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Journal of Artificial Intelligence and Technology Information
Published by Tech Cart Press
ISSN : 29855306     EISSN : 29856396     DOI : doi.org/10.58602/jaiti
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Journal of Artificial Intelligence and Technology Information (JAITI) is a peer-review journal focusing on Artificial Intelligence and Technology Information issues. Journal of Artificial Intelligence and Technology Information (JAITI) invites academics and researchers who do original research in artificial intelligence and technology 1nfromation.Journal of Artificial Intelligence and Technology Information (JAITI) is published by Tech Cart Press in March, June, September, and December every year. Journal of Artificial Intelligence and Technology Information (JAITI) accept articles in Bahasa Indonesia and English.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 85 Documents
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Performance Analysis of ECS Architecture in 2D Mobile Game Development: Ocean Hero Raynaldi Irfansya Regar; Benny Pinontoan; Christian A. J. Soewoeh
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 2 (2026): Volume 4 Number 2 June 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i2.267

Abstract

Mobile game development frequently encounters computational performance bottlenecks when a system must render and update the logic of many objects simultaneously in each frame. Conventional Object-Oriented Programming (OOP) architecture produces high memory overhead and elevated cache miss rates because game objects are allocated in scattered, non-contiguous memory locations. This research aims to design, implement, and analyze the performance of an Entity Component System (ECS) architecture in a 2D Android educational arcade game titled Ocean Hero. The development process followed the Game Development Life Cycle (GDLC). ECS separates identity, data, and behavior into entities, components, and systems, allowing game logic to process homogeneous component data sequentially through Unity DOTS. Evaluation was conducted on a Samsung Galaxy A15 4G by comparing ECS and OOP implementations through white-box functional verification and stress testing across six entity workloads from 500 to 3,000 entities, each observed over a 20-second tracking period. The ECS implementation maintained a stable 30 FPS and 33.3 ms frame time across all tested entity levels. In contrast, the OOP implementation degraded to 11 FPS and 90.73 ms frame time at 3,000 entities. Based on the relative performance improvement formula, ECS achieved approximately 172.7% higher runtime performance than OOP at the highest workload. These results confirm that ECS is an effective architectural solution for improving scalability and computational efficiency in real-time 2D mobile games with large entity counts.
Pengelompokkan Titik Panas Menggunakan Algoritma DBSCAN di Provinsi Sumatera Selatan Nawa Fatimi Fauziah; Febri Dwi Irawati; Muhajir Hasibuan
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 2 (2026): Volume 4 Number 2 June 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i2.269

Abstract

Kebakaran hutan dan lahan yang berulang setiap tahun di Provinsi Sumatera Selatan menimbulkan kerugian ekologis dan ekonomi yang signifikan, didukung oleh dominasi lahan gambut sebesar 21,88% dari total gambut Sumatera. Meskipun pemantauan titik panas dari citra satelit MODIS telah banyak digunakan sebagai indikator kebakaran, analisis pengelompokkan spasial titik panas secara multi-tahun dengan evaluasi kualitas clustering di wilayah ini belum pernah dilakukan. Penelitian ini menerapkan algoritma Density Based Clustering Application with Noise dengan penentuan parameter adaptif menggunakan metode k-distance untuk mengelompokkan 17.353 titik panas tahun 2019–2023 dan mengidentifikasi pola spasial wilayah dengan kepadatan titik panas tertinggi. Parameter optimal ditentukan secara independen per tahun menggunakan Silhouette Coefficient ( ) sebagai kriteria seleksi, menghasilkan MinPts sebesar 11 sampai 13 dengan ε sebesar 0,1 untuk data padat (2019 dan 2023), serta MinPts sebesar 2 dengan ε sebesar 0,10 sampai 0,13 untuk data jarang (2020 sampai 2022), dengan kualitas cluster sebesar 0,296–0,670 dan Davies-Bouldin Index sebesar 0,273–0,595. Kabupaten Ogan Komering Ilir dan Kota Palembang teridentifikasi sebagai wilayah dengan kepadatan titik panas tertinggi secara konsisten selama lima tahun analisis, dengan cluster dominan mencakup 61,87% (2019) dan 73,92% (2023) dari seluruh titik panas yang terdeteksi.
Enhancing Sentiment Classification Performance on Tentang Anak Application Reviews Using Optimized Support Vector Machine Riska Aryanti; Eka Fitriani; Royadi Royadi; Dian Ardiansyah
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 2 (2026): Volume 4 Number 2 June 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i2.271

Abstract

The increasing use of parenting and child development applications has generated a large volume of user reviews containing valuable insights regarding application quality, usability, and user satisfaction. One of the widely used applications in Indonesia is Tentang Anak: Kehamilan & Anak. However, manually analyzing these reviews is inefficient due to the large amount of unstructured textual data. Therefore, this study aims to enhance sentiment classification performance on user reviews of the Tentang Anak: Kehamilan & Anak application using an optimized Support Vector Machine (SVM) model. The dataset consisted of user reviews collected from application platforms, which were processed through several text preprocessing stages, including cleaning, normalization, tokenization, stopword removal, and stemming. Sentiment labeling was conducted using polarity scores to classify reviews into positive and negative sentiments. The proposed model was evaluated using different test size scenarios (0.1, 0.2, 0.3, and 0.4) and random state configurations to identify the optimal parameter setting. Experimental results demonstrate that the best performance was achieved at a test size of 0.1 with random state 0, obtaining an accuracy of 89.8%, precision of 91.7%, recall of 55.0%, and F1-score of 68.8%. The findings indicate that the optimized SVM model is effective in classifying sentiment in reviews of the Tentang Anak: Kehamilan & Anak application, particularly in achieving high precision and classification stability across multiple testing scenarios. Furthermore, the study highlights the importance of parameter optimization in improving sentiment analysis performance for user-generated textual data.
A Pythagorean Fuzzy-Based MUNRA Method for Handling Uncertainty in Complex Decision Environments Setiawansyah Setiawansyah
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 2 (2026): Volume 4 Number 2 June 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i2.273

Abstract

This research develops the Pythagorean Fuzzy Multi-Normalized Rating Analysis (PF-MUNRA) method as a novel approach to address uncertainty and ambiguity in multi-criteria decision making. The main contribution of this study lies in the integration of Pythagorean Fuzzy Sets with a multi-normalization framework consisting of linear, vector, and non-linear normalization within a single decision-making model, enabling more flexible, comprehensive, and unbiased evaluation results compared to conventional single-normalization approaches. This method integrates the concept of Pythagorean Fuzzy Sets, which can represent degrees of membership and non-membership more flexibly, with the multi-normalization approach in MUNRA. Unlike previous studies that generally apply fuzzy environments and normalization techniques separately, the proposed PF-MUNRA simultaneously combines fuzzy uncertainty handling, multi-normalization mechanisms, and objective weighting to improve ranking consistency and decision robustness. In addition, weighted aggregation is used to produce more accurate preference values and reflect the relative importance of each criterion. The experimental results demonstrate that PF-MUNRA produces stable alternative rankings with Spearman correlation values ranging from 0.9464 to 1.0000 under various weight-change scenarios, indicating a very strong level of ranking consistency and robustness. Comparative analysis shows changes in alternative positions that reflect the capability of the proposed method to capture data complexity more effectively than the initial approach, while sensitivity analysis confirms that variations in criterion weights do not significantly affect the final ranking results, thereby proving that PF-MUNRA has high stability and reliability in dynamic and uncertain decision-making environments.
Analisis Sentimen Masyarakat terhadap Profesionalisme Generasi Z di Dunia Kerja Menggunakan Support Vector Machine (SVM) Bulan Kirana Subrata; Yuwan Jumaryadi; Febryo Ponco Sulistyo; Sarwati Rahayu
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 2 (2026): Volume 4 Number 2 June 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i2.279

Abstract

Generasi Z yang lahir pada rentang tahun 1997–2012 telah menjadi bagian penting dari angkatan kerja modern. Karakteristik generasi ini yang berbeda dibandingkan generasi sebelumnya sering memunculkan berbagai persepsi dan diskusi terkait profesionalisme di lingkungan kerja, terutama melalui media sosial. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat Indonesia terhadap profesionalisme Generasi Z di dunia kerja berdasarkan data yang diperoleh dari platform X. Dataset penelitian terdiri atas 2.095 tweet yang dikumpulkan melalui proses crawling. Pelabelan sentimen dilakukan menggunakan pendekatan berbasis leksikon yang menghasilkan 1.092 tweet (52,12%) berkategori negatif, 855 tweet (40,81%) berkategori positif, dan 145 tweet (6,92%) berkategori netral. Hasil tersebut menunjukkan bahwa persepsi masyarakat terhadap profesionalisme Generasi Z cenderung didominasi oleh sentimen negatif. Selanjutnya, data diproses melalui tahapan text preprocessing dan ekstraksi fitur menggunakan metode Term Frequency–Inverse Document Frequency (TF-IDF), kemudian diklasifikasikan menggunakan algoritma Support Vector Machine (SVM) dengan tiga skenario pembagian data, yaitu 70:30, 80:20, dan 90:10. Hasil pengujian menunjukkan bahwa model SVM memperoleh performa terbaik pada rasio pembagian data 90:10 dengan nilai akurasi sebesar 69,52%, presisi 66%, dan recall 70%. Temuan penelitian ini memberikan gambaran empiris mengenai persepsi publik terhadap profesionalisme Generasi Z di dunia kerja serta menunjukkan bahwa SVM mampu digunakan untuk mengklasifikasikan sentimen pada data media sosial dengan tingkat performa yang cukup baik.

Page 9 of 9 | Total Record : 85

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