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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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INDONESIA
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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Penerapan Metode EUCS dan SMARTPLS Terhadap Pengukuran Tingkat Kepuasan Pengguna Aplikasi Zalora Budiman, Muhammad Arif; Sanjaya, M. Rudi; Endang Lestari Ruskan; Indah Dwi Rosa
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1044

Abstract

Zalora is an E-Commerce aplplication founded on 2012 in Singapore and now operates in various countries, including Indonesia. Although widely known, this application still has a number of complaints from users such as product incompatibility, delays in process updates, and less than optimal application performance. This study aims to evaluate user satisfaction with the Zalora application using the EUCS method, which includes five variables, namely Content, Accuracy, Format, Ease of Use, and Timeliness. This study provides benefits for application, with the subjects being student college members of the Faculty of Computer Science at Sriwijaya University. This study employs a quantitative approach, utilizing data collection through questionnaires and analysis with SmartPLS software. As a result, the five EUCS method variables positively impact user satisfaction, particularly the Content, Accuracy, Ease of Use and Timeliness variables.
Analisis Efisiensi Waktu Bubble, Insertion, Merge, Dan Quick Sort Menggunakan Python Sutanto, Daniel Septhiady; Chandra, Chandra Kirana; Wahyuningsih, Delpiah
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1053

Abstract

Sorting is a fundamental process in data processing that plays an important role in increasing the efficiency of other algorithms. This study aims to conduct a comparative analysis of four classic sorting algorithms: Bubble Sort, Insertion Sort, Merge Sort, and Quick Sort. The comparison is based on three main paRAMeters: Execution time, algorithm Complexity. Testing was carried out experimentally using small to large random Datasets with test scenarios of 100, 150, 300 using the Python progRAMming language. The results showed that Merge Sort is the most efficient algorithm in terms of time because the average time required is 0.000165 seconds.
Sistem Informasi Geografis Prioritas Penanganan Kerusakan Infrastruktur Menggunakan Metode MOORA Silalahi, Adi Gunawan; Triase
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1054

Abstract

The prioritization of road infrastructure damage repairs in Sibolga City has so far been conducted manually, resulting in inefficient, time-consuming processes that are prone to subjectivity. This study aims to develop a Geographic Information System (GIS) based on the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method to provide an objective and measurable solution for determining road repair priorities. Data were collected through field observations and interviews, and the system was developed using the Waterfall model. The MOORA method was applied to evaluate road segments based on several key criteria such as damage level, traffic volume, repair cost, and road width. The analysis results show that the system successfully ranks road segments accurately, with segments A21, A22, and A23 receiving the highest priority scores of 0.1096. The GIS feature enables visualization of priority locations on an interactive map, supporting coordination among stakeholders and enhancing public participation in monitoring and reporting processes.
Analisis Sentimen Publik Terkait Danantara Menggunakan Algoritma IndoBERT pada Platform Media Sosial Pratama, Ahlul Yoga; Sanjaya, Gauri Ananda; Lubis , Nadya Khairunisa; Rangga Aditya , Muhammad; Yennimar, Yennimar
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1055

Abstract

This study aims to analyze public sentiment toward the Indonesia Investment Authority (Badan Pengelola Investasi – BPI) Danantara using artificial intelligence technology. Data was collected through crawling using an X API token, resulting in 4,269 tweets stored in CSV format, consisting of 15 columns including tweet text and user metadata. The data underwent a pre-processing stage, including text cleaning, case folding, and tokenization, to prepare it for analysis. Manual labeling was conducted to classify sentiment into three categories: positive (32%), negative (45%), and neutral (23%). Due to class imbalance, a data augmentation technique was applied, increasing the total number of records to 23,623. The IndoBERT-base model was employed using a transfer learning approach for three-class sentiment classification. After five training epochs, the model achieved an accuracy of 97.71%. Evaluation results demonstrate high computational efficiency, with the model capable of processing data quickly. This study highlights the importance of applying artificial intelligence technologies, particularly BERT-based language models, in sentiment analysis in the digital era.
Systematic Literature Review: Penilaian Rasa Nyeri Melalui Analisis Ekspresi Wajah Di Area Sekitar Mata Christnatalis HS; Horas Antonius Saragi Sidauruk; Michael Fernandes Simangunsong; Gary Fontanna; Christo
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1059

Abstract

In the medical field, accurate pain assessment poses a significant challenge. This study explores the use of facial expression recognition technology to detect pain, with a particular focus on the sensitive area around the eyes. A comprehensive review of 50 articles in the field suggests that changes in facial expressions, including forehead wrinkles, drooping eyebrows, and eyelid changes, can serve as significant indicators of pain. The integration of advanced sensors with deep learning and machine learning algorithms has demonstrated significant potential in the effective recognition of pain indicators. Recent innovations, including more advanced neural networks and biological marker measurements, offer efficient solutions for real-time applications. Nevertheless, challenges persist, including variations in individual expression and limitations in high-quality datasets. Consequently, this study proposes the development of more diverse pain expression datasets and the optimization of algorithms for this technology. Integration of systems with facial expression-based pain scales and the conduction of clinical trials are pivotal in enhancing the accuracy and reliability of pain assessment, thereby facilitating healthcare professionals in caring for patients and improving the quality of healthcare services.
Penerapan XLM-R dan TD-IF+Consiner Similarity untuk Deteksi Berita Hoax Multilanguage pada Aplikasi Mobile Berbasis Natural Language Processing Achmad Rizqon Subarkah; Imanda, Rahmi
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1066

Abstract

The spread of hoaxes has become an increasingly alarming global issue, in line with the growing use of social media. This study aims to develop a mobile application system for automatic hoax detection using a Natural Language Processing (NLP) approach by leveraging the XLM-RoBERTa model. The system development process follows the CRISP-DM methodology and the Agile approach. The dataset used consists of 1,928 verified news articles, divided into training and testing data. The model architecture is designed by adding three hierarchical classification layers, consisting of linear layers, batch normalization, ReLU activation, and a dropout rate of 0.4, with the [CLS] token as the main representation for classification. Optimization is performed using the Adam algorithm with a learning rate of 0.001. To enhance the interpretability of the classification results, the system is equipped with an explainability feature based on TF-IDF and Cosine Similarity. The model is integrated into a Flutter-based mobile application with a REST API backend developed using the Flask framework, enabling real-time news analysis. The application provides outputs in the form of prediction labels (hoax/fact), confidence scores, and a summary of the news content. Evaluation results show that the model achieves an accuracy of 94.51%, indicating excellent performance in identifying hoax news automatically and efficiently. By integrating this intelligent model into a mobile application, the system is expected to help users quickly and reliably assess the truthfulness of information, thereby contributing to efforts to mitigate the spread of hoaxes in the digital era.
Tinjaun Literatur Sistematis Terhadap Teknologi Kecerdasan Buatan untuk Deteksi Nyeri (2020-2024) Dharma, Abdi; Veron, Veron; Wijaya, Jeremy; Valentino, Bue; Wijaya, Vincent
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1068

Abstract

Pain is a complex sensory and emotional experience, often difficult to assess objectively. In recent years, artificial intelligence (AI) has shown great potential in improving the accuracy and efficiency of pain assessment. This study aims to conduct a systematic review of AI-based pain detection methods developed in the period 2020 to 2024. Using the PRISMA 2020 approach, a literature search was conducted in three major databases: PubMed, Scopus, and Google Scholar, with keywords related to pain detection and perception. Of the 1,685 articles found, 44 studies were selected through a rigorous selection process. The analysis of five showed the main approaches in pain detection: Neuroimaging & Neurological, Physiological & Biometric, Visual-Only (facial recognition), Audio/Speech-based, and Behavioral/Observational. Neuroimaging-based approaches such as EEG and fMRI were the most dominant, followed by the use of biometric sensors and facial recognition technology. However, significant challenges remain, including the limitations of global data standards, difficulties in model generalization, and ethical and privacy issues. This study highlights that the integration of non-invasive sensors with deep learning models and personalized approaches can improve the effectiveness of automated pain detection systems.
Implementasi Metode Bubble Sort Descending pada Aplikasi Sealed Bid Auction Toko Indonesia Art Shop Syuhada, Muhammad Randa; Samsudin
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1071

Abstract

Indonesia Art Shop is a shop that sells all kinds of antiques for collection, these collectibles range from small to large. Small items such as: rings, coins, agate, ancient money and large items such as clocks, old school radios, typewriters, paintings, urns, etc. This research aims to design a sealed bid auction application that is useful to facilitate and benefit Indonesia Art Shop shop owners in selling antiques. By implementing the bubble sort descending method in the sealed bid auction application which aims to assist in sorting data from the highest bid to the lowest bid, so that it can be implemented in the application and make it easier for the admin to determine the winner of the auction, the system automatically determines the winner with the highest bid, the application also uses a closed auction system or sealed bid auction which is useful for maintaining user confidentiality in bidding for antiques made by users or bidders. With the creation of a web-based sealed bid auction application at the Indonesia Art Shop store as a medium for selling antiques with a closed auction system, so it is hoped that antiques are sold at the highest price among the antique collector market and also as a promotional medium to expand the market.
Pemilihan Abang Kakak Tebing Tinggi dengan Metode Profile Matching Berbasis Website Harahap, Frans Adetya; Ali Ikhwan; Dedi Irawan, Muhammad
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/metik.v9i2.1081

Abstract

In today's digital era, advances in information technology have brought about major transformations in various aspects of life, including in the decision-making process in the social field. One of the important activities for the young generation in Tebing Tinggi City is the selection of Abang Kakak Tebing Tinggi as a representation of regional culture and potential. The conventional selection process that has been used so far often takes a long time and is not entirely objective. Therefore, this study designs a web-based decision support system using the Profile matching method. This system facilitates participant assessment based on the level of conformity between ideal criteria and actual profiles. The implementation results show that the system provides transparent and fair recommendations. System testing shows a good level of calculation accuracy, with the highest score for male participants being 4.58 and female participants being 4.42. With the blackbox testing method, all system features are also proven to run as they should.
Klasifikasi Penyakit Kanker Paru Menggunakan Algoritma Random Forest Berbasis Streamlit Dimas Aprilianto; Rizal, Erian
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/svz4r327

Abstract

Lung cancer is one of the leading causes of global mortality, often difficult to detect early due to its nonspecific initial symptoms. This study proposes a Machine Learning-based approach to classify lung cancer risks using the Random Forest algorithm optimized with GridSearchCV. The identified research gap is the lack of interactive web-based implementations that deliver real-time classification results with a user-friendly interface for general users. The objective of this study is to develop an accurate and efficient classification model and integrate it into a web application using Streamlit. The dataset was sourced from Kaggle, consisting of 5,000 patient records and 18 clinical and lifestyle-related features. The preprocessing steps included data cleaning, normalization, encoding, and feature recategorization. Model performance evaluation using Accuracy, Precision, Recall, and F1-Score metrics showed an accuracy of 90%. Feature importance analysis identified smoking habits, throat discomfort, and respiratory issues as dominant predictors of lung cancer. The model was then deployed into a Streamlit-based web application and tested via a User Acceptance Test (UAT) involving 50 respondents, resulting in a Mean Opinion Score (MOS) average above 84%. These findings indicate that the developed prediction system is not only technically accurate but also well-accepted by users, highlighting its potential as a practical and efficient tool for early lung cancer screening.

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