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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Prosiding SNATIKA Vol 01 (2011) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Terapan Abdimas Jurnal Farmasi Galenika (Galenika Journal of Pharmacy) Jurnal SOLMA Conference on Innovation and Application of Science and Technology (CIASTECH) JURNAL NUSANTARA APLIKASI MANAJEMEN BISNIS Teknika Insyst : Journal of Intelligent System and Computation Journal of Technology and Informatics (JoTI) Journal of Community Practice and Social Welfare (JaCiPS) Seminar Nasional Ilmu Terapan Prosiding Seminar Nasional Universitas Ma Chung Prosiding Seminar Nasional Pengabdian Masyarakat Jurnal Difusi Ipteks Legowo
Windra Swastika
Program Studi Teknik Informatika, Universitas Ma Chung
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Deteksi dan Klasifikasi Merek Mobil untuk Penentuan Iklan Billboard Menggunakan Convolution Neural Network Swastika, Windra; Kurniawan, Ardian; Setiawan, Hendry
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 4: Agustus 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020742183

Abstract

Dunia periklanan di Indonesia saat ini memiliki perkembangan yang sangat pesat. Hal ini dibuktikan dengan semakin bertambah banyaknya media periklanan yang diciptakan, salah satunya adalah iklan billboard pada jalan raya. Iklan billboard ini memiliki kelemahan, yaitu materi atau konten dari iklan yang ditampilkan tidak dapat berubah-ubah, dengan demikian maka target dari periklanan tidak bisa tertuju pada konsumen yang tepat. Untuk mengatasi masalah tersebut maka dibutuhkan pemanfaatan teknologi untuk mendukung keefektifan kinerja dari iklan billboard. Pada penelitian ini dibuat sebuah sistem yang dapat mendeteksi mobil dan mengenali merek dari mobil yang terdeteksi, sehingga materi iklan dapat berubah sesuai dengan merek mobil yang dikenali oleh sistem. Untuk deteksi pada mobil digunakan metode You Only Look Once (YOLO) dan untuk klasifikasi pada merek mobil digunakan metode MiniVGGNet. Proses latih dilakukan dengan menggunakan 1100 buah gambar dan terdapat 11 macam merek mobil yang dapat diklasifikasikan. Dari pengujian yang dilakukan, didapatkan akurasi akhir 93% pada deteksi mobil. Untuk klasifikasi dari merek mobil dilakukan pengujian dengan fungsi optimasi Adam dengan ukuran masukan gambar 64x64 piksel. Untuk akurasi terbaik yang didapatkan adalah 60%.AbstractThe world of advertising in Indonesia today has a very rapid development. This is proven by the increasing number of advertising media created, one example is billboard advertising on the highway. Billboard advertising has a weakness, namely the material or the content of the ads displayed cannot change, therefore the target of advertising cannot be directed at the right consumer. To overcome this problem, the use of technology is needed to support the effectiveness of billboard advertising. In this study a system was created which is can detect the car and recognize the brand of the car detected, so the advertising material can change according to the brand of the car that is recognized by the system. For the detection of cars, using You Only Look Once (YOLO) method and for the classification of car brands, using MiniVGGNet method. The training process is carried out using 1100 pictures and there are 11 kinds of car brands that can be classified. From the tests performed, 93% final accuracy was found in car detection. The classification of the car brand was tested with Adam optimization functions with an image input size of 64x64 pixels. For the best accuracy obtained is 60% using the Adam optimization function with the input image size of 64x64 pixels.
Studi Awal Deteksi COVID-19 Menggunakan Citra CT Berbasis Deep Learning Swastika, Windra
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 3: Juni 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020733399

Abstract

Pada bulan Desember 2019, virus COVID-19 menyebar ke banyak negara, termasuk di Indonesia yang kemudian menjadi pandemi dan menimbulkan masalah serius karena masih belum adanya vaksin untuk mencegah penularan. Uji spesimen saluran nafas atas dan saluran nafas bawah saat ini merupakan salah satu metode yang efektif untuk mengetahui apakah seseorang terinfeksi COVID-19 atau tidak. Salah satu indikasi dari infeksi COVID-19 adalah sesak nafas atau pneumonia serta munculnya ground-glass opacity pada citra CT. Penelitian ini merupakan studi awal untuk melihat apakah citra CT dari organ thorax dapat digunakan sebagai alternatif untuk mendeteksi infeksi virus COVID-19. Deep learning digunakan untuk membuat sebuah model dengan citra CT sebagai masukan. Total 140 data citra CT yang terbagi menjadi 2 yaitu citra dari pasien terinfeksi dan citra dari subjek normal digunakan sebagai masukan pada deep learning. Proses pelatihan dilakukan menggunakan CNN dengan arsitektur VGG16 dan optimizer SGD dan Adam. Hasil yang didapatkan adalah akurasi sebesar 92,86% untuk mengklasifikasikan infeksi COVID-19 dan normal. Nilai spesifisitas dan sensitivitas sebesar 100% dan 85,71% untuk pelatihan dengan menggunakan optimizer SGD. AbstractIn December 2019, the COVID-19 virus spread to many countries, including Indonesia which later became a pandemic and caused serious problems because there was still no vaccine to prevent transmission. Tests of upper and lower respiratory tract specimens are now an effective method of finding whether a person is infected with COVID-19 or not. One indication of COVID-19 infection is shortness of breath or pneumonia and the appearance of ground-glass opacity on CT images. This research is a preliminary study to see whether CT images of the thorax organs can be used as an alternative to detect COVID-19 virus. The deep learning is used to create a model with CT images as input. A total of 140 CT image data which are divided into 2 images from infected patients and images from normal subjects are used as input for deep learning. The training process is carried out using CNN with VGG16 architecture and SGD and Adam optimizers. The results obtained are 92.86% accuracy for classifying COVID-19 infections and normal. Specificity and sensitivity values were 100% and 85.71% for training using the SGD optimizer.
Rancang Bangun Website Akademik dengan Penyimpanan Sertifikat Digital Menggunakan Teknologi Blockchain Swastika, Windra; Wirasantosa, Hermawan; Kelana, Oesman Hendra
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 1: Februari 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021863645

Abstract

Sertifikat merupakan sebuah bentuk penghargaan yang didapat seseorang setelah menyelesaikan suatu uji kompetensi atau pembelajaran tertentu. Sertifikat harus dibuat dan disimpan dengan metode dan keamanan yang baik untuk mencegah terjadinya perubahan isi atau bahkan pemalsuan. Teknologi blockchain merupakan teknologi yang memungkinkan proses penyimpanan yang aman dengan ongkos yang rendah. Keamanan terjamin karena semua orang bisa mengambil bagian untuk menyimpan data dengan ledger yang terdistribusi. Berdasarkan hasil penelitian dan perancangan sistem yang dibuat dapat disimpulkan bahwa proses pembuatan teknologi blockchain sebagai media penerbitan sertifikat dan validasinya dapat dibuat menggunakan program milik Ethereum yaitu Geth dan penyimpanan data menggunakan smart contract yang diterbitkan pada jaringan blockchain. Hasil dari pengujian reliabilitas terhadap sistem menunjukkan bahwa bahwa sistem berhasil memproses 200 transaksi dalam waktu kurang lebih 8 detik. Untuk pengujian skalabilitas didapatkan estimasi 10 juta blok membutuhkan kapasitas penyimpanan sebesar 22,6 GB untuk menjadi node atau miner pada jaringan blockchain ini. AbstractA certificate is a form of appreciation that a person receives after completing a certain competency or learning test. Certificates must be created and stored with good methods and security to prevent changes in content or even forgery. Blockchain technology is technology that allows secure storage processes at a low cost. Security is guaranteed because everyone can take part in storing data with a distributed ledger. Based on the results of research and design of the system, it can be concluded that the process of making blockchain technology as a media for issuing certificates and validation can be made using Ethereum's proprietary program Geth and data storage using smart contracts issued on the blockchain network. The results of the reliability testing of the system indicate that the system successfully processed 200 transactions in approximately 8 seconds. For scalability testing, it is estimated that 10 million blocks require a storage capacity of 22.6 GB to become a node or miner on this blockchain network.
Perbandingan Akurasi Deteksi Emosi Pada Suara Menggunakan Multilayer Perceptron, Random Forest, Decision Tree dan K-NN Swastika, Windra; Widodo, Romy Budhi; Oepojo, Alvin Andrius
Intelligent System and Computation Vol 5 No 1 (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.v5i1.264

Abstract

Penelitian ini bertujuan untuk membandingkan akurasi pengenalan emosi melalui suara dengan menggunakan beberapa jenis classifier. Emosi dasar yang akan dikenali ada 4, yaitu senang, sedih, neutral dan marah. Metodologi penelitian dimulai dengan memperoleh dataset suara dari database RAVDESS, yang terdiri dari 24 aktor dengan jumlah suara sebanyak 60 per aktor. Namun, hanya 28 suara yang dipilih dari setiap aktor, sehingga total ada 672 suara yang digunakan dalam penelitian ini. Untuk mengekstraksi fitur dari dataset suara, digunakan tiga teknik yaitu mel frequency cepstral coefficient (MFCC), Chroma, dan Skala Mel. Kemudian, empat jenis classifier digunakan dalam pembuatan model yaitu Multilayer Perceptron Classifier (MLPC), Decision Tree, Random Forest, dan K-NN. Dataset dibagi menjadi data train dan data test dalam 3 uji coba untuk masing-masing classifier, yait 85% train – 25% test, 80% train – 25% test, dan 75% train dan 25% test. Hasil penelitian menunjukkan bahwa model dengan menggunakan Random Forest Classifier memiliki akurasi tertinggi yaitu sebesar 79% dengan pembagian dataset 80% train - 20% test. Sedangkan, model dengan Decision Tree Classifier memiliki akurasi terendah sebesar 57% dengan pembagian dataset menjadi 75% train - 25% test. Dalam penelitian ini, teknik ekstraksi fitur yang digunakan yaitu MFCC, Chroma, dan Skala Mel, yang terbukti efektif dalam menghasilkan fitur dari dataset suara. Selain itu, hasil penelitian juga menunjukkan bahwa Random Forest Classifier lebih unggul dalam mengenali emosi melalui suara jika dibandingkan dengan jenis classifier yang lain.
Implementation of Hand Gesture Recognition as Smart Home Devices Controller Dewangga, Stanley; Subianto, Mochamad; Swastika, Windra
Intelligent System and Computation Vol 6 No 2 (2024): 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.v6i2.372

Abstract

Some current virtual assistant products such as Alexa, Siri and Google Home facilitate features to control smart home devices through voice input, which has become increasingly popular in recent years. In addition to voice input, smart home devices can also be monitored and controlled through smartphones or computers using applications that provide users with flexibility. However, both control systems are less efficient, as they consume time and voice input utilization may sometimes not be recognized in crowded conditions. Therefore, this research introduces an application to recognize real-time hand gestures and utilize them for a new control system that provides time and energy efficiency. This application processes images using the Mediapipe framework, generating hand landmark outputs. These landmark outputs are utilized to determine the combination of raised or lowered fingers, which is then used to control smart home devices. The application is developed with ESP32 and ESP01s modules as data receivers from gesture recognition, and ESP32-CAM for image acquisition. Meanwhile, the gesture recognition computation process is executed on a Raspberry Pi 3 Model B. The gesture recognition application achieves good accuracy at 88%, but may experience occasional failures for certain gestures. However, the response time generated by the smart home control system is still relatively long, averaging 7.88 seconds.
Implementasi Strategi Trading Cryptocurrency Menggunakan Indikator QMI: Perspektif Sebelas Tahun pada Bitcoin (2013–2024) Swastika, Windra; Suganda, Tarsisius Renald; Cahyadi, Rino Tam
Jurnal Nusantara Aplikasi Manajemen Bisnis Vol 10 No 2 (2025): Jurnal Nusantara Aplikasi Manajemen Bisnis
Publisher : UNIVERSITAS NUSANTARA PGRI KEDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/nusamba.v10i2.25032

Abstract

  Research aim : This study aims to develop and test the effectiveness of a Quantitative Market Indicator (QMI)-based trading strategy to optimize Bitcoin investment returns while managing risk through controlled position allocation.. Design/Methode/Approach : The research follows five stages: data preprocessing, market indicator calculation, trading strategy design, performance analysis, and visualization. The QMI integrates three key indicators—Puell Multiple, Golden Fibonacci Index, and Pi Cycle—to identify market cycles and trend reversals. The systematic trading strategy applies buying rules when QMI < 20 and selling when QMI > 80, allocating 3% of capital for each transaction. Research Finding : The QMI strategy generated 1,101 transactions (479 buys, 622 sells), achieving a 12,670.71% return with an initial capital of USD 1,000 growing to USD 127,700.32. The maximum drawdown was 32.4%, the win rate 68.5%, and the Sharpe ratio 2.1, indicating strong performance with controlled risk. Theoretical contribution/Originality : This study introduces the integration of the Fibonacci sequence and the golden ratio in market cycle detection, enhancing prediction accuracy in volatile crypto markets. Practitioner/Policy implication : The QMI model provides a structured decision-making framework for crypto investors, emphasizing disciplined trading and adaptive risk management. Research limitation : Further development should integrate market sentiment and dynamic thresholds to refine model robustness.
Web-Based Automatic Code Evaluation System Using Claude AI for Programming Education Irawan, Paulus Lucky Tirma; Swastika, Windra
Journal of Technology and Informatics (JoTI) Vol. 8 No. 1 (2026): Vol. 8 N. 1 (2026)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v8i1.1305

Abstract

Algorithm and Programming learning face challenges in providing fast and personalized feedback to students. The manual evaluation process conducted by lecturers requires considerable time, hindering students' iterative learning process. This study aims to develop a web submission platform prototype with automatic feedback based on Claude AI to support and enhance the programming learning process. The research method employs a Research and Development (R&D) approach with four stages: needs analysis, system design and planning, platform implementation, and testing and evaluation. The platform was developed using a PHP backend, MySQL database, and Claude AI integration through RESTful web services with a cascading AI evaluation strategy. Evaluation was conducted on 9 students with 39 submissions for three Java assignments with different difficulty levels. Results show the system successfully provides high-quality feedback with an average response time of 2.8 seconds and 100% evaluation success rate. Score distribution shows average improvement from the first assignment (82.3) to the third assignment (87.1), indicating a positive trend in iterative learning. A satisfaction survey of 8 respondents shows the system interface is user-friendly, and AI feedback helps identify syntax and program logic errors. Students made an average of 3.2 attempts per assignment, demonstrating high engagement in the learning process.
Co-Authors Albert Wahyudi Nur Bahardica, Dan Julio Chandra, Melisa Dwi Dewangga, Stanley Godeliva Adriani Hendra Gregorius Guntur Sunardi Putra Handoko, Nico Alexander Haryasena, Agustinus Bohaswara Haryasena Bohaswara Hendry Setiawan Hideaki Haneishi Irawan, Paulus Lucky Tirma Kestrilia Rega Prilianti Kurniawan, Ardian Leona, Ossie Leona, Ossie Mochamad Subianto Muhammad Hilmi Afthoni Nugraha, Arief Oepojo, Alvin Andrius Oesman Hendra Kelana Oesman Hendra Kelana Paulus Lucky Tirma Irawan Paulus Lucky Tirma Irawan Pramesti, Agatha Tri Endha Rahadi, Theresa Dhea Lonika Rino Tam Cahyadi, Rino Tam Rollando, Rollando Romy Budhi Widodo Tarsisius Renald Suganda Wirasantosa, Hermawan Yudhi Kurniawan Yudhi Kurniawan Yuswanto Yuswanto