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All Journal JURNAL CRANKSHAFT Jurnal Optimalisasi JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK)
Tsany, Rahmat Basya Shahrys
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Implementation of the K-Means Algorithm on Household Electric Load Maulidhia, Alief Nur Aisyi; Widyastuti, Indri Ika; Sukarno, Friska Intan; Tsany, Rahmat Basya Shahrys; Brian, Thomas
Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Vol 5 No 1 (2025): JTECS Januari 2025
Publisher : FAKULTAS TEKNIK UNIVERSITAS ISLAM KADIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32503/jtecs.v5i1.6739

Abstract

Peningkatan konsumsi energi listrik rumah tangga menuntut adanya strategi pengelolaan daya yang lebih efisien. Salah satu pendekatan yang dapat digunakan untuk menganalisis pola konsumsi listrik adalah dengan menerapkan algoritma K-Means Clustering. Algoritma ini memungkinkan pengelompokan data konsumsi listrik berdasarkan kesamaan pola penggunaan daya, sehingga dapat membantu dalam memahami perilaku konsumsi energi rumah tangga serta memberikan rekomendasi efisiensi listrik. Penelitian ini bertujuan untuk mengimplementasikan algoritma K-Means pada data konsumsi listrik rumah tangga yang dikumpulkan dengan interval satu menit selama periode tertentu. Data yang digunakan mencakup berbagai parameter kelistrikan seperti daya aktif, daya reaktif, tegangan, dan nilai sub-metering dari beberapa perangkat listrik. Tahapan penelitian meliputi pengumpulan dan praproses data, penerapan algoritma K-Means untuk pengelompokan pola konsumsi daya, serta evaluasi hasil clustering. Hasil penelitian menunjukkan bahwa algoritma K-Means mampu mengelompokkan pola konsumsi listrik rumah tangga menjadi beberapa kategori berdasarkan tingkat penggunaan daya. Pengelompokan ini dapat membantu pengguna dalam mengidentifikasi perangkat dengan konsumsi energi tinggi serta merancang strategi penghematan energi. Selain itu, hasil analisis dapat digunakan oleh penyedia layanan listrik untuk mengoptimalkan distribusi daya dan merancang kebijakan yang lebih efisien. Hasil penelitian optimal menunjukkan jumlah cluster=3.
Optimasi Parameter Operasional Mini Pembangkit Listrik Tenaga Angin Berbasis Machine Learning untuk Meningkatkan Output Daya Parman, Parman; Hamzah, Fais; Basya Shahrys Tsany, Rahmat; Brian, Thomas; Nizar Zulfika, Dicki
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.7578

Abstract

The utilization of renewable energy is experiencing significant growth, with wind turbines emerging as a key solution for generating environmentally friendly electricity. However, the efficiency of wind turbines is highly dependent on their operational parameters, such as wind speed, blade size, angular velocity, and torque. This research aims to optimize the operational parameters of small-scale wind turbines using an XGBoost-based Machine Learning model and an L-BFGS-B algorithm-based optimization method. A simulation dataset was generated based on the physical equations of wind turbine power and a MATLAB Simulink model, incorporating added noise to approximate real-world conditions. The XGBoost model was trained to predict the turbine's output power based on its operational parameters. Subsequently, an optimization method was employed to identify the parameter combination that yields maximum power. The experimental results demonstrate that the model exhibits strong performance, characterized by a low Mean Squared Error (MSE) and a high R-squared score. The optimization process successfully achieved a significant increase in power output compared to the initial configuration. Through this approach, wind turbine systems can operate more efficiently and generate optimal electrical power. This study contributes to the advancement of artificial intelligence-based optimization strategies for renewable energy systems.
Optimasi Parameter Proses Injeksi Molding Material Biokomposit Serat Sisal dan Polypropylene Terhadap kekuatan Impak Tsany, Rahmat Basya Shahrys; Sholihah, Mar’atus; Fajardini, Ridhani Anita; Ahmad, Mahasin Maulana; Maulidhia, Alief Nur Aisyi; Ilman, Abdillah Fashiha
Jurnal Optimalisasi Vol 11, No 2 (2025): Oktober
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The demand for environmentally friendly materials has driven the utilization of natural fiber–based biocomposites as an alternative engineering material. This study aims to optimize the injection molding process parameters of polypropylene (PP)–sisal fiber biocomposites with the aid of maleic anhydride polypropylene (MAPP) as a compatibilizer. The research scope covers the effect of process parameter variations on the mechanical properties, particularly impact strength. The composite material consists of 85% PP, 10% sisal fiber, and 5% MAPP, which were extruded into pellets prior to processing using an injection molding machine. Optimization was carried out using the Taguchi method with an L9 (3⁴) orthogonal array design. Four main parameters were investigated: barrel temperature (200°C, 210°C, 220 °C), injection pressure (50 bar, 55 bar, 60 bar), holding pressure (40 bar, 45 bar, 50 bar), and injection velocity (60 mm/s, 65 mm/s, 70 mm/s). The response variable was impact strength (kJ/m²) according to ASTM D256-04 standards, while other parameters were kept constant. Data were analyzed using the Signal-to-Noise Ratio (S/N ratio) with the “larger-the-better” criterion to obtain the optimum condition. The results showed that the optimum parameter combination A1B3C3D2 (200 °C, 60 bar, 50 bar, 65 mm/s) provided the best response, as this combination yielded the highest Signal-to-Noise ratio with a more stable impact performance. Under these conditions, the material flowed well into the mold, fiber distribution was uniform, and stronger bonding occurred between fiber and matrix, thereby enhancing the mechanical properties.
ANALISIS EXPERIMENTAL STRATEGI PERLAKUAN PANAS UNTUK PENYUSUNAN WPS SAMBUNGAN LAS BAJA COR AAR M201 GR.B+ Augustino, Immanuel Freddy; Zulfika, Dicki Nizar; Azhad, Faizur Rijal; Tsany, Rahmat Basya Shahrys
JURNAL CRANKSHAFT Vol 8, No 3 (2025): Jurnal Crankshaft Vol. 8 No. 3 (2025)
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/cra.v8i3.15697

Abstract

Penelitian ini bertujuan mengevaluasi dan mengoptimalkan strategi perlakuan panas terhadap kualitas sambungan las komponen bogie kereta api berbahan casting AAR M201 Grade B+ berdasarkan standar AWS D15.1. Pengelasan dilakukan menggunakan metode Shielded Metal Arc Welding (SMAW) dengan elektroda E8018-B2, melalui empat variasi perlakuan: tanpa perlakuan panas, preheat 100°C, PWHT saja, serta kombinasi preheat dan PWHT. Pengujian meliputi uji tarik, tekuk, kekerasan, impak, radiografi, dan analisis struktur mikro. Hasil menunjukkan bahwa perlakuan panas tidak secara signifikan memengaruhi kekuatan tarik, namun mampu meningkatkan ketangguhan zona HAZ hingga 62%, menurunkan kekerasan hingga 19% akibat pelunakan dan pelepasan tegangan, serta menghasilkan struktur mikro yang lebih homogen. Kombinasi preheat dan PWHT dinilai paling optimal dalam meningkatkan performa sambungan, meskipun ketangguhan logam las sedikit menurun. Temuan ini mendasari penyusunan Welding Procedure Specification (WPS) dan Procedure Qualification Record (PQR) yang valid dan mendukung proses pengelasan bogie kereta api yang aman dan andal.
Co-Authors Ahmad, Mahasin Maulana Augustino, Immanuel Freddy Azhad, Faizur Rijal Brian, Thomas Hamzah, Fais Ilman, Abdillah Fashiha Maulidhia, Alief Nur Aisyi Nizar Zulfika, Dicki parman parman Ridhani Anita Fajardini Sholihah, Mar’atus Sukarno, Friska Intan Widyastuti, Indri Ika Zulfika, Dicki Nizar