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All Journal INTEGER: Journal of Information Technology Jurnal Optimalisasi JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer ORAHUA : Jurnal Pengabdian Kepada Masyarakat
Maulidhia, Alief Nur Aisyi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Other

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Mengenal Dunia Maritim dengan Beraneka Warna Wibowo, Sekarsari; Sukarno, Friska Intan; Widyastuti, Indri Ika; Maulidhia, Alief Nur Aisyi; Brian, Thomas
ORAHUA : Jurnal Pengabdian Kepada Masyarakat Vol. 2 No. 02 (2025): Januari
Publisher : Faatuatua Media Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70404/orahua.v2i02.128

Abstract

Kegiatan Pengenalan Dunia Maritim Melalui Aneka Warna bertujuan untuk meningkatkan pengetahuan dan kreativitas anak-anak di tingkat pendidikan usia dini. Program ini dirancang secara interaktif dengan melibatkan aktivitas pengenalan dengan media gambar dan vidio serta aktivitas mewarnai yang berfokus pada objekobjek dunia maritim, kapal, ikan dan terumbu karang. Kegiatan ini dibagi menjadi beberapa tahap, yaitu perencanaan, pelaksanaan, serta monitoring dan evaluasi. Hasil kegatan menunjukkan peningkatan pengetahuan, dan kreativitas siswa PAUD Sepuluh Nopember tentang dunia maritim. Siswa yang sebelumnya memiliki pengetahuan terbatas tentang laut sekarang dapat memahami laut sebagai sarana transportasi dan habitat makhluk hidup, mengenal berbagai transportasi dan biota laut. Program ini juga menumbuhkan kreativitas siswa melalui kegiatan mewarnai. Ketertarikan dan partisipasi siswa dalam rangkaian kegiatan menunjukkan bahwa program ini efektif dan menarik bagi anak-anak usia dini
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.
Perbandingan Model Decision Tree, Support Vector Machine dan K-Nearest Neighbors untuk Memprediksi Kualitas Air Minum Brian, Thomas; Maulidhia, Alief Nur Aisyi; Sholikhah, Evi Nafiatus; Wibowo, Sekarsari
INTEGER: Journal of Information Technology Vol 10, No 1: April 2025
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2024.v10i1.7523

Abstract

The need for drinking water is increasing so that appropriate method support is needed to determine water potability. In this study, machine learning models will be implemented including Decision Tree, Support Vector Machine, and K-Nearest Neighbors to determine the best model in classifying drinking water quality from the Kaggle Water Quality dataset. The dataset consists of 3,276 data with 9 parameters consisting of ph, Hardness, Solids, Chloramines, Sulfate, Conductivity, Organic_carbon, Trihalomethanes and Turbidity, and one Potability attribute as a target that indicates the feasibility of consumption. This study will apply several machine learning models consisting of Decision Tree, Support Vector Machine, and K-Nearest Neighbors. Based on the results of the trial using 20% and 30% testing data, the results are close to the same for the confusion matrix model evaluation metrics (Accuracy, F1 Score, Precision and Recall). So it can be concluded that the Decision Tree classification model gets the best Accuracy value among other classification models of 70.50% on 20% testing data and 70.98% on 30% testing data. However, the one chosen as the final classification model is Support Vector Machine because it has the highest value by meeting three requirements with F1 Score, Precision and Recall values of 82.40% each) from the four requirements tested.
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.
Co-Authors Ahmad, Mahasin Maulana Brian, Thomas Ilman, Abdillah Fashiha Ridhani Anita Fajardini Sholihah, Mar’atus Sholikhah, Evi Nafiatus Sukarno, Friska Intan Tsany, Rahmat Basya Shahrys Wibowo, Sekarsari Widyastuti, Indri Ika