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All Journal Jurnal Edukasi Elektro JURNAL NASIONAL TEKNIK ELEKTRO Journal of Energy, Mechanical, Material and Manufacturing Engineering JOMLAI: Journal of Machine Learning and Artificial Intelligence Engineering Science Letter Jurnal Pengabdian Masyarakat Bangsa Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknik Elektro
Tengku Reza Suka Alaqsa
Universitas Islam Negeri Sultan Syarif Kasim Riau
Agriculture, Biological Sciences & Forestry Arts Humanities 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 Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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Proyeksi Perencanaan Solar PV Di Mushola Al Ikhlas Bagan Besar Timur, Dumai Sebagai Solusi Energi Terbarukan Arbi, Arbi; Alaqsa, Tengku Reza Suka; Altof, Muhammad Sayid; Farhan, Aulia Muhammad; Safka, Lutvi Nabilla; Ardila, Melati; Juliana, Juliana; Fadilah, Fadilah; Lindasari, Ewil; Zahra, Annisa; Maulana, Hamsah
Jurnal Pengabdian Masyarakat Bangsa Vol. 2 No. 6 (2024): Agustus
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v2i6.1142

Abstract

Penelitian ini menyoroti kebutuhan mendesak bagi Indonesia untuk beralih dari bahan bakar fosil ke energi terbarukan, khususnya energi surya, mengingat potensinya yang besar dan keuntungan geografis negara tersebut. Tujuan penelitian ini adalah mengembangkan rencana pemanfaatan PV surya di Mushola Al Ikhlas di Bagan Besar Timur, Dumai, sebagai bagian dari KKN pengabdian masyarakat oleh mahasiswa UIN Suska Riau. Menggunakan metode perangkat lunak PVsyst, penelitian ini melibatkan penyusunan basis data meteorologi, pengumpulan data radiasi matahari dan suhu, serta merancang sistem PV surya yang terhubung ke jaringan listrik. Hasil penelitian menyoroti perencanaan modul PV dan sistem inverter, dengan tujuan mencapai kinerja optimal dan integrasi ke dalam infrastruktur jaringan yang ada. Data output energi menunjukkan variasi yang signifikan sepanjang tahun, dengan produksi puncak pada bulan Maret karena radiasi matahari yang tinggi dan output lebih rendah pada bulan November karena tingkat radiasi yang berkurang. Total tahunan radiasi global horizontal sebesar 1606,1 kWh/m², dengan output energi efektif sebesar 31,925 MWh dan energi yang dimasukkan ke jaringan mencapai 29,904 MWh. Rasio kinerja (PR) sebesar 0,775 mencerminkan efisiensi sistem dalam mengubah energi surya menjadi tenaga listrik. Inisiatif ini tidak hanya mendukung adopsi energi terbarukan, tetapi juga berfungsi sebagai aplikasi praktis teknologi surya dalam pengabdian masyarakat.
Pemberdayaan Masyarakat Melalui Perancangan Website Usaha Wedding Organizer di Kelurahan Bagan Besar Timur Alaqsa, Tengku Reza Suka
Jurnal Pengabdian Masyarakat Bangsa Vol. 2 No. 6 (2024): Agustus
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v2i6.1155

Abstract

Kegiatan pengabdian ini bertujuan untuk merancang website bagi Budi Wedding Organizer, sebuah usaha kecil di Kelurahan Bagan Besar Timur, sebagai bentuk pengabdian masyarakat yang dilakukan oleh mahasiswa UIN Suska Riau dalam kegiatan KKN. Melalui studi literatur dan konsultasi dengan pemilik usaha, desain website dilakukan menggunakan Jotform. Proses perancangan meliputi integrasi logo, introduction, galeri foto, alasan memilih usaha, informasi lokasi, fitur konsultasi, paket dekorasi, nomor WhatsApp, galeri tambahan, dan testimoni klien. Pelatihan pengelolaan website diberikan kepada pemilik usaha untuk memastikan mereka dapat mengelola dan memperbarui konten secara mandiri. Hasil kegiatan pengabdian menunjukkan bahwa website ini dapat meningkatkan visibilitas, efisiensi operasional, dan daya saing Budi Wedding Organizer di pasar.
Evaluasi Pengaruh Tekanan-Arus pada Kehilangan Fiber melalui NIRS DA1650 Tengku Reza Suka Alaqsa; Zulfatri Aini; Liliana
JURNAL NASIONAL TEKNIK ELEKTRO Vol 13, No 3: November 2024
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v13n3.1233.2024

Abstract

This study focuses on enhancing the yield of crude palm oil (CPO) during the pressing process by thoroughly examining the oil losses that occur throughout production. The primary aim is to evaluate how different pressures and electric currents impact oil losses from palm fiber at a specific palm oil mill in Pantai Cermin, Kec. Tapung, Kampar, Riau. A systematic methodology was employed to achieve this, which involved detailed measurements conducted using the FOSS NIRS DA1650. This advanced technology allowed for precise assessment and quantification of oil losses during the pressing phase. Following the data collection, a rigorous statistical analysis was performed utilizing determination coefficients to interpret the relationship between the variables. The analysis results revealed a coefficient of determination (R²) of 49.96% concerning pressure, suggesting that nearly half of the variability in oil losses can be explained by fluctuations in pressing pressure. Additionally, the examination of current showed a higher coefficient of determination of 60.09%, underscoring a substantial influence of electric current on fiber oil losses. These findings highlight the critical importance of optimizing pressure and current in palm oil extraction. By making informed adjustments to these parameters, mill operators can significantly reduce oil losses, thus enhancing the overall extraction efficiency. The study provides practical recommendations for operators aiming to improve their processes, ultimately contributing to better resource utilization and increased profitability in the palm oil industry.
Steam requirements and mass balance in digesters and screw presses at palm oil mill Zulfatri Aini; Tengku, Tengku Reza Suka Alaqsa; Sri Basriati
Journal of Energy, Mechanical, Material, and Manufacturing Engineering Vol. 9 No. 2 (2024)
Publisher : University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jemmme.v9i2.37043

Abstract

Fresh Fruit Bunches (FFB) are the primary component in Crude Palm Oil (CPO) production. Palm oil mills face challenges in optimizing CPO yield, particularly in reducing oil losses during processing, which affects efficiency and profitability. The pressing station, including the digester and screw press, plays an important role in oil extraction. The digester uses steam to heat and soften the fruit for better oil release, while the screw press performs the mechanical extraction of oil. Insufficient steam can hinder oil separation, leading to increased losses. This research aimed to analyze steam requirements for the digester and evaluate the mass balance of the screw press. Using energy and mass balance methods, the optimal steam requirement was 359,870 kg/hour with a mass balance error of 6.58%. Corrective actions in steam valve settings reduced oil losses to 1.57%, which improved processing efficiency and product quality.
Feasibility Study of a Microgrid for Electrifying the Isolated Area Suka Alaqsa, Tengku Reza; Aini, Zulfatri; Eng, Ewe Win
Jurnal Edukasi Elektro Vol. 9 No. 1 (2025): Jurnal Edukasi Elektro Volume 9, No. 1, May 2025
Publisher : DPTE FT UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jee.v9i1.78828

Abstract

Guha Village in Aceh Singkil lacks access to electricity from PLN, with challenging road conditions further complicating energy access. This study explores a feasible microgrid solution, assessing local renewable energy potential and economic viability using HOMER Pro software. Calculations reveal an annual energy demand of 97,334 kWh for 41 households. The analysis identifies a standalone micro-hydro system as the most cost-effective option, capable of partially meeting this demand with an annual output of 296,077 kWh. However, due to a distribution efficiency of 79.6%, the effective supply is 235,961 kWh per year. Financially, the micro-hydro system offers significant advantages, reducing the Net Present Cost (NPC) from Rp5.82 billion to Rp1.10 billion, despite a higher initial capital requirement of Rp825 million. With minimal O&M costs of Rp15 million annually and a Levelized Cost of Energy (LCOE) of Rp987.66/kWh, the system demonstrates substantial long-term savings. Investment metrics show a 49% Internal Rate of Return (IRR), 44% Return on Investment (ROI), and a two-year payback period, making the micro-hydro system a sustainable as cost effective energy solution for Guha Village.
Forecasting Electricity Consumption in Riau Province Using the Artificial Neural Network (ANN) Feed Forward Backpropagation Algorithm for the 2024-2027 Tengku Reza Suka Alaqsa; Zulfatri Aini; Liliana; Nanda Putri Miefthawati
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/7eeq7029

Abstract

Electricity production in Riau Province fluctuates between surplus and deficit, as reported by the Central Statistics Agency. From a peak of 3,758.75 GWh in 2017, production fell to 525.19 GWh in 2019, mainly due to lack of investment in new power plants and dependence on external electricity supply. This study addresses these challenges by using the Artificial Neural Network (ANN) Feed Forward Backpropagation method to forecast electricity demand from 2024 to 2027. This study aims to analyze the accuracy of the prediction through the Mean Absolute Percentage Error (MAPE), evaluate electricity consumption projections, and calculate the annual growth rate. The gap in this study is the inclusion of previously ignored variables, namely the GRDP of Government Buildings and the number of Government Building customers. The methodology used is Artificial Neural Network Feed Forward Backpropagation. In the training data training, the MAPE was obtained at 4,315%. The electricity consumption prediction obtained is 8,679 GWh in 2024, 9,690 GWh in 2025, 10,959 GWh in 2026, and 12,681 GWh in 2027. The growth rate is also projected to increase, namely 5.67% from 2023 to 2024, 11.65% from 2024 to 2025, 13.10% from 2025 to 2026, and 15.71% from 2026 to 2027.
Forecasting Electricity Demand In Indonesia: Recommendation for Prediction Models to Support PLN’s RUPTL Zulfatri Aini; Rahmadeni; Tengku Reza Suka Alaqsa
Engineering Science Letter Vol. 4 No. 03 (2025): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.001696

Abstract

The Electricity Supply Business Plan (RUPTL) prepared annually by PLN still shows a high error rate in predicting electricity consumption, exceeding 10% in various provinces, such as North Sumatra (36.92%), DKI Jakarta (24.87%), West Kalimantan (40.24%), and South Sulawesi (31.56%), due to the limitations of the linear regression method used in the RUPTL. This study aims to evaluate and recommend the best electricity consumption forecasting model based on artificial intelligence using a Feed Forward Backpropagation Neural Network (FFBP-NN) combined with six training algorithms: Bayesian Regularization (BR), Conjugate Gradient (CG), Levenberg-Marquardt (L-M), Gradient Descent (GD), Quasi-Newton (Q-N), and Resilient Backpropagation (RB), resulting in a total of 13 algorithmic combinations. The data used consists of RUPTL indicators for DKI Jakarta from 2018 to 2023. Testing results of the 13 training functions on the FFBP-NN demonstrate that the TRAINOSS (Quasi-Newton) algorithm achieves the best performance with the lowest Mean Square Error (MSE) of 0.0000065546 and Mean Absolute Percentage Error (MAPE) of 0.06696%. This algorithm outperforms the linear regression method currently used in PLN’s RUPTL, which has a MAPE of approximately 21.14%. The second and third best algorithms are TRAINSCG and TRAINLM, with MAPE values of 0.09455% and 0.10020%, and MSE values of 0.0012160450 and 0.0012229340, respectively. The FFBP-NN model trained with TRAINOSS is highly recommended as the primary alternative to support long-term electricity load planning such as in PLN’s RUPTL.
Comparative Analysis of Static Var Compensator and Distributed Generation Installation on Voltage Profile Zulfatri Aini; Muhammad Guido Randa Febiant Guido; Tengku Reza Suka Alaqsa; Liliana
Jurnal Teknik Elektro Vol. 16 No. 1 (2024)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v16i1.8824

Abstract

In Indonesia, electricity is a basic need with demand that continues to grow. PT PLN (Persero) projects an increase in electricity consumption of 8.9% by early 2022, highlighting the urgent need to address frequent problems such as blackouts, power losses, and voltage sags in the power distribution system. Effective solutions, including Static VAR Compensator (SVC) and Distributed Generation (DG), have been proposed to improve voltage stability and reduce power losses. This study evaluates and compares the performance of SVC and DG on a standard IEEE 14-bus system under increased load conditions. Using power flow analysis in ETAP, we simulate the installation of SVC at 15.99 Mvar and DG at 20.58 Mvar on bus 9, which shows optimal results. The findings show that DG slightly outperforms SVC in improving voltage stability and reducing power losses, with a 0.16% greater voltage increase and a 3.2 MW or 17.3% reduction in power losses. These results indicate that although both devices meet PLN’s voltage standards and improve power system efficiency, DG provides a slightly superior improvement in overall system performance.
Performance Evaluation of NARX-CG Model for Electricity Forecasting: Bali Blackout Case Study Tengku Reza Suka Alaqsa; Zulfatri Aini
Jurnal Teknik Elektro Vol. 17 No. 2 (2025)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v17i2.35519

Abstract

Bali experienced a widespread blackout in May 2025 that disrupted economic and social activities across the island, revealing weaknesses in electricity demand forecasting and system resilience. This study evaluates the performance of a Hybrid Nonlinear Autoregressive with Exogenous Inputs-Conjugate Gradient (NARX-CG) model as an advanced electricity forecast. The dataset covers the 2018-2023 period and includes six variables: electricity energy, connected capacity, number of customers, tariffs, Gross Regional Domestic Product (GRDP), and population, aligned with the national electricity planning framework. The NARX-CG model was developed using a 6-12-6-1 network architecture and trained with tansig transfer function. Forecasting performance was evaluated using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) metrics. Results show that the NARX-CG model achieved an MSE of 0.09853 and an average MAPE of 8.12%, outperforming conventional projections with a MAPE of 28.48%. Yearly evaluations show consistent model stability, with the lowest MAPE values of 1.93% and 5.86% in 2023 and 2022, respectively. The NARX-CG model effectively captures nonlinear temporal dependencies, enhances predictive accuracy, and contributes to improved power system reliability and resilience, providing valuable insights for adaptive energy planning following the 2025 Bali blackout.
Entropy-Regularized Nonlinear Auto-Regressive Network with eXogenous Inputs (ER-NARX): A Mathematical Framework for Scalable and Robust Big Data Forecasting Using ITL and Fractional Dynamics Zulfatri Aini; Tengku Reza Suka Alaqsa
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 4 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i4.6689

Abstract

This study proposes the Entropy-Regularized NARX (ER-NARX) model, which integrates nonlinear autoregressive modeling, entropy-based regularization, and information-theoretic learning for big data forecasting. The NARX model captures temporal dependencies between past outputs and exogenous inputs, while entropy regularization is incorporated to control the uncertainty of model predictions and prevent overfitting. The innovation of this model is its ability to control information flow through entropy regularization, which helps balance predictive accuracy with uncertainty, preventing the model from becoming overly deterministic. By combining these components, the ER-NARX model enhances the stability and robustness of the forecasts and improves its generalization to complex, high-dimensional data. Additionally, fractional dynamics are employed to model long-range memory effects in temporal data to enhancing the model's ability to handle datasets with extended dependencies. The resulting ER-NARX framework provides a mathematically grounded approach to big data forecasting improved performance in a computationally efficient manner. Future research may explore advanced entropy regularization techniques and apply the model to more diverse real-world data with intricate dependencies.
Co-Authors Altof, Muhammad Sayid Arbi, Arbi Ardila, Melati Eng, Ewe Win Fadilah Fadilah, Fadilah Farhan, Aulia Muhammad Juliana Juliana Liliana Liliana Liliana Lindasari, Ewil Maulana, Hamsah Muhammad Guido Randa Febiant Guido Nanda Putri Miefthawati Rahmadeni Safka, Lutvi Nabilla Sri Basriati Syarifah Adriana Zahra, Annisa Zulfatri Aini