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LP3M Universitas Mulia Jl. Letjen Z.A. Maulani No. 9 Kelurahan Damai Bahagia Kecamatan Balikpapan Selatan Kota Balikpapan Provinsi Kalimantan Timur Indonesia
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METIK JURNAL
Published by Universitas Mulia
ISSN : 24429562     EISSN : 25801503     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering
Media Teknologi Informasi dan Komputer (METIK) Jurnal adalah jurnal teknologi dan informasi nasional berisi artikel-artikel ilmiah yang meliputi bidang-bidang: sistem informasi, informatika, multimedia, jaringan serta penelitian-penelitian lain yang terkait dengan bidang-bidang tersebut. Terbit dua kali dalam setahun bulan Juni dan Desember.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 243 Documents
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Impelementasi Algoritma LSTM Dan SVR Untuk Prediksi Harga Bitcoin Menggunakan Data Yahoo Finance: Indonesia Riziq, Ihsannur Fathan; Dzikrillah, Ahmad Rizal
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: 10.47002/90ypad22

Abstract

Technological developments in the financial sector have facilitated the emergence of various digital investment instruments, one of which is cryptocurrency. Bitcoin and Ethereum are digital assets with the largest market capitalization, while the USD remains a significant player in global trade. The high price volatility of these three assets demands accurate and adaptive prediction methods. This study aims to apply the Long Short-Term Memory (LSTM) learning algorithm to predict Bitcoin, Ethereum, and USD prices based on historical data from Yahoo Finance from 2019 to 2024. Preprocessing includes data normalization with a Min-Max Scaler and feature engineering in the form of daily returns. Model evaluation was conducted using the Mean Absolute Error (MAE) and Mean Squared Error (MSE) metrics. The results showed that the LSTM model performed best, with the lowest MAE value of 1,320.41 and an MSE of 3,464,596.53 for the highest price prediction. These findings demonstrate that LSTM excels in consistently handling complex and fluctuating data patterns. This research is expected to serve as a reference in the development of a machine learning-based digital asset price prediction system, particularly for assets with high volatility.
Desain UI/UX Aplikasi Edukasi FIDEXA-SD Menggunakan Design Thinking dan Figma Yaasiin, Muhammad; Hanif, Isa Faqihuddin
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: 10.47002/803mgn28

Abstract

This study explores the UI (User Interface) and UX (User Experience) design process of an educational application called FIDEXA-SD, developed specifically for elementary school students and teachers. The core problem identified is the limited availability of digital learning tools that are both engaging for young learners and practical for educators. To address this, the Design Thinking approach was implemented through five iterative stages: Empathize, Define, Ideate, Prototype, and Test. Using Figma, the research team designed interactive prototypes for student and teacher interfaces. Data were gathered through interviews, questionnaires, and usability testing involving 5 students from grades 2 to 4 and 3 teachers. Results showed that students found the visual design attractive and intuitive, while teachers appreciated the clean and functional dashboard layout. Some usability issues, such as confusing quiz navigation and unclear material upload options, were uncovered and addressed through design iteration. This study highlights how combining Design Thinking with Figma can effectively produce user-centered designs tailored to the needs of both learners and educators. The findings also provide a solid foundation for future development and real-world classroom implementation.
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.
Desain Protokol Keamanan Data Berbasis Blockchain pada Pengolahan Data Pengguna Aplikasi E-commerce Purba, Risky Pradipa; Wijaya, Bayu Angga; Nazara, Leni Wati; Utami, Sekar Suryati
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/gbywas22

Abstract

Data security is a crucial aspect in developing e-commerce applications, considering the high risk of user data leakage. This study aims to design, implement simulations, and analyze blockchain technology-based security protocols to comprehensively improve protection of user data in e-commerce systems. The methods used include literature studies, system requirements analysis, and protocol architecture design using a smart contract approach on the Ethereum Testnet platform. The results of this study indicate that this protocol design is very effective in the form of a protocol design that supports data integrity, transparency, and authentication through decentralization and cryptography features in the blockchain. Specifically, testing proves 100% success in data encryption using AES-256 and 100% success in recording data hashes using SHA-256 to the blockchain. This design has also been shown to be able to distinguish and reject unauthorized access and record all activities, both on-chain and off-chain, supporting the principle of non-repudation. Operational efficiency is also seen with an average data transaction completion time of 4.2 seconds, indicating that this system is quite responsive for an e-commerce environment. With this approach, the developed protocol significantly minimizes the potential for data manipulation and misuse by irresponsible parties. This simulation implementation clearly demonstrates how blockchain can be adopted in the context of cybersecurity to support higher user trust in e-commerce platforms, as well as ensure data privacy protection through encrypted off-chain storage of data.
Pembuatan Desain Website Penyakit Tanaman Jagung Berbasis User Centered Design Fajriansyah, Alif; Arsi, Primadani; Suliswaningsih
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/a8jv0880

Abstract

Maize (Zea mays), as one of Indonesia’s primary agricultural commodities, often suffers from diseases such as stem rot, leaf spot, and fungal infections, which reduce its productivity. This study focuses on designing a user interface for a maize disease detection system using the User-Centered Design (UCD) approach—an approach that remains rarely applied in the context of agricultural AI systems. Unlike previous studies that applied UCD in areas such as waste management, web interface audits, and educational websites, this study emphasizes the integration of UCD into an AI-based crop disease detection tool. The design process followed the standard UCD stages understanding the context of use, specifying user requirements, developing design solutions, and evaluating the design. The interface was developed using Figma and evaluated through the System Usability Scale (SUS) method. The evaluation yielded a SUS score of 79, categorized as "Good" (Grade B), indicating a high level of usability and user satisfaction. This study contributes to expanding the implementation of user-centered design in the agricultural sector, offering a novel and practical approach to enhancing AI system adoption among end-users in farming.
Implementasi Algoritma YOLOv11 Dan Roboflow Untuk Deteksi Tingkat Kematangan Anggur Berbasis Web Nofita Sary; Pasaribu, Hendra Handoko Syahputra; Situmeang, Rahel Juliana; Darus, Rizky Darmawan
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/xeze6v30

Abstract

Manual detection of grape ripeness is inefficient and prone to subjective errors. This study developed a web-based automatic classification system using the YOLOv11 algorithm with the YOLO11s.pt model and the Roboflow platform. A deep learning approach was applied to automate the classification of grapes into four ripeness categories: unripe, semi-ripe, ripe, and rotten. The dataset used consisted of 897 images obtained directly from the vineyard, then expanded to 6,135 images through preprocessing and augmentation. The labeling process was carried out using Roboflow, and model training was carried out on Google Colab for 200 epochs. The training results showed high performance, with a recall value of 0.95, a precision of 0.98, and a mean Average Precision (mAP) of 0.84. The system was able to distinguish multi-class objects with an average detection time of 1,02 seconds per image, thus supporting semi real-time operations. However, the accuracy of the semi-ripe class classification is still a challenge due to visual similarities with other classes. This system has been integrated into a web application that displays classification results in semi real-time, and has the potential to be applied in a digital agricultural system. For further research, it is recommended to optimize the dataset, especially by adding the amount of training data on the rotten and half-ripe grape classes. In addition, the development of the application into a mobile application is recommended to increase accessibility and flexibility of use.
Systematic Literature Review: Menilai Tingkat Nyeri Melalui Pola Suara Kristanti, Inka; Br Sitohang, Sondang Agustina; Margaretha, Yulia; Daya, Onita; HS, Christnatalis
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/mn6wfj51

Abstract

In the medical field, Accurate detection of pain levels is a crucial aspect of healthcare, especially for patient groups who cannot directly communicate their pain, such as infants, individuals in critical condition, or those with neurological dysfunction. This study aims to test the effectiveness of a voice pattern analysis approach in detecting pain levels through a Systematic Literature Review (SLR) method. From 500 articles, 13 relevant inclusion studies were selected based on PRISMA criteria. The review results indicate that sounds such as crying and moaning can serve as objective pain indicators, and have great potential for integration into clinical systems. Supported by artificial intelligence algorithms such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), the accuracy level of pain detection based on sound reaches 83% to 96% depending on the type of data and methods. Although the results are promising, there are several challenges such as limited dataset variability, background noise interference, and the absence of a standardized voice-based pain classification. Therefore, further research is needed for direct validation of the system in clinical environments, development of classification standards, and exploration of multimodality to improve accuracy. This research is expected to serve as a foundation for the development of more objective, adaptive, and inclusive pain assessment technologies for patients with communication limitations.
Redesign UI/UX dengan Metode SUS dan UCD pada Website Akademik UHAMKA Kurniyawan, Ahmad Barkah; Irwansyah, Irwansyah
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/g7qxcr44

Abstract

The advancement of information technology has encouraged higher education institutions to provide more efficient and accessible digital academic services. Universitas Muhammadiyah Prof. Dr. Hamka (UHAMKA) utilizes a web-based academic information system to support student activities such as course registration (KRS), grade management, payments, and schedule information. However, the existing system still faces challenges related to user interface and user experience, including monotonous design, unintuitive navigation, and inconsistent interface elements. This study aims to redesign the user interface (UI) and user experience (UX) of UHAMKA’s academic system using the User-Centered Design (UCD) approach and to evaluate the results through the System Usability Scale (SUS) method. The prototype was developed using the Figma platform. The research methodology involved observation, questionnaire distribution, and interviews with 35 active students. The initial evaluation yielded a SUS score of 40.07, which falls into the “Poor” and “Not Acceptable” categories. After redesigning seven main pages and one additional page, the prototype was re-evaluated and achieved a SUS score of 80.71, categorized as “Excellent” and “Acceptable.” Post-redesign interviews also indicated improvements in user comfort, information clarity, and ease of navigation. The primary contribution of this study lies in demonstrating a significant improvement in system usability through a systematic application of the UCD approach, and the resulting design may serve as a reference for developing more user-centered digital academic systems.
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.  
Implementasi Dan Deteksi Tong Sampah Pintar Menggunakan Esp8266 dengan Algoritma CNN Denice; Ravellia oska amanda; Eric Pradana Nst; Jaya Bardi; Dhanny Rukmana Manday
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: 10.47002/3w7fmh51

Abstract

Waste accumulation due to ineffective management has become a challenge in the modern era. This study designs an Internet of Things(IoT)-based smart trash bin system using ESP8266, an HC-SR04 ultrasonic sensor, and a servo motor to monitor waste volume and automatically open or close the lid. The ESP8266-CAM and a Convolutional Neural Network (CNN) model are utilized for classifying organic and inorganic waste, with data communicated to a Flutter-based mobile application via Wi-Fi. The system also integrates a GPS module and Google Maps API navigation to guide an RC car to the trash bin location. Results show the CNN model achieved a validation accuracy of 83.18%, and the system functions effectively for capacity detection and automation. This solution offers efficient household waste management, real-time monitoring, and automatic classification, helping to reduce emptying frequency, minimize physical contact, and raise environmental awareness. This research provides a foundation for further IoT development in environmental and sustainable waste management.

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