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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer EMITTER International Journal of Engineering Technology Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Knowledge Engineering and Data Science JICTE (Journal of Information and Computer Technology Education) Applied Information System and Management Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Information System,Graphics, Hospitality and Technology Teknika Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology Insyst : Journal of Intelligent System and Computation Jurnal Nasional Teknik Elektro dan Teknologi Informasi Eduvest - Journal of Universal Studies Jurnal Inovasi Teknologi dan Edukasi Teknik Bulletin of Social Informatics Theory and Application
Esther Irawati Setiawan
Institut Sains Dan Teknologi Terpadu Surabaya
Aerospace Engineering Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

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Evaluating Player Experience for Fear Modeling of 2D East Java Horror Game Alas Tilas Herman Thuan To Saurik; Harits Ar Rosyid; Aji Prasetya Wibawa; Esther Irawati Setiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.5043

Abstract

Developing a 2D horror game and evaluating the reliability of the player experience are two things that are interrelated and equally important. Developers must ensure that the game can provide a satisfying and reliable gaming experience for their players. This study aims to evaluate the reliability of the player's experience in the game entitled Alas Tilas, East Java. This study used the User Experience Questionnaire (UEQ) in Indonesian as a survey approach method, which was given to 30 teenagers who at least played horror games once. UEQ may provide feedback to developers on the attractiveness, clarity, efficiency, accuracy, stimulation, and novelty aspects of the game. From the results of the UEQ, a reliability test will be carried out using the Cronbach Alpha technique. The results of the descriptive analysis show that these variables are Attractiveness (mean, 0.933), Clarity (mean, 1.808), Efficiency (mean, 1.508), Accuracy (mean, 0.217), Stimulation (mean, 0.667) and Novelty (mean, 0.242). Attractiveness, clarity, and efficiency averaged positive results. The average aspects of accuracy, stimulation, and novelty of the game get neutral results. The results of the reliability test conducted on UEQ data obtained a Cronbach alpha value > 0.6 which indicates that the research data used to test the player experience are considered reliable so that they can be used to provide input for future development of the Alas Tilas game. To increase the average score, the researcher provides recommendations for improvement, namely, adjusting the accuracy and novelty aspects of the horror scenario game entitled Alas Tilas East Java. Therefore, it is expected to improve the quality of the game.
Maximum Marginal Relevance and Vector Space Model for Summarizing Students' Final Project Abstracts Gunawan Gunawan; Fitria Fitria; Esther Irawati Setiawan; Kimiya Fujisawa
Knowledge Engineering and Data Science Vol 6, No 1 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i12023p57-68

Abstract

Automatic summarization is reducing a text document with a computer program to create a summary that retains the essential parts of the original document. Automatic summarization is necessary to deal with information overload, and the amount of data is increasing. A summary is needed to get the contents of the article briefly. A summary is an effective way to present extended information in a concise form of the main contents of an article, and the aim is to tell the reader the essence of a central idea. The simple concept of a summary is to take an essential part of the entire contents of the article. Which then presents it back in summary form. The steps in this research will start with the user selecting or searching for text documents that will be summarized with keywords in the abstract as a query. The proposed approach performs text preprocessing for documents: sentence breaking, case folding, word tokenizing, filtering, and stemming. The results of the preprocessed text are weighted by term frequency-inverse document frequency (tf-idf), then weighted for query relevance using the vector space model and sentence similarity using cosine similarity. The next stage is maximum marginal relevance for sentence extraction. The proposed approach provides comprehensive summarization compared with another approach. The test results are compared with manual summaries, which produce an average precision of 88%, recall of 61%, and f-measure of 70%.
Klasifikasi Nyeri pada Video Ekspresi Wajah Bayi Menggunakan DCNN Autoencoder dan LSTM Yosi Kristian; I Ketut Eddy Purnama; Effendy Hadi Sutanto; Lukman Zaman; Esther Irawati Setiawan; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 3: Agustus 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1508.119 KB)

Abstract

Babies are still unable to inform the pain theyexperience, therefore, babies cry when experiencing pain. With the rapid development of computer vision technologies, in the last few years, many researchers have tried to recognize pain from babies expressions using machine learning and image processing. In this paper, a research using Deep Convolution Neural Network (DCNN) Autoencoder and Long-Short Term Memory (LSTM) Network is conducted to detect cry and pain level from baby facial expression on video. DCNN Autoencoder isused to extract latent features from a single frame of baby face. Sequences of extracted latent features are then fed to LSTM sothe pain level and cry can be recognized. Face detection and face landmark detection is also used to frontalize baby facial imagebefore it i s processed by DCNN Autoencoder. From the testing on DCNN autoencoder, the result shows that the best architecture used three convolutional layers and three transposed convolutional layers. As for the LSTM classifier, the best model is using four frame sequences.
FlashCard Mobile Web App untuk Pembelajaran Matematika dengan Sencha Touch FrameWork Tjwanda Putera Gunawan; Esther Irawati Setiawan; Heppi Siswanto; Setya Ardhi; Joan Santoso
Jurnal Inovasi Teknologi dan Edukasi Teknik Vol. 3 No. 2 (2023)
Publisher : Universitas Ngeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um068v3i22023p99-104

Abstract

A flashcard is a learning card that is used by children. This study card has two sides, the front and rear. The front section usually contains questions, and the back contains the answers. The way to learn this card is by opening the front of the card and thinking about the answer. Then the card is reversed, if the answer is like the answer on the back of the card, it is correct. If the answer is wrong, this process is repeated until the answer is correct. Sencha Touch is a mobile web app framework. This framework is used by developers who want to develop web applications like the original, but Sencha only runs on the client side. If the developers want to run the application on the server side, they can use PHP which is called by using Ajax request. This application aims to develop a mobile flashcard application using Sencha Touch. Features such as quizzes and group will be added to share the flashcard or quiz questions with friends and find out their learning activities. There is also a multimedia feature, by which users can add images, voice, or video on flashcards. The use of Sencha Touch mobile web is very helpful because the GUI for web app development using Sencha Architect. Sencha Touch handles only the client side, so it is necessary to have an application to handle the server side for database processing, which is done by using PHP called by using Ajax request. Flashcard merupakan kartu belajar yang pada umumnya digunakan untuk belajar anak-anak pada usia balita. Kartu belajar tersebut memiliki dua sisi, bagian depan dan bagian belakang. Pada bagian depan biasanya berisi pertanyaan, dan bagian belakang berisi jawaban. Cara mempelajarinya adalah dengan membuka kartu bagian depan, kemudian pengguna memikirkan jawabannya. Setelah itu kartu dibalik, jika jawaban yang dipikirkan sama dengan jawaban pada bagian belakang kartu, maka jawabannya benar. Jika jawabannya salah pembelajaran diulangi berkali-kali hingga jawabannya benar. Sencha Touch merupakan framework mobile web app. Framework ini digunakan para pengembang yang ingin membuat aplikasi web seperti aplikasi asli, tetapi pada Sencha hanya berjalan pada client side. Jika pengembang ingin menjalankan aplikasi server side, pengembang dapat menggunakan PHP yang dipanggil menggunakan Ajax request. Aplikasi ini bertujuan membuat suatu aplikasi flashcard dengan Sencha Touch. Fitur yang akan ditambahkan antara lain fitur quiz, fitur grup untuk dapat berbagi kartu flashcard atau soal quiz kepada teman, dan mengetahui aktifitas belajar teman. Juga ada fitur multimedia, dimana pengguna dapat menambahkan gambar, suara, atau video pada flashcard yang dibuat. Penggunaan Sencha Touch sangat membantu pembuatan mobile web app, karena adanya GUI untuk pembuatan web app dengan menggunakan Sencha Architect. Sencha Touch hanya menangani aplikasi secara client side, sehingga dibutuhkan aplikasi server side untuk pengolahan database, yaitu dengan menggunakan PHP yang dipanggil menggunakan Ajax request.
Deteksi Komentar Cyberbullying Pada YouTube Dengan Metode Convolutional Neural Network - Long Short-Term Memory Network (CNN-LSTM) Albertus Josef Andika; Yosi Kristian; Esther Irawati Setiawan
Teknika Vol 12 No 3 (2023): November 2023
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v12i3.677

Abstract

Pada era digital seperti sekarang cyberbullying kerapkali terjadi di berbagai belahan dunia termasuk di Indonesia, hal ini dapat terjadi pada siapa saja dan dimana saja terutama media sosial seperti YouTube melalui fitur komentar semua pengguna yang memiliki akun dapat dengan mudah terlibat cyberbullying hanya melalui berbalas komentar. Penelitian ini bertujuan untuk melakukan deteksi adanya cyberbullying melalui pengumpulan serta pengklasifikasian komentar negatif video pada kanal YouTube dengan konten tertentu berbasis bahasa Indonesia (serta bahasa-bahasa daerah tertentu, seperti Jawa dan Surabaya) melalui metode deep-learning Convolutional Neural Network — Long Short-Term Memory Network (CNN-LSTM). Dataset komentar yang dipakai dalam penelitian dikumpulkan dengan menggunakan Application Program Interface (API) yang telah disediakan oleh Youtube secara gratis dan berbatas kuota secara kumulatif. Terkumpul data komentar total sebanyak 26.918 komentar dengan perincian 9.834 komentar terklasifikasi cyberbullying dan 17.084 komentar terklasifikasi sebagai bukan cyberbullying. Setelah dataset dipakai dalam proses training pada model CNN-LSTM dan menghasilkan sebuah model dengan nilai F1-score sebesar 0,84, model tersebut dipakai dalam sebuah API sederhana yang menerima input beberapa kalimat yang akan dideteksi konten cyberbullying dan menghasilkan output berupa JSON yang berisi hasil klasifikasi dari setiap kalimat yang akan dideteksi.
Klasifikasi Sentimen Opini Publik Pada Instagram Pemerintah Kabupaten Bojonegoro Menggunakan LSTM Titis Arwindarti; Esther Irawati Setiawan; Syaiful Imron
Teknika Vol 13 No 1 (2024): Maret 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i1.699

Abstract

Media sosial banyak membantu masyarakat dalam mendapatkan informasi terbaru terkait peristiwa atau kejadian dilingkungan sekitar maupun lebih luas. Masyarakat dapat menyampaikan pendapat mereka melalui tulisan dan dapat mengekspresikannya melalui fitur emoticon pada platform media sosial. Pemerintah Kabupaten Bojonegoro menggunakan platform Instagram sebagai salah satu sarana dalam menyampaikan informasi kepada masyarakat. Selaku pembuat kebijakan pelayanan publik membutuhkan feedback dari masyarakat agar kebijakan yang dibuat bisa tepat sasaran dan bermanfaat bagi masyarakat. Sentimen opini publik merupakan aspek penting dalam memahami respon masyarakat terhadap layanan masyarakat, program dan kebijakan yang dibuat. Peneliti mengumpulkan dan mengolah data yang diperoleh dari proses scrapping akun resmi Instagram Pemerintah Kabupaten Bojonegoro sebanyak 4.637 dataset yang selanjutnya dilakukan pelabelan data. Penelitian ini menggunakan word embbeding Word2Vec untuk mengubah teks menjadi representasi vektor dan Long Short-Term Memory (LSTM) untuk melakukan klasifikasi. Dengan menggunakan confusion matrix menunjukkan bahwa model LSTM yang dibuat hasilnya mencapai akurasi 84,16%. Hasil analisa tersebut dapat memberikan kontribusi positif dan dapat menjadi bahan pertimbangan Pemerintah Kabupaten Bojonegoro dalam upaya meningkatkan layanan masyarakat, program dan kebijakan yang dibuat.
Sequential Pattern Mining to Support Customer Relationship Management at Beauty Clinics Setiawan, Esther Irawati; Natalie, Valerynta; Santoso , Joan; Fujisawa, Kimiya
Bulletin of Social Informatics Theory and Application Vol. 6 No. 2 (2022)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v6i2.602

Abstract

The increasing competition for beauty clinics, makes management need to think of methods to survive in this competition. For that, the company needs to improve CRM in its service to customers. Customer Relationship Management is a series of activities managed in an effort to better understand, attract attention, and maintain customer loyalty. Sequential Pattern Mining is one of the data mining techniques that is useful for finding patterns sequential / sequence of a set of items. The algorithm that is used is the Generalized Sequential Pattern (GSP). GSP performs candidate generation and support counting processes that is, the union of L1−k with itself which generates a candidate sequence that cannot exist as twin candidate, after that deletion candidate who does not meet the minimum support. While carrying out the process through existing data, is also carried out increasing the number of supports from the included candidates in data sequences. The output to be produced by the program are all frequent itemsets that satisfy minimum support in the form of rules. Sales transaction data will be processed by using the Generalized Sequential Pattern algorithm so that it can produce a rule, namely the purchase order that meets the minimum support. The result of the rule used by management to support enterprise CRM activities such as acquiring new customers, increasing the profits from existing customers, and retaining existing customers.
Long short-term memory-based chatbot for vocational registration information services Langgeng, Yudo Sembodo Hastoro; Setiawan, Esther Irawati; Imron, Syaiful; Santoso, Joan
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.128

Abstract

The development of chatbots can communicate fluently like humans thanks to the Natural Language Processing (NLP) technology. Using this technology, chatbots can provide more accurate and natural responses, providing an almost the same experience as human interaction. Therefore, chatbot technology is in great demand by companies and government agencies as a cost-effective solution for information and administrative services that require little human effort and can operate 24/7. The registration information service at BLK Surabaya still uses an operator who serves prospective trainees and answers questions via social media or chat. However, these operators have limitations in terms of time and effort. The purpose of this study is to examine how to use chatbots to answer questions about registration information training at BLK Surabaya using the Long Short Term Memory (LSTM) algorithm with a dataset of questions collected in the form of Frequently Asked Questions (FAQ) in Indonesian. The dataset consists of 2,636 labeled samples of questions, which were divided into three sets: 60% for training (1,581 pieces), 20% for validation (527 samples), and 20% for testing (528 samples) to evaluate the model's performance. Several steps were taken in implementing this research, including changing the list of questions and answers into the JSON data format, preprocessing, creating LSTM modeling, data training, and data testing. The test results show that Chatbot can provide accurate solutions related to training registration questions with Precision of 88.4%, Accuracy of 87.6%, and Recall of 87.3%.
Timbre Style Transfer for Musical Instruments Acoustic Guitar and Piano using the Generator-Discriminator Model Nagari, Widean; Santoso, Joan; Setiawan, Esther Irawati
Knowledge Engineering and Data Science Vol 7, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v7i12024p101-116

Abstract

Music style transfer is a technique for creating new music by combining the input song's content and the target song's style to have a sound that humans can enjoy. This research is related to timbre style transfer, a branch of music style transfer that focuses on using the generator-discriminator model. This exciting method has been used in various studies in the music style transfer domain to train a machine learning model to change the sound of instruments in a song with the sound of instruments from other songs. This work focuses on finding the best layer configuration in the generator-discriminator model for the timbre style transfer task. The dataset used for this research is the MAESTRO dataset. The metrics used in the testing phase are Contrastive Loss, Mean Squared Error, and Perceptual Evaluation of Speech Quality. Based on the results of the trials, it was concluded that the best model in this research was the model trained using column vectors from the mel-spectrogram. Some hyperparameters suitable in the training process are a learning rate 0.0005, batch size greater than or equal to 64, and dropout with a value of 0.1. The results of the ablation study show that the best layer configuration consists of 2 Bi-LSTM layers, 1 Attention layer, and 2 Dense layers.
Aspect-Based Sentiment Analysis of Healthcare Reviews from Indonesian Hospitals based on Weighted Average Ensemble Setiawan, Esther Irawati; Tjendika, Patrick; Santoso, Joan; Ferdinandus, FX; Gunawan, Gunawan; Fujisawa, Kimiya
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.328

Abstract

Public assessments are essential for evaluating hospital quality and meeting patient demand for superior medical treatment. This study offers a novel approach to aspect-based sentiment analysis (ABSA), which consists of aspect extraction, emotion categorization, and aspect classification. The goal is to examine patient reviews (6,711 reviews) from Google assessments of 20 Indonesian hospitals, broken down by categories including cost, doctor, nurse, and other categories. For example, there are 469 good, 66 negative, and 7 neutral ratings for cleanliness and 93 positive, 125 negative, and 19 neutral reviews for pricing in the sample, which covers a range of attitudes. Using the Conditional Random Field (CRF) approach, aspect phrase extraction was refined and word characteristics and positional tags were adjusted, resulting in an improvement in the F1-score from 0.9447 to 0.9578. The Support Vector Machine (SVM) model had the greatest F1-score of 0.8424 out of two strategies used for aspect categorization. With the addition of sentiment words, sentiment classification improved and led by SVM to an ideal F1-score of 0.7913. For aspect and sentiment classification, a Weighted Average Ensemble approach incorporating SVM, Naïve Bayes, and K-Nearest Neighbors was employed, yielding F1-scores of 0.7881 and 0.8413, respectively. The use of an ensemble technique for sentiment and aspect classification and the incorporation of hyperparameter optimization in CRF for aspect term extraction, which led to notable performance gains, are the innovative aspects of this work.
Co-Authors Adi Sutanto Adriel Ferdianto Aidil Aji Prasetya Wibawa Albertus Josef Andika Arif Priyambodo Arya Tandy Hermawan Christian Nathaniel Purwanto Dhaniswara, Erwin Edwin Pramana Edwin Pramana Effendy Hadi Sutanto Eko Mulyanto Yuniarno Endang Setyati F.X. Ferdinandus Fachrul Kurniawan Ferdinandus, F. X. Fitria Fitria Fujisawa, Kimiya Gunawan Gunawan Gunawan Halim, Kevin Jonathan Hans Juwiantho Hans Keven Budi Prakoso Harianto, Reddy Alexandro Harits Ar Rosyid Hartono, Luluk Setiawati Hartono, Patrick Heppi Siswanto Herman Budianto Herman Thuan To Saurik Honoris Setiahadi I Ketut Eddy Purnama Ika Lestari Imron, Syaiful Joan Santoso Johanes, Sugiharto Kimiya Fujisawa Kristian, Yosi Langgeng, Yudo Sembodo Hastoro Leonel Hernandez Lim, Ernest Lukman Zaman Machfudin, Mohammad Farid Mauridhi Hery Purnomo Mauridhi Hery Purnomo Mohamad Fahmi Yusuf Muhammad Arief Rahman Mustaqin, Farhan Faisal Zainul Nagari, Widean Natalie, Valerynta Putra, Adi Surya Rachmatullah, Sholeh Rahmatullah, Dewangga Ramadhan Ramadhan Safitri, Lia Samuel Budi Wardhana Kusuma Santoso , Joan Saputra, Daniel Gamaliel Setya Ardhi Singh, Vrijraj Sugiono, Judi Prajetno Surya Sumpeno Syaiful Imron Syaiful Imron Titis Arwindarti Tjendika, Patrick Tjwanda Putera Gunawan Vu, Tong Nam Tuan Wardoyo, Nikko Riestian Putra Yosi Kristian Yuliana Yuliana YUNI YAMASARI Zaman, Lukman