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All Journal Jurnal Teknologi Sistem Informasi dan Aplikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) METIK JURNAL Infotech: Journal of Technology Information Jurnal Pengabdian kepada Masyarakat Nusantara JMBA (Jurnal Manajemen dan Bisnis asmi) Prosiding Seminar Nasional Unimus Akiratech
Pratiwi, Nunik
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Nursing Public Health Social Sciences

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Journal : METIK JURNAL

 1 
Implementasi YOLOv11 dan Google ML Kit untuk Pembacaan Struk pada Aplikasi Keuangan Mobile Zufar, Muhammad Viddya; Pratiwi, Nunik
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 2 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/pcpv7618

Abstract

This study aims to develop an Android-based system capable of automatically recapping shopping data from cashier receipts. The system integrates the YOLOv11 object detection method to identify key information areas such as product names, quantity, unit price, and total amount, and utilizes Google ML Kit as the Optical Character Recognition (OCR) module to extract text from receipt images. The research stages include problem identification, system design, prototype development, and performance evaluation using the Confusion Matrix method. The testing results show a precision of 100%, recall of 74.96%, and an F1-score of 85.7%, indicating that the system performs with high accuracy and effectiveness in extracting receipt information. Therefore, this system offers a practical and efficient solution for automatic expense recording through mobile devices.
Deteksi Tipe Sidik Jari Untuk Mengenali Kepribadian Menggunakan Metode Support Vector Machine Jasmine, Putri; Pratiwi, Nunik
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 2 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/x7msax09

Abstract

This study discusses the development of a fingerprint type classification system based on digital image processing using the Support Vector Machine (SVM) method. The system is designed to recognize three main fingerprint patterns: arch, loop, and whorl. The data processing stages include binarization of the fingerprint image and feature extraction using the Histogram of Oriented Gradients (HOG) method. Once the features are extracted, classification is performed using the SVM algorithm with a Radial Basis Function (RBF) kernel to improve separation performance between classes. The dataset used in this study was obtained from the Kaggle platform, and the system was implemented using MATLAB software, complete with a graphical user interface (GUI) to facilitate user interaction. The system’s performance was evaluated by dividing the dataset into 80% training data and 20% testing data. The results show that the model is capable of classifying fingerprint patterns with an accuracy of 89.25%. These findings indicate that the SVM method is effective and can serve as an initial solution for automatic fingerprint-based identification systems.  
Perancangan Sistem Pengenalan Tulisan Tangan pada Jawaban Esai Menggunakan Metode CNN-LSTM Berbasis Android Elsa Apriani; Pratiwi, Nunik
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 2 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/wn518489

Abstract

Technological developments drive educational innovation, one of which is a handwriting recognition system to accelerate essay answer assessment. This study designs an Android application that recognizes students' handwriting using the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) methods. The system was developed using a prototyping approach through the stages of identifying needs, designing interfaces, implementing features, and testing. The evaluation results showed an accuracy of 60.37%, Character Error Rate (CER) of 16.84%, and Word Error Rate (WER) of 78.41%. Although the WER is still high, character accuracy is good enough for the early stages of development and provides a promising basis for future system improvements. Testing using Black Box Testing ensures that all features run according to their functions. This system is expected to make it easier for teachers to correct essay answers more efficiently, quickly, and consistently, as well as support the digitalization of assessment in the educational environment.
Klasifikasi Ukuran Baju Berdasarkan Pengukuran Tubuh Menggunakan MediaPipe dan Support Vector Machine Nurul Laily, Tasya; Pratiwi, Nunik
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 2 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/tqvapv07

Abstract

Inaccurate clothing size selection is a common issue in online shopping, as many consumers do not know their exact body measurements. This study developed an automatic clothing size recommendation system based on image processing using MediaPipe to detect body keypoints from user images. Body parameters such as height, shoulder width, and chest circumference were calculated using the Euclidean Distance method and converted into centimeters through height-based calibration. These values were then used as input for clothing size classification using the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel. The system was built with a MATLAB-based interface. A total of 231 body image data were used as the dataset. The classification testing results showed an accuracy of 91%, with high precision, recall, and F1-score values. Based on the evaluation, the system’s Mean Absolute Error (MAE) was 1.66 cm for body height, 1.08 cm for shoulder width, and 2.99 cm for chest circumference. The system proved to be sufficiently accurate and can assist users in automatically and efficiently determining their clothing size.
Pengembangan Game 3D Menggunakan Batik Sido Mukti dalam Sentai’s Martial Mayhem Rahayu, Anida Sri; Pratiwi, Nunik
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 2 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/zz98x012

Abstract

The main visual component of this project is the Sido Mukti batik motif, which will be used to design characters in Sentai's Martial Mayhem video game. This motif was chosen for its deep philosophical meaning and its ability to preserve regional culture through digital media. Research and Development (R&D) was the process employed, encompassing problem identification, data collection, graphic design, character implementation into a Unity-based game, and testing using black-box and System Usability Scale (SUS) approaches. Based on the test results, 14 out of 15 scenarios ran as planned, with an accuracy rate of 93.75%. The average SUS score was 68.25, or ‘sufficiently adequate,’ given by 20 respondents. Both technically and visually, the batik-themed Gold Ranger character was successfully integrated into the game. Based on these results, the addition of regional cultural elements to digital games enhances their visual appeal and serves as a useful educational tool for cultural preservation. As a result, this study significantly promotes the development of games that emphasise regional cultural values.  
Co-Authors Akram, Abdullah Alam, Indera Nurul Alifandra, Dhafa Ana Hidayati Mukaromah Ariyansyah, Riyan Atwiyandani, Indah Audina, Risti Ifa Davy Wiranata, Ade Dianson, Arkila Destha Elsa Apriani Gunadi, Reza Jasmine, Putri Lusti, Nur Fara Miftahuddin Miftahuddin Musaidah, Siti Nurul Laily, Tasya Pinardi, Sofia Prasetio, Bima Rahayu, Anida Sri Ramza, Harry Rivai, Ahmad Rosalina Rosalina Sinduningrum, Estu Widodo, Muh. Adnan Zufar, Muhammad Viddya