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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 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Knowledge Engineering and Data Science SMARTICS Journal 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 JOINCS (Journal of Informatics, Network, and Computer Science) 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
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 Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering 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.
Klasifikasi Helpdesk Menggunakan Metode Support Vector Machine Stefanie Hilda Kusumahadi; Hartarto Junaedi; Joan Santoso
Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 1 (2019): JPIT, Januari 2019
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v4i1.1125

Abstract

The online helpdesk with ticketing system with the help of operators often experiences problems such as inappropriate delegation processes, the duration of the helpdesk waiting time to be delegated, even the helpdesk is missed to be handled. The ticket delegation checked manually by the operator has risks creating an error in delegating helpdesk tickets to inappropriate technicians. The helpdesk classification system is needed so that every incoming helpdesk ticket can be classified to the right technician according to the job description. The incoming Helpdesk is classified into 6 types of requests, namely multimedia, documentation, internet, server, hardware, software and miscellaneous. This helpdesk grouping is needed so that related technicians for each helpdesk can work and help the helpdesk according to their respective job descriptions. The Support Vector Machine method is used to classify text on the helpdesk. The use of Linear and Polynomial kernels produces an accuracy of 78%, the RBF or Gaussian kernel produces the highest accuracy of 81% while the Sigmoid kernel produces the smallest accuracy of 51%. The helpdesk classification results with the Support Vector Machine method can produce quite good accuracy.
Klasifikasi Micro-Expression Menggunakan K-Nearest Neighbors Menggunakan Fitur CAS dan HOG Nikko Riestian Putra Wardoyo; Joan Santoso; Esther Irawati Setiawan
Intelligent System and Computation Vol 5 No 2 (2023): 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.v5i2.346

Abstract

Micro-Expression adalah ekspresi yang muncul dalam waktu singkat, hanya berlangsung sepersekian detik. Hal ini mungkin merupakan akibat dari aktivitas komunikasi antar manusia selama interaksi sosial. Reaksi ekspresi mikro wajah terjadi secara alami dan segera, sehingga hanya menyisakan sedikit ruang untuk manipulasi. Namun, karena Micro-Expression bersifat sementara dan memiliki intensitas rendah, pengenalan dan pengenalannya sulit dan sangat bergantung pada pengalaman para ahli. Karena kekhususan dan kompleksitas intrinsiknya, klasifikasi Micro-Expression menggunakan 2 ekstraksi yaitu CAS dan HOG menarik tetapi menantang, dan baru-baru ini menjadi area penelitian yang aktif. context-aware saliency (CAS) yang bertujuan untuk mendeteksi wilayah gambar yang mewakili pemandangan. Tutujuannya adalah untuk mendeteksi objek dominan. Histogram Oriented Gradient (HOG) Bertujuan sebagai deskriptor yang efektif untuk pengenalan dan deteksi objek. Metode K-Nearest Neighbors (K-NN) digunakan untuk klasifikasi Micro-Expression berdasarkan fitur HOG dari citra saliency. Dataset yang digunakan pada penelitian ini dari data sampel siswa SMK Ma’arif NU Prambon jurusan Multimedia sebanyak 45 siswa dan ditambahkan dataset dari affecnet. Hasil yang didapatkan dari total dataset sebanyak 4116 citra yang dibagi menjadi 6 Micro-Expression yaitu anger, disgust, fear, happy, sad dan surprise, mendapatkan hasil akurasi diatas 80% dari perbandingan dataset sejumlah 4116 terbagi menjadi 2 dengan persentase 70% training dan 30% data testing.
Factors That Influence Repurchase Intention: A Systematic Literature Review Muhammad Amfahtori Wijarnoko; Edwin Pramana; Joan Santoso
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.v12i3.693

Abstract

This research is a systematic literature review of factors that influence repurchase intention. Repurchase intention is important for companies because it will shape customer behavior to become loyal, customers usually tend to have an interest in buying products or services repeatedly so that the company will benefit from products or services that have been sold. The aim of this research is to provide insights into the research trends and issues in the studies of Repurchase Intention. The literature search focused on finding journals published between 2018 and 2023. Only English-language journals with the keyword Repurchase Intention were used in this research. Researchers found 80 journals that matched these keywords but after reading the collected articles thoroughly and removing duplicate and irrelevant articles, the authors produced 50 articles to be used in this research. The findings highlight key drivers for increasing sales: Satisfaction, Trust, Perceived Value, Price, and Word of Mouth. Additionally, 14 moderating factors were identified, with Age being the most prominent in four articles. Korea, India, and Indonesia lead research contributions, each with six articles. Structural Equation Modeling (SEM) is the prevailing measurement method, while other approaches persist. Companies are recommended to prioritize these core factors for consumer engagement. Future research should delve into unexplored moderating factors and alternative measurement methods, enriching our understanding of this vital field.
Dragonfly Algorithm for Crowd NPC Movement Simulation in Metaverse Santoso, Ong, Hansel; Junaedi, Hartarto; Santoso, Joan
Bulletin of Social Informatics Theory and Application Vol. 6 No. 1 (2022)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v6i1.551

Abstract

During The Pandemic Period The Development Of Virtual Reality (Vr) In The Field Of Social Media (Metaverse) Is Very Fast To Give New Experiences. To Provide A New Experience, The Development Of A Supporting Virtual World As A Gathering Place Is Needed, To Support The Presence Of Others That Become A Factor Of Social Virtual Presence (Svr) Npc Is Required. Npc Crowds Will Be Tested In Job Fair Case Study By Compared Dragonfly And Particle Swarm Optimization Algorithms. Algorithm Testing Will Be Adjustable With The Same Parameters And Profiles For Individuals And Objectives. After Experiment And Evaluation, Dragonfly Algorith Was More Optimal And Provided Better SVR.
PEMODELAN PREDIKSI KUANTITAS PENJUALAN MAINAN MENGGUNAKAN LightGBM Febriantoro, Erfan; Setyati, Endang; Santoso, Joan
SMARTICS Journal Vol 9 No 1 (2023): SMARTICS Journal (April 2023)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v9i1.8279

Abstract

The main characteristic of the toy industry is its rapid change and uncertainty. Demand, influenced by certain trends, can change abruptly and suddenly disappear when the next viral product takes over the market. Constant product innovation, short life cycles and high cannibalization rates have the potential to incur higher relative costs compared to other industries in terms of inventory obsolescence, lost sales and reduced prices. Based on these problems, a study was conducted to predict toy sales using the LightGBM algorithm model in a time-series form with a sales dataset of 460 toy items classified into 14 categories within a time span of 1,353 days with a prediction period of 1, 3, and 6 months. This study produced 42 models based on product category and prediction period, with the best RMSE value of 0.0042 in the KARTU toy model, and 3 models for all categories based on the prediction period with the best RMSE value of 0.0380 in the 1 month prediction period.
Implementing UTAUT Model to Analyze Consumer Behaviour in Mobile Recycling Application Elizabeth Shirley, Stephanie; Santoso, Joan; Kristina, Natalia
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 16, No 1 (2024): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v16i1.26930

Abstract

Abstract—Waste disposal continues to increase globally, causing environmental issues to worsen. Indonesia, with its rapidly growing cities, also struggles to manage all this waste. Numerous mobile applications were released to recycle waste more effectively. However, The rate of mobile recycling application adoption is still low. We contend that lack of awareness and knowledge on recycling is the main cause of low adoption from people, that makes them deny the need for recycling and individual responsibility. Hence, the purpose of this study is to find out if sociopreneur awareness implementation, workshop, on recycling application influence the adoption of recycling applications. TPB and UTAUT model is used to attest the acceptance of workshop on recycling application. Quantitative approach is employed in this study, using a questionnaire of 139 respondents. The structural model is calculated using Smart PLS tools, and the results are validation data. According to the results, the user's intention to use recycling applications with a workshop feature is positively and significantly impacted by four variables (T-Values ≥ 1.96), which are Functional Expectancy, Attemption, Support System, and Perceived Control. While, Society Influence have negative effects (T-Values 1.96) on the user's intention to use recycling applications with a workshop feature.
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 Aditya Dwi Aryanto Adriel Ferdianto Agung Dewa Bagus Soetiono Ahmad Syaifuddin Ali Djamhuri Ananta Tio Putra Andik Jatmiko Anita Guterres Bayu Anggara Putra Budi Irawan 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 Ernest Lim Esther Irawati S. Esther Irawati Setiawan Esther Irawati Setiawan Eunike Kardinata F.X. Ferdinandus Fachrul Kurniawan Febriantoro, Erfan Francisca Chandra Fujisawa, Kimiya Gunawan Gunawan Gunawan Gunawan Gunawan Gunawan Hans Juwiantho Hans Keven Budi Prakoso Hartarto Junaedi Hendrawan Armanto Heppi Siswanto Herman Budianto Imron, Syaiful Indra Maryati Jatmiko, Andik Kristian Indradiarta Gunawan Kristina, Natalia Kurniawan S, Putu Widiarsa Langgeng, Yudo Sembodo Hastoro Leonel Hernandez Luhfita Tirta Lukman Zaman Machfudin, Mohammad Farid Mauridhi Hery Purnomo Miftah Farid Mochamad Hariadi Muhammad Amfahtori Wijarnoko Mustaqin, Farhan Faisal Zainul Nagari, Widean Nikko Riestian Putra Wardoyo Nindian Puspa Dewi Ong, Hansel Santoso Patrick Sutanto Reddy Alexandro Harianto Ricky Sutanto Rossy P. C. Rully Widiastutik Samuel Budi Wardhana Kusuma Saputra, Daniel Gamaliel Setya Ardhi Soetiono, Agung Dewa Bagus Stefanie Hilda Kusumahadi Stella Vania Surya Sumpeno Syabith Umar Ahdan Syaiful Huda Syaiful Imron Tjendika, Patrick Tjwanda Putera Gunawan Tri Septianto Tuesday saka gustaf Ubaidi Ubaidi Ubaidi, Ubaidi Yosi Kristian