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All Journal Engineering and Technology International Journal (EATIJ) Jurnal Indonesia Sosial Sains JT-IBSI (Jurnal Teknik Ibnu Sina) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika
Ghea Paulina Suri
Universitas Ibnu Sina
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Neuroscience Physics Social Sciences

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Analisis Kebutuhan Sistem Informasi untuk Pengelolaan Homestay Syariah Melalui Stakeholder Interviews Abdul Rohmad Basar; Muhammad Ropianto; Hanafi Hanafi; Ghea Paulina Suri; Indah Kusuma Dewi; Aprizal Y; Revi illya Badri
Jurnal Responsive Teknik Informatika Vol 8 No 01 (2024): 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.v8i01.1468

Abstract

Pengelolaan homestay syariah merupakan bagian penting dalam perkembangan industri pariwisata yang terus berkembang di era digital. Homestay syariah menawarkan alternatif penginapan yang sesuai dengan prinsip-prinsip syariah, mencakup nilai-nilai kebersihan, privasi, dan ketertiban yang diharapkan oleh para tamu. Penelitian ini bertujuan untuk menganalisis kebutuhan sistem informasi yang mendukung pengelolaan homestay syariah secara optimal. Melalui pendekatan kualitatif dengan wawancara mendalam dengan pemangku kepentingan, termasuk pemilik homestay, administrator, dan tamu, penelitian ini mengidentifikasi kebutuhan utama dalam manajemen reservasi, keuangan, pelayanan tamu, dan integrasi nilai-nilai syariah. Temuan penelitian menunjukkan bahwa sistem informasi yang terintegrasi dan sesuai dengan prinsip syariah sangat penting untuk meningkatkan efisiensi operasional, meningkatkan pengalaman tamu, dan memastikan kepatuhan terhadap nilai-nilai syariah. Penelitian ini memberikan panduan untuk pengembangan sistem informasi yang relevan dan penting bagi pengelolaan homestay syariah, serta memberikan kontribusi dalam meningkatkan daya saing homestay syariah dalam industri pariwisata.
Perancangan E-Commerce Berbasis Content Management System di PT. ABC Muhammad Fikry Bayhaqi; Atman Lucky Fernandes; Ghea Paulina Suri; Willy Rizki Perdana
Jurnal Responsive Teknik Informatika Vol 9 No 01 (2025): 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.v9i01.1451

Abstract

Perkembangan teknologi informasi mendorong perusahaan untuk mengadopsi sistem berbasis web guna meningkatkan layanan dan efisiensi operasional. PT. Sentral Berkat Teknologi, yang bergerak di bidang penjualan dan perakitan PC gaming serta perangkat teknologi lainnya, menghadapi kendala dalam layanan e-commerce dan manajemen pelanggan karena keterbatasan fitur pada website yang berbasis CMS WordPress. Penelitian ini bertujuan untuk mengembangkan sistem e-commerce berbasis CMS WordPress dengan metode Agile menggunakan model Scrum guna meningkatkan layanan pelanggan, efisiensi pemesanan perakitan PC, serta optimalisasi penjualan daring. Penelitian ini dilakukan dengan tahapan pengolahan data yang mencakup editing, klasifikasi, verifikasi, dan analisis kebutuhan sistem. Pengembangan sistem mencakup implementasi fitur e-commerce, layanan pelanggan online, serta perakitan PC daring yang diintegrasikan dalam website. Evaluasi sistem dilakukan melalui pengujian fungsional, keamanan, serta uji kepuasan pengguna. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu meningkatkan efisiensi transaksi, mempermudah pelanggan dalam mengakses layanan, serta meningkatkan kepuasan pelanggan secara signifikan.
Analisis Kebutuhan Sistem Informasi untuk Pengelolaan Homestay Syariah Melalui Stakeholder Interviews Abdul Rohmad Basar; Muhammad Ropianto; Hanafi Hanafi; Ghea Paulina Suri; Indah Kusuma Dewi; Aprizal Y; Revi illya Badri
Jurnal Responsive Teknik Informatika Vol 8 No 01 (2024): 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.v8i01.1468

Abstract

Pengelolaan homestay syariah merupakan bagian penting dalam perkembangan industri pariwisata yang terus berkembang di era digital. Homestay syariah menawarkan alternatif penginapan yang sesuai dengan prinsip-prinsip syariah, mencakup nilai-nilai kebersihan, privasi, dan ketertiban yang diharapkan oleh para tamu. Penelitian ini bertujuan untuk menganalisis kebutuhan sistem informasi yang mendukung pengelolaan homestay syariah secara optimal. Melalui pendekatan kualitatif dengan wawancara mendalam dengan pemangku kepentingan, termasuk pemilik homestay, administrator, dan tamu, penelitian ini mengidentifikasi kebutuhan utama dalam manajemen reservasi, keuangan, pelayanan tamu, dan integrasi nilai-nilai syariah. Temuan penelitian menunjukkan bahwa sistem informasi yang terintegrasi dan sesuai dengan prinsip syariah sangat penting untuk meningkatkan efisiensi operasional, meningkatkan pengalaman tamu, dan memastikan kepatuhan terhadap nilai-nilai syariah. Penelitian ini memberikan panduan untuk pengembangan sistem informasi yang relevan dan penting bagi pengelolaan homestay syariah, serta memberikan kontribusi dalam meningkatkan daya saing homestay syariah dalam industri pariwisata.
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.
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.
Peran IoT dalam Transformasi Jaringan Multimedia: Tinjauan Literatur Sistematis Ghea Paulina Suri; Atman Lucky Fernandes; Hidayatul Ikhsan; Putri Devina; Ledi Alde Nopa; Sherly Agustini
Jurnal Responsive Teknik Informatika Vol 8 No 02 (2024): 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.v8i02.1430

Abstract

Integrasi Internet of Things (IoT) dengan jaringan multimedia telah mengubah paradigma pengelolaan, pengiriman, dan pemrosesan konten multimedia secara fundamental. Penelitian ini bertujuan mensintesis perkembangan terkini melalui tinjauan literatur sistematis terhadap 25 artikel ilmiah yang dipublikasikan antara 2019–2024, bersumber dari Scopus, IEEE Xplore, dan Google Scholar, mengikuti protokol PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Hasil tinjauan mengungkap empat tema utama: (1) arsitektur jaringan berbasis IoT memanfaatkan edge computing dan 5G untuk optimasi latensi; (2) manajemen Quality of Service (QoS) secara adaptif; (3) keamanan data dan privasi dalam ekosistem perangkat heterogen; serta (4) penerapan kecerdasan buatan untuk otomasi pengelolaan data multimedia. Analisis sintesis menunjukkan adopsi 5G mampu mengurangi latensi hingga 85% dibanding 4G, sementara model keamanan berbasis blockchain menjadi solusi yang paling banyak dikaji. Gap penelitian teridentifikasi pada minimnya studi tentang standarisasi interoperabilitas antar-vendor dan model konsumsi energi IoT skala besar.
Peran IoT dalam Transformasi Jaringan Multimedia: Tinjauan Literatur Sistematis Ghea Paulina Suri; Atman Lucky Fernandes; Hidayatul Ikhsan; Putri Devina; Ledi Alde Nopa; Sherly Agustini
Jurnal Responsive Teknik Informatika Vol 8 No 02 (2024): 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.v8i02.1430

Abstract

Integrasi Internet of Things (IoT) dengan jaringan multimedia telah mengubah paradigma pengelolaan, pengiriman, dan pemrosesan konten multimedia secara fundamental. Penelitian ini bertujuan mensintesis perkembangan terkini melalui tinjauan literatur sistematis terhadap 25 artikel ilmiah yang dipublikasikan antara 2019–2024, bersumber dari Scopus, IEEE Xplore, dan Google Scholar, mengikuti protokol PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Hasil tinjauan mengungkap empat tema utama: (1) arsitektur jaringan berbasis IoT memanfaatkan edge computing dan 5G untuk optimasi latensi; (2) manajemen Quality of Service (QoS) secara adaptif; (3) keamanan data dan privasi dalam ekosistem perangkat heterogen; serta (4) penerapan kecerdasan buatan untuk otomasi pengelolaan data multimedia. Analisis sintesis menunjukkan adopsi 5G mampu mengurangi latensi hingga 85% dibanding 4G, sementara model keamanan berbasis blockchain menjadi solusi yang paling banyak dikaji. Gap penelitian teridentifikasi pada minimnya studi tentang standarisasi interoperabilitas antar-vendor dan model konsumsi energi IoT skala besar.
Co-Authors Abdul Rohmad Basar Agus Suryadi Aprizal Y Dasman Johan David Saro Defnizal Defnizal Devega, Army Trilidia Didin Setyawan Fernandes, Atman Lucky Hanafi Hanafi Herli Marchiano Hidayatul Ikhsan Hilda Herasmus Indah Kusuma Dewi, Indah Kusuma Ledi Alde Nopa Ledi Alde Nopa Muhammad Fikry Bayhaqi Muhammad Fikry Bayhaqi Muhammad Ropianto Nofri Yudi Arifin Novita Anggraini Panji Priantono Putra Putri Devina Revi Illya Badri Ririt Dwiputri Permatasari Romiko Afriantoni sherly agustini Syahrul Bahri Tommy Saputra Willy Rizki Perdana Zulfirman Syahputra Hasibuan