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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 106 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.
Manajemen Proyek Sistem Informasi Persediaan pada PT. Bandung Scientific Technical Indonesia (PT.BSTI) Tuti Haryanti; Alfrino Patriando Adam; Yehezkhiel Michael Mulyadi; Najwa Nur Aziz; Mario Ivan Raphael Maningkas; Aileen Paike Budihardjo
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.v4i2.300

Abstract

Penelitian ini mengkaji perencanaan proyek pengembangan Sistem Informasi Persediaan pada PT. Bandung Scientific Technical Indonesia (PT. BSTI) sebagai upaya memperkuat keteraturan, ketepatan, dan efisiensi pengelolaan data persediaan. Permasalahan yang menjadi dasar kajian adalah data persediaan yang masih berpotensi tersebar pada dokumen fisik, email, dan Microsoft Excel, sehingga proses pencarian data, pemantauan stok, pencatatan barang masuk dan keluar, serta penyusunan laporan belum berjalan optimal. Tujuan penelitian ini adalah menyusun rancangan manajemen proyek perangkat lunak yang memuat kebutuhan fungsional, jadwal pelaksanaan, pembagian sumber daya manusia, estimasi anggaran, asumsi, kendala, dan risiko proyek. Metode yang digunakan berupa perencanaan proyek perangkat lunak melalui identifikasi kebutuhan pengguna, penyusunan fungsi sistem, pengembangan Work Breakdown Structure, penyusunan Gantt Chart, estimasi biaya, serta analisis risiko. Hasil pembahasan menunjukkan bahwa sistem informasi persediaan berbasis web dapat mendukung bagian persediaan, bagian penjualan, bagian pembelian, dan manajemen dalam mengelola data barang, pemasok, pelanggan, transaksi barang masuk, barang keluar, faktur, surat jalan, serta laporan stok secara terintegrasi. Perencanaan proyek yang sistematis juga membantu mengurangi potensi keterlambatan, perubahan ruang lingkup, dan ketidaksesuaian hasil sistem dengan kebutuhan pengguna. Dengan demikian, rancangan proyek ini dapat menjadi landasan awal bagi pengembangan Sistem Informasi Persediaan PT. BSTI yang lebih terarah, efektif, terukur, serta mudah dievaluasi dalam pelaksanaan proyek berikutnya.
Evaluasi Efektivitas Teknologi Internet of Things untuk Deteksi Dini Risiko Kebakaran dan Paparan Gas Berbahaya di Kamar Mesin Kapal Muhammad Saleh
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.v4i2.371

Abstract

Ship engine rooms are high-risk working environments due to the presence of fuel, lubricating oil, hot surfaces, electrical installations, and potential exposure to hazardous gases. This study aimed to evaluate the effectiveness of Internet of Things (IoT) technology in supporting the early detection of fire risks and hazardous gas exposure based on crew experience, and to identify the technical, human, environmental, and organizational factors affecting its implementation. This study employed a qualitative approach with an evaluative case study design. Data were collected from 85 purposively selected informants through in-depth interviews, observations of 85 units, and documentation. The data were analyzed thematically, while their validity was strengthened through source and technique triangulation. The findings indicate that IoT is sufficiently effective in supporting continuous monitoring, accelerating warning delivery, identifying hazard types and locations, and facilitating the crew’s initial response. Observations showed that 91.8% of units had fire and/or hazardous gas sensors, 90.6% of devices were functional and in good condition, 85.9% of systems clearly displayed hazard types and locations, 88.2% had maintenance or calibration records, and 94.1% demonstrated crew readiness to respond to alarms. However, effectiveness remained suboptimal because of false alarms, connectivity disruptions, transmission delays, unclear information, engine-room environmental conditions, and varying crew competencies. It is concluded that IoT is an effective detection and decision-support system but cannot replace direct inspection and professional judgment. Improved effectiveness requires marine-grade devices, risk-based sensor placement, scheduled maintenance, backup communication, regular training, clear procedures, and integration with the ship’s Safety Management System to enhance early-warning reliability and prevent hazardous conditions from developing into shipboard accidents.
Kombinasi Metode MEREC dan TOPSIS dalam Seleksi Penerimaan Calon Karyawan Baru Ilham Nasul Fathon Muhaji. P; Adhie Thyo Priandika
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 3 (2026): Volume 4 Number 3 September 2026 (Issue in Progress)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i3.254

Abstract

The employee candidate selection process in many companies is still often carried out manually, resulting in decision-making that is subjective, inconsistent, and ineffective, especially when having to evaluate many applicants based on various criteria simultaneously. This situation makes it difficult for companies to determine the best candidate objectively and accurately. Therefore, this study aims to develop a Decision Support System (DSS) in the selection of new employee candidates by combining the MEREC (Method based on the Removal Effects of Criteria) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods. The MEREC method is used to determine the weight of criteria objectively based on the influence of each criterion on the change in alternative performance, while the TOPSIS method is used to rank prospective employees based on their closeness to the positive ideal solution and their distance from the negative ideal solution. The criteria used in this study include education, work experience, technical skills, communication, and age. The results of the study indicate that the proposed method combination is able to produce a more objective, systematic, and accurate employee selection process. Based on the ranking results, alternative A7-IP obtained the highest preference value of 0.9945 and ranked first, followed by A5-EK with a value of 0.9427 in second place, and A1-AR with a value of 0.8946 in third place. The application of the MEREC and TOPSIS methods also successfully increased consistency and reduced subjectivity in the employee selection decision-making process.
Analisis Sistem Informasi Pelayanan Konsumen Dengan Metode Pieces Pada PT Bintang Property Lampung Amelia Maharani; Ajeng Savitri Puspaningrum
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 3 (2026): Volume 4 Number 3 September 2026 (Issue in Progress)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i3.268

Abstract

Penelitian ini membahas analisis dan pengembangan sistem informasi pelayanan konsumen pada PT Bintang Property Lampung yang masih menggunakan sistem manual berupa dokumen fisik dan spreadsheet yang belum terintegrasi. Kondisi tersebut menyebabkan keterlambatan pelayanan, kesulitan pengelolaan data, proses verifikasi yang lambat, risiko kehilangan data, serta rendahnya efektivitas pelayanan konsumen. Penelitian ini bertujuan untuk menganalisis kelemahan sistem yang berjalan dan mengembangkan sistem berbasis website menggunakan metode PIECES yang mencakup aspek Performance, Information, Economy, Control, Efficiency, dan Service. Metode penelitian yang digunakan adalah pendekatan kuantitatif melalui observasi, wawancara, dokumentasi, dan penyebaran kuesioner kepada 15 konsumen aktif. Hasil analisis menunjukkan nilai rata-rata sebesar 2,681 dengan kategori “Cukup”, di mana aspek Economy dan Efficiency berada pada kategori “Buruk”. Sebagai solusi, dikembangkan sistem berbasis website dengan fitur pengelolaan data, tracking keluhan, verifikasi pembayaran, serta laporan otomatis. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu meningkatkan efektivitas, efisiensi, dan kualitas pelayanan konsumen pada PT Bintang Property Lampung.
Pendekatan Machine Learning Untuk Prediksi Motif Batik Indonesia Wahyu Marzian Putra; Erliyan Redy Susanto; Neneng Neneng
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 3 (2026): Volume 4 Number 3 September 2026 (Issue in Progress)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i3.270

Abstract

Batik merupakan warisan budaya tak benda Indonesia yang diakui UNESCO sejak tahun 2009. Perkembangan teknologi kecerdasan buatan, khususnya deep learning, membuka peluang otomatisasi klasifikasi visual batik. Namun, klasifikasi otomatis batik Indonesia masih menghadapi tantangan utama, yaitu ketidakseimbangan kelas pada dataset (imbalanced dataset) dan kemiripan visual antarkelas yang tinggi . Penelitian ini mengembangkan sistem klasifikasi 20 jenis batik Indonesia menggunakan model YOLOv11 yang diintegrasikan dengan teknik Synthetic Minority Over-sampling Technique (SMOTE) serta skema 5-Fold Stratified Cross-Validation guna memperoleh evaluasi yang objektif. Dataset terdiri atas 983 gambar yang diakuisisi dari repositori Kaggle. SMOTE diterapkan pada representasi fitur piksel berdimensi tinggi (d = 12.288) untuk menyeimbangkan distribusi antarkelas tanpa mengorbankan informasi kelas mayoritas. Model YOLOv11 dilatih pada setiap lipatan dengan konfigurasi hyperparameter terkontrol menggunakan pengoptimasian AdamW. Hasil penelitian menunjukkan nilai mean Average Precision (mAP) keseluruhan sebesar 0,8251 dan rata-rata mAP per lipatan sebesar 0,8788 ± 0,0264, dengan konsistensi metrik yang tinggi di seluruh lipatan validasi. Analisis matriks kebingungan mengindikasikan bahwa misclassification cenderung terjadi antara kelas batik yang memiliki kemiripan visual tinggi. Kerangka kerja yang diusulkan berpotensi diimplementasikan sebagai sistem identifikasi batik otomatis untuk mendukung pelestarian warisan budaya, autentifikasi produk pada platform e-commerce, serta dokumentasi koleksi museum di era digital.

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