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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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Integrasi Machine Learning dengan Geographically Weighted Regression untuk Analisis Indeks Pembangunan Manusia Ismi Rizqa Lina; Dia Cahya Wati; Ibnu Mansyur Hamdani; Yulia Resti
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/nakv4774

Abstract

The success of national development is determined not only by economic growth but also by the quality of human development, which is measured through the Human Development Index (HDI). Although Indonesia's HDI has continued to improve, regional disparities in Western Indonesia remain a significant development challenge. This study aims to classify and analyze the factors influencing HDI by integrating the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Geographically Weighted Regression (GWR) methods with adaptive Gaussian weighting. Class imbalance in the training data was addressed using the Synthetic Minority Oversampling Technique (SMOTE), while model performance was evaluated using a confusion matrix, accuracy, precision, recall, and F1-score. The results show that SVM with a Radial Basis Function (RBF) kernel outperformed KNN, achieving an accuracy of 78.8%, a precision of 0.89, a recall of 0.78, and an F1-score of 0.82. In comparison, KNN achieved an accuracy of 76.9%, a precision of 0.89, a recall of 0.76, and an F1-score of 0.80. Furthermore, the GWR analysis identified 16 spatial clusters characterized by different dominant factors, including population size, the Community Literacy Development Index, per capita expenditure, the open unemployment rate, and the number of sub-districts. The GWR model produced a coefficient of determination (R²) of 84.17%, indicating strong explanatory power. These findings demonstrate that the integration of machine learning techniques and GWR is effective in classifying HDI and revealing spatial variations in human development factors, providing valuable insights for the formulation of more targeted development policies aimed at reducing regional HDI disparities.
Perancangan Game Edukasi Matematika Berbasis Android untuk Siswa Sekolah Dasar Menggunakan Metode GDLC M. Ziad Mubarok; Anugerah Bagus Wijaya; Banu Dwi Putranto
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/a41pdy88

Abstract

This study aims to design and develop an Android-based educational mathematics game as an interactive learning medium for elementary school students using the Game Development Life Cycle (GDLC) method. The game adopts an educational monopoly concept integrated with basic arithmetic learning materials, including addition, subtraction, multiplication, and division. The development process consists of the planning, design, development, testing, and implementation stages. The game was developed using the Unity Engine and designed by integrating educational monopoly gameplay with interactive features such as dice rolling, character movement, and reward–punishment mechanisms to create a more engaging and enjoyable learning experience for elementary school students. Black-box testing showed that all game features functioned properly without any functional errors. User testing involving 17 elementary school students and one mathematics teacher resulted in an average feasibility score of 78.8%, which falls into the "Feasible" category as an interactive learning medium. The results indicate that the developed educational game has the potential sto support mathematics learning by making the learning process more engaging and interactive for elementary school students.
Modularisasi Perangkat Lunak dengan Integrasi Multi-Fitur Menggunakan Pembelajaran Representasi Graf Kenrick Ortonsius; Sandi Tendean
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/zwpwrk85

Abstract

Software modularization remains an issue until this day. As software grows over time, software becomes more complex and difficult to maintain. To address this issue, software module clustering was used by grouping software components such as classes, objects and files. Many approaches have been conducted by using single-feature clustering and multi-features clustering. The result shows that multi-features clustering performs better than single-feature clustering. The application of GNN also gains significant attention for software modularization and proven to produce better performance. In this study, we analyze how integrating structural features, semantic features and graph relations using Attention-drive Graph Clustering Network (AGCN) affects the quality of software clustering. We use four input combinations which are structural features with graph, semantic features with graph, identity matrix with graph, and the fusion of graph, semantic and structural features across all three java projects (JavaCC, Dom4J, and JEdit). The result shows that by using the structural feature only the model could predict the total cluster that is closest to the original total cluster and multi-feature fusion achieves the best NMI score across all input variants.
Analisis Sentimen Terhadap Program Makan Bergizi Gratis (MBG) pada Media Sosial Menggunakan Algoritma IndoBERT Bunga Tribuana; Saikin; Wafiah Murniati
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: 10.47002/0ac2j454

Abstract

The Free Nutritious Meal (MBG) Program is one of the Indonesian government's strategic policies that has generated various public responses on social media, particularly TikTok. This study aims to analyze public sentiment toward the MBG program using the IndoBERT Deep Learning model. The data were collected through TikTok comment scraping using the Apify platform, resulting in 14,447 raw comments. After the data cleaning process, 13,574 valid comments were obtained, and a 50% sample was selected, resulting in 6,787 comments for modeling purposes. Sentiment labeling was performed automatically using a lexicon-based approach with three sentiment categories: positive, negative, and neutral. The class imbalance problem was addressed using the Synthetic Minority Over-sampling Technique (SMOTE) on the training data prior to the IndoBERT fine-tuning process. The results showed that the IndoBERT model with SMOTE achieved an accuracy of 72.39% and a weighted F1-score of 0.73. Although SMOTE improved the representation of the minority class, it reduced the overall accuracy when compared to the model without SMOTE. Nevertheless, the model was still able to classify public sentiment toward the MBG program reasonably well. The findings of this study are expected to provide useful insights for the government in understanding public perceptions of the MBG policy through social media.

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