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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Teknologi dan Manajemen Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Inovasi Teknologi Pendidikan Register: Jurnal Ilmiah Teknologi Sistem Informasi Briliant: Jurnal Riset dan Konseptual Sinkron : Jurnal dan Penelitian Teknik Informatika Syntax Literate: Jurnal Ilmiah Indonesia JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL EDUCATION AND DEVELOPMENT MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pendidikan dan Pengabdian Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Evan Tanuwijaya
Universitas Ciputra Surabaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Implementasi Ekonomi Sirkular Melalui Studi Kelayakan Pembuatan Bata Limbah Plastik Evan Tanuwijaya; Tota Pirdo Kasih; Rudy Susanto
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (528.314 KB) | DOI: 10.36418/syntax-literate.v7i11.12000

Abstract

Pertumbuhan ekonomi dan peningkatan jumlah populasi mengakibatkan peningkatan jumlah konsumsi. Konsumsi yang meningkat menimbulkan masalah limbah, terutama limbah plastik yang bisa berpengaruh negatif pada ekosistem dan kesehatan manusia. Maka dari itu diperlukan manajemen pengelolaan limbah plastik melalui program ekonomi sirkular, yang bertujuan untuk melakukan daur ulang limbah sehingga dapat menciptakan kegiatan ramah lingkungan. Salah satu pengelolaan limbah adalah pengembangan produk bata limbah plastik. Tujuan penelitian adalah untuk menganalisis dan mengevaluasi pengembangan bata berbahan limbah plastik dalam implementasi ekonomi sirkular. Sampel penelitian adalah bata limbah plastik. Analisis yang digunakan adalah menggunakan metode kuantitatif dan kualitatif dengan melakukan analisa rasio experiment. Hasil penelitian memperoleh kesimpulan bahwa hasil percobaan memperoleh 4 buat jenis bata, bata plastik yang dihasilkan mampu menahan beban tumpukan setinggi 4 m dengan tekanan terbesar sebesar 114, dan dapat mewujudkan penerapan ekonomi sirkular dengan melakukan daur ulang.
Development of innovative behaviors Android application and website for teachers using the waterfall method Tanuwijaya, Evan; Kurniawan, Jimmy Ellya; Rahmawati, Kuncoro Dewi
Jurnal Inovasi Teknologi Pendidikan Vol. 11 No. 3 (2024): September
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jitp.v11i3.68133

Abstract

This research explores the pivotal role of innovation in education, particularly in response to the COVID-19 pandemic. It focuses on developing the Guru Inovatif Android app and website to enhance educators' innovative teaching practices. These platforms feature a survey to assess innovation levels and training modules to develop innovative skills, aiming to revolutionize teaching methodologies. (In this research, the waterfall methodology is employed, which is used to develop software from start to finish. This model involves a sequential design process, where progress flows in one direction through phases such as requirements, design, implementation, verification, and maintenance. It is utilized to create both an Android-based application and a Laravel-based website.) Testing involved 109 teachers from various educational levels across six regions in Java. Data were collected via a structured Likert-scale survey and analyzed for usability and satisfaction. Results indicated high user-friendliness (4.43) and satisfaction (4.28) but highlighted the need for design consistency (2.37) and error reduction (2.27). By addressing these issues, the Guru Inovatif platforms can better support educators in adopting innovative teaching practices.
DEVELOPMENT OF VERTICAL-AXIS WIND TURBINE MODELS AS STRENGTHENING FOR SUSTAINABLE RENEWABLE ENERGY Kenneth Sutjiawan; Tota Pirdo Kasih; Rudy Susanto; Noviando Yuliando; Evan Tanuwijaya
JURNAL EDUCATION AND DEVELOPMENT Vol 12 No 2 (2024): Vol 12 No 2 Mei 2024
Publisher : Institut Pendidikan Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37081/ed.v12i2.5630

Abstract

The use of electricity in the world continues to increase over time, the main source of producing electricity in the world is coal which is a basic material that takes a long time to form. Since 2021, British patrol has recorded that coal supplies in the world are available at 1,074,108 million tonnes. In Indonesia, coal still provides 60% of the country's electricity supply and the other 40% uses other energy such as water, steam, gas and other energy. New renewable energy development goals through the Sustainable Development Goals (SDG) in utilizing wind energy through vertical axis wind turbines. The research made three turbine models with different shapes, which then collected voltage data with an air compressor. The experimental results show that the average voltage generated by the two turbine models is 240 mV and 229 mV respectively. For the third model using two sampling methods obtained 353 mV and 292 mV. The use of vertical shaft wind turbines can be used in urban areas, especially in some tall buildings and parks. Research can still be developed by adding several other factors such as wind speed, turbine rotation speed and the aerodynamics of the turbine shape. and using a dc generator that requires a small torque so that it can increase the electric power produced. (KS)
COMPARATIVE STUDY OF CNN-BASED ARCHITECTURES ON EYE DISEASES CLASSIFICATION USING FUNDUS IMAGES TO AID OPHTHALMOLOGIST Yaurentius, Evelyn Callista; Saputri, Theresia Ratih Dewi; Tanuwijaya, Evan; Sutanto, Richard Evan
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.3699

Abstract

Eye health has a significant impact on quality of life, with more than 2.2 billion people experiencing vision problems. Many of these cases can be prevented or treated. The use of AI for eye disease classification helps healthcare professionals provide optimal care. However, the complexity of fundus images challenges classification performance. This study examines various Convolutional Neural Network (CNN) architectures using Transfer Learning and Adam optimization. Fundus images are processed using CLAHE (clip limit and grid size) and the Wiener filter (size) to enhance contrast and reduce noise. Afterward, ResNet-152, EfficientNet, MobileNetV1, and DenseNet-121 are tested to identify the most effective model. The study aims to determine the optimal CNN architecture for eye disease classification, assisting ophthalmologists in diagnosing eye diseases through fundus images. The best CNN model, ResNet-152, achieved an accuracy of 94.82%, outperforming other models by 3.95 - 8.29%.
Rancang Bangun Aplikasi Penitipan Hewan Peliharaan Berbasis Android Evan Tanuwijaya
Jurnal Teknik Informatika dan Sistem Informasi Vol 4 No 3 (2018): JuTISI
Publisher : Maranatha University Press

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

Abstract

A lot of people in Indonesia keeps their pets at their home. Therefore, the pets must be taken care at all time. But we as humans have many other activities besides taking care of our pets. This causes the pet not to be monitored. To solve the problem, an app is required to search for a pet daycare. This app can search for healthy, comfortable, and safe pet daycare. Applications have been widely developed in the community, especially for mobile applications. Mobile applications have many advantages and ease in accessing them. In addition, in Indonesia technology is growing very fast. Many Indonesians are also using smartphones in their daily lives. Based on the above data, a solution appears to create an app for pet daycare where pet owners can easily search for pet daycare just with their smartphones. This app uses a client-server architecture where the client is a smartphone that has an Android operating system and a server using Google Firebase. Development method used is the Systems Development Life Cycle (SDLC) method with the Waterfall model. The result of this thesis, it can be concluded that Petqper mobile application can look for healthy, convenient, and safe daycare. In addition, reliable pet daycare can use this application to offer their services and simplify the process of booking a place.
HUMAN FACE RECOGNITION ON IMAGE VIDEO CONFERENCE APPLICATION USING SIAMESE NETWORK WITH SKIP CONNECTION SMALLER VGG MODEL Tanuwijaya, Evan; Setiawan, Averill Saladin Atma; Arianindita, Achmad Rijalu; Kristanto, Timothy
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.981

Abstract

Attendance recording is needed to find out someone's attendance at a meeting or meeting. These meetings are sometimes conducted online through the video conferencing application. Recording attendance at online meetings is using an online form that is distributed via chat. There are several problems such as chats piling up and meeting participants arriving late so they cannot access the form link. Therefore, facial recognition can be used to record attendance using screenshots as an attendance record with the aim of helping to facilitate attendance recording through video conferencing applications using computer vision technology. This study proposes a method of using the Siamese network with the Smaller VGG skip connection model to improve human face recognition in video conferencing application images. Has validation accuracy results in the training phase of 98%, precision of 98%, and recall of 98%. For the similarity phase where the model is applied to the Siamese network, the accuracy is 95%, the precision is 53%, and the recall is 78%. Then the model is applied to the pipeline system with the YOLO-face model to classify the results of face detection from Yolo with the faces in the database so that the model does not need to be retrained if there are new faces, it only needs to add facial images to the database to be compared with the query image..
Implementation of YOLOv12 and PaddleOCR for Indonesian Bank Statement Table Extraction Kristanto, Samuel Miracle; Tanuwijaya, Evan
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15383

Abstract

The increasing reliance on digital financial documents has highlighted the need for automated methods to extract structured information from bank statements. Traditional optical character recognition (OCR) systems often fail to capture complex tabular structures, leading to incomplete or error-prone transaction records. To address this challenge, this research proposes a two-stage detection and recognition pipeline that combines YOLOv12 for table and structural element detection with PaddleOCR for text extraction, followed by automated Excel conversion. The objective of this study is to improve accuracy in localizing tables, detecting rows and columns, and generating structured financial data that can be directly utilized for downstream applications. The methods involve training a YOLOv12-n model in two stages: Stage 1 focuses on detecting entire table regions, while Stage 2 focuses on identifying row and column structures within the detected tables. A lightweight AdamW optimizer with conservative augmentation strategies was applied to preserve the geometric integrity of document layouts. Results show that Stage 1 achieved precision of 0.998, recall of 1.0, and mAP50-95 of 0.989, while Stage 2 achieved precision of 0.992, recall of 0.964, and mAP50-95 of 0.899, demonstrating strong localization and structural recognition. The conclusions confirm that the proposed two-stage pipeline is effective for financial document processing, with potential applications in digital banking, auditing, and automated record management. Future research may focus on expanding datasets and addressing domain-specific variability.
Application of YOLO11 and Long Short-Term Memory Architecture for Exercise Form Evaluation in Weightlifting Dylan Lienardi , Nicholas; Evan Tanuwijaya
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16112

Abstract

Exercise provides significant benefits for physical health, and weightlifting has become increasingly popular among fitness enthusiasts. However, improper lifting techniques often lead to injuries, discouraging beginners and affecting long-term training consistency. To address this issue, this study proposes a deep learning approach that automatically evaluates weightlifting form through movement classification. The proposed method integrates the YOLO11n-pose algorithm for detecting keypoints from exercise video recordings and the Long Short-Term Memory (LSTM) network for classifying movement types and determining the correctness of form execution. The model achieved a mean average precision of 88.8% using side-view recordings of single- repetition weightlifting exercises. YOLO11n-pose extracts the coordinates of body keypoints, which are converted into joint angle data and analyzed over time using LSTM to identify movement quality based on expert-validated training data. The trained model was implemented into an iOS application called KorForm, developed using FastAPI, to provide real-time feedback for users. The results demonstrate that combining YOLO11n-pose and LSTM effectively supports weightlifting form evaluation and offers a practical solution for promoting safer and more consistent exercise habits.
Rule-Based Pitch Inference in Optical Music Recognition on Polyphonic Scores using YOLOv12 Derend Marvel Hanson Prionggo; Tanuwijaya, Evan
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16291

Abstract

Optical Music Recognition (OMR) faces significant challenges when applied to polyphonic music scores, due to the high symbol density and the overlapping of notes. This study proposes a hybrid method of combining the detection of noteheads using YOLOv12 with rule-based pitch inference, which converts the spatial position of the detected noteheads into accurate pitch information. The dataset used in this study is DeepScoresV2-Dense, which is processed through annotation conversion, image normalization, and staff extraction as a reference to infer the pitch of a note. The YOLOv12 model was trained for 30 epochs using a transfer learning approach, resulting in an mAP50 value of 0.75, a precision of 0.85, and a recall of 0.58 on the validation data. The implementation of rule-based pitch inference successfully achieved a pitch accuracy of 0.87 with an F1 score of 0.87, demonstrating a balance between accuracy and completeness of prediction. This result shows that the integration of YOLOv12 and rule-based pitch inference can be an effective solution for pitch extraction in polyphonic music scores, with potential applications in music information retrieval, digital music score conversion, and an artificial intelligence-based music learning system.
Comparative Analysis of 1D CNN Architectures for Guitar Chord Recognition from Static Hand Landmarks Naya, Rafi Abhista; Tanuwijaya, Evan
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11339

Abstract

Vision-based guitar chord recognition offers a promising alternative to traditional audio-driven methods, particularly for silent practice, classroom environments, and interactive learning applications. While existing research predominantly relies on full-frame image analysis using 2D convolutional networks, the use of structured hand landmarks remains underexplored despite their advantages in robustness and computational efficiency. This study presents a comprehensive comparative analysis of three one-dimensional convolutional neural network architectures—CNN-1D, ResNet-1D, and Inception-1D—for classifying seven guitar chord types using 63-dimensional static hand-landmark vectors extracted via MediaPipe Hands. The methodology encompasses extensive dataset preprocessing, targeted landmark augmentation, Bayesian hyperparameter optimization, and stratified 5-fold cross-validation. Results show that CNN-1D achieves the highest mean accuracy (97.61%), outperforming both ResNet-1D and Inception-1D, with statistical tests confirming significant improvements over ResNet-1D. Robustness experiments further demonstrate that CNN-1D maintains superior resilience under Gaussian noise, landmark occlusion, and geometric scaling. Additionally, CNN-1D provides the fastest inference and most stable computational performance, making it highly suitable for real-time or mobile deployment. These findings highlight that, for structured and low-dimensional landmark data, simpler convolutional architectures outperform deeper or multi-branch designs, offering an efficient and reliable solution for vision-based guitar chord recognition.
Co-Authors AA Sudharmawan, AA Agus Zainal Arifin Angelica Roseanne Arianindita, Achmad Rijalu Arif Mudi Priyatno Baskoro Nugroho Chastine Fatichah Christian Tanjono David Christian Kartamihardja Derend Marvel Hanson Prionggo Dini Adni Navastara, Dini Adni Dylan Lienardi , Nicholas Eha Renwi Astuti Jimmy Ellya Kurniawan Kenneth Sutjiawan Kristanto, Samuel Miracle Kristanto, Timothy Kuncoro Dewi Rahmawati Laura Mahendratta Tjahjono Lestari, Caecilia Citra Mohammad Fatoni Anggris, Mohammad Fatoni Mychael Maoeretz Engel Naya, Rafi Abhista Noviando Yuliando Rarasmaya Indraswari Reinaldo Lewis Lordianto Reiner Anggriawan Jasin Rinabi Tanamal Rudy Susanto Rudy Susanto Safri Adam Setiawan, Averill Saladin Atma Siswanto, Felicia Devina Sutanto, Richard Evan Theresia Ratih Dewi Saputri Timotius Timotius Leonardo Lianoto Tota Pirdo Kasih Tota Pirdo Kasih Yaurentius, Evelyn Callista Yuwono Marta Dinata