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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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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%.
Kuntilanak as a Runtime Entity: Technical Integration of Javanese Folklore Using Manga Matrix in a 2D Horror Game Saurik, Herman Thuan To; Rosyid, Harits Ar; Wibawa, Aji Prasetya; Setiawan, Esther Irawati
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.v5i3.961

Abstract

In this work, Kuntilanak, a mythological creature from Javanese mythology, is used as a dynamic element in a 2D horror game to provide a technical framework for integrating culturally infused folklore into interactive gaming. The design process breaks down the character's appearance, attire, and personality into workable technical specifications using the Manga Matrix framework as a guide. With C# scripted behaviours like unexpected appearances, animation state changes (controlled by Unity's Animator Controller), audio triggers (laughing, crying), and interactive reactions to in-game objects like yellow Bamboo (for hiding) and scissors (for repelling), Kuntilanak was created as a sprite-based runtime entity inside the Unity game engine. The character can be dynamically instantiated thanks to this technical approach, which supports procedural horror encounters and is consistent with traditional narratives. The effectiveness of the suggested technological integration was validated by a quantitative assessment using a Likert scale (N=50), which showed 82.2% agreement on cultural authenticity and 79.5% on emotional impact. The findings support the methodology's capacity to turn folklore characters into functional game entities and offer a replicable model for serious games that consider cultural sensitivity. The findings support the methodology's capacity to turn folklore characters into functional game entities and provide a replicable model for serious games that consider cultural sensitivity, with direct implications for designing engaging educational experiences that promote cultural heritage preservation.
Optimization of LPG Distribution for a Multiplatform-Based LPG Marketplace Budianto, Herman; Mustaqin, Farhan Faisal Zainul; Setiawan, Esther Irawati; Santoso, Joan
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.v5i3.994

Abstract

Marketplace applications have become an essential digital solution supporting online transactions, including LPG distribution. The development of this application adopts a multiplatform approach, enabling the application to run on various devices, particularly Android platforms and websites. Using the React Native framework, developers can build applications with a single, efficient codebase for multiple platforms. This study aims to provide users with convenience in purchasing LPG without leaving their homes while offering a more practical and effective user experience. This research includes features for selling, buying, payment, and delivery via courier. The transaction feature facilitates sellers' recording of sales within the application. The results of alpha testing indicate that the Elpijiku marketplace app works well despite some significant errors or bugs. However, acceptance testing results were very positive, with 91% of respondents rating the application and user experience as good. These findings indicate that the Elpijiku application meets user needs in terms of convenience and efficiency and is suitable for use as a digital solution for LPG distribution.
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.
Evaluating User Experience of a Virtual Reality-Based Adaptive Learning Application on Chemical Compound Structures for High School Students Setiawan, Esther Irawati; Machfudin, Mohammad Farid; Saputra, Daniel Gamaliel; Santoso, Joan; Gunawan, Gunawan; Kusuma, Samuel Budi Wardhana
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1445

Abstract

Recognizing the significant spatial visualization challenges that high school students face in understanding abstract chemical compound structures—a limitation often inherent in conventional teaching methods based on 2D diagrams—this research presents the comprehensive development and user experience (UX) evaluation of an innovative adaptive learning application in Virtual Reality (VR). The application, developed using the Unity 3D engine and configured via XR Plugin Management to ensure broad hardware compatibility, places students in an interactive virtual laboratory. Within it, students can directly manipulate meticulously designed 3D atomic models to build molecules, observe the formation of covalent and ionic bonds, and interact with dynamic chemical processes. Its key innovation is the integration of an intelligent adaptive learning algorithm, which utilizes a Firebase cloud database to analyze user performance metrics—such as accuracy, completion time, and recurring areas of difficulty. Based on this data, the system dynamically personalizes learning pathways by recommending remedial content or more challenging topics. Furthermore, assessment materials such as quizzes were efficiently generated using large language models (LLMs) to ensure relevance and quality. An in-depth UX evaluation was conducted with high school students using a mixed-methods approach, combining standardized questionnaires to quantitatively measure metrics like usability, engagement, and satisfaction, with qualitative feedback sessions for contextual insights. The results indicate a highly positive user experience; participants reported that the ability to directly manipulate molecules in 3D space significantly enhanced their conceptual understanding, bridging the gap between theory and visualization. The adaptive system was highly valued for its ability to adjust to individual learning paces, which was shown to boost confidence and reduce frustration. This research provides strong evidence that VR-based adaptive learning platforms are powerful pedagogical tools, capable of transforming chemistry education by making complex scientific concepts more accessible, engaging, and comprehensible.
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%.
Factors Affecting The Adoption Of Mobile Learning In Vocational High Schools And High Schools Using Extended UTAUT Safitri, Lia; Pramana, Edwin; Setiawan, Esther Irawati
Eduvest - Journal of Universal Studies Vol. 4 No. 8 (2024): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v4i8.1718

Abstract

M-Learning is a learning process that uses technology or mobile devices such as smartphones, tablets or wearable devices to support the learning process. This is still being done because there are many different theoretical models proposed. However, there is no model that can be generally accepted as an established theoretical model in the application of M-learning in vocational and high school education environments in Sidoarjo. This research is expected to make a significant contribution to the development of a better theoretical understanding of the determining factors that influence M-learning adoption using the Unified Theory of Acceptance and Use of The Technology (UTAUT). To collect data, researchers distributed questionnaires to respondents using Google Form. The data used were 444 M-learning users. Theoretical model research was carried out using Structural Equation Modeling (SEM) analysis, then SPSS and Amos as analysis support. There are seven factors that determine the results of acceptance of M-Learning adoption in this research, namely Facilitating Condition, Performance Expectancy, Effort Expectancy, Perceived Convenience, Social Influence, School Management Support. The six factors that show a positive and significant relationship are Facilitating Condition, Performance Expectancy, Effort Expectancy, Perceived Convenience, Social Influence, School Management Support. Perceived Convenience has the first strongest positive and significant value, and Performance Expectancy has the second strongest value. Each factor has a moderate influence on Intention to Use. This factor is the most influential in implementing M-Learning in vocational and high schools in the Sidoarjo area.
Klasifikasi SMS Center RSUD SMART Berdasarkan Jenis Keluhan Pelayanan Menggunakan Naive Bayes Rachmatullah, Sholeh; Setiawan, Esther Irawati; Harianto, Reddy Alexandro
Intelligent System and Computation Vol 1 No 1 (2019): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v1i1.37

Abstract

RSUD SMART memiliki layanan SMS Center yang digunakan untuk berinteraksi dengan masyarakat dalam menerima pertanyaan, masukan, saran dan kritik maupun pengaduan. Informasi yang diterima dalam bentuk sms hanya disimpan dan tidak dikelompokkan berdasarkan unit atau layanan yang dituju sehingga pihak manajemen rumah sakit tidak bisa mengukur tingkat pelayanan di tiap unit. Penelitian ini melakukan klasifikasi terhadap data SMS dan saran responden dari masyarakat yang ditujukan kepada Direktur Rumah Sakit. Data SMS yang diklasifikasi berasal dari database aplikasi SMS Center RSUD SMART Pamekasan. Data SMS dan saran responden diklasifikasikan dalam 10 kelas yaitu Rawat Inap, Rawat jalan, Laboratorium, Farmasi, BPJS, Humas, Loket Pembayaran, Sarana dan Prasarana, Profesi dan tidak Terklasifikasi, serta melakukan scoring sms. Sebelum melakukan proses klasifikasi terlebih dahulu dilakukan pre-processing seperti penyamaan karakter, penghapusan tanda baca, mengembalikan singkatan, terjemah bahasa daerah (Bahasa Madura), penghapusan angka, penghapusan kata yang tidak penting dalam SMS, dan stemming untuk mengubah kata menjadi kata dasar. Penelitian ini menggunakan algoritma Naive Bayes dengan Two Stage (TS) Smoothing. Dalam beberapa uji coba yang telah dilakukan terhadap 2292 data dengan presentase data traning sebesar 20%, 30%, 40% dan 50% mendapatkan rata-rata akurasi sebesar 82,97% dengan nilai λ=0.2, μ=2000 dan threshold=3. Bahkan dalam salah satu uji coba klasifikasi dengan threshold statis mencapai akurasi 86,73% sedangkan akurasi terendah dengan threshold dinamis mencapai 74,28%. Pengaturan threshold statis terbukti meningkatkan akurasi klasifikasi sebesar 6,14%
Aspect Based Sentimen Analysis Opini Publik Pada Instagram dengan Convolutional Neural Network Muhammad Arief Rahman; Herman Budianto; Setiawan, Esther Irawati
Intelligent System and Computation Vol 1 No 2 (2019): INSYST:Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v1i2.83

Abstract

Internet sebagai sarana informasi dan komunikasi sudah sangat dikenal di kalangan masyarakat dalam menawarkan kemudahan dan fleksibilitas yang cukup memadai ketika menjadi media. Oleh karena itu opini publik terhadap Operator Telekomunikasi merupakan hal yang sangat penting untuk dijadikan patokan. Namun, untuk mengevaluasi umpan balik online itu, bukan masalah sederhana. Kadang-kadang ketika menganalisis ulasan online yang berkembang pesat ini, menjadi sulit untuk mengkategorikan apakah opini pelanggan puas atau tidak puas terhadap produk dan layanan. Selain itu, sebagai bagian dari peningkatan kualitas mereka, organisasi seperti jasa ini perlu mengklasifikasikan aspek produk dan layanan yang paling disukai pelanggan. Deep Learning adalah area baru dalam penelitian Machine Learning, yang telah diperkenalkan dengan tujuan menggerakkan Machine Learning lebih dekat dengan salah satu tujuan aslinya yaitu Artificial Intelligence. Deep Learning adalah tentang belajar beberapa tingkat representasi dan abstraksi yang membantu untuk memahami data seperti gambar, suara, dan teks. Convolutional Neural Network adalah salah satu contoh metode Deep Learning. Metode Convolutional Neural Network diharapkan dapat digunakan dalam pengimplementasian opini publik untuk keperluan data training yang dikumpulkan dari beragam data yang dianotasikan kelas sentimennya secara otomatis. Hasil dari penelitian menunjukkan dari 4 aspek dan 3 sentimen maka didapatkan nilai rata-rata precision, recall, dan f1-score adalah precision 97.6%, recall 84%, f1-score 90.3%. Bisa disimpulkan score representation ini dapat digunakan untuk klasifikasi sentimen.
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