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All Journal Media Komunikasi Geografi JOIV : International Journal on Informatics Visualization IT JOURNAL RESEARCH AND DEVELOPMENT Communications in Science and Technology Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Abdimas Mandiri Zonasi: Jurnal Sistem Informasi Buletin Pembangunan Berkelanjutan ABDINE Jurnal Pengabdian Masyarakat International Journal of Information Systems and Technology (IJOINT) Data Science Insights
Apri Siswanto
Universitas Islam Riau
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Maximizing oil recovery in sandstone reservoirs through optimized ASP injection using the super learner algorithm Putra, Dike Fitriansyah; Jaafar, Mohd Zaidi; Khalif, Ku Muhd Na’im; Siswanto, Apri; Lukman, Ichsan; Kurniawan, Ahmad
Communications in Science and Technology Vol 10 No 1 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.1.2025.1649

Abstract

Optimizing the Alkaline-Surfactant-Polymer (ASP) injection process remains a persistent challenge in Enhanced Oil Recovery (EOR), particularly in heterogeneous sandstone reservoirs where traditional reservoir simulators are constrained by high computational demands and limited flexibility. This study introduces a novel application of the Super Learner (SL) ensemble, a stacking-based machine learning algorithm integrating multiple base models (XGBoost, SVR, BRR, and Decision Tree), to systematically predict and optimize ASP injection parameters. Unlike previous approaches, our method blends high-fidelity CMOST simulation data with machine learning precision in which it enables real-time optimization with field-scale relevance. Using 500 simulation scenarios validated by laboratory input, the SL model achieved exceptional predictive performance (R² = 0.988, RMSE = 0.304), outperforming all individual learners. The optimal recovery factor (RF) of 79.49% was obtained with the finely tuned concentrations of surfactant (5483.29 ppm), polymer (2242.61 ppm), SO?²? (5610.15 ppm), CO?²? (7053.59 ppm), and Na? (9939.35 ppm). Remarkably, the SL approach could reduce optimization time from 10 hours (CMOST) to under 1 minute; this underscored its potential for real-time operational deployment. The novelty of this work lies in its integrated use of ensemble learning to capture the complex and non-linear interactions between ionic chemistry and oil mobilization behavior, offering a field-ready AI framework for rapid and adaptive EOR design. This approach paves the way for the intelligent optimization of ASP schemes by minimizing the reliance on computationally intensive simulations while ensuring chemical and economic efficiency in marginal or complex reservoirs.
PREDIKSI PERUBAHAN JUMLAH PENDUDUK DI KOTA PEKANBARU MENGGUNAKAN ALGORITMA NAIVE BAYES Efendi, Akmar; Siswanto, Apri; Andika, Rio Fit
ZONAsi: Jurnal Sistem Informasi Vol. 6 No. 3 (2024): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode September 2024
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v6i3.22346

Abstract

Penelitian ini mengaplikasikan Algoritma Naive Bayes untuk memprediksi perubahan jumlah penduduk di Kota Pekanbaru berdasarkan data historis dari tahun 1995 hingga 2022. Tujuan dari penelitian ini adalah untuk memahami tren demografis dan memberikan prediksi yang dapat digunakan sebagai dasar dalam pengambilan keputusan terkait perencanaan pembangunan kota. Data jumlah penduduk diolah untuk mengkategorikan perubahan tahunan sebagai peningkatan ("Increase") atau penurunan ("Decrease"). Algoritma Naive Bayes dilatih dan diuji untuk memprediksi kategori perubahan tersebut. Hasil penelitian menunjukkan bahwa Algoritma Naive Bayes mencapai akurasi sebesar 89.29%. Algoritma ini cukup efektif dalam memprediksi peningkatan jumlah penduduk, namun memiliki keterbatasan dalam memprediksi penurunan jumlah penduduk, yang diidentifikasi sebagai akibat dari ketidakseimbangan data. Meskipun demikian, hasil prediksi ini memberikan wawasan yang penting bagi pengambil kebijakan dalam merencanakan pembangunan dan penyediaan layanan publik di Kota Pekanbaru. Untuk penelitian lebih lanjut, disarankan untuk mengeksplorasi metode penyeimbangan data dan model pembelajaran mesin alternatif untuk meningkatkan akurasi prediksi.
Optimizing Pigeon-Inspired Algorithm to Enhance Intrusion Detection System Performance Internet of Things Environments Ratnawati, Fajar; Siswanto, Apri; Jaroji, -; Effendy, Akmar; Tedyyana, Agus
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1724

Abstract

Intrusion Detection Systems (IDS) are crucial in maintaining network security and safeguarding sensitive information against external and internal threats. This study proposes a novel approach by utilizing a Pigeon-Inspired Algorithm optimized with the Hyperbolic Tangent Function (Tanh) function to enhance the performance of IDS in threat detection specifically tailored for Internet of Things (IoT) environments. We aim to create a more robust solution for optimizing intrusion detection systems by integrating the efficient and effective Tanh function into the Pigeon-Inspired Algorithm. The proposed method is evaluated on three widely-used datasets in the field of IDS: NSL-KDD, CICIDS2017, and CSE-CIC-IDS2018. Experimental results demonstrate that integrating the Tanh function into the Pigeon-Inspired Algorithm significantly improves the performance of the intrusion detection system. Our method achieves higher accuracy, True Positive Rate (TPR), and F1-score while reducing the False Positive Rate (FPR) compared to traditional Pigeon-Inspired Algorithms and several other optimization algorithms. The Pigeon-Inspired Algorithm optimized with the Tanh function offers an efficient and effective solution for enhancing intrusion detection system performance, specifically in Internet of Things environments. This method holds great potential for application in diverse network environments, bolstering information security and safeguarding systems from evolving cybersecurity threats. By extending the applicability and effectiveness of the Pigeon-Inspired Algorithm optimized with the Tanh function, researchers can contribute to developing more comprehensive and robust security solutions, addressing the ever-evolving landscape of IoT-based cybersecurity threats.
Application of Geolocation Methods in Student Attendance System Design Rizya Pratama , Yoga; Siswanto, Apri
Data Science Insights Vol. 2 No. 1 (2024): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v2i1.10

Abstract

Universitas Islam Riau is one of the universities in Riau province that is of interest to high school graduate students as a place to continue their studies at a higher level. Implementing the student attendance process at the Universitas Islam Riau is still done manually; this causes less efficiency and effectiveness of attendance activities, starting from data collection, processing presence data, and storing and searching processes, which take time. In some cases, fraud may occur, such as falsifying the presence of someone represented by another party. Then, we need a system that can record the attendance of students whose positions are within the scope of the class radius. Geolocation can capture device coordinates by utilizing latitude and longitude, which will be used to measure the distance between classes and students. If the student's position is outside the class radius determined by each lecturer, then the student cannot fill in attendance. If the student's position is within the scope of the class radius that has been determined, students can fill in attendance. In the research, we succeeded in designing a student attendance system based on the geolocation method. Security to overcome fake GPS managed to function properly, and fingerprints to take attendance can work properly. From the results of Black box testing, the system can run well and is free from syntax and functional errors.
Co-Authors Abdul Syukur Adiguna Setiawan Agus Tedyyana, Agus Andika, Rio Fit Aprilia Aprilia, Aprilia Ause Labellapansa, Ause Budi Prayitno Devi Wedayanti, Made Efendi, Akmar Efendi, Akmar Effendy, Akmar Evizal Abdul Kadir Faiser, Adli Fikram, Muhammad Ismail, Mohd Suhaili Bin Jaafar, Mohd Zaidi Jaroji, - Khalif, Ku Muhd Na’im Kurniawan, Ahmad Kusmeli, Kusmeli Lukman, Ichsan Merlang, Ronald Prafes, Prendy Putra, Dike Fitriansyah Ratnawati, Fajar Reza Amelia Rizya Pratama , Yoga Sundari, Eva Wedayanti, Made Devi Yeni Kusumawaty Yoldi Novendra Yudhi Arta Yudhi Arta