 dalam format satu kolom. Panjang ideal sebuah abstrak adalah 150 sampai 250 kata. Jika terdapat istilah-istilah asing yang belum dibakukan ditulis italic.(1 baris kosong, 10pt)
Evaluasi Kualitas Website Terhadap Kepuasan Masyarakat Menggunakan Metode Webqual 4.0 dan Importance Performance Analysis (IPA)(Studi Kasus pada Website disperkim. malangkota …
Authors: Ikrom Septian Hadi, Satrio Hadi Wijoyo, Fitra Abdurrachman Bachtiar
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Web Development; User Experience
Abstract: Tujuan dari penelitian ini untuk mendapatkan hasil evaluasi dari website disprekim. malangkota. go. id menggunakan webqual 4.0 dan mendapatkan rekomendasi untuk website disperkim. malangkota. go. id menggunakan guideline dan metode IPA. Metode yang digunakan pada evaluasi website Dinas Perumahan dan Permukiman Kota Malang ini yaitu webqual 4.0 dengan 3 dimensinya serta metode lmportance and Performance Analysis (IPA) yang terdiri dari analisis tingkat kesesuaian, analisis gap, serta analisis kuadran IPA. Hasil analisis kualitas Website Dinas Perumahan dan Kawasan Permukiman Kota Malang menunjukkan bahwa Website tersebut memiliki tingkat kesesuaian< 100% yaitu sebesar 94, 53%, Artinya tingkat kinerja Website Dinas Perumahan dan Kawasan Permukiman Kota Malang masih dibawah tingkat kepentingan atau tidak sesuai dengan harapan pengguna. Rata Rata nilai kesenjangan (GAP) pada Website Dinas Perumahan dan Kawasan Permukiman Kota Malang menunjukkan hasil negatif (< 0) yaitu sebesar-0, 24, Hasil tersebut menunjukkan bahwa tingkat kinerja Website Dinas Perumahan dan Kawasan Permukiman Kota Malang masih kurang dan belum memenuhi harapan pengguna
Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine (SVM)
Authors: Arsya Monica Pravina, Imam Cholisoddin, Putra Pandu Adikara
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Sentiment Analysis; Machine Learning
Abstract: Dengan meningkatnya penggunaan Twitter, media sosial yang bekerja secara real-time untuk masyarakat dapat menyampaikan keluh kesah maupun apresiasinya terhadap maskapai-maskapai penerbangan, perlu dibuat sebuah sistem yang dapat melakukan klasifikasi suatu tweet yang berisikan opini termasuk ke dalam kelas apa, dalam penelitian ini terdapat kelas positif dan negatif. Hal tersebut dilakukan agar dapat membantu perusahaan maskapai penerbangan dalam hal evaluasi peningkatan pelayanan serta dapat membantu masyarakat dalam memilih maskapai penerbangan dengan tepat. Sehingga dilakukan klasifikasi sentimen dengan fitur Lexicon Based yang dapat menerima opini berbahasa lain selain Bahasa Indonesia (dalam penelitian ini digunakan Bahasa Inggris) untuk melakukan analisis sentimen. Digunakan algoritme support vector machine untuk melakukan klasifikasi. Hasil dari penelitian ini menunjukkan parameter optimal dan pengaruh penggunaan Lexicon Based Features. Dengan digunakan parameter C bernilai 10 dan learning rate bernilai 0, 03 serta digunakan Lexicon Based Features dengan iterasi sebanyak 50 kali memberikan hasil accuracy sebesar 40%, precision 40%, 100% recall, dan f-measure sebesar 57, 14%.
Klasifikasi Citra Makanan Menggunakan HSV Color Moment dan Local Binary Pattern dengan Naïve Bayes Classifier
Authors: Karunia Ayuningsih, Yuita Arum Sari, Putra Pandu Adikara
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Image Classification; Machine Learning
Abstract: Makanan merupakan kebutuhan dasar yang harus dipenuhi bagi kehidupan manusia. Kebiasaan makan dapat menimbulkan kebiasaan yang baik dan buruk. Kebiasaan makan buruk dapat menimbulkan berbagai macam penyakit. Komunikasi, informasi, dan edukasi (KIE) dapat memberikan edukasi terhadap kebiasaan makan. Makanan memiliki jenis beraneka ragam, diperlukan pengenalan jenis makanan agar mempermudah dalam melakukan klasifikasi jenis makanan yang baik. Tujuan dari penelitian ini agar dapat memberikan edukasi mengenali jenis makanan. Proses diawali dengan melakukan klasifikasi citra menggunakan pre-processing untuk memisahkan antara objek makanan dan background. Tahapan selanjutnya yaitu dengan menggunakan esktraksi fitur warna Hue Saturation Value (HSV) yang terdiri dari fitur Mean, Standard Deviation, dan Skewness. Kemudian, dengan menggunakan ekstraksi fitur tekstur Local Binary Pattern (LBP) menghasilkan nilai keabuan dalam histogram. Hasil ekstraksi fitur dari setiap citra kemudian dilakukan klasifikasi menggunakan Naive Bayes Classifier. Berdasarkan hasil pengujian jika hanya menggunakan metode HSV menghasilkan nilai akurasi 65%. Selain itu, hasil dari pengujian yang telah dilakukan menggunakan metode HSV menghasilkan akurasi sebesar 65% dan metode LBP menghasilkan akurasi sebesar 60%.