Lab-IS/filkom-research-papers huggingface dataset:
Text-based emotion recognition in indonesian tweet using bert
Authors: Kuncahyo Setyo Nugroho, Fitra Abdurrachman Bachtiar
Year: 2021
Topics: Natural Language Processing; BERT
Abstract: Human uses social media platform such as Twitter to express feelings and opinions through text about the surrounding issues. Understanding emotions at the subtle level of expressed feelings are essential for better human and computer interactions. The previous emotion recognition approach required many training data and lexical databases. Unfortunately, the availability of very little labeled training data is a limitation and challenge to achieving high model performance. Therefore, we investigate the BERT language model for emotion recognition in Indonesian-language Tweets in this study. We choose to use fine-tuning instead of pre-training, which requires extensive data and resources. Two pre-trained models were used to determine the effectiveness and performance of the proposed model. Experiments show that the proposed model outperforms all existing baseline models, with the highest accuracy is 77 …
Implementasi Metode Naive Bayes untuk Mendeteksi Stres Siswa Berdasarkan Tweet pada Sistem Monitoring Stres
Authors: Diva Fardiana Risa, Fajar Pradana, Fitra Abdurrachman Bachtiar
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Year: 2021
Topics: Machine Learning; Naive Bayes
Abstract: Gangguan mental saat ini masih menjadi permasalahan bagi bidang kesehatan di seluruh dunia. Salah satu jenis dari gangguan mental yang dapat diprediksi saat ini adalah stres. Stres memiliki dampak yang sangat besar bagi Kesehatan penderitanya, namun masih banyak masyarakat yang terlalu meremehkan perihal keberadaan penyakit stres ini. Hal ini salah satunya disebabkan oleh media yang dapat digunakan untuk melakukan pengecekan tingkat stres masih sangat sedikit. Sejauh ini, pengecekan kondisi kesehatan mental khususnya stres dapat dilakukan melalui konsultasi ke psikolog terdekat. Namun, tidak banyak masyarakat yang mengetahui hal itu. Ketika seseorang mengalami gangguan kecemasan khususnya stres, maka ia akan cenderung melakukan tindakan yang dapat mengekspresikan kecemasannya di media sosial. Kegiatan ini dinamakan Self Disclosure. Hal ini dianggap dapat mengurangi beban penderita gangguan mental tersebut. Mengenai hal itu, saat ini penggunaan media sosial menjadi hal yang sangat lumrah dimasyarakat khususnya remaja. Salah satu jenis sosial media yang banyak digunakan oleh masyarakat adalah Twitter. Salah satu keunggulan Twitter adalah dikarenakan twitter lebih mudah digunakan dan memiliki tampilan yang sederhana. Selain itu, penulisan tweet pada akun twitter memiliki pembatasan jumlah karakter sehingga tweet yang ditulis pengguna lebih jelas dan ringkas. Oleh karena itu, pada penelitian ini akan dibangun fitur untuk mendeteksi tingkat stres melalui tweet pada akun twitter dengan menggunakan metode Naïve Bayes yang mana akan dapat mengklasifikasikan tingkat …
Komparasi metode klasifikasi untuk deteksi ekspresi wajah dengan fitur facial landmark
Authors: Fitra A Bachtiar, Muhammad Wafi
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer
Year: 2021
Topics: Computer Vision; Classification Methods
Abstract: Human machine interaction, khususnya pada facial behavior mulai banyak diperhatikan untuk dapat digunakan sebagai salah satu cara untuk personalisasi pengguna. Kombinasi ekstraksi fitur dengan metode klasifikasi dapat digunakan agar sebuah mesin dapat mengenali ekspresi wajah. Akan tetapi belum diketahui basis metode klasifikasi apa yang tepat untuk digunakan. Penelitian ini membandingkan tiga metode klasifikasi untuk melakukan klasifikasi ekspresi wajah. Dataset ekspresi wajah yang digunakan pada penelitian ini adalah JAFFE dataset dengan total 213 citra wajah yang menunjukkan 7 (tujuh) ekspresi wajah. Ekspresi wajah pada dataset tersebut yaitu anger, disgust, fear, happy, neutral, sadness, dan surprised. Facial Landmark digunakan sebagai ekstraksi fitur wajah. Model klasifikasi yang digunakan pada penelitian ini adalah ELM, SVM, dan k-NN. Masing masing model klasifikasi akan …
Fuzzy tsukamoto membership function optimization using PSO to predict diabetes mellitus risk level
Authors: Risqy S Pradini, Cantika N Previana, Fitra A Bachtiar
Year: 2020
Topics: Fuzzy Logic; Particle Swarm Optimization
Abstract: Diabetes Mellitus (DM) is known as the silent killer because the sufferer often goes unnoticed and when it is known, complications usually already occur. The number of people with DM in Indonesia is a lot, which may increase the health costs and may cause trouble for health workers. Therefore, researchers conducted research to make predictions of the risk level of DM. By creating a prediction model the risk of DM can be identified in the early stage. The prediction of DM risk is based on the input variable consisting of age, body mass index and blood pressure (systolic) which are proven to be the basis for determining the risk ratio of DM and the output variable is the level of diabetes risk (low, high). This study uses a combination of Fuzzy Tsukamoto and PSO to predict the risk level of DM. The membership function for Fuzzy Tsukamoto will be optimized with PSO. Membership optimization is expected to increase the …
Generating angry birds-like levels with domino effects using constrained novelty search
Authors: Febri Abdullah, Pujana Paliyawan, Ruck Thawonmas, Fitra A Bachtiar
Year: 2020
Topics: Game Development; Novelty Search
Abstract: This paper proposes a method to generate interesting Angry Birds-like game levels featuring the Rube Goldberg machine (RGM) mechanism using Constrained Novelty Search (CNS). An RGM level in Angry Birds emphasizes a domino effect, which allows it to be completed by only one bird shooting. By evolving the feasible population and infeasible population in CNS, our results show that the entropy of block-type frequencies is higher than the entropy by our previous generator and that two requirements to achieve playable RGM levels - the 100% stability and the perfect-shot rate - are met. The results indicate that the proposed method can generate levels with more diversity than our previous generator while maintaining their playability.
Improving Performance for Diabetic Nephropathy Detection Using Adaptive Synthetic Sampling Data in Ensemble Method of Machine Learning Algorithms
Authors: Lailil Muflikhah, Fitra A Bachtiar, Dian Eka Ratnawati, Riski Darmawan
Journal: Jurnal Ilmiah Teknik Elektro Komputer dan Informatika
Year: 2024
Topics: Machine Learning; Ensemble Methods
Abstract: Nephropathy is a severe diabetic complication affecting the kidneys that presents a substantial risk to patients. It often progresses to renal failure and other critical health issues. Early and accurate prediction of nephropathy is paramount for effective intervention, patient well-being, and healthcare resource optimization. This research used medical records from 500 datasets of diabetic patients with imbalanced classes. The main goal of this study is to get high-performance predictive models for nephropathy. So, this study suggests a new way to deal with the common problem of having too little or too much data when trying to predict nephropathy: adding more data through adaptive synthetic sampling (ADASYN). This technique is particularly pertinent in ensemble machine-learning methods like Random Forest, AdaBoost, and bagging (Adabag). By increasing the number of instances of minority classes, it tries to reduce the bias that comes with imbalanced datasets, which should lead to more accurate and strong predictive models in the long run. The experimental results show an improving 4% rise in performance evaluation such as precision, recall, accuracy, and f1-score, especially for the ensemble methods. Two contributions of this research are highlighted here: first, the utilization of adaptive synthetic sampling data to improve the balance and diversity of the training dataset. The second contribution is incorporating ensemble methods within machine learning algorithms to enhance the accuracy and robustness of diabetic nephropathy detection.
Detection of online review spam: a literature review
Authors: Raga SH Istanto, Wayan Firdaus Mahmudy, Fitra A Bachtiar
Year: 2020
Topics: Natural Language Processing; Literature Review
Abstract: Online reviews have become an important resource for customers. It has become a habit for customers to first read a review before deciding to make a purchase. But it can be used by fraudsters to make review spam. This activity can result in the wrong customer purchase decision. Automatic opinion mining methods can also provide inaccurate conclusions due to this activity. This paper aims to provide a literature review on the online review spam detection topic. We identify papers relevant to related topics since 2015, understanding each paper to extract findings, similarities, and research gaps. We find that studies on this topic can be categorized into three focus groups. Focus on review spam detection methods, studies on individuals who write review spam, and studies that examine the spammer groups. Each focus of research has its strengths and weaknesses method which provide benefits in the field of review …
Pengenalan Citra Makanan Tradisional menggunakan Fitur Hue Saturation Pengenalan Citra Makanan Tradisional menggunakan Fitur Hue Saturation Value dan Fuzzy k-Nearest Neighbor
Authors: Refi Fadholi, Yuita Arum Sari, Fitra Abdurrachman Bachtiar
Journal: Pengembangan Teknologi Informasi Dan Ilmu Komputer
Year: 2019
Topics: Computer Vision; Fuzzy k-Nearest Neighbor
A generative-based chatbot for daily conversation: A preliminary study
Authors: Fitra A Bachtiar, Alfirsa Damasyifa Fauzulhaq, Marvel Timothy Raphael Manullang, Fabiansyah Raam Pontoh, Kuncahyo Setyo Nugroho, Novanto Yudistira
Year: 2022
Topics: Natural Language Processing; Generative Models
Abstract: Currently AI has been integrated in our daily life. One of basic example of AI in our daily life is chatbot. The common chatbot that is developed is a rule-based chatbot. Rule-based chatbot have several drawbacks that is easy to predict, repetitive, an unnatural conversation might happened. In addition rule-based chatbot would rely on fixed pair question and answer, developed in a closed domain conversation, and has limited self-learning. Due to this problem, there is a need to develop a chatbot that is able to dynamically response to the question. The chatbot model in this study is based on Simple Dialog and Daily Dialog dataset. The dataset are then merged into single dataset. In this research, we proposed the Seq2seq model architecture with LSTM and GRU cell to create a Generative-Based chatbot. The Seq2seq model consists of two parts, encoder and decoder. The proposed model is evaluated using Cross …
Perbandingan Pretrained Model Transformer pada Deteksi Ulasan Palsu
Authors: Aisyah Awalina, Fitra A Bachtiar, Fitri Utaminingrum
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Year: 2022
Topics: Natural Language Processing; Transformer Models
Abstract: Kemudahan untuk memperoleh informasi saat ini, telah sedikit membantu hidup kita. Seperti mencari ulasan untuk menimbang tempat atau barang yang akan dipilih. Beberapa orang memanfaatkan hal tersebut dengan membuat ulasan palsu untuk kepentingan mereka sendiri. Sehingga deteksi ulasan palsu sangat dibutuhkan. Model Transformer saat ini banyak diterapkan pada pemrosesan bahasa alami karena kinerja yang diperoleh nya sangat baik. Ada dua pendekatan yang dapat dilakukan dalam model Transformer yaitu pre-training dan fine-tuning. Penelitian sebelumnya telah banyak menggunakan fine-tuning dari model Transformer dikarenakan adanya kemudahan dalam pelatihan, waktu yang lebih sedikit, biaya dan kebutuhan lingkungan yang lebih rendah dibanding proses pre-training. Akan tetapi penelitian sebelumnya masih sedikit yang membandingkan model deep learning dengan fine-tuning yang khusus diterapkan pada deteksi ulasan palsu. Penelitian ini melakukan perbandingan model Transformer menggunakan pendekatan fine-tuning dengan metode deep learning yaitu CNN dengan berbagai pretrained word embedding untuk mengatasi deteksi ulasan palsu pada dataset Ott. Model RoBERTa mengungguli model Transformer dan deep learning dimana nilai akurasi 90, 8%; precision 90%; recall 91, 8% dan f1-score 90, 8%. Namun dari segi waktu komputasi model pelatihan, DistilBERT memperoleh waktu komputasi terkecil yaitu dengan nilai 200, 5 detik. Meskipun begitu, hasil yang diperoleh model Transformer maupun deep learning memiliki kinerja yang baik untuk deteksi ulasan palsu pada dataset Ott.
Metode deteksi intrusi menggunakan algoritme extreme learning machine dengan correlation-based feature selection
Authors: Sulandri Sulandri, Achmad Basuki, Fitra Abdurrachman Bachtiar
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer
Year: 2021
Topics: Machine Learning; Feature Selection
Abstract: Deteksi intrusi pada jaringan komputer merupakan kegiatan yang sangat penting dilakukan untuk menjaga keamanan data dan informasi. Deteksi intrusi merupakan proses monitor traffic pada sebuah jaringan untuk mendeteksi adanya pola data yang dianggap mencurigakan, yang memungkinkan terjadinya serangan jaringan. Penelitian ini melakukan analisis pada traffic jaringan untuk mengetahui apakah paket tersebut mengandung intrusi atau merupakan paket normal. Data traffic yang digunakan untuk deteksi intrusi pada penelitian ini diambil dari dataset KDD Cup. Metode yang digunakan untuk melakukan deteksi intrusi dengan cara klasifikasi yaitu dengan menggunakan metode Extreme Learning Machine (ELM). Namun, dengan menggunakan metode ELM saja tidak mampu untuk menghasilkan akurasi yang baik maka, pada metode ELM perlu ditambahkan metode seleksi fitur Correlation-Based …
Pengenalan Aktivitas Manusia Menggunakan Sensor Akselerometer Dan Giroskop Pada Smartphone Dengan Metode K-Nearest Neighbor
Authors: Zainal Arifien, Fitra A Bachtiar, Novanto Yudistira
Journal: vol
Year: 2021
Topics: Machine Learning; Sensor Data Analysis
Abstract: Pengenalan aktivitas manusia atau Human Activity Recognition (HAR) merupakan salah satu topik yang populer karena besarnya peluang untuk diterapkan di kehidupan sehari-hari. Tujuan dari pengenalan ini adalah untuk mengenali, mendeteksi, dan mengklasifikasikan aktivitas yang dilakukan manusia. Pengenalan aktivitas manusia adalah salah satu teknologi penting untuk memantau dinamisme seseorang sehingga dapat bermanfaat di berbagai hal. Selain untuk menjaga kesehatan, pencegahan penyakit, dan membantu menentukan jenis olah raga, HAR dapat dimanfaatkan juga untuk diterapkan pada bidang keamanan dan pengembangan teknologi. Penelitian ini menggunakan smartphone sebagai teknologi utama dalam memperoleh data dengan memanfaatkan sensor akselerometer dan giroskop yang telah tertanam di dalamnya. Terdapat 8 macam aktivitas yang diteliti dengan kombinasi lama waktu eksperimen 5, 10, dan 15 detik serta posisi smartphone dipegang bebas maupun di dalam saku celana kanan. Data yang diperoleh terdiri dari 3 sumbu (x, y, dan z) pada setiap sensor yang digunakan. Data tersebut kemudian melalui proses pengolahan dan klasifikasi menggunakan algoritme k-Nearest Neighbor (k-NN). Hasil akurasi yang didapat dalam penelitian ini mencapai 79, 56%. Hasil yang diperoleh melalui penelitian ini menunjukkan bahwa perbedaan peletakan smartphone mempengaruhi hasil pengenalan aktivitas manusia secara stabil. Selain itu, perbedaan jumlah data akibat perbedaan lamanya waktu eksperimen dapat mengakibatkan perbedaan lamanya waktu komputasi. Penelitian ini menjadi penting karena …
Penggalian Perilaku Pemain dalam Penentuan Tipe Permainan pada E-Learning Pemrograman Berbasis Gamification
Authors: Bayu Priyambadha, Fajar Pradana, Fitra Abdurrachman Bachtiar
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer
Year: 2020
Topics: Gamification; Behavior Analysis
Abstract: Salah satu kompetensi utama yang harus dimiliki oleh lulusan dari jurusan atau program studi bidang keilmuan komputer adalah kemampuan programming (membuat program). Berbagi informasi untuk meningkatkan kualitas pembelajaran pemrograman telah dilakukan di banyak kampus di Indonesia. Salah satunya adalah penerapan media pembelajaran online atau disebut juga sebagai E-Learning. Salah satu sistem pembelajaran yang paling umum, yang didukung oleh teknologi informasi, adalah e-learning. Namun, banyak juga sistem e-learning tidak mencapai tujuan yang diinginkan karena ketidakpatuhan dan kurangnya pengetahuan tentang teknik dan metode untuk pengembangan sistem informasi online. Tujuan yang dicapai antara lain efisiensi, efektifitas, motivasi dan keterlibatan siswa. Pendekatan gamification dapat digunakan untuk meningkatkan beberapa hal tersebut demi tercapainya tujuan pembelajaran online. Dalam sistem Code Maniac, terdapat beberapa elemen gamification yang digunakan, yaitu poin pengalaman, poin aktivitas dan badge. Namun, hal tersebut terbukti masih kurang meningkatkan motivasi mahasiswa dalam menggunakan Code Maniac. Pendekatan player-centric memungkinkan sistem menyesuaikan gameplay yang sesuai dengan gaya bermain seorang pemain. Untuk dapat mewujudkan sebuah media pembelajaran yang berorientasi pada pemain, maka dibutuhkan sebuah mekanisme untuk mengenali karakteristik pemainnya. Pada penelitian ini menekanan metode yang digunakan untuk menggali perilaku permainan. Penggalian pola dilakukan pada data log proses per pemain dan per sesi …
Agent-based Modeling and Simulation for Evacuation of Landslides Natural Disaster
Authors: Afta Ramadhan Zayn, Fatwa Ramdani, Fitra Abdurrachman Bachtiar
Journal: Journal of Information Technology and Computer Science
Year: 2020
Topics: Agent-Based Modeling; Simulation
Abstract: Landslides are natural disasters that pose a threat which is quite high in the area of Batu, East Java, Indonesia. The occurrence of landslides has a negative impact on environmental damage and even fatalities. These impacts can arise due to a lack of planning in disaster management preparedness. Therefore better planning is needed to minimize the negative impacts that arise. Improvement of planning can be done by conducting evacuation simulations. However, the existing evacuation simulation is still static with one scenario that is done repeatedly. Therefore, a more dynamic evacuation simulation is needed to represent the various parties involved in it and to apply various scenarios. Such dynamic simulations can be facilitated using agent technology. Agents can describe autonomous behaviour and can communicate in their environment to achieve a goal. Apply the capabilities of these agents by modelling and simulating the evacuation process can provide an illustration for a more dynamic process of landslide evacuation. This research presents an agent-based landslide evacuation model and the simulation results from this model. The results are concerned that, by using agent technology can apply simulations with various conditions. So with these results can be used as a reference in the handling of natural disasters that occur landslides.
Evaluating conceptual framework for landslides natural disaster management using feature analysis
Authors: Puad Siswahyudi, Fatwa Ramdani, Fitra A Bachtiar
Journal: Journal of Information Technology and Computer Science
Year: 2019
Topics: Disaster Management; Feature Analysis
Abstract: Landslides is the top rank disaster in the number of incidents in Batu City. Currently, the handling of landslides in Batu City still lack preparation in preparedness phase. In preparedness phase, as an early step before a landslide take place, need a systematic disaster management. The disaster management refers to the use of a framework as a guideline to understand disasters that will occur. Batu City have developed a framework as a guideline for landslide management, but the quality and maturity are questionable. Therefore, this study proposes an evaluation of conceptual framework for landslides natural disaster management. The specific objective is to know the quality of proposed framework, find the weakness and strength. Evaluation is held by using featured based analysis which using seven criteria ie background, goal, model, content, legitimation, implementation and contribution. The result shows that the framework Â is feasible to use as a guideline to manage the landslide management in preparedness phase in Batu City.
Weakening Feature Independence of Naïve Bayes Using Feature Weighting and Selection on Imbalanced Customer Review Data
Authors: Reiza Adi Cahya, Fitra A Bachtiar
Year: 2019
Topics: Machine Learning; Feature Selection
Abstract: E-commerce sites have provided review section for users to take advantage of and express their opinion about products or services. Decision makers, on other hand, can also take advantage of the abundant reviews to analyze which aspects of products or services to be improved, which is known as sentiment analysis. naïve Bayes (NB) is popular method for sentiment analysis because it is considerably faster than other methods but has comparable performance. One weakness of NB however, is that NB assumes each feature is independent with other features. This assumption is not fulfilled in sentiment analysis because terms are correlated with others. Two approaches, i.e. feature weighting (FW) and feature selection (FS) are used to weaken this assumption. Two approaches use genetic algorithm (GA) to find optimal weights and subset based on correlation and odds ratio to take imbalanced review data into …
Eyeball Movement Detection Using Sector Line Distance Approach and Learning Vector Quantization
Authors: Gusti Pangestu, Fitra A Bachtiar
Year: 2019
Topics: Machine Learning; Computer Vision
Abstract: The shifting of education to learning supported by technology facing challenges. One of the challenges is to able to detect student learning processes, especially student affective state. Some studies implemented affective detection. However, the detection in obtrusive manner. Thus, there is a need to propose a method to detect student learning state in unobtrusive manner. This research propose a method to take advantage of eyeball movements as the measuring media as a feedback in the e-learning process. This study focuses in detecting eyeball movement. The eye movement to be detected in this study is in 5 directions center, upward, downward, leftward, and rightward. The eyeball movements is detected using the Sector Line Distance and classification method namely LVQ (Learning Vector Quantization). The result of the experiment shows that the average accuracy in detecting 5 eyeball direction shows a …
Klasifikasi Aktivitas Manusia Menggunakan Algoritme Computed Input Weight Extreme Learning Machine Dengan Reduksi Dimensi Principal Component Analysis
Authors: M Sofyan Irwanto, Fitra A Bachtiar, Novanto Yudistira
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Year: 2022
Topics: Machine Learning; Dimensionality Reduction
Abstract: Salah satu bidang penelitian yang sangat penting yaitu pengenalan aktivitas manusia secara otomatis dikarenakan potensi penerapannya di berbagai bidang lain seperti pengawasan, lingkungan cerdas, maupun kesehatan. Dari berbagai pendekatan yang pernah dilakukan, teknik berbasis sensor diketahui lebih unggul daripada teknik lain seperti teknik berbasis visi komputer. Teknik berbasis sensor juga dapat dilakukan menggunakan ponsel cerdas, sehingga akan lebih dapat diterima dalam kehidupan sehari-hari. Meski begitu, pada penelitian terkait pengenalan aktivitas manusia menggunakan ponsel cerdas masih terdapat beberapa permasalahan, yaitu jumlah dimensi fitur yang tinggi serta keterbatasan prosesor yang ada pada ponsel cerdas untuk melakukan komputasi algoritme yang kompleks. Penelitian ini dilakukan untuk mengenali aktivitas sederhana seperti berjalan, menaiki tangga, menuruni tangga, duduk, berdiri, dan berbaring. Data yang digunakan pada penelitian ini adalah data sekunder dengan jumlah data sebanyak 10299 baris data dan 561 fitur. Dimensi fitur dari data tersebut direduksi menggunakan Principal Component Analysis (PCA) agar jumlah fiturnya berkurang. Data hasil reduksi dimensi tersebut kemudian digunakan untuk melakukan klasifikasi menggunakan Computed Input Weight Extreme Learning Machine (CIWELM). Pada penelitian ini juga dilakukan pemilihan hyperparameter terbaik menggunakan Grid Search Cross Validation. Hyperparameter terbaik yang didapatkan untuk algoritme PCA adalah dengan nilai k= 207, serta untuk algoritme CIW-ELM adalah dengan jumlah hidden neuron= 600 dan …
Classification of electroencephalogram data on massive open online course studying process using gated recurrent unit
Authors: Kresna Hafizh Muhaimin, Fitra Abdurrachman Bachtiar
Year: 2022
Topics: Machine Learning; Deep Learning
Abstract: Massive Open Online Course (MOOC) is an education service that provides an online learning system for people. MOOCs implement asynchronous learning and there is no limit to how many people join a class. Thus, it allows many people to receive the education that is needed by the people or even people who are interested in some topic that they want to learn. Online learning shows that a learning process does not require direct interaction between lecturer and student, that also brings up a problem where it is hard to determine if the lecture video is being understood by the student or not. A group of researchers collects brain signal data which generates EEG data from a few students while watching a few lecture videos from MOOC, which the EEG data can be used for further research to detect how the brain works when watching MOOC videos. This research implements Gated Recurrent Unit method to do …
Klasifikasi Ulasan Palsu Menggunakan Borderline Over Sampling (BOS) dan Support Vector Machine (SVM)(Studi Kasus: Ulasan Tempat Makan)
Authors: Aisyah Awalina, Fitra Abdurrachman Bachtiar, Indriati Indriati
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer
Year: 2022
Topics: Machine Learning; Natural Language Processing
Abstract: Kemudahan memperoleh informasi saat ini telah banyak membantu manusia, salah satu mencari ulasan untuk tempat makan baru. Pencarian ulasan ini dipicu karena pengunjung tidak mengetahui layanan dari tempat tersebut. Ulasan juga dapat menguntungkan penjual, karena mereka mengetahui pengalaman yang dimiliki pengunjungnya. Oleh karena itu, ulasan palsu dimanfaatkan banyak orang untuk membuat ulasan palsu. Ulasan palsu bisa secara efektif dibedakan menggunakan machine learning. Namun, banyak dari dataset ulasan palsu ini tidak seimbang (imbalanced dataset) sehingga dapat mempengaruhi hasil klasifikasi. Oleh karena itu, penelitian ini menggunakan metode BOS untuk mengatasi tidak seimbangnya data dan melakukan klasifikasi dengan metode SVM. Adapun tahapan dari penelitian yaitu preprocessing, lalu pembobotan kata dengan TF-IDF dan fitur sentimen menggunakan …
Evaluasi Usability Aplikasi Elektronik-Kesehatan Ibu dan Anak (e-KIA)
Authors: Fitra Abdurrachman Bachtiar, Retno Indah Rokhmawati, Fajar Pradana, Intan Yusuf Habibie
Journal: J. Teknol. Inf. dan Ilmu Komput
Year: 2021
Topics: Human-Computer Interaction; Usability Evaluation
Abstract: Teknologi informasi sudah berkembang disemua bidang dan tidak terkecuali sudah diterapkan ke bidang kesehatan. Salah satu langkah awal penerapan teknologi informasi pada bidang kesehatan adalah dengan membangun aplikasi untuk memonitor kesehatan ibu dan anak yang disebut dengan e-KIA. Aplikasi e-KIA yang sudah dibangun saat ini masih belum diketahui tingkat usabilitas dari penggunaan aplikasi tersebut. Pengujian usability ini penting untuk dilakukan sebelum aplikasi tersebut digunakan oleh masyarakat secara luas dan dapat digunakan sebagai early warning untuk mendeteksi kemungkinan-kemungkinan kesalahan yang terjadi. Untuk mengatasi hal tersebut pengujian usability dilakukan pada aplikasi e-KIA. Pengujian usability dimulai dengan melakukan studi litearatur terkait. Kemudian dilakukan pemilihan evaluator sekaligus melakukan briefing proses evaluasi. Tahap selanjutnya adalah merancang instrumen untuk evaluasi dan merancang skenario evaluasi. Evaluasi usability yang dilakukan difokuskan pada 3 hal yaitu Severity Ranking (SR), Ease of Fixing Ranking (EFR), dan Kategori Masalah. Masalah yang ditemukan kemudian dikategorikan berdasarkan 10 prinsip heuristik Nielsen dan disederhanakan menjadi kategori Selection Driven Command (perbaikan interaksi), Content Organization (kelengkapan konten informasi), dan Visual Representation (pemilihan komponen desain yang tepat). Dari hasil evaluasi tersebut didapatkan temuan masalah Selection Driven Command (perbaikan interaksi) sejumlah 18 temuan, Content Organization (kelengkapan konten informasi) sejumlah 10 temuan, dan Visual …
Comparison of bagging ensemble combination rules for imbalanced text sentiment analysis
Authors: Reiza Adi Cahya, Fitra A Bachtiar, Wayan Firdaus Mahmudy
Journal: Journal Homepage: www. jitecs. ub. ac. id
Year: 2021
Topics: Machine Learning; Ensemble Methods
Abstract: The wealth of opinions expressed by users on micro-blogging sites can be beneficial for product manufacturers of service providers, as they can gain insights about certain aspects of their products or services. The most common approach for analyzing text opinion is using machine learning. However. opinion data are often imbalanced, eg the number of positive sentiments heavily outnumbered the negative sentiments. Ensemble technique, which combines multiple classification algorithms to make decisions, can be used to tackle imbalanced data to learn from multiple balanced datasets. The decision of ensemble is obtained by combining the decisions of individual classifiers using a certain rule. Therefore, rule selection is an important factor in ensemble design. This research aims to investigate the best decision combination rule for imbalanced text data. Multinomial Naïve Bayes, Complement Naïve Bayes, Support Vector Machine, and Softmax Regression are used for base classifiers, and max, min, product, sum, vote, and meta-classifier rules are considered for decision combination. The experiment is done on several Twitter datasets. From the experimental results, it is found that the Softmax Regression ensemble with meta-classifier combination rule performs the best in all except in one dataset. However, it is also found that the training of the Softmax Regression ensemble requires intensive computational resources.
2D and 3D Geovisualization: learning user preferences in landslide vulnerability
Authors: Hafif Bustani Wahyudi, Fatwa Ramdani, Fitra Abdurrachman Bachtiar
Journal: Journal of Information Technology and Computer Science
Year: 2020
Topics: Geovisualization; User Studies
Abstract: Geovisualization in both 2D and 3D forms can be used as a medium for disseminating information of landslide vulnerability. Determining proper geovisualization is very important so that the information carried about landslide vulnerability is truly helpful and can be understood by the community as enduser. There are very few research in current literature explaining the user preferences on geovisualization 2D and 3D related to landslide vulnerability. In this paper, the user preferences of both 2D and 3D geovisualization will be evaluated. This study will focus to find out which geovisualization suits most users and their literacy spatial among those provided geovisualizations. Geovisualization of landslide disaster vulnerabilities will be displayed to users in 2D and 3D. Then to find out their spatial literacy and preferences, interviews were conducted with users regarding the geovisualization presented. The results of the user's answers are then collected and evaluated. From our results, 90% of users prefer 3D geovisualization over 2D. Furthermore, our analysis shows that 2D geovisualization has the advantage of being easily understood by users in all ages. Meanwhile, 3D geovisualization is better at increasing users' spatial literacy at all ages and levels of education in knowing the causes of landslide vulnerability. Choosing the proper geovisualization will provide information and knowledge that is useful for communities in regards of landslide vulnerability for better disaster awareness.
Optimasi Fungsi Keanggotaan Fuzzy Tsukamoto dengan Algoritme Genetika pada Peramalan Harga Emas untuk Stock Trading
Authors: Ficry Agam Fathurrachman, Fitra Abdurrachman Bachtiar, Imam Cholissodin
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Fuzzy Logic; Genetic Algorithms
Abstract: Investor dan pedagang saham memerlukan pengetahuan tentang peramalan kapan harga emas akan naik atau akan turun agar dapat meminimalkan risiko dalam berinvestasi. Peramalan ini membutuhkan sebuah metode yang tepat agar dapat memberikan hasil yang baik. FIS Tsukamoto digunakan untuk meramalkan harga emas berdasarkan data kurs mata uang yang ada. Parameter yang digunakan oleh FIS Tsukamoto adalah kurs mata uang USD/GBP, CHF/USD, JPY/USD, EUR/USD berdasarkan tiga hari sebelumnya dan harga emas berdasarkan satu hari sebelumnya. Untuk memaksimalkan performa FIS Tsukamoto maka fungsi keanggotaan FIS Tsukamoto akan dioptimasi menggunakan Algoritme Genetika. Representasi kromosom yang digunakan adalah real-coded dengan tipe data double. Pada reproduksi metode crossover menggunakan one-cut point, sedangkan untuk metode mutasi menggunakan random mutation. Pada proses seleksi, metode yang digunakan adalah elitism selection untuk mendapatkan individu yang terbaik. Berdasarkan pengujian parameter yang dilakukan dengan 10 kali percobaan setiap parameter didapatkan ukuran populasi terbaik sebanyak 180, kombinasi cr= 0.9 dan mr= 0.1, serta jumlah generasi terbaik sebesar 325 nilai fitness terbaik sebesar 8, 6870. Nilai Root-Mean Squared Error (RMSE) yang didapatkan sebelum optimasi adalah 13, 3611, sedangkan setelah dilakukan optimasi didapatkan nilai RMSE yang lebih kecil yaitu sebesar 12, 5801. Hasil tersebut menunujukkan adanya peningkatan nilai akurasi pada FIS Tsukamoto setelah dioptimasi dengan menggunakan Algoritme Genetika.
Modified MobileNetV2 with convolutional block attention module for facial expression recognition
Authors: Muh Hafidh Ilmi Nafi’An, Fitra Abdurrachman Bachtiar, Budi Darma Setiawan
Year: 2023
Topics: Computer Vision; Deep Learning
Abstract: Facial emotion recognition is one of the artificial intelligence implementations used to recognize emotions based on data learned by computers. Unlike humans, who can recognize a person’s emotions directly, computers need several trainings procedures conducted by humans to be able to recognize a person’s emotions. Previous research has proposed various methods with deep learning and traditional machine learning approaches to classify emotions based on faces. Some studies obtained relatively high accuracy, but on evaluation by cross-validation, the results were much lower than the accuracy obtained. Therefore, this study proposes an approach using a modified MobileNetV2 deep learning architecture in the residual layer by adding a Convolutional Block Attention Module (CBAM) to improve accuracy and data generalization. This experiment uses the Karolinska Directional Emotional Faces (KDEF …
Comparative Analysis of Summarization Methods for Skin Care Product Reviews: A Study on BERT, BART, and T5 Models
Authors: Nisrina Arintia Maghfiroh, Fitra Abdurrachman Bachtiar, Lailil Muflikhah
Year: 2023
Topics: Natural Language Processing; Machine Learning
Abstract: The growth of the skin care industry is driving consumers to look for product review information. The skincare product currently widely used by women and men is facial wash products. On the Female Daily website, the most frequently purchased facial wash products are Cetaphil products. With so many purchases of this product, potential buyers want to try the product by reading the reviews first. However, the large number of reviews is a barrier to accessing relevant information. Therefore, effectively summarizing reviews can solve this problem by presenting key information more concisely and digestibly. This study aims to evaluate the effectiveness of three different summary methods, namely BERT, BART, and T5, in summarizing skin care product reviews. The stages in these three models are explained in detail. Evaluation results using the ROUGE Score method with the Cetaphil facial wash product review dataset …
Fluent: Factoid retrieval based chatbot using lstm
Authors: Fitra Abdurrachman Bachtiar, Andi Alifsyah Dysham, Alfirsa Damasyifa Fauzulhaq, Ja'far Shidqul Azzam, Himawat Aryadita
Year: 2023
Topics: Natural Language Processing; Deep Learning
Abstract: Advancement in technology especially in the Industrial Revolution 4.0 drive various automation in various field including the education field. Automation in the education filed may improve or support the daily business process. However, not all automation is implemented to support the student services. For example, a staff still needs to reply student questions repetitively and try of respond every inquiry shortly. These activities is hardly done by the staff since the number of staff is limited and could only serve few student at a time. One of the example to overcome this problem is the usage of chatbot. This study presents a development of a model that can act as an agent to answer students' questions about the faculty's general services and academic information. This study compares two types of models: LSTM and DNN which is built based on the data from a knowledge base collected in this study. The LSTM model …
Geometrial: Development of Educational Digital Game for Combined Two-Dimensional Figure Learning
Authors: Ahmad Fairuzabadi, Herman Tolle, Fitra A Bachtiar, Ahmad Afif Supianto
Journal: Journal of Information Technology and Computer Science
Year: 2022
Topics: Educational Technology; Game Development
Abstract: Mathematics is one of the subjects that is often considered difficult and boring for students. This is evidenced by the poor scores obtained by the students. One of the chapters that are considered difficult is the geometry chapter, especially on the topic of combined two-dimensional figures studied by students at the Vocational High School (SMK) level. Spatial skills are needed for students to be able to solve combined two-dimensional figures questions, which to learn will be very difficult without using assisted learning media. While the learning so far is still using the conventional learning approach which is considered boring for students. This is due to the absence of learning media that is fun and can be easily accessed by students. This study tries to present a solution to this problem in the form of a mobile-based educational digital game design that can be accessed by all students. This digital educational game is called Geometry. This study uses Research & Development (R&D) combined with the Agile-Extreme Programming method to develop this educational digital game. Tests were carried out using an expert validation approach to game prototypes. This study uses a questionnaire that adapts the Computer System Usability Questionnaire (CSUQ) to assess the usability aspect of the game system built. Meanwhile, to assess this game from the point of view of educational media, this study used an evaluation questionnaire of material experts and media experts. The development process occurs in 3 iterations of development which includes the ideation, conceptualization, and prototyping stages. The results obtained from the assessment of …
Myers-Briggs Type Indicator Personality Model Classification in English Text using Convolutional Neural Network Method
Authors: Joseph Ananda Sugihdharma, Fitra Abdurrachman Bachtiar
Journal: Jurnal Ilmu Komputer dan Informasi
Year: 2022
Topics: Natural Language Processing; Deep Learning
Abstract: Myers-Briggs Type Indicator (MBTI) is a personality model developed by Katharine Cooks Briggs and Isabel Briggs Myers in 1940. It displays a combination of preferences from four domains. Generally, test takers need to answer about 50 to 70 questions, and it is relatively expensive to know MBTI personality. The researcher developed a personality classification system using the Convolutional Neural Network (CNN) method and GloVe (Global Vectors for Word Representation) word embedding to solve this problem. The dataset used in this research consists of 8,675 data from the Kaggle site. The steps in this research are downloading the dataset from Kaggle, text preprocessing, GloVe weighting, classification using the CNN method, and evaluation using accuracy from the Confusion Matrix. Based on the tests carried out, using GloVe weighting can improve the model accuracy rather than random weighting. The best GloVe word dimensions depend on the metrics used to measure the model performance and the data of the classes contained in the dataset. From the CNN hyperparameter tuning test, the Adamax optimizer performs better and produces higher accuracy than the Adam optimizer. In addition, the CNN hyperparameter tuning increased model accuracy more significantly compared with the best GloVe word embedding dimensions.
A Study of Machine Learning Based Stressed Speech Recognition System.
Authors: Barlian Henryranu Prasetio, Edita Rosana Widasari, Fitra Abdurrachman Bachtiar
Journal: International Journal of Intelligent Engineering & Systems
Year: 2022
Topics: Machine Learning; Speech Processing
Abstract: The nonverbal communication processes a critical parcel. In some cases verbal communication is incapable since the speaker does not utilize non-verbal communication well at the same time. Non-verbal communication which falls in unconscious emotion is important in determining in function of cognition, language comprehension, and decision making. However, a little research studied in this area. Many years, researchers are amazed by the reliability of Mel-Frequency Cepstral Coefficients (MFCC) feature extraction technique in recognizing stressed speech. In this paper, we propose a simple feature extraction technique that effective but strong enough to recognize stressed speech. There are the speech energy and frequency. We attempted a basic approach to classify unbiased or stretch on female and male discourse. The highlight extraction is based on control and recurrence. This investigate utilized 10 female and 10 male discourse datasets. There are 5 classification strategies utilized. The classification models are Neural Arrange, k-Nearest Neighbour, Bolster Vector Machine, combination of NN-k-NN and combination of NN-SVM. Test comes about approved utilizing k-fold cross-validation strategy. The tests are assessed utilizing R-index to distinguish whether the highlights contributing to the push discourse acknowledgment. Based on exploratory comes about, the number of inputs impacts the esteem of R-index. In general, combining the Neural Organize and Back Vector Machine is the most excellent classification strategy by appearing stretch acknowledgment rate of 85% precision.
Perbandingan Convolutional Neural Network VGG16 dan ResNet34 pada Sistem Klasifikasi Sampah Botol
Authors: William Hutamaputra, Rifky Yunus Krisnabayu, Marrisaeka Mawarni, Novanto Yudistira, Fitra Abdurrachman Bachtiar
Journal: Jurnal Teknologi Dan Sistem Komputer
Year: 2022
Topics: Computer Vision; Deep Learning
Abstract: Hampir semua botol minuman kemasan yang beredar di masyarakat terbuat dari bahan plastik dikarenakan plastik merupakan bahan yang murah dan mudah dibentuk. Plastik adalah bahan non-organik yang sulit diuraikan sehingga botol plastik dapat menyebabkan pencemaran lingkungan. Sehingga diperlukan suatu solusi yang efektif untuk mengatasi kerusakan lingkungan yang disebabkan oleh sampah botol plastik. Salah satu solusi yag dapat dilakukan yaitu melakukan klasifikasi dan daur ulang sampah botol plastik. Pengklasifikasian sampah botol plastik dan sampah botol bukan plastik ke dalam kategori yang ditentukan sesuai dengan persyaratan kemudian didaur ulang agar dapat diolah kembali agar tidak merusak lingkungan. Artikel ini mengusulkan model VGG16 dan ResNet34 berbasis deep learning menggunakan CNN (Convolutional Neural Network) untuk mengidentifikasi dan mengklasifikasikan sampah botol. Berdasarkan hasil pengujian menggunakan Convolutional Neural Network, arsitektur VGG16 memiliki akurasi sebesar 90% dan ResNet34 memiliki akurasi sebesar 50% pada klasifikasi botol plastik dan bukan botol plastik. Masing-masing arsitektur menggunakan 10 epoch, 32 batch, 1655 gambar.
The Heron Formula Approach for Head Movements Detection for Student Focus Detection during Pandemic Online Class
Authors: Gusti Pangestu, Farhana Mar’i, Fitra A Bachtiar, Fairuz Iqbal Maulana
Year: 2021
Topics: Computer Vision; Educational Technology
Abstract: Head movement are widely used and utilized, there are many things that applying head movement as a main subject such as to control a device, control a smart wheelchair, playing games and many more. In this study, we propose a preliminary study to use the head movements as a focus detector for student in online learning. We use several approaches such as Face Landmark until Heron Triangle approach to detect and recognize the movement of the head. In this study, we utilize the Heron formulas to obtain the large of triangle that shaped using the marking point from Face Landmark method. By using the large of triangle, this proposed method could obtain and recognize the gaze of head movements. Our proposed method can reach 85% of accuracy and has an ability to detect the horizontal and vertical movements. This study is an important as an initial step to create a robust head movement detection …
Implementasi Support Vector Machine untuk Deteksi Stres pada Pengguna E-Learning
Authors: Fajar Pradana, Fitra A Bachtiar, Muhammad Zulfikarrahman
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer
Year: 2021
Topics: Machine Learning; Educational Technology
Abstract: Pada masa ini, e-learning cenderung monoton yang hanya digunakan untuk otomasi pekerjaan saja. Pada pengembangan e-learning yang akan datang, e-learning menerapkan lingkungan adaptif agar hasil yang didapatkan dari penggunaan e-learning dapat menjadi lebih optimal. Salah satu strategi agar e-learning menjadi adaptif adalah adaptasi dengan kondisi mental pengguna. Contoh kasus ketika pengguna stres maka sistem e-learning yang adaptif akan memberikan materi latihan yang lebih mudah atau memberi notifikasi untuk istirahat. Deteksi stres dapat dilakukan dengan pengolahan data dari sinyal fisiologis, yaitu heart rate. Metode klasifikasi Support Vector Machine diterapkan untuk deteksi stres. Fitur yang digunakan untuk klasifikasi stres adalah fitur yang berasal dari domain Heart Rate Statistical. Pengujian akurasi metode Support Vector Machine terhadap kasus pengguna e-learning mampu …
Promoting Emotions with Angry Birds-like Gameplay on Rube Goldberg Machine Levels
Authors: Febri Abdullah, Changeun Yang, Pujana Paliyawan, Ruck Thawonmas, Tomohiro Harada, Fitra A Bachtiar
Year: 2019
Topics: Game Development; Human-Computer Interaction
Abstract: This paper proposes a method for generating Angry Birds-like game levels, and their gameplays, that promote spectator's emotions. Each of such levels features a domino effect generated based on Rube Goldberg Machine (RGM) mechanisms, allowing it to be completed by one perfect shot of a bird. We evaluate the effects on spectator's emotions by comparing two sets of gameplay videos; one with only perfect shots and the other with only imperfact shots (i.e., gameplays that were not successfully completed by one shot). We conducted an online survey using Positive and Negative Affect Schedule (PANAS) to evaluate the spectator's emotions before and after watching a video of interest. Our results show that perfect-shot videos lead to higher positive affect and lower negative affect of the spectator in the same series of generated RGM levels. In addition, the perfect-shot video has a stronger evidence in …