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All Journal Sistemasi: Jurnal Sistem Informasi Fidelity : Jurnal Teknik Elektro Journal of Artificial Intelligence and Digital Business
Artiyasa, Marina
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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A Novel Interval Type-2 Fuzzy Logic Controller for Shunt Active Power Filters in Power Quality Enhancement Sidik, Aryo De Wibowo Muhammad; Efendi, Efendi; Lumbantobing, Harurikson; Indrawan, Bayu; Junfithrana, Anggy Pradiftha; Ula, Rini Khamimatul; Suryana, Anang; Tambunan, Handrea Bernando; Narputro, Panji; Artiyasa, Marina
Fidelity : Jurnal Teknik Elektro Vol 6 No 2 (2024): Edition for May 2024
Publisher : Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/fidelity.v6i2.248

Abstract

This paper proposes a novel control strategy for enhancing power quality through the implementation of an Interval Type-2 Fuzzy Logic Controller (T2FLC) for Shunt Active Power Filters (SAPF). The presented method effectively addresses harmonic distortions introduced by non-linear loads, a common issue in modern electrical systems. Extensive simulations conducted in MATLAB/Simulink demonstrate the T2FLC's ability to regulate the DC bus voltage while significantly reducing Total Harmonic Distortion (THD). The method achieves superior performance compared to both traditional passive and active filtering techniques, reducing THD within the limits specified by IEEE 519-2014 standards. The results confirm the robustness and adaptability of the proposed approach across varying load conditions, establishing it as a reliable and scalable solution for improving power quality in complex electrical networks.
Penerapan H2O AutoML untuk Prediksi Kanker Kolorektal Artiyasa, Marina; Yuda, Gina Syahbani
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 1 (2025): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i1.395

Abstract

Penelitian ini bertujuan untuk memprediksi hasil kanker kolorektal dan mengidentifikasi faktor risiko utama menggunakan dataset global dari UCI Machine Learning Repository . Dataset mencakup 28 variabel terkait demografi pasien (usia, jenis kelamin, negara), gaya hidup (merokok, konsumsi alkohol, aktivitas fisik), riwayat medis (keluarga, diabetes, mutasi genetik), stadium kanker, pengobatan, tingkat kelangsungan hidup, dan biaya perawatan kesehatan. Metode yang digunakan adalah H2O AutoML , sebuah alat automated machine learning (AutoML) yang melatih berbagai model pembelajaran mesin secara otomatis, termasuk Gradient Boosting Machines (GBM) , Random Forest , dan Deep Learning . Proses analisis melibatkan feature engineering , optimasi hiperparameter, dan evaluasi model berdasarkan metrik seperti akurasi, AUC-ROC, presisi, recall, dan F1-score. Hasil menunjukkan bahwa faktor risiko utama seperti usia, ukuran tumor, gaya hidup, riwayat medis, akses layanan kesehatan, dan biaya perawatan memiliki dampak signifikan terhadap perkembangan dan prognosis penyakit. Variabel seperti biaya perawatan kesehatan (Healthcare_Costs) berkontribusi sebesar 21.53% terhadap prediksi, diikuti oleh ukuran tumor (Tumor_Size_mm) sebesar 11.19% , dan usia pasien (Age) sebesar 10.38% . Model prediksi yang dihasilkan mampu memprediksi tingkat kelangsungan hidup dan respons terhadap pengobatan dengan performa tinggi, serta mengidentifikasi pola data tidak biasa menggunakan teknik seperti Isolation Forest dan Principal Component Analysis (PCA) . Implikasi penelitian ini penting untuk meningkatkan strategi pencegahan, diagnosis dini, dan perawatan kanker kolorektal secara global, terutama dalam mengatasi disparitas akses layanan kesehatan dan biaya pengobatan.
Sistem Pendukung Keputusan Tingkat Stres Mahasiswa dengan Fuzzy Mamdani Sutisna, Muhamad Galuh; Yudono, Muchtar Ali Setyo; Artiyasa, Marina; Narputo, Panji; Jakfar, Any Elvia
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 1 (2025): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i1.403

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan yang dapat mengevaluasi tingkat stres mahasiswa berdasarkan data akademik, yaitu nilai IPK, absensi, dan beban SKS. Metode yang digunakan adalah logika fuzzy Mamdani, dengan implementasi menggunakan Python. Sistem dibangun melalui tahapan pengumpulan data kuesioner, pembentukan fungsi keanggotaan, penentuan aturan fuzzy, hingga proses defuzzifikasi. Hasil sistem menunjukkan akurasi hingga 80% saat dibandingkan dengan hasil kuesioner manual, menunjukkan konsistensi dalam penilaian tingkat stres mahasiswa. Sistem ini dapat menjadi alat bantu dalam perancangan kebijakan akademik guna meningkatkan kesejahteraan mahasiswa.
Adaptive Traffic Signal System Utilizing YOLOv11 and Fuzzy Logic for Congestion Mitigation Permadi, Dio Damas; Yudono, Muchtar Ali Setyo; Kuspranoto, Abdul Haris; Rozandi, Ardin; Artiyasa, Marina; Mubarok, Alvin; Septiani, Dwi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5865

Abstract

The increasing number of vehicles in urban and suburban areas has led to traffic congestion, resulting in longer travel times, higher exhaust emissions, and an increased risk of accidents. Conventional fixed-time traffic signal systems often fail to respond dynamically to changing traffic conditions, leading to inefficient vehicle queues. This study proposes the development of an adaptive traffic signal system that utilizes YOLOv11 and fuzzy logic to detect vehicle volume and adjust green light durations in real time. YOLOv11 is employed to detect vehicles in each lane, while fuzzy logic is used to regulate green signal durations based on the detected vehicle counts. Experimental results demonstrate a detection accuracy of 0.92 and a recall of 0.93. The green light duration varies from 80 seconds for low traffic volumes to 100 seconds for high traffic volumes. The traffic signal cycle is dynamically adjusted according to vehicle density, with a maximum total cycle time of 100 seconds. Overall, the proposed system is proven effective in reducing congestion and improving traffic management efficiency at intersections with high vehicle volumes.
Co-Authors Anang Suryana Anggy Pradiftha Junfithrana Any Elvia Jakfar Aryo De Wibowo Bayu Indrawan Dwi Septiani Efendi Efendi Handrea Bernando Tambunan Harurikson Lumbantobing Kuspranoto, Abdul Haris Mubarok, Alvin Muchtar Ali Setyo Yudono Narputo, Panji Panji Narputro Permadi, Dio Damas Rozandi, Ardin Sutisna, Muhamad Galuh Ula, Rini Khamimatul Yuda, Gina Syahbani