Lab-IS/filkom-research-papers huggingface dataset:
Penilaian Penerimaan Teknologi E-Learning Pemrograman berbasis Gamification dengan Metode Technology Acceptance Model (TAM)
Authors: Fajar Pradana, Fitra Abdurrachman Bachtiar, Bayu Priyambadha
Journal: Jurnal Teknologi Informasi Dan Ilmu Komputer
Year: 2019
Topics: Educational Technology; 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 (PU), Perceived Ease of Use (PEU), Perceived Social Influence (PSI), Perceived Enjoyment (PE), Intention of Engagement (IoE), dan Elearning Gamification Attitude (EGA). Model TAM didasarkan beberapa hipotesis dan faktor yang berpengaruh. Hasil menunjukan …
Analisis performa pre-trained model convolutional neural network Dalam Mendeteksi Penyakit tuberkulosis
Authors: Ovy Rochmawanti, Fitri Utaminingrum, Fitra A Bachtiar
Journal: J. Teknol. Inf. dan Ilmu Komput
Year: 2021
Topics: Deep Learning; Medical Imaging
Abstract: Abstrak (1 baris kosong, 10pt) Tuberkulosis (TB) merupakan salah satu penyakit berbahaya yang dapat menular lewat udara dan sering menyebabkan kematian apabila tidak cepat ditangani. Penyakit TB bisa disembuhkan dengan deteksi dini sehingga penderita dapat segera mendapatkan pengobatan yang tepat. Metode Convolutional Neural Network (CNN) digunakan untuk mendeteksi penyakit TB melalui foto rontgen dada. Penelitian ini bertujuan untuk menentukan model CNN yang mampu menghasilkan performa paling baik dalam mendeteksi penyakit TB. Pengujian dilakukan dengan menggunakan lima pre-trained model yang telah disediakan oleh Keras yaitu
Text Mining for Aspect Based Sentiment Analysis on Customer Review: A Case Study in the Hotel Industry.
Authors: Fitra Abdurrachman Bachtiar, Wirdhayanti Paulina, Alfi Nur Rusydi
Year: 2020
Topics: Text Mining; Natural Language Processing
Abstract: The development of the role of the OTA (Online Travel Agent) site has become one of the E-WOM (Electronic Word of Mouth) media in addition to its main function as a platform for ticket reservations to encourage stakeholders in the hotel industry to utilize E-WOM for business continuity. One of the guest houses in Malang realized the importance of E-WOM because 90 percent of the booking process originated from the OTA website. However, the process of processing customer reviews only focuses on physical reviews, namely Guest Reviews. Meanwhile, information from online sources can have a more significant impact on E-WOM. One of the techniques of text mining is sentiment analysis which can be used to process and group text reviews. Sentiment analysis can be done to determine the sentiment of opinions on customer reviews to determine customer satisfaction with guest house services that aim to produce a positive E-WOM. Sentiment analysis is carried out at the aspect level using aspects of location, room, food, price, and service. The text of the review used in Indonesian originates from the sites Agoda. com, Expedia, Pegi-Pegi, Booking. Com, TripAdvisor and has a timeline from 2012 to 2019. This research yields findings in the form of customer satisfaction analysis of the five aspects where food aspects have urgency to be addressed and corrected immediately. Evaluation of the classification results also proves the effectiveness of the SVM method from Naïve Bayes
Perbandingan algoritme machine learning untuk memprediksi pengambil matakuliah
Authors: Fitra A Bachtiar, Indra K Syahputra, Satrio A Wicaksono
Journal: Jurnal Teknologi Informasi Dan Ilmu Komputer
Year: 2019
Topics: Machine Learning; Educational Data Mining
Abstract: Pada setiap awal semester bagian akademik melakukan penjadwalan dan penentuan matakuliah yang akan dibuka untuk semester berikutnya. Akan tetapi proses tersebut memiliki permasalahan antara lain kelas yang dibuka terlalu banyak dibanding jumlah siswa yang berminat atau sebaliknya. Selain itu, dalam permasalahan prediksi data yang terkumpul memiliki kecenderungan tidak seimbang pada setiap kelas (imbalance class). Hal ini akan berdampak pada proses penjadwalan yang kurang tepat. Sehingga dibutuhkan sistem yang dapat memprediksi mahasiswa pengambil mata kuliah. Akan tetapi ada banyak algoritme yang dapat digunakan untuk proses prediksi. Penelitian ini membandingkan performa algoritma untuk klasifikasi mahasiswa pengambil matakuliah. Pada penelitian ini prediksi dilakukan berdasarkan atribut dari data mahasiswa. Atribut-atribut tersebut yaitu Nilai, IP, IPK, SKS, SKSK dan …
Penerapan metode association rule mining untuk asosiasi ulasan terhadap aspek tempat wisata Jawa Timur Park 3
Authors: Aisyatul Maulidah, Fitra A Bachtiar
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Year: 2021
Topics: Data Mining; Association Rule Mining
Abstract: Google Review pada salah satu fitur Google Maps dapat menjadi salah satu media untuk mengukur tingkat kepuasan pengunjung Jawa Timur Park 3 (Jatim Park 3). Akan tetapi jumlah ulasan yang mencapai ribuan dan belum tersedianya media pengelola data ulasan dapat mempersulit manajemen Jatim Park 3 dalam mengeksplorasi dan menganalisis masukan pengunjung secara mendetail. Penelitian ini memanfaatkan teknik Association Rule Mining (ARM) dalam mengelola data ulasan sehingga dapat menemukan hubungan kata yang sering muncul pada ulasan. Teknik ini paling populer untuk menemukan hubungan tersembunyi antar variabel. Algoritma yang digunakan dalam mengimplementasikannya adalah algoritma Apriori karena dianggap paling efisien. Pada penelitian ini menggunakan data ulasan sebanyak 1067 ulasan dalam Bahasa Indonesia dari bulan Januari sampai bulan April tahun 2019. Berdasarkan wawancara, data tersebut digolongkan menjadi 8 aspek berdasarkan kata kunci yang sudah ditentukan sebelumnya. Aspek tersebut antara lain akses jalan, biaya, kebersihan, kepuasan, keramaian, pelayanan, keamanan, dan teknologi. Pengujian dilakukan untuk mengetahui pengaruh minimum support dan minimum confidence terhadap rule yang terbentuk. Keseluruhan aspek mampu menghasilkan asosiasi kata dengan algoritma Apriori. Selain itu, Keseluruhan rule yang terbentuk menghasilkan rata-rata lift ratio di atas 1 dimana rule dengan nilai lift ratio diatas 1 tersebut merupakan rule yang unik diantara rule-rule lain yang terebentuk dari asosiasi tersebut. Pada penelitian ini, rule yang terbentuk divisualisasikan …
Augmented reality adapted book (AREmotion) design as emotional expression recognition media for children with autistic spectrum disorders (ASD)
Authors: Tika Miningrum, Herman Tolle, Fitra A Bachtiar
Journal: International Journal of Advanced Computer Science and Applications
Year: 2021
Topics: Augmented Reality; Assistive Technology
Abstract: One of the Autism Spectrum Disorder (ASD) characteristics is their difficulty understanding other people's emotions. Their lack of skill of understanding emotion includes expression and appropriate emotional response for a certain situation. This paper proposes an adapted book that helps therapists and parents guide ASD children to learn facial emotional expression. The adapted book combined with video, animation, and Augmented Reality increases children with ASD at recognizing emotional expression. This research uses User-
Comparison of machine learning methods for android malicious software classification based on system call
Authors: Mochammad Anshori, Farhanna Mar'i, Fitra A Bachtiar
Year: 2019
Topics: Machine Learning; Cybersecurity
Abstract: The development of the Android operating system is very rapid accompanied by the development of various types of malicious software (malware). The malware application can enter automatically into an Android device in an unintentional way by Android smartphone users so that there are many cases of data theft that are very detrimental to the user. In this study, malware detection will be based on the system call feature on Android using several machine learning methods, namely Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, Log Regression, and K-nearest Neighbor (KNN). The purpose of this study is to find out the machine learning method that can provide the best value of accuracy, TPR, and FPR in resolving the problem of malware detection on android by classification of types of malware using a system call on Android. Based on the results of this study, it can be seen that the …
Classification of stress in office work activities using extreme learning machine algorithm and one-way ANOVA F-test feature selection
Authors: Dariswan Janweri Perangin-Angin, Fitra A Bachtiar
Year: 2021
Topics: Machine Learning; Feature Selection
Abstract: Stress is a condition when humans feel psychological pressure consciously or unconsciously comes from within themselves and the environment. Based on the survey, more than 50% of workers experienced stress at work. Neglecting stressful conditions repeatedly can worsen the performance and health of workers. Identifying stress condition manually is not effective and may take some time. There is a need to build a system to diagnose stress quickly and accurately. Machine learning classification is one of the solutions to problems that are applied to intelligent systems. One of the machine learning methods that can be used is the Extreme Learning Machine (ELM) algorithm. One-way ANOVA F-test is used as a method of feature selection to require a quick decision so the reduction in features is expected to accelerate the results of the classification. The dataset used is the Heart Rate Variability totaling 5000 …
Reporting sleepy driver into channel telegram via telegram bot
Authors: Choirul Huda, Fitra Abdurrachman Bachtiar, Ahmad Afif Supianto
Year: 2019
Topics: Telegram Bots; Driver Safety
Abstract: Road accident is one of the biggest causes of death. The case is dominated by a sleepy driver. Today, research related to the method of sleepiness detection has been developed. However, the detection accuracy in the previous study need to be improved. Moreover, the detection system only inform the driver itself if the driver is sleepy or drowsy while other person, such as family, or authorities did not receive this information. As a result, if accidents happen peoples would not be noticed. This has an impact on handling post-accident victims. These series could increase the risk and even bring casualties if do not taken care seriously. Therefore, it needs further development to broadcast sleepy driver information to other people such as families, the rescue center and even the highway police department. This study proposed a sleepy driver detection and information notification system implemented on smartphones …
Analisis Ulasan Pelanggan Menggunakan Multinomial Naïve Bayes Classifier dengan Lexicon-Based dan TF-IDF Pada Formaggio Coffee and Resto
Authors: Sarah Anggina, Nanang Yudi Setiawan, Fitra A Bachtiar
Journal: @ is The Best: Accounting Information Systems and Information Technology Business Enterprise
Year: 2022
Topics: Text Mining; Natural Language Processing
Abstract: Formaggio Coffee and Resto Tangerang menyajikan hidangan western dengan rasa yang disesuaikan pada selera masyarakat Indonesia. Adanya peningkatan jumlah restoran di Kota Tangerang setiap tahunnya membuat Formaggio Coffee and Resto harus memiliki keunggulan kompetitif dengan cara meningkatkan kepuasan pelanggan. Kepuasan pelanggan bisa didapatkan jika ekspektasi pelanggan terpenuhi. Pihak manajemen Formaggio menganggap kritik dan saran yang diberikan oleh pelanggan merupakan sebuah hal positif yang dapat meningkatkan kinerja mereka. Namun, banyaknya ulasan pelanggan yang tersebar di berbagai situs membuat pihak restoran sulit dalam mengelola pendapat pelanggan. Hal tersebut dapat diatasi dengan melakukan web scraping pada situs Traveloka, PergiKuliner, Zomato, dan Google Review, dimana data yang berhasil terkumpul adalah sebanyak 741 ulasan dengan rentang waktu mulai tahun 2018 hingga tahun 2021. Kemudian, salah satu cara untuk mendapatkan informasi dari ulasan pelanggan adalah dengan melakukan analisis sentimen menggunakan fitur kamus Indonesian Sentiment Lexicon (InSet Lexicon) dan pembobotan TF-IDF, serta algoritme klasifikasi Multinomial Naïve Bayes. Model klasifikasi selanjutnya diuji menggunakan Confusion Matrix dengan empat parameter, yaitu accuracy, recall, precision, dan f1-score. Didapatkan nilai rata-rata dari setiap parameter tersebut sebesar 95%, 68%, 85%, dan 72%. Hasil penelitian selanjutnya divisualisasikan ke dalam sebuah dashboard dan diuji menggunakan kuesioner System Usability Scale (SUS) dengan nilai akhir sebesar 67, 5 …
Optimization of k value in knn algorithm for spam and ham email classification
Authors: Eko Laksono, Achmad Basuki, Fitra Bachtiar
Journal: Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi)
Year: 2020
Topics: Machine Learning; Algorithm Optimization
Abstract: There are many cases of email abuse that have the potential to harm others. This email abuse is commonly known as spam, which contains advertisements, phishing scams, and even malware. This study purpose to know the classification of email spam with ham using the KNN method as an effort to reduce the amount of spam. KNN can classify spam or ham in an email by checking it using a different K value approach. The results of the classification evaluation using confusion matrix resulted in the KNN method with a value of K= 1 having the highest accuracy value of 91.4%. From the results of the study, it is known that the optimization of the K value in KNN using frequency distribution clustering can produce high accuracy of 100%, while k-means clustering produces an accuracy of 99%. So based on the results of the existing accuracy values, the frequency distribution clustering and k-means clustering can be used to optimize the K-optimal value of the KNN in the classification of existing spam emails.
Employee Recruitment Recommendation Using Profile Matching and Naïve Bayes
Authors: Fitra A Bachtiar, Fajar Pradana, Rizkia Desi Yudiari
Year: 2019
Topics: Machine Learning; Human Resources
Abstract: Finding the right employee in the employee recruitment process in an organization is a challenging tasks. For example, enormous candidates participated in the selection, various parameters to be examined from the candidates. Furthermore, the selection process done manually could take long time during the selection process. In addition, there is a case that sometimes the position of the employee's work is not suitable with the strengths and weaknesses of the candidates. Moreover, in some cases using Curriculum Vitae (CV), Interview, and Assessment Test could not give the optimal selection results. There is a need to overcome this problems to create more comprehensive and could suggest the employee placement based on their characteristics. In this study, Profile Matching is used to comprehensively assess the candidates. In addition, Naïve Bayes is used in the next step to determine or predict the employee …
Eye State Recognition Using Multiple Methods for Applied to Control Smart Wheelchair.
Authors: Gusti Pangestu, Fitri Utaminingrum, Fitra A Bachtiar
Journal: International Journal of Intelligent Engineering & Systems
Year: 2019
Topics: Computer Vision; Assistive Technology
Abstract: People with multi-disability order need more handling than normal people. Smart wheelchair also developed with conventional mechanism that has more improvement in any aspect such as automatic system until control system. We develop a way for controlling smart wheelchair with movement of eyelid. In other ways, our system have capability to recognize state of the eye whether the eyes in opened or closed state. Opened and closed state in the eye can made a benchmark to control a wheelchair. In our research, we develop a system to recognize whether eye in the opened or closed state by using Multiple Method consisting of Haar cascade to detect eye location, Vertical Image Projection (VIP) to extract the feature and Learning Vector Quantization (LVQ) Neural Networks to recognize state of the eye. Our method produce an accuracy until 90.32% to recognize eye state. This approach can be used as an alternate way to control movement such go forward or stop by using their eyelid movement in smart wheelchair for disabled people.
Perbandingan Performa Metode Klasifikasi SVM, Neural Network, dan Naive Bayes untuk Mendeteksi Kualitas Pengajuan Kredit di Koperasi Simpan Pinjam
Authors: Iqbal Taufiq Ahmad Nur, Nanang Yudi Setiawan, Fitra Abdurrachman Bachtiar
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Machine Learning; Classification Algorithms
Abstract: Mendeteksi kualitas kredit sejak dini merupakan satu tahapan penting yang wajib dilakukan oleh koperasi simpan pinjam guna meminimalisir adanya risiko kredit. Dalam penelitian ini, kami menggunakan tiga metode klasifikasi yaitu SVM, Neural Network, dan Naïve Bayes untuk menemukan metode dengan performa yang paling baik dan optimal pada kasus pendeteksian kualitas kredit di koperasi simpan pinjam. Proses yang dilakukan adalah dengan mengimplementasikan data hasil pre processing menggunakan algoritme SVM, Neural Network, dan Naïve Bayes dengan proses evaluasi menggunakan 5-fold cross validation. Hasil yang didapatkan adalah metode Neural Network menjadi metode dengan performa paling baik. Rerata tingkat akurasi yang dihasilkan sebesar 86,81%, rerata precision sebesar 0,8194, rerata recall sebesar 0,8236, dan rerata nilai AUC sebesar 0,9158. Namun, waktu eksekusi …
Perancangan dan Implementasi Fuzzy Inference System (FIS) Metode Tsukamoto pada Penentuan Penghuni Asrama
Authors: Aulia Akhrian Syahidi, Fajerin Biabdillah, Fitra Abdurrachman Bachtiar
Journal: J. Teknol. Inf. dan Ilmu Komputer
Year: 2019
Topics: Fuzzy Logic; Inference Systems
Abstract: Asrama mahasiswa dibangun sebagai tempat tinggal bagi sekelompok orang yang sedang manjalankan suatu tugas atau kegiatan yang sama. Untuk menentukan mahasiswa yang berhak menjadi penghuni asrama, maka dalam penelitian ini memberikan rekomendasi untuk menggunakan Metode Fuzzy Tsukamoto. Metode Fuzzy Tsukamoto dipilih karena ada beberapa kelebihan yang menonjol yaitu dapat mendefinisikan nilai yang kabur dari inputan penilaian, membangun, serta mengaplikasikan pengalaman-pengalaman dari pakar-pakar secara langsung sehingga tidak melalui proses pelatihan. Hasil analisis menyimpulkan bahwa:(1) Cara kerja Metode Fuzzy Tsukamoto memiliki tiga bagian yaitu: fuzzifikasi, inferensi fuzzy, dan defuzzifikasi,(2) Implementasi Metode Fuzzy Tsukamoto dapat menghitung penentuan penerimaan penghuni asrama mahasiswa pada studi kasus asrama mahasiswa putera “Negara Dipa Amuntai Malang”, berdasarkan 19 data dengan membandingkan antara hasil penilaian pakar, hasil perhitungan Fuzzy Tsukamoto secara manual, dan hasil perhitungan Fuzzy Tsukamoto secara otomatis menggunakan sistem yang terprogram, telah diuji mempunyai tingkat akurasi keberhasilan sebesar 63, 15% dengan predikat cukup.
Feature extraction comparison for facial expression recognition using adaptive extreme learning machine.
Authors: Muhammad Wafi, Fitra A Bachtiar, Fitri Utaminingrum
Journal: International Journal of Electrical & Computer Engineering (2088-8708)
Year: 2088
Topics: Machine Learning; Feature Extraction
Abstract: Facial expression recognition is an important part in the field of affective computing. Automatic analysis of human facial expression is a challenging problem with many applications. Most of the existing automated systems for facial expression analysis attempt to recognize a few prototypes emotional expressions such as anger, contempt, disgust, fear, happiness, neutral, sadness, and surprise. This paper aims to compare feature extraction methods that are used to detect human facial expression. The study compares the gray level co-occurrence matrix, local binary pattern, and facial landmark (FL) with two types of facial expression datasets, namely Japanese female facial expression (JFFE), and extended Cohn-Kanade (CK+). In addition, we also propose an enhancement of extreme learning machine (ELM) method that can adaptively select best number of hidden neurons adaptive ELM (aELM) to reach its maximum performance. The result from this paper is our proposed method can slightly improve the performance of basic ELM method using some feature extractions mentioned before. Our proposed method can obtain maximum mean accuracy score of 88.07% on CK+ dataset, and 83.12% on JFFE dataset with FL feature extraction.
Customer clustering based on RFM features using K-means algorithm
Authors: Wafa Essayem, Fitra A Bachtiar, Diah Priharsari
Year: 2022
Topics: Clustering; Machine Learning
Abstract: Offering targeted products and services to customers is the key driver to a successful business. In recent years and with the simplified access and gathering of data, companies are adjusting their marketing strategies to retain and attract new customers. One of the methods organizations adopt, is customer clustering. Customer clustering, as part of Customer Relationship Management, is useful when companies wish to offer services, discounts and targeted advertising campaigns to specific customers based on their preferences. One of the techniques widely used in this task is RFM based clustering using K-Means clustering algorithm. The clusters obtained by the algorithm are then further analyzed to set marketing strategies. In this research we cluster customers of a retail store based on RFM features using K-Means clustering algorithm. For the task, we use the available POS data of the store. Clusters obtained are …
Klasifikasi kinerja akademik siswa menggunakan neighbor weighted k-nearest neighbor dengan seleksi fitur information gain
Authors: Rizky Adinda Azizah, Fitra A Bachtiar, Sigit Adinugroho
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Year: 2022
Topics: Machine Learning; Feature Selection
Abstract: Kinerja akademik siswa merupakan indikator kesuksesan dari pembelajaran di sekolah. Mengukur kinerja akademik siswa dapat membantu tenaga didik mengembangkan pembelajaran yang sesuai untuk siswa sehingga meningkatkan keberhasilan pembelajaran sekolah. Kinerja akademik siswa dapat diamati melalui suatu Learning Management System bernama Kalboard 360 yaitu sistem yang berhubungan dengan perilaku siswa menggunakan alat pelacak aktivitas siswa yang memantau aktivitas pembelajaran. Data sekunder dari aktivitas tersebut dapat digunakan untuk mengetahui kinerja siswa dengan salah satu caranya adalah klasifikasi. Klasifikasi menggunakan metode Neighbor Weighted K-Nearest Neighbor dengan seleksi fitur Information Gain diterapkan pada penelitian ini untuk membantu klasifikasi kinerja siswa karena metode NWKNN mempunyai kelebihan memperhitungkan metode pembobotan kelas dan mengatasi data tidak seimbang. Seleksi fitur dengan Information Gain digunakan agar dapat mengoptimalkan hasil kerja classifier. Berdasarkan pengujian dan analisis penelitian, didapatkan nilai akurasi terbaik sebesar 0,604, dengan nilai precision adalah 0,719, nilai recall sebesar 0,676, dan nilai f-measure diperoleh adalah 0,661. Nilai tersebut dihasilkan saat menggunakan 9 fitur yaitu VisitedResource,
Web-based application for visual representation of learners' problem-posing learning pattern
Authors: Ahmad Afif Supianto, Satrio Agung Wicaksono, Fitra A Bachtiar, Admaja Dwi Herlambang, Yusuke Hayashi, Tsukasa Hirashima
Journal: Journal of Information Technology and Computer Science
Year: 2019
Topics: Educational Technology; Data Visualization
Abstract: Studentsâ€™ learning process analysis normally involves massive amount of data. This study explores the pattern and relationship of studentsâ€™ learning process data in an interactive learning media to identify their learning process patterns to ease the needs of sophisticated data analysis for class instructors and educational researchers. This study focuses on the development of a web-based software application that creates a visual representation of studentsâ€™ learning process in a learning media. The result of this software is a visualization of studentsâ€™ activity sequence. This result is then used to infer studentsâ€™ learning patterns as well as identifying their learning behavior and to create a better feedback via the learning instructors. As a case study, this research uses the data log of Monsakun, a digital learning environment that focuses on the subject of mathematic for grade school students on the topic of arithmetic using story-based question and problem-posing approach. Investigation result shows four distinct learning activity patterns which are: smart pattern, adventure pattern, peer pattern and cyclic pattern. Each pattern requires different feedback to optimize learning progression, by using this web-based application, appropriate feedback to specific learning pattern is then applied to each student based on its learning activity pattern.
Klasifikasi Tingkat Stress dari Data Berbentuk Teks dengan Menggunakan Algoritma Support Vector Machine (SVM) dan Random Forest
Authors: Naufal Fathirachman Mahing, Alifi Lazuardi Gunawan, Ahmad Foresta Azhar Zen, Fitra Abdurrachman Bachtiar, Satrio Agung Wicaksono
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer
Year: 2024
Topics: Machine Learning; Text Analysis
Abstract: Stres merupakan keadaan dimana seseorang merasakan adanya tekanan yang berlebih pada dirinya. Pemantauan tingkat stres menjadi hal yang penting bagi manusia. Tingkat stres yang tinggi dapat menimbulkan dampak negatif terhadap kesehatan manusia. Deteksi dini stres menjadi sesuatu yang sangat penting untuk dilakukan. Salah satu cara mengetahui tingkat stres seseorang adalah melalui analisis teks. Penelitian ini dilakukan untuk melakukan klasifikasi tingkat stres berdasarkan data berupa teks menggunakan algoritma Support Vector Machine (SVM) dan Random Forest. Pada penelitian ini melakukan perbandingan beberapa metode transformasi. Transformasi yang dilakukan pada penelitian ini menggunakan TF-IDF, CountVectorizer, NRCLex, dan Word Affect Intensities. Data yang digunakan dalam penelitian ini berupa sebuat teks berbahasa Inggris yang diambil dari media sosial Twitter. Total data yang digunakan yaitu 8439 data. Pelatihan model baik untuk Support Vector Machine dan Random Forest menggunakan 6751 data. Sedangkan untuk pengujian menggunakan 1688 data. Hasil penelitian menunjukkan bahwa algoritma SVM dengan pembobotan menggunakan TF-IDF memiliki performa yang paling baik dibandingkan dengan algoritma Random Forest dan metode transformasi lainnya yang digunakan dalam penelitian. Model algoritma SVM dengan transformasi TF-IDF yang dibangun berhasil mendapatkan akurasi sebesar 84%. Model ini mendapatkan akurasi yang lebih tinggi dibanding model Random Forest yang memperoleh akurasi tinggi sebesar 80% dengan menggunakan transformasi CountVectorizer …
Opinion spam classification on steam review using support vector machine with lexicon-based features
Authors: Rafif Taqiuddin, Fitra A Bachtiar, Welly Purnomo
Journal: Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Year: 2021
Topics: Machine Learning; Natural Language Processing
Abstract: Steam is a video game digital distribution platform developed by Valve Software. Steam provides a user review feature, where users can write about criticism or comments on games that can contain positive or negative sentiments. Based on the questionnaire that the author conducted to Steam users from all over Indonesia, the user review feature provided by Steam was not sufficient. This is because there are fake reviews that allow biased opinions from certain parties so that a phenomenon called review bombing often occurs where users review only to drop or raise the image of a product, not to review it sincerely. From these problems, a solution design is needed that can classify fake reviews on the Steam service. The Support Vector Machine (SVM) classification method was chosen as the model in combination with lexicon-based feature retrieval and Term Frequency–Inverse Document Frequency (TF-IDF) weighting. Of the 236 classification test data conducted by SVM, it produced 105 reviews which were categorized as Valid Reviews. Meanwhile, those categorized as Opinion Spam by SVM are 131 reviews. The accuracy level of the data classification model using Support Vector Machine method is of 81% by dividing training data by 70% and test data by 30% with a random state level of 109. A dashboard in the form of a web application has also been made that contains the classification model to be used for buying reference for Steam user.
Implementasi Weighted Product untuk memberikan Rekomendasi Prospek Pelanggan bagi Sales Marketing Berdasarkan Web Analytics
Authors: Fajar Pradana, Fitra Abdurrachman Bachtiar, Mochammad Dearifaldi Al Ikhsan
Journal: Jurnal Teknologi Informasi Dan Ilmu Komputer
Year: 2020
Topics: Decision Making; Web Analytics
Abstract: Keberhasilan sebuah perusahaan dalam memasarkan produk atau jasa yang ditawarkan sangat tergantung dari kinerja marketing. Kegiatan marketing saat ini berkembang tidak hanya dilakukan secara kovensional melalui tatap muka langsung dengan pelanggan. Salah satu pemasaran yang dilakukan pada perusahaan adalah dengan digital marketing. Digital marketing menggunakan Internet dan World Wide Web untuk mendekati pelanggan. Dalam mencapai tujuan ini, perusahaan harus mengadopsi Web analytics, yang didefinisikan sebagai pengukuran, pengumpulan, analisis dan pelaporan data Internet untuk tujuan memahami dan mengoptimalkan penggunaan Web. Dengan melakukan web analytics, marketing dapat mengenali calon pelanggan prospek yang sering mengakses website perusahaan. Tidak seperti kegiatan marketing konvensional, kegiatan mengenali pengunjung website menjadi …
Breast cancer classification using GLCM and BPNN
Authors: Syam Julio A Sarosa, Fitri Utaminingrum, Fitra A Bachtiar
Journal: Int J Adv Soft Comput Appl
Year: 2019
Topics: Machine Learning; Medical Imaging
Abstract: Among all the cancer in women, breast cancer is the most common and deadliest cancer. In 2018, there were 22.692 Indonesian women dead because of breast cancer. Until now, the main cause of breast cancer is still unknown. However, the possibility of recovery and survival rates can be increased through early detection. One of the most efficient ways of early detection is through mammography. Mammography produces images called mammograms. The main objective of this paper is to develop Computer Aided Diagnosis (CADx) system that can help radiologist determine breast cancer cases based on mammogram image. In this paper, a combination of the Gray-level Co-Occurrence matrix (GLCM) and Backpropagation Neural Network (BPNN) is used to classify normal-abnormal patient based on mammogram image. Using mammogram image provided by Mammography Imaging Analysis Society (MIAS), a test for the proposed method was concluded. The result was, accuracy 94.06%, Sensitivity 90.16%, and Specificity 95.57%.
Eyeball movement detection system using corner triangle similarity, naïve Bayes, and ear approach
Authors: Gusti Pangestu, Fitri Utaminingrum, Fitra A Bachtiar
Journal: Int J Adv Soft Comput Appl
Year: 2019
Topics: Computer Vision; Machine Learning
Abstract: Eye movement detection is one of the most developed technology, especially in human and computer interaction. Many research is developed by using this technology such as lying detection, security using captcha of movement eye until fatigue detection. Eye movement detection also applied for controlling an electronic device like a wheelchair. Eye movement can furnish an easy input source by tracking user point of view and applied it as guided. In general, the process of tracking eyeball movement required a face detection first and also detect an eye area. Unlike the face and eye detection method, eye movement detection still needs a lot of improvement. In this paper, we use multiple approaches to detect eyeball movement by included corner triangle similarity methods, face landmark methods, and Naïve Bayes classifier. This proposed approach is able to handle many various directions of movement of the eye and produce an accuracy of detection about 85%. This multiple approaches also can be proposed as an alternative option to detect the eyeball movement.
Violence recognition on videos using two-stream 3d cnn with custom spatiotemporal crop
Authors: Raka Aditya Pratama, Novanto Yudistira, Fitra Abdurrachman Bachtiar
Journal: Multimedia Tools and Applications
Year: 2024
Topics: Computer Vision; Deep Learning
Abstract: Violence may happen anywhere. One of the ways to know and oversee the violence in some places is by installing Closed-circuit Television (CCTV). The recorded video captured by CCTV can be used as proof in a law court. Violence video classification is also one of the topics being discussed in deep learning. The latest violence video dataset is RWF-2000. That dataset contains violent and non-violent videos, 5 seconds duration, 30 frames per second, with the amount of 2000 videos. That publication also has the best accuracy of 87.25% by their proposed method. In this study, we will use a Residual Network known to have the advantage of solving the vanishing gradient problem. Beside that, we also implement transfer learning from Kinetics and Kinetics + Moments in Time pre-trained data. We also test the number of frames and the location range of the sampled frames. RGB and optical flow inputs are …
Engagement emotion classification through facial landmark using convolutional neural network
Authors: Aulia Nurrahma Rosanti Paidja, Fitra A Bachtiar
Year: 2022
Topics: Computer Vision; Deep Learning
Abstract: The concept of engagement is the way people are involved in an activity that relates to emotional feelings and attention. In an e-learning environment, engagement can be used as a benchmark for evaluating learning activities because emotional involvement refers to students' affective reactions such as interest, boredom, confusion, or frustration. A person's emotions can be recognized through facial expressions. However, facial expression data of images have high dimensions, resulting in large computational time in the model learning process. To reduce computation time and data dimensions, feature extraction methods such as facial landmarks can be used. Therefore, this study aims to build an emotional engagement recognition system through facial landmarks by implementing the Convolutional Neural Network (CNN) method. Five facial landmarks and Euclidean distance between points and center point from …
Go story: Design and evaluation educational mobile learning podcast using human centered design method and gamification for history
Authors: Fajerin Biabdillah, Herman Tolle, Fitra A Bachtiar
Journal: Journal of Information Technology and Computer Science
Year: 2021
Topics: Educational Technology; Gamification
Abstract: Technological developments, especially in the field of education, can help students learn more effectively and help the learning process. The learning method used in high school for history learning still uses conventional methods. The use of this conventional method often experiences problems such as students being less motivated in learning. Previous research data on the entry and development of Islam in the archipelago showed that the learning outcomes of 28 students had an average score of only 41.8 on a scale of 1-100. so that this research designs and develops learning media that can increase student learning scores. One of the solutions proposed in this article is to design an androidbased learning media that can support the activities of the learning process named go-story. Interface design for students as application users and (UI/UX) based on human centered design methodology and the concept of gamification. The human centered design approach and the concept of gamification will be used in the analysis and design process to maximize the usability and engagement of the application. The application will be implemented and tested on students to measure its effectiveness. The trials that have been carried out show the results of improvements. The test results using The Post-Study System Usability Questionnaire (PSSUQ) method show a final score of 86.84.
Implementasi Topsis untuk Menentukan Rekomendadi Makanan Anak Usia 1-3 Tahun pada Sistem Monitoring Tumbuh Kembang Anak
Authors: Fajar Pradana, Fitra A Bachtiar, Rona Salsabila
Journal: J. Teknol. Inf. dan Ilmu Komput
Year: 2021
Topics: Decision Making; Health Monitoring
Abstract: Penting bagi orang tua untuk memperhatikan pertumbuhan anak secara teratur terutama pada saat periode emasnya. Usia emas pada anak berada pada saat 1000 hari pertama sejak kelahiran atau hingga anak berusia 2 tahun, tumbuh kembang anak dapat meningkat sangat signifikan pada usia ini. Pertumbuhan anak dapat maksimal apabila nutrisi yang diberikan juga tepat sejak usia lahir sampai 3 tahun. Stunting (kerdil) merupakan salahsatu penyakit yang disebabkan karena kurangnya nutrisi pada anak. Stunting adalah sebuah kondisi dimana bayi memiliki panjang dan tinggi badan yang lebih rendah daripada bayi pada umumnya. Pola asuh orang tua terhadap bayi secara mandiri menjadi sangat diperlukan. Untuk membantu orang tua dalam memantau tumbuh kembang anak serta mengurangi peningkatan jumlah bayi stunting maka dibangun sistem monitoring tumbuh kembang anak berbasis web. Pada sistem ini terdapat fitur untuk memberikan rekomendasi makanan berdasarkan kebutuhan kalori setiap anak. Dalam menentukan rekomendasi makanan diperlukan metode Sistem Pendukung Keputusan (SPK) sesuai dengan kebutuhan kalori anak. Dalam penerapan SPK, terdapat metode yang dapat digunakan untuk melakukan analisis data antara lain adalah metode Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Alternatif yang digunakan meliputi nama makanan yang dapat dikonsumsi oleh anak usia 1 sampai dengan 3 tahun. Sedangkan kriteria yang digunakan adalah kalori yang didalamnya terdapat karbohidrat, lemak, protein, dan kalsium. 3. Hasil perankingan yang diberikan oleh TOPSIS …
Development of English learning system using gamification approach
Authors: Ilham Pambudi, Fitra A Bachtiar, Fajar Pradana
Year: 2019
Topics: Education Technology; Gamification
Abstract: English has been an international language for communication amongst global citizens. A good English ability is highly required for Indonesians to be able to communicate with other people around the world. However, learners in Indonesia frequently encounter a myriad of challenges in learning English, including their reluctance to study new English vocabulary, worry of making mistakes, and feeling bored with the conventional teaching methods. English learning system using gamification approach serves as one of the solutions to improve learners' motivation in learning English. The use of gamification concept integrates several elements embedded in game applications, such as point collection, achievement, ranking, which are wrapped as English learning materials. Website-based learning system can be accessed without any place and time constraint, so that learners can study English autonomously and …
Modeling agent-oriented methodologies for landslide management
Authors: Very Sugiarto, Fatwa Ramdani, Fitra Bachtiar
Journal: Journal of Information Technology and Computer Science
Year: 2019
Topics: Agent-Oriented Modeling; Environmental Management
Abstract: One of the most fatal natural disasters in Batu City was a landslide. The percentage of casualties if directly affected by landslides is 47%. This number is quite large when compared to other natural disasters. Even though the potential of the tsunami is the biggest, when compared to the intensity of the occurrence, landslides are the most common and most often cause fatalities. One of the causes of many fatalities in natural disasters is the lack of preparedness management. For that reason the need to develop a technology that can support to reduce fatalities in landslides is needed. One of the technologies used to prevent the number of fatalities caused by natural disasters is to use multi-agent system. One of the advantages of an agent is use Belief-Desire-Intention (BDI) models for building the Muiti-Agent Systems (MAS). The specific objective of this study was to model a simulated natural disaster landslide using the Prometheus Methodology. Evaluation is done by generating a model using jackCode and implementing it using the Java Agent Development Framework. The results of this study indicate that a model made using the Prometheus Methodology can be used as a simulation of natural disaster preparedness for landslides.
Outlier detection with supervised learning method
Authors: Aditya Hari Bawono, Fitra Abdurrachman Bachtiar
Year: 2019
Topics: Machine Learning; Supervised Learning
Abstract: Outliers are data points that can affect the quality of data and the results of analysis from data mining. Outlier detection can also be seen as a pre-processing step to find data points that do not properly placed in the data set. Previously outlier detection methods are unsupervised. Today, ODDS provide data set with outlier information as a ground truth for supervised learning. However, outliers are commonly minor in any data set. For supervised learning this will lead to an imbalanced data classification problem. This paper presents the result of popular classification method, k-Nearest neighbor, Centroid Classifier, and Naive Bayes to handle outlier detection task. Even the mentioned methods were not designed to detect outlier, they proved by achieving 81% average sensitivity which is good for further research.
An angry birds level generator with rube goldberg machine mechanisms
Authors: Febri Abdullah, Pujana Paliyawan, Ruck Thawonmas, Tomohiro Harada, Fitra A Bachtiar
Year: 2019
Topics: Game Development; Procedural Generation
Abstract: This study proposes a method for generating Angry Birds-like game levels featuring a domino effect generated based on Rube Goldberg Machine (RGM) mechanisms, which allow them to be completed by one shot of a bird. The proposed method generates a level by selecting predefined segments consisting of several objects arranged in a way that creates a domino effect among them. To increase the variability of generated levels, the proposed method procedurally generates a varying structure on the top of certain blocks in a predefined segment. Our results show that the proposed RGM generator is comparable to two existing generators, including the winner of the 2018 AIBIRDS Level Generation Competition, in terms of stability while it outperforms both baseline generators with respect to running time and an expressivity metric called "dynamic" which is introduced in this work to measure the time period where …
Fuzzy Tsukamoto Implementation to Detect Physiological Condition on IoT-Based e-Learning Users
Authors: F Pradana, FA Bachtiar, ER Widasari
Journal: International Journal of Information and Education Technology
Year: 2022
Topics: Fuzzy Logic; IoT
Abstract: Science and technology advancement drives humans to adapt to the digital world. IT development is proven to positively affect the education area through the concept of electronic learning (e-learning). This is especially true during the COVID-19 pandemic where traditional classrooms teaching was transferred to e-learning. This technological development demands individuals to adapt to the advancement. Despite its benefits, technological advancement may affect the physical condition of e-learning users. When the e-learning users fail to adjust, they might have physical condition problems that cause depression. Therefore, we propose an Internet of Things (IoT)-based system to detect the physiological conditions of e-learning users. By implementing Fuzzy Tsukamoto as artificial intelligence on IoT technology, we can identify the physiological condition of e-learning users such as relaxed, calm, anxious, and stressed conditions. Structurally, the proposed system consists of three stages: 1) Sensor data acquisition, 2) Physiological condition detection using Fuzzy Tsukamoto, 3) Display the output directly to the website. We evaluate the effectiveness of the proposed system in the task of detecting the physiological condition of the ten e-learning users. Based on experimental results, the proposed system presents 84.01% of accuracy. This result indicates that the proposed system is able to reliably detect physiological conditions on IoT-based e-learning users. By detecting psychological conditions, e-learning is expected to become an adaptive learning system so that it can adapt to the characteristics of each user.
Detecting Emotion in Indonesian Tweets: A Term-Weighting Scheme Study.
Authors: Kuncahyo Setyo Nugroho, Fitra A Bachtiar, Wayan Firdaus Mahmudy
Journal: Journal of Information Systems Engineering & Business Intelligence
Year: 2022
Topics: Natural Language Processing; Sentiment Analysis
Abstract: Background: Term-weighting plays a key role in detecting emotion in texts. Studies in term-weighting schemes aim to improve short text classification by distinguishing terms accurately. Objective: This study aims to formulate the best term-weighting schemes and discover the relationship between n-gram combinations and different classification algorithms in detecting emotion in Twitter texts. Methods: The data used was the Indonesian Twitter Emotion Dataset, with features generated through different n-gram combinations. Two approaches assign weights to the features. Tests were carried out using ten-fold cross-validation on three classification algorithms. The performance of the model was measured using accuracy and F1 score. Results: The term-weighting schemes with the highest performance are Term Frequency-Inverse Category Frequency (TF-ICF) and Term Frequency-Relevance Frequency (TF-RF). The scheme with a supervised approach performed better than the unsupervised one. However, we did not find a consistent advantage as some of the experiments found that Term Frequency-Inverse Document Frequency (TF-IDF) also performed exceptionally well. The traditional TF-IDF method remains worth considering as a term-weighting scheme. Conclusion: This study provides recommendations for emotion detection in texts. Future studies can benefit from dealing with imbalances in the dataset to provide better performance.
Spam email detection with affect intensities using recurrent neural network algorithm
Authors: Nurafifah Alya Farahisya, Fitra A Bachtiar
Year: 2022
Topics: Machine Learning; Neural Networks
Abstract: A large number of email users triggers an increase in the occurrence of spam in emails to gain benefits for some parties but harm others and also email users. Spam emails usually contain advertisements or criminal acts such as phishing which implicitly contain human emotions in them. It is quite difficult and takes time to differentiate between a large number of spam and ham emails. This problem can be overcome by using deep learning technology. One of which is a neural network that can classify spam emails. This paper uses the spam and ham Enron email corpus dataset. This study will add emotional features in extracting its features. The steps taken include text preprocessing, feature extraction using tf-idf, and lexicon-based emotion features, followed by classification using RNN to detect spam in emails. A comparison with other methods is also provided by comparing the proposed method to Naïve Bayes …