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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 264 Documents
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Sistem Rekomendasi Skincare Berdasarkan Jenis Kulit Menggunakan Content-Based Filtering dan Knowledge-Based Normalization Joy Raphaela; Arif Nur Rohman; Ika Nur Fajri
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/aa1yds57

Abstract

The rapid growth of the skincare industry has triggered information overload, complicating consumer decision-making particularly among Generation Z users on e-commerce platforms. Conventional Collaborative Filtering approaches are limited by popularity bias and the cold-start problem, and are unable to account for ingredient-level compatibility with individual skin conditions. Addressing this gap, this study proposes a novel Content-Based Filtering recommendation system that integrates TF-IDF and Cosine Similarity with a Knowledge-Based Normalization layer. This original framework maps informal consumer terminology into standardized dermatological categories, effectively reducing semantic inconsistency in unstructured product descriptions. Data were obtained from the Kaggle public repository (third-party extracted dataset) and underwent a validation process, yielding a final dataset of 91 skincare products. The system was evaluated using Precision@K across five skin-condition scenarios. Results yield an average Precision@5 of 0.80 (80%), with a peak cosine similarity score of 0.3606. The low absolute cosine value is attributable to TF-IDF vector sparsity in short-text descriptions, a characteristic acknowledged in prior literature. Implementation as a web application confirms the system's practical utility in guiding users toward biologically appropriate skincare choices, independent of market-trend bias.
Ensemble Learning untuk Model Prediksi Risiko Preeklamsia dan Explainable AI Berbasis SHAP Yudhi Fajar Saputra; Milkhatun; Mahmoud Ahmad Al-Khasawneh; Yazeed Al Moaiad; Aldi Bastiatul Fawait; Sitti Rahmah; Zakaria Ahmad Dahlan
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/kp377403

Abstract

Preeclampsia is a pregnancy complication that poses significant risks to both mother and fetus. Early prediction of preeclampsia risk is crucial to improve maternal healthcare outcomes. This study aims to develop a predictive model for preeclampsia risk using ensemble learning approaches and to enhance model interpretability through Explainable Artificial Intelligence (XAI). The dataset consists of 332 pregnant women who received antenatal care, with 330 complete clinical records after data cleaning. Two ensemble learning algorithms, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), were implemented and evaluated using Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC), and additional classification metrics. The best-performing model was further analyzed using SHapley Additive exPlanations (SHAP) to assess feature contributions at both global and individual levels. The results indicate that XGBoost outperformed Random Forest with an AUC of 0.81 compared to 0.72 after applying class weighting and 5-fold cross-validation. XGBoost also demonstrated more balanced performance with an accuracy of 0.83, recall of 0.85, and specificity of 0.60. In contrast, Random Forest achieved an accuracy of 0.91 and specificity of 0.98 but failed to detect positive cases, with a recall of 0.00, indicating bias toward the majority class. SHAP analysis reveals that height, weight, age at menarche, and the number of antenatal care (ANC) visits significantly influence prediction, while hypertension consistently contributes to increased risk. This study demonstrates that integrating ensemble learning with XAI improves both predictive performance and model transparency for preeclampsia risk assessment.
Pengaruh Persepsi Dukungan Guru dan Kecemasan Penggunaan Artificial Intelligence terhadap Motivasi Belajar Siswa Kelas XI di MAN 2 Situbondo Dina Komariya; Nur Azizah; Dyan Yuliana
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/dmjqfy91

Abstract

This study aims to examine the effect of perceived teacher support and Artificial Intelligence (AI) use anxiety on the learning motivation of Grade XI students at MAN 2 Situbondo. A correlational quantitative approach was employed, involving 35 students as respondents selected using total sampling. Data were collected through Likert-scale questionnaires and analyzed using multiple linear regression, t-test, F-test, and coefficient of determination with SPSS version 20. Results indicate that perceived teacher support did not significantly affect learning motivation partially (t=1.288; sig.=0.207>0.05), although all students reported very high levels of perceived support, AI use anxiety did not significantly affect learning motivation partially (t=1.482; sig.=0.148>0.05), with a positive direction suggesting facilitative anxiety, both variables simultaneously did not significantly affect learning motivation (F=2.160; sig.=0.132>0.05), with an R Square of 0.119. These findings indicate that the high homogeneity of perceived teacher support and motivation in a conducive madrasah environment limits statistical detection of their effects. Future research is recommended to expand the population and include variables such as self-efficacy and parental support.
Penerapan Sistem Informasi Berbasis Database untuk Optimalisasi Pengelolaan Data Pelanggan pada Usaha Kecil Indra Setiawan; Nur Azizah; Firman Jaya
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/me7h3q46

Abstract

Small businesses play a crucial role in the economy, most small businesses in Indonesia still rely on manual systems or applications that are not well-integrated, leading to difficulties in monitoring, analyzing, and utilizing customer data for business development. This study aims to develop and implement a database-based information system to optimize customer data management in small businesses, focusing on efficiency, accuracy, and improved data analysis. The research method used is the design and development of an information system, involving several key stages: needs analysis, system design, system development, and implementation evaluation. The needs analysis was conducted by collecting data through interviews, observations, and surveys with small business owners to understand their current data management practices and the challenges they face. The system developed uses MySQL for data storage and PHP or Python for the user interface, with main features such as customer data recording, data search, and transaction history management. The results show that the implementation of the database-based information system has significantly improved the efficiency of customer data management. The data search time, which previously took up to 180 seconds, was reduced to 30 seconds after the system was implemented. In addition, data accuracy improved due to the more structured data storage. A survey also revealed that 80% of users found the system easy to use. The discussion indicates that despite the technological limitations faced by small businesses, this system proved effective in helping manage customer data more efficiently and accurately, supporting faster and data-driven decision-making.
Klusterisasi Kemisikinan Berbasis Konsumsi dan Penilaian Kerentanan: Pendekatan Machine Learning untuk Penargetan Perlindungan Sosial di Indonesia (2013-2025) Muhammad Dawam Amali; Yudha Riwanto
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/x8tpdk58

Abstract

Poverty measurements based on static indicators often fail to capture the dynamics of economic vulnerability reflected in changes in consumption patterns over time. This study proposes a machine learning framework to identify the consumption vulnerability profiles of rural households in Indonesia using aggregate per capita expenditure data from the Central Statistics Agency (BPS) for the 2013–2024 period at the expenditure stratum level. The main stage of the analysis was conducted using K-Means clustering to form consumption pattern segments, which were then evaluated using internal validation metrics and compared with the Hierarchical Clustering and Gaussian Mixture Model approaches. The K-Means results at K=3 yielded three consumption profiles: STabel, Volatile, and Extreme, with a Silhouette Score of 0.5474, a Davies-Bouldin Index of 0.6471, and a Calinski-Harabasz Score of 291.57. To evaluate the separability of cluster labels, a Random Forest model was used for supervised validation and achieved an accuracy of 96.84% with a macro-F1 of 0.9552 under a stratified cross-validation scheme. SHAP analysis indicated that expenditure structure, particularly the ratio of non-food to food expenditures, was the most contributing feature in distinguishing cluster profiles. These findings suggest that a consumption-pattern-based approach can provide additional insights in economic vulnerability analysis and support the development of proxy simulations for social protection targeting. However, since this study uses aggregate data at the expenditure stratum level, the results are not intended to determine vulnerability or aid recipients at the individual household level without further validation using microdata.
Penerapan E-CRM Berbasis Web untuk Optimalisasi Layanan Pelanggan pada Toko Ponsel Alqorni Evi Mawardani; Suparmadi; Sri Rezki Maulina Azmi
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/pk9ykn29

Abstract

Advances in information technology are driving businesses to improve the quality of customer service through the use of digital systems. Alqorni Mobile Phone Store still faces challenges in managing customer data, recording transactions, and handling complaints, all of which are done manually. This results in suboptimal service and low customer loyalty. This study aims to implement a web-based Electronic Customer Relationship Management (E-CRM) system to improve service quality, customer satisfaction, and loyalty. The research methods employed include observation, interviews, and literature review, as well as system development using a structured approach. The system implementation resulted in key features such as integrated customer data management, transaction history, product reviews, and complaint handling. The results of the study indicate an increase in customer satisfaction, marked by increased interaction through the review feature and faster service responses compared to before the system’s implementation. Additionally, customer loyalty has also improved, as evidenced by customers’ tendency to make repeat purchases and their continued use of the system. Thus, the implementation of a web-based E-CRM has a positive impact on improving service quality, satisfaction, and customer loyalty at Alqorni Mobile Phone Store.
Penerapan Algoritma EDDSA dalam Menjamin Keamanan dan Keaslian Dokumen Elektronik Berbasis Konsensus Proof of Work Rico Wijaya Dewantoro; Hendra Tampan Mangatur Sagala; Angelina Tambunan; Medima Ronauli Sitorus; Putri V Sui Minarti Pane
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/dtyxpr72

Abstract

The exchange of digital documents remains vulnerable to alteration, forgery, and manipulation due to third-party attacks and the weaknesses of centralized systems. This study aims to implement the Edwards-curve Digital Signature Algorithm (EdDSA), integrated with blockchain technology based on the Proof of Work (PoW) consensus mechanism, to ensure the security, integrity, and authenticity of electronic documents. The novelty of this research lies in the integration of the EdDSA digital signature mechanism with a Proof of Work consensus-based blockchain to create an electronic document verification system. The research method used is experimental, involving the development of a blockchain-based system using the Python programming language and the Flask framework on a local network simulation consisting of 5 nodes. Each document is processed using the SHA-256 hash function to generate a unique digital fingerprint, which is then digitally signed using EdDSA before being validated through a Proof of Work consensus mechanism and stored on the blockchain network. Research results show that the system achieves a 100% success rate in document verification and manipulation detection, with the EdDSA algorithm generating consistent 64-byte signatures, an average signing time of 0.00048 seconds, and a verification time of 0.00108 seconds. An average block size of 863 bytes and a mining time of 1.02 seconds on a 5 node network demonstrate the system’s efficiency and strong performance. Thus, the combination of EdDSA and a Proof of Work-based blockchain has proven capable of forming a secure, transparent, and decentralized electronic document security system that enhances trust in the use of electronic documents.
Deteksi Video Deepfake Berbasis CNN dan Metadata untuk Forensik Digital Muhammad Wishnu; Yudi Prayudi
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/fta8sq84

Abstract

Deepfakes are AI-generated media that enable highly realistic face replacement and other identity manipulations in video. Their rapid progress has intensified the challenge of distinguishing forged content from genuine recordings, necessitating effective and reliable detection techniques. This study develops a deepfake detection method based on Convolutional Neural Networks (CNNs) that learns discriminative visual patterns characteristic of manipulated videos, integrated with metadata extraction as a complementary signal. The proposed pipeline comprises dataset acquisition by scraping YouTube, frame extraction, data preprocessing, supervised labeling into genuine versus manipulated classes, and model training. The hybrid model was evaluated using a dataset of 287 genuine and 3 manipulated videos. Experimental results show that the integrated model achieved an accuracy of 99.65%, precision of 100%, recall of 66.67%, F1-score of 80.00%, and an Area Under Curve (AUC) of 1.0. These results demonstrate that combining metadata extraction with visual feature analysis is robust in minimizing false positives, making it highly relevant for enhancing the reliability of digital forensic investigations.
Penerapan Algoritma Logistic Regression dalam Deteksi Komentar Promosi Judi Online pada YouTube Alyudani; Wahyuni; Rizky Zakariyya Rasyad
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/1mjskk23

Abstract

The growing popularity of video-based social media platforms such as YouTube has significantly increased user interaction through comment sections. However, this high level of activity has also led to the misuse of comment sections to promote online gambling through various writing patterns intended to disguise specific keywords. This study aims to detect online gambling promotional comments on YouTube using a text mining approach. The framework applied in this study consists of several stages, including text preprocessing, keyword-based feature engineering, TF-IDF feature extraction using character n-grams, data balancing through the Synthetic Minority Over-sampling Technique (SMOTE), and classification using the Logistic Regression algorithm. The dataset used in this study consists of 11,972 YouTube comments obtained from a YouTube video comment section and manually labeled into two classes: normal comments and online gambling promotional comments. The evaluation results show that the model achieved a precision of 1.000, recall of 0.912, F1-score of 0.954, and overall accuracy of 0.999 on the test data. These findings indicate that the combination of keyword-based features, character n-gram TF-IDF, SMOTE, and Logistic Regression can effectively detect online gambling promotional comments, particularly in minimizing the misclassification of normal comments as promotional gambling content.
Systematic Literature Review: Pemanfaatan Sinyal EEG dalam Sistem IoT untuk Monitoring dan Diagnosis Dini Penyakit Jantung Selwa Nander Kumar; Muhammad Balira Safa; Calvin Susanto; Toni Saputra; Saut Dohot Siregar
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/z9v32805

Abstract

This study aims to conduct a Systematic Literature Review (SLR) on the utilization of Electroencephalogram (EEG) signals in Internet of Things (IoT)-based systems for the early monitoring and diagnosis of heart disease. Literature was collected from IEEE Xplore, Scopus, Semantic Scholar, MDPI, and ResearchGate, covering publications from 2024 to 2026. Article selection was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach, while the quality of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) method. The review findings indicate that the integration of EEG with other biosignals, particularly Electrocardiogram (ECG), in wearable IoT-based systems can improve the accuracy of real-time cardiovascular monitoring. The application of artificial intelligence and machine learning further enhances the capability for early heart disease detection. In addition, multimodal biosignal approaches provide more comprehensive insights into patients’ neurological and cardiovascular conditions. However, challenges remain regarding EEG signal quality, device integration complexity, energy consumption, and data security and privacy. The novelty of this study lies in its comprehensive synthesis of EEG, IoT, and artificial intelligence integration for cardiovascular monitoring, supported by a quality assessment of the literature using the GRADE approach. These findings demonstrate the significant potential of EEG in supporting the development of more accurate and efficient smart healthcare systems.

Page 26 of 27 | Total Record : 264

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