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All Journal JURNAL PENDIDIKAN TAMBUSAI Journal of Computer Networks, Architecture and High Performance Computing Jurnal Sains Informatika Terapan (JSIT) Malcom: Indonesian Journal of Machine Learning and Computer Science Engineering and Technology International Journal (EATIJ) Jurnal Pengabdian West Science JT-IBSI (Jurnal Teknik Ibnu Sina) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika JURNAL LIGA ILMU SERANTAU Journal of Engineering and Technology Innovation MPKM
Nofri Yudi Arifin
Universitas Ibnu Sina
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Transportation Other

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Api Response Time Prediction Using a Deep Learning Model on Web Application Load Testing Simulation Data Defnizal Defnizal; Atman Lucky Fernandes; Aprizal Y; David Saro; Ghea Paulina Suri; Nofri Yudi Arifin; Romiko Afriantoni; Willy Rizki Perdana
Jurnal Responsive Teknik Informatika Vol 10 No 01 (2026): JR : Jurnal Responsive Teknik Informatika
Publisher : LPPM Universitas Ibnu Sina Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v10i01.1539

Abstract

Web application performance, particularly application programming interface (API) response time, is one of the primary determinants of user experience and system reliability, especially under high-load conditions. Conventional load testing is reactive, as it can only measure actual response time after a given load has been applied to the system, creating a need for a predictive approach capable of estimating API response time from load and server-resource conditions. This study applies a deep learning model in the form of a Multi-Layer Perceptron (MLP) with three hidden layers to predict API response time, and compares its performance against two baseline models, namely Linear Regression and Random Forest Regressor. Due to limited access to sensitive real-world production data, this study uses a simulated load-testing dataset of 500 samples generated programmatically, comprising ten load and server-resource features such as concurrent users, requests per second, payload size, database query count, cache hit ratio, and CPU and memory utilization, with non-linear patterns representing resource-contention effects under high load. The data was split into 70% training and 30% testing, then evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). Experimental results show that the Deep Learning (MLP) model achieved the best predictive performance with an RMSE of 24.86 ms, MAPE of 19.33%, and R² of 0.9009, outperforming Linear Regression (RMSE 26.25 ms, R² 0.8895) and Random Forest (RMSE 27.90 ms, R² 0.8751), although Linear Regression's MAE was slightly lower (19.34 ms versus 19.53 ms). Feature-importance analysis indicates that the number of concurrent users, database query count, and CPU utilization are the most dominant factors influencing API response time. To ground the simulated scenario in a concrete example, a small real demonstration web application was also built and load-tested; its qualitative behavior (response time increasing with concurrency) is consistent with the assumptions underlying the simulated dataset. These findings indicate that the deep learning model is better able to capture non-linear patterns caused by resource contention compared to a linear model or a tree-based ensemble, suggesting potential use as a predictive component in auto-scaling strategies or web-application capacity planning, with the caveat that these results remain a preliminary study requiring further validation using real production data.
PENGEMBANGAN SISTEM INFORMASI LAYANAN PENGIRIMAN KARGO DOMESTIK Sherly Agustini; Nofri Yudi Arifin; Okta Veza; Albertus Laurensius; Hermansyah
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

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Abstract

Penelitian ini bertujuan untuk (1) merancang sistem informasi jasa pengiriman cargo domestic berbasis web pada Perusahaan PT Mandiri Express Logistics (2) mempermudah pekerjaan karyawan PT Mandiri Express Logistics serta meminimalisir terjadinya kesalahan. Untuk melakukan penelitian ini terdapat 3 penelitian sejenis yang menjadi referensi bagi penulis dalam membangun perancangan sistem informasi jasa pengiriman cargo domestik ini. Metodologi penelitian yang digunakan yaitu metode wawancara, observasi studi Pustaka dan studi literatur sejenis dan metodologi pengembangan sistem menggunakan metode Object Oriented Analysis and Design (OOAD) dan menggunakan pemodelan Unified Modelling Language (UML) serta menggunakan PHP sebagai Bahasa pemrograman, MySQL sebagai database server dan Blackbox testing sebagai tahap pengujian sistem. Hasil penelitian ini berupa Sistem Informasi Jasa Pegiriman Cargo Domestik Berbasis Web Pada PT Mandiri Express Logistics.
TINJAUAN LITERATUR SISTEMATIS TERHADAP METODE HISTOGRAM OF ORIENTED GRADIENTS (HOG) PADA PENGOLAHAN CITRA Okta Veza; Nofri Yudi Arifin
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

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Abstract

Histogram of Oriented Gradients (HOG) merupakan metode ekstraksi fitur berbasis gradien yang banyak digunakan dalam visi komputer karena efisiensi dan kemampuannya merepresentasikan struktur tepi objek. Dalam bidang inspeksi nondestruktif pengelasan berbasis citra, HOG telah diterapkan untuk mendeteksi dan mengklasifikasikan cacat pengelasan. Namun, sebagian besar penelitian masih menggunakan konfigurasi HOG konvensional yang kurang adaptif terhadap variasi intensitas, noise, dan kompleksitas pola cacat pengelasan. Penelitian ini menyajikan tinjauan literatur sistematis terhadap penerapan metode HOG pada inspeksi cacat pengelasan berdasarkan artikel-artikel ilmiah yang terindeks Scopus. Literatur diklasifikasikan berdasarkan kesamaan metode dan objek penelitian untuk mengidentifikasi tren, keunggulan, serta keterbatasan metode yang ada. Hasil kajian menunjukkan bahwa penerapan HOG secara spesifik pada objek pengelasan masih terbatas, sehingga membuka peluang pengembangan metode HOG yang lebih adaptif dan robust. Temuan ini diharapkan menjadi dasar pengembangan metode ekstraksi fitur yang lebih akurat dan aplikatif dalam mendukung inspeksi pengelasan di lingkungan industri.
Towards Integrated Smart Tourism Systems in Urban Destinations: A Systematic Literature Review on End-to-End Journey and SME Digital Integration Okta Veza; Nofri Yudi Arifin; Sherly Agustini
Engineering and Technology International Journal Vol 8 No 01 (2026): Engineering and Technology International Journal (EATIJ)
Publisher : YCMM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55642/eatij.v8i01.1238

Abstract

The rapid development of digital technologies has significantly transformed the tourism sector, particularly in urban destinations characterized by complex ecosystems and diverse stakeholders. Smart tourism systems have emerged as a key approach to enhancing service efficiency, improving tourist experiences, and enabling data-driven decision-making. However, existing studies are still fragmented and largely focus on partial implementations, lacking comprehensive end-to-end integration. This study aims to conduct a systematic literature review on smart tourism systems in urban destinations, with a focus on system integration, end-to-end tourist journey, and Small and Medium Enterprises (SMEs) digital integration. The review was conducted using selected articles from reputable international journals published between 2023 and 2025. The analysis categorizes the studies into three groups: same system and same scope, same system and similar scope, and same system and different scope. The results indicate that only a limited number of studies have developed fully integrated smart tourism systems, while most studies focus on specific components or are applied in different domains. In addition, stakeholder integration and SME digital inclusion remain key challenges in developing comprehensive smart tourism ecosystems. This study contributes by identifying research gaps and proposing future research directions focused on developing integrated, scalable, and inclusive smart tourism systems. The findings are expected to support the advancement of smart tourism system design in urban destinations.
Data Driven Smart Tourism Management: A Literature Review on System Integration, Digital Tourist Journey, and UMKM Connectivity in Smart Cities Sherly Agustini; Okta Veza; Nofri Yudi Arifin; Albertus Laurensius Setyabudhi
Engineering and Technology International Journal Vol 8 No 01 (2026): Engineering and Technology International Journal (EATIJ)
Publisher : YCMM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55642/eatij.v8i01.1239

Abstract

The rapid development of smart city initiatives has significantly transformed the tourism sector through the adoption of digital technologies and data-driven systems. This study aims to analyze the development of data-driven smart tourism management by focusing on system integration, digital tourist journey, and UMKM connectivity within smart city environments. A Systematic Literature Review (SLR) method was employed to examine 30 relevant articles published between 2020 and 2025. The findings indicate that most studies utilize similar methodological approaches but are applied to different research objects, resulting in fragmented research outcomes. Furthermore, the lack of integration among systems and limited involvement of UMKM in digital platforms remain major challenges in developing effective smart tourism ecosystems. This study highlights the need for integrated, interoperable, and scalable smart tourism systems supported by advanced technologies such as artificial intelligence, big data analytics, and Internet of Things (IoT). The results of this study provide a conceptual foundation and research directions for developing more comprehensive and sustainable smart tourism systems in smart city contexts.
Co-Authors Adi Nugroho Susanto Putro Ajat Sudrajat Al Rusman Albertus L. Setyabudhi Albertus Laurensius Alex Sandri Sikumbang Aprizal Y David Saro Defnizal Defnizal Deo Bagus Prayoga Devega, Army Trilidia Devin Mahendika Didik Cahyono Didin Setyawan Doni Syofiawan Dra. Nur Iffah Elly Prasetyo Eri Mardiani Etika, Erdyna Dwi Fernandes, Atman Lucky Ghea Paulina Suri Hadi, Anto J. Hasan Bashori Helmi Ali Hermansyah Ikhsan Amar Jusman Jasiah Jasiah Kristiana Lylya Larisang Laurensius Setyabudhi, Albertus Legito . Mardiati Mardiati Moh. Erkamim Mutiara Ayu Mawaddah Nanda Jarti Nindi Permata Riau Novi Widiana Putri Okta Veza Okta Veza Revi Illya Badri Rian Dwi Guntoro Romiko Afriantoni Sepriano Sepriano sherly agustini Tiara Aziza Villia Anggraini Willy Rizki Perdana Yulia Suryani Zainol Hasan