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All Journal Semantik MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Knowledge Engineering and Data Science SMARTICS Journal Applied Information System and Management Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Information System,Graphics, Hospitality and Technology Applied Technology and Computing Science Journal Teknika Journal of Electrical Engineering and Computer (JEECOM) Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology Insyst : Journal of Intelligent System and Computation J-Intech (Journal of Information and Technology) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Sistem Cerdas dan Rekayasa Jurnal Inovasi Teknologi dan Edukasi Teknik Nusantara Journal of Computers and its Applications Bulletin of Social Informatics Theory and Application Jurnal Informatika: Jurnal Pengembangan IT Journal of Information Technology
Joan Santoso
Institut Sains Dan Teknologi Terpadu Surabaya
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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Pemanfaatan Deep Convolutional Auto-encoder untuk Mitigasi Serangan Adversarial Attack pada Citra Digital Kurniawan S, Putu Widiarsa; Kristian, Yosi; Santoso, Joan
J-INTECH (Journal of Information and Technology) Vol 11 No 1 (2023): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v11i1.845

Abstract

Adversarial attacks on digital images pose a serious threat to the utilization of machine learning technology in various real-life applications. The Fast Gradient Sign Method (FGSM) technique has proven to be effective in conducting attacks on machine learning models, including digital images found in the ImageNet dataset. This research aims to address this issue by utilizing the Deep Convolutional Auto-encoder (AE) technique as a method for mitigating adversarial attacks on digital images.The results of the study demonstrate that FGSM attacks can be performed on the majority of digital images, although there are certain images that are more resilient to such attacks. Furthermore, the AE mitigation technique proves to be effective in reducing the impact of adversarial attacks on most digital images. The accuracy of the attack and mitigation models is measured at 14.58% and 91.67%, respectively.
Prediksi Student Performance Pada Hasil Penilaian Proses Pembelajaran Online Mata Pelajaran Informatika Di SMA Dipa, Sasra; Santoso, Joan; Chandra, Francisca H.
J-INTECH (Journal of Information and Technology) Vol 12 No 1 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i1.1259

Abstract

In the Corona Endemic, we are not just returning to offline education patterns but are already moving towards education 5.0. Online, normal, blended learning patterns have become commonplace. Online learning assessment requires fast and precise predictions of student performance (high accuracy). The reason is first, due to limited direct interaction. Second, normal learning usually involves an assessment of the learning process and character assessment to be able to provide an accurate final assessment, which is difficult to implement in online learning accurately. Third, there is a lot of data to be processed quickly and precisely so that it can be reported to educational institutions and to students' families. Fourth, Informatics is a lesson that is 80% practical and 20% theory so that the assessment instruments used are 80% performance instruments (Bloom's taxonomy: C2, C3, C4, C5) and 20% multiple choice instruments (C1). Informatics correction and assessment requires more time because 80% cannot be assessed automatically. This research aims to predict student performance (Pass (1) or Intervention (0)) on the results of the online learning process assessment for informatics subjects in high school. If the student performance prediction results in an intervention, it will be immediately followed up by providing an intervention strategy to increase student performance. The target of the research results is to achieve > 70% accuracy on the processed dataset. This research uses the ensemble learning method random Forest Classification and XG Boosting classification. The research results of Student Performance Prediction using XG Boost Classification produce higher accuracy than RF Classification which has an average accuracy value = 93% while RF Classification has an average accuracy result = 92%. The research objectives have been achieved because the results of the 2 methods used have met the desired targets.
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%.
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.
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%.
Recommender System Based on Social Network Analysis of Student Workshop and Event Activities Compared to GPA and Department Setiawan, Esther; Santoso, Joan; Cahyadi, Billy Kelvianto; Afandi, Acxel Derian; Saputra, Daniel Gamaliel; Ferdinandus, FX; Fujisawa, Kimiya; Purnomo, Mauridhi Hery
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2943

Abstract

This research uses social network connections and academic data to create a recommender system that helps students choose seminars and events that suit their interests. The aim is to address the issue of students' hesitation in selecting activities. This project investigates the use of social network analysis (SNA) to provide individualized suggestions by analyzing student involvement in workshops and events, as well as their grade point average (GPA). The materials contain student data gathered between 2018 and 2023 from Institut Sains dan Teknologi Terpadu Surabaya (ISTTS), emphasizing the student's social media interactions and event participation. Metrics like centrality are employed to identify prominent nodes inside the network, and the approach combines graph-based SNA and cosine similarity for event recommendation. The network of student involvement in events was represented by a dataset comprising 2,293 edges and 602 nodes. The results show that the relevance of recommendations is improved when social network data is integrated with GPA, rather than GPA-based systems alone. The system identified key nodes, such as specific lectures, that significantly impacted student involvement and were rated highly in terms of centrality. Future research implications recommend expanding the dataset to encompass a broader range of events and refining the algorithm by including content-based filtering. The system's application is not limited to educational environments; it may also be tailored for career counselling or professional development.
Peringkasan Teks Ekstraktif pada Dokumen Tunggal Menggunakan Metode Restricted Boltzmann Machine Rully Widiastutik; Lukman Zaman P. C. S. W; Santoso, Joan
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.v1i2.84

Abstract

Penelitian yang dilakukan yaitu menghasilkan peringkasan teks ekstratif secara otomatis yang dapat membantu menghasilkan dokumen yang lebih pendek dari dokumen aslinya dengan cara mengambil kalimat penting dari dokumen sehingga pembaca dapat memahami isi dokumen dengan cepat tanpa membaca secara keseluruhan. Dataset yang digunakan sebanyak 30 dokumen tunggal teks berita berbahasa Indonesia yang diperoleh dari www.kompas.com pada kategori tekno. Dalam penelitian ini, digunakan sepuluh fitur yaitu posisi kalimat, panjang kalimat, data numerik, bobot kalimat, kesamaan antara kalimat dan centroid, bi-gram, tri-gram, kata benda yang tepat, kemiripan antar kalimat, huruf besar. Nilai fitur setiap kalimat dihitung. Nilai fitur yang dihasilkan ditingkatkan dengan menggunakan metode Restricted Boltzmann Machine (RBM) agar ringkasan yang dihasilkan lebih akurat. Untuk proses pengujian dalam penelitian ini menggunakan ROUGE-1. Hasil yang diperoleh dalam penelitian yaitu dengan menggunakan learning rate 0.06 menghasilkan recall, precision dan f-measure tertinggi yakni 0.744, 0.611 dan 0.669. Selain itu, semakin besar nilai compression rate yang digunakan maka hasil recall, precision dan f-measure yang dihasilkan akan semakin tinggi. Hasil peringkasan teks dengan menggunakan RBM memiliki nilai recall lebih tinggi 2.1%, precision lebih tinggi 1.6% dan f-measure lebih tinggi 1.8% daripada hasil peringkasan teks tanpa RBM. Hal ini menunjukkan bahwa peringkasan teks dengan menggunakan RBM hasilnya lebih baik daripada peringkasan teks tanpa RBM.
Stance Classification Post Kesehatan di Media Sosial Dengan FastText Embedding dan Deep Learning Lim, Ernest; Setiawan, Esther Irawati; Santoso, Joan
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.v1i2.86

Abstract

Misinformasi merupakan fenomena yang semakin sering terjadi di media sosial, tidak terkecuali Facebook, salah satu media sosial terbesar di Indonesia. Beberapa penelitian telah dilakukan mengenai teknik identifikasi dan klasifikasi stance di media sosial Indonesia. Akan tetapi, penggunaan Word2Vec sebagai word embedding dalam penelitian tersebut memiliki keterbatasan pada pengenalan kata baru. Hal ini menjadi dasar penggunaan fastText embedding dalam penelitian ini. Dengan menggunakan pendekatan deep learning, penelitian berfokus pada performa model dalam klasifikasi stance suatu judul post kesehatan di Facebook terhadap judul post lainnya. Stance berupa for (setuju), observing (netral), dan against (berlawanan). Dataset terdiri dari 3500 judul post yang terdiri dari 500 kalimat klaim dengan enam kalimat stance terhadap setiap klaim. Model dengan fastText pada penelitian ini mampu menghasilkan F1 macro score sebesar 64%.
Analisis Trending Topik untuk Percakapan Media Sosial dengan Menggunakan Topic Modelling Berbasis Algoritme LDA Syaifuddin, Ahmad; Harianto, Reddy Alexandro; Santoso, Joan
Intelligent System and Computation Vol 2 No 1 (2020): 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.v2i1.150

Abstract

Aplikasi WhatsApp merupakan salah satu aplikasi chatting yang sangat populer terutama di Indonesia. WhatsApp mempunyai data unik karena memiliki pola pesan dan topik yang beragam dan sangat cepat berubah, sehingga untuk mengidentifikasi suatu topik dari kumpulan pesan tersebut sangat sulit dan menghabiskan banyak waktu jika dilakukan secara manual. Salah satu cara untuk mendapatkan informasi tersirat dari media sosial tersebut yaitu dengan melakukan pemodelan topik. Penelitian ini dilakukan untuk menganalisis penerapan metode LDA (Latent Dirichlet Allocation) dalam mengidentifikasi topik apa saja yang sedang dibahas pada grup WhatsApp di Universitas Islam Majapahit serta melakukan eksperimen pemodelan topik dengan menambahkan atribut waktu dalam penyusunan dokumen. Penelitian ini menghasilkan model topic dan nilai evaluasi f-measure dari model topik berdasarkan uji coba yang dilakukan. Metode LDA dipilih untuk melakukan pemodelan topik dengan memanfaatkan library LDA pada python serta menerapkan standar text-preprocessing dan menambahkan slang words removal untuk menangani kata tidak baku dan singkatan pada chat logs. Pengujian model topik dilakukan dengan uji human in the loop menggunakan word instrusion task kepada pakar Bahasa Indonesia. Hasil evaluasi LDA didapatkan hasil percobaan terbaik dengan mengubah dokumen menjadi 10 menit dan menggabungkan dengan reply chat pada percakapan grup WhatsApp merupakan salah satu cara dalam meningkatkan hasil pemodelan topik menggunakan algoritma Latent Dirichlet Allocation (LDA), didapatkan nilai precision sebesar 0.9294, nilai recall sebesar 0.7900 dan nilai f-measure sebesar 0.8541.
Co-Authors Aditya Dwi Aryanto Adriel Ferdianto Afandi, Acxel Derian Agung Dewa Bagus Soetiono Ahdan, Syabith Umar Ahmad Syaifuddin Ali Djamhuri Ananta Tio Putra Andik Jatmiko Anita Guterres Budi Irawan Cahyadi, Billy Kelvianto Chandra, Francisca H. Christian Nathaniel Purwanto Devi Dwi Purwanto Dewi, Nindian Puspa Dipa, Sasra Edwin Pramana Eka Rahayu Setyaningsih Eko Mulyanto Yuniarno Elizabeth Shirley, Stephanie Endang Setyati Esther Irawati S. Esther Irawati Setiawan Eunike Kardinata F.X. Ferdinandus Fachrul Kurniawan Febriantoro, Erfan Ferdinandus, F. X. Francisca Chandra Fujisawa, Kimiya Gunawan Gunawan Gunawan Gunawan Gunawan Gunawan Halim, Kevin Jonathan Hans Juwiantho Hans Keven Budi Prakoso Harianto, Reddy Alexandro Hartarto Junaedi Hartono, Patrick Hendrawan Armanto Heppi Siswanto Herman Budianto Imron, Syaiful Indra Maryati Irawati Setiawan, Esther Jatmiko, Andik Kristian Indradiarta Gunawan Kristina, Natalia Kurniawan S, Putu Widiarsa Langgeng, Yudo Sembodo Hastoro Leonel Hernandez Lim, Ernest Luhfita Tirta Lukman Zaman Machfudin, Mohammad Farid Mauridhi Hery Purnomo Mochamad Hariadi Muhammad Amfahtori Wijarnoko Mustaqin, Farhan Faisal Zainul Nagari, Widean Nindian Puspa Dewi Ong, Hansel Santoso Putra, Bayu Anggara Putu Widiarsa Kurniawan S Rossy P. C. Rully Widiastutik Samuel Budi Wardhana Kusuma Saputra, Daniel Gamaliel Setiawan, Esther Setya Ardhi Soetiono, Agung Dewa Bagus Stefanie Hilda Kusumahadi Surya Sumpeno Sutanto, Patrick Sutanto, Ricky Syaiful Huda Syaiful Imron Tjendika, Patrick Tjwanda Putera Gunawan Tri Septianto Tuesday saka gustaf Ubaidi Ubaidi Ubaidi, Ubaidi Vania, Stella Vu, Tong Nam Tuan Wardoyo, Nikko Riestian Putra Yosi Kristian