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All Journal Engineering and Technology International Journal (EATIJ) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika Journal of Engineering and Technology Innovation
David Saro
Universitas Ibnu Sina
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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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.
Improvement of IoT Security with a Machine Learning-Based Intrusion Detection System Approach Nur Halizzah; Aisyah Chayani; Atman Lucky Fernandes; David Saro; Ramdhani Yusli Arbain Sugoro
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.v8i02.1434

Abstract

The development of the Internet of Things (IoT) has brought convenience to various aspects of life, but it also presents significant challenges regarding cybersecurity. One solution to address this issue is the development of an Intrusion Detection System (IDS) based on machine learning. This study aims to design an efficient and adaptive IDS for IoT environments using machine learning algorithms such as Random Forest and Support Vector Machine (SVM). The methodology includes system design, data collection, algorithm selection, model training, and system performance evaluation. The results show that Random Forest and SVM algorithms are effective in detecting attacks such as Distributed Denial of Service (DDoS) and malware, with a relatively high accuracy rate. However, the main challenges faced are the need for representative datasets and computational efficiency issues on resource-constrained IoT devices. This study concludes that machine learning-based intrusion detection systems can improve IoT security by accurately detecting cyber-attacks. Further development is expected to address efficiency constraints and enhance the system's reliability in facing increasingly complex threats.
Prediksi Kelulusan Mahasiswa Menggunakan Algoritma Naive Bayes: Studi Kasus Universitas Ibnu Sina Batam Willy Rizki Perdana; Romiko Afriantoni; Sherly Agustini; David Saro; Aprizal Y
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.v8i02.1438

Abstract

Tingkat kelulusan mahasiswa merupakan indikator penting kualitas pendidikan tinggi. Penelitian ini bertujuan mengembangkan model prediksi kelulusan mahasiswa menggunakan algoritma Naive Bayes dengan memanfaatkan data akademik, sosial-demografis, dan ekonomi mahasiswa di Universitas Ibnu Sina Batam. Dataset mencakup 1.247 rekaman data mahasiswa Program Studi Teknik Informatika dan Sistem Informasi angkatan 2017–2021. Metode validasi menggunakan stratified 10-fold cross-validation. Hasil menunjukkan akurasi 89,74%, presisi 88,31%, recall 91,05%, dan F1-Score 89,66%. Perbandingan dengan Decision Tree dan SVM menunjukkan Naive Bayes unggul dalam efisiensi komputasi. IPK semester 1–4 dan tingkat kehadiran terbukti sebagai prediktor paling signifikan.
Improvement of IoT Security with a Machine Learning-Based Intrusion Detection System Approach Nur Halizzah; Aisyah Chayani; Atman Lucky Fernandes; David Saro; Ramdhani Yusli Arbain Sugoro
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.v8i02.1434

Abstract

The development of the Internet of Things (IoT) has brought convenience to various aspects of life, but it also presents significant challenges regarding cybersecurity. One solution to address this issue is the development of an Intrusion Detection System (IDS) based on machine learning. This study aims to design an efficient and adaptive IDS for IoT environments using machine learning algorithms such as Random Forest and Support Vector Machine (SVM). The methodology includes system design, data collection, algorithm selection, model training, and system performance evaluation. The results show that Random Forest and SVM algorithms are effective in detecting attacks such as Distributed Denial of Service (DDoS) and malware, with a relatively high accuracy rate. However, the main challenges faced are the need for representative datasets and computational efficiency issues on resource-constrained IoT devices. This study concludes that machine learning-based intrusion detection systems can improve IoT security by accurately detecting cyber-attacks. Further development is expected to address efficiency constraints and enhance the system's reliability in facing increasingly complex threats.
Prediksi Kelulusan Mahasiswa Menggunakan Algoritma Naive Bayes: Studi Kasus Universitas Ibnu Sina Batam Willy Rizki Perdana; Romiko Afriantoni; Sherly Agustini; David Saro; Aprizal Y
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.v8i02.1438

Abstract

Tingkat kelulusan mahasiswa merupakan indikator penting kualitas pendidikan tinggi. Penelitian ini bertujuan mengembangkan model prediksi kelulusan mahasiswa menggunakan algoritma Naive Bayes dengan memanfaatkan data akademik, sosial-demografis, dan ekonomi mahasiswa di Universitas Ibnu Sina Batam. Dataset mencakup 1.247 rekaman data mahasiswa Program Studi Teknik Informatika dan Sistem Informasi angkatan 2017–2021. Metode validasi menggunakan stratified 10-fold cross-validation. Hasil menunjukkan akurasi 89,74%, presisi 88,31%, recall 91,05%, dan F1-Score 89,66%. Perbandingan dengan Decision Tree dan SVM menunjukkan Naive Bayes unggul dalam efisiensi komputasi. IPK semester 1–4 dan tingkat kehadiran terbukti sebagai prediktor paling signifikan.
Co-Authors Abd Basith Aisyah Chayani Ali Basriadi Aprizal Y Defnizal Defnizal Doni Damara Fandi Fernandes, Atman Lucky Firman Firman Firnam Firnam Ghea Paulina Suri Harmen Harmen Hilda Herasmus Indah Kusuma Dewi, Indah Kusuma Larisang Muhammad Aris Fadilla Muhammad Aris Fadilla Muhammad Noreza Setiawan Muhammad Ropianto Muhammad Ropianto Nofri Yudi Arifin Nur Halizzah Nur Halizzah Okta Veza Ramdhani Yusli Arbain Sugoro Romiko Afriantoni Sabtu Sabtu sherly agustini Tiara Aziza Willy Rizki Perdana