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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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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%.
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.
MultiResUNet for COVID-19 Lung Infection Segmentation Based on CT Image Ferdinandus, F.X.; Setiawan, Esther Irawati; Santoso, Joan
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.85386

Abstract

Image segmentation plays a crucial role in medical image analysis, facilitating the identification and characterization of various pathologies. During the COVID-19 pandemic, this technique has proven valuable for detecting and assessing the severity of infection. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have significantly enhanced the efficacy of image segmentation. Numerous CNN-based architectures have been proposed in the literature, with MultiResUNet emerging as a promising approach. This study investigates the application of the MultiResUNet architecture for segmenting regions of COVID-19 infection within patient lung CT images. Experimental results demonstrate the effectiveness of MultiResUNet, achieving an average Dice score of 73.10%.
Retrieval Augmented Generation-Based Chatbot for Prospective and Current University Students Hartono, Luluk Setiawati; Setiawan, Esther Irawati; Singh, Vrijraj
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.951

Abstract

Universities utilize chatbots as assistants for users, especially prospective and current students, to access information and answer questions with relevant answers. This study introduces a new approach to an open-source model-based QA system using Gemma2-2b-it by combining Retrieval Augmented Generation (RAG) and Fine-tuning (FT) techniques. Previously, some studies have focused on only one approach, but this study will combine and compare both methods separately. Raw conversation data from WhatsApp, the main university website, and university PDF documents are used. The Retrieval Augmented Generation Assessment (RAGAS) framework will be used to evaluate the performance of the RAG model. In contrast, precision, recall, and similarity are used to assess the comparative performance of RAG and fine-tuning. The results of the RAGAS show that RAG using the base model is better than RAG using a fine-tuned model, which has 0.78 faithfulness, 0.64 answer relevancy, 0.81 context precision, and 0.68 context recall, so the overall RAGAS Score is 0.72. The comparison of precision and recall of fine-tuning are higher than those of using RAG, but the similarity score is not much different. Furthermore, the potential improvement for RAG of this study can be increased by adding a reranking process in the retrieved context, and fine-tuning of the embedding model can also be added to increase the retrieval process's performance. In addition, further experiments on various datasets and the challenge of overfitting in fine-tuning must be overcome so that the model can also perform better generalization.
Market Basket Analysis untuk Penjualan Perlengkapan Cetak dengan Algoritma FP Growth Rahmatullah, Dewangga; Setiawan, Esther Irawati; Yuliana
Journal of Information System,Graphics, Hospitality and Technology Vol. 7 No. 1 (2025): Journal of Information System, Graphics, Hospitality and Technology
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37823/insight.v7i1.435

Abstract

Perusahaan yang bertumbuh adalah perusahaan yang terus berkembang dan berinovasi menemukan berbagai macam strategi seiring dengan berjalannya waktu agar meningkatkan omzet usaha yang ditandai dengan penjualan barang. Namun apabila perusahaan serupa atau kompetitor juga melakukan pendekatan strategi yang sama, maka perlu mempersiapkan strategi pemasaran baru untuk meningkatkan penjualan.             Market Basket Analysis merupakan pendekatan analisis data untuk mengenali pola perilaku konsumen terhadap keterkaitan antar produk dalam transaksi penjualan. Metode yang digunakan dalam analisis ini adalah association rule mining, yang berfokus pada pencarian relasi produk yang dibeli secara bersamaan. Terdapat tiga metrik utama dalam metode ini, yaitu support, confidence, dan lift, yang digunakan untuk menilai relevansi aturan asosiasi. Algoritma FP-Growth dipakai karena mampu menemukan aturan asosiasi secara lebih efisien melalui pembuatan struktur data FP-Tree, yang memungkinkan penemuan frequent itemset tanpa perlu menghasilkan kombinasi kandidat secara eksplisit.                 Pengujian dilakukan pada data transaksi penjualan dari tahun 2022-2023 dengan total sebanyak 118.709 transaksi dengan bahasa Python lalu menghasilkan 9 aturan asosiasi. Pelaku bisnis dapat melakukan strategi pemasaran seperti membuat promosi product bundling dan peletakan produk yang berdekatan. Produk-produk tertentu yang memiliki keterkaitan satu sama lain seperti HEAD L210 L1110 L3110 L3150 DUS KECIL NEW dengan FP HEAD CLEANER PREMIUM 20ML (93,99%) dan FP PERMANENT STAMP 10ML – BLACK dengan FP PERMANENT STAMP REMOVER 5ML (97,91%) dapat menjadi kandidat bundel produk yang menjanjikan dikarenakan memiliki nilai confidence yang tinggi.
Sentiment Analysis Twitter Bahasa Indonesia Berbasis WORD2VEC Menggunakan Deep Convolutional Neural Network Juwiantho, Hans; Setiawan, Esther Irawati; Santoso, Joan; Purnomo, Mauridhi Hery
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 1: Februari 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Media sosial sebagai media informasi dan komunikasi mulai berkembang pesat sejak internet mudah diakses. Orang dengan mudah menyatakan pendapat, ekspresi, opini, dan informasi melalui tulisan pada media sosial. Opini atau informasi pada media sosial dapat digunakan untuk menilai baik atau buruk suatu brand perusahaan. Orang cenderung jujur dalam mengungkapkan perasaan terhadap sesuatu pada media sosial. Dengan menggunakan sentiment analysis terhadap opini dari pelanggan, analisis opini dapat dilakukan secara otomatis. Perusahaan dapat secara langsung mengetahui tingkat kepuasan pelanggan dan digunakan untuk meningkatkan kualitas pelayanan hingga menaikan brand perusahaan. Penggunaan metode classical machine learning yang sudah banyak diterapkan pada sentiment analysis, tetapi metode tersebut tidak memperhatikan pentingnya urutan kata pada suatu kalimat. Metode deep learning dengan algoritme Deep Convolutional Neural Network ditawarkan untuk menjawab permasalahan tersebut dengan melakukan operasi convolution menggunakan filter sebesar ukuran window untuk mendapatkan fitur berdasarkan urutan kata. Model Word2Vec untuk Bahasa Indonesia digunakan sebagai representasi kata dalam bentuk vektor. Penggunaan Word2Vec juga mempercepat proses pelatihan dan meningkatkan akurasi algoritme Deep Convolutional Neural Network. Data yang digunakan dalam makalah ini adalah data Twitter Bahasa Indonesia dengan jumlah 999 tweet. Hasil percobaan yang telah dilakukan dengan algoritme Deep Convolutional Neural Network memiliki nilai akurasi terbaik sebesar 76,40%. AbstractSocial media as information media and communication is growing rapidly since the internet is easily accessible. People easily express opinions, expressions, and information by writing on social media. Opinion or information on social media can be used to assess how good or bad a companies is. People tend to be honest in expressing feelings towards something on social media. With sentiment analysis, analysis of the opinions of customers can be done automatically. The company will know the level of customer satisfaction and can be used to improve the quality of service to raise the company's brand. The use of classical machine learning methods that have been widely applied to sentiment analysis ignoring the importance of the word order in a sentence. Deep Convolutional Neural Network algorithm is offered to answer these problems by carrying out convolution operations using filters as large as window size to get features based on word order. Word2Vec model for Indonesian is used as a word vector representation. The use of Word2Vec also reduce the training time and improve the accuracy of the Deep Convolutional Neural Network algorithm. The data used in this paper is Indonesian Twitter data with 999 tweets. The results of experiments that have been carried out with the Deep Convolutional Neural Network algorithm have the best accuracy value of 76.40%.
Co-Authors Adi Sutanto Adriel Ferdianto Aidil Aji Prasetya Wibawa Albertus Josef Andika Arif Priyambodo Arya Tandy Hermawan Christian Nathaniel Purwanto Collantes, Leonel Hernandez Daniel Gamaliel Saputra Dhaniswara, Erwin Edwin Pramana Edwin Pramana Effendy Hadi Sutanto Eko Mulyanto Yuniarno Endang Setyati F. X. Ferdinandus F.X. Ferdinandus Fachrul Kurniawan Fachrul Kurniawan Farhan Faisal Zainul Mustaqin Ferdinandus, Fransiskus Xaverius Fitria Fitria Fitria Fitria Fujisawa, Kimiya Gunawan Gunawan Gunawan Hans Juwiantho Hans Keven Budi Prakoso Harianto, Reddy Alexandro Harits Ar Rosyid Hartono, Luluk Setiawati Heppi Siswanto Herman Budianto Herman Thuan To Saurik Honoris Setiahadi I Ketut Eddy Purnama Ika Lestari Imron, Syaiful Joan Santoso Johanes, Sugiharto Kevin Jonathan Halim Kimiya Fujisawa Kristian, Yosi Langgeng, Yudo Sembodo Hastoro Leonel Hernandez Lia Safitri Lim, Ernest Lukman Zaman Mauridhi Hery Purnomo Mauridhi Hery Purnomo Mohamad Fahmi Yusuf Mohammad Farid Machfudin Muhammad Arief Rahman Nagari, Widean Natalie, Valerynta Patrick Hartono Purwanto, Christian Nathaniel Putra, Adi Surya Rachmatullah, Sholeh Rahmatullah, Dewangga Samuel Budi Wardhana Kusuma Santoso , Joan Setya Ardhi Singh, Vrijraj Sugiono, Judi Prajetno Surya Sumpeno Syaiful Imron Syaiful Imron Titis Arwindarti Tjendika, Patrick Tjwanda Putera Gunawan Tong Nam Tuan Vu Wardoyo, Nikko Riestian Putra Yosi Kristian Yuliana Yuliana Yuni Yamasari Zaman, Lukman