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Setiawan Wibisono, Iwan
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Computer Science & IT

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Pemantauan Daya Listrik Real-Time Menggunakan IoT untuk Efisiensi Energi Rumah Tangga Munir, Misbahul; Setiawan Wibisono, Iwan
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2391

Abstract

Teknologi Internet of Things (IoT) hadir sebagai penggerak utama dalam era transformasi digital yang memungkinkan perangkat saling terhubung dan beroperasi secara otomatis. Penggunaan daya listrik yang tidak terkontrol dapat menyebabkan pemborosan energi dan peningkatan biaya operasional. Penelitian ini merancang sebuah sistem monitoring pengelolaan konsumsi daya listrik berbasis IoT (Internet of Things), sehingga memungkinkan pengguna untuk memantau tingkat konsumsi energi secara real-time. Sistem ini memanfaatkan sensor daya PZEM004T yang terhubung ke platform smartphone berbasis aplikasi Blynk melalui mikrokontroler NodeMCU ESP8266. Metode yang diterapkan dalam penelitian ini adalah Research and Development, yang mencakup tahapan perencanaan, pengembangan, serta evaluasi sistem. Hasil pengujian menunjukkan bahwa sistem mampu menurunkan konsumsi daya listrik hingga 20%, meningkatkan akurasi sensor sebesar 3%, serta menurunkan latensi transmisi data hingga 75%. Temuan ini menunjukkan bahwa sistem mampu meningkatkan kesadaran pengguna terhadap pola konsumsi energi dan mendorong perubahan perilaku ke arah yang lebih hemat energi. Selain memberikan solusi praktis untuk pengendalian energi rumah tangga, sistem ini juga menawarkan potensi pengembangan lebih lanjut, seperti integrasi kecerdasan buatan (AI) dan energi terbarukan. Penelitian ini memberikan kontribusi penting terhadap pengembangan sistem IoT di bidang efisiensi energi dengan menghadirkan pendekatan yang aplikatif, hemat biaya, serta ramah lingkungan, sekaligus memperkaya khazanah penelitian sebelumnya di bidang monitoring konsumsi energi berbasis IoT yang belum banyak mengeksplorasi integrasi sistem dengan aplikasi mobile secara langsung dan real-time.
Sistem Informasi Penjualan Tembakau Berbasis Web dengan Laravel: Implementasi Metode Waterfall dan Pengujian Black-Box Anugrah, Harun; Setiawan Wibisono, Iwan
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2511

Abstract

Tobacco sales by local businesses often face challenges in recording transactions and managing customer data. This study focuses on the design and development of a Laravel-based sales information system using the Waterfall methodology approach. The development of this system involves a series of processes that include needs identification, architecture design, design, testing, and system maintenance. This system contributes to the digitization of tobacco MSMEs with a time efficiency in recording of up to 45%. System testing indicates an increase in effectiveness in transaction data management and sales report presentation. With this system, it is hoped that business actors will achieve improvements in digitizing their business processes effectively and measurably.
SmartTraffic-CNN: Deteksi dan Estimasi Jumlah Kendaraan Secara Otomatis Menggunakan Deep Learning dan Ekstraksi Fitur Putri, Marsiska Ariesta; Riyono; Setiawan Wibisono, Iwan
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2943

Abstract

With the rapid pace of urbanization, the number of vehicles traveling between cities has increased significantly. As a result, various traffic-related problems have emerged, such as congestion and excessive vehicle volume and types. To address these issues, comprehensive road data collection is essential. Therefore, in this study, we developed an intelligent traffic monitoring system based on You Only Look Once (YOLO) and a Fuzzy Convolutional Neural Network (CFNN), which records traffic volume and vehicle-type information from the roadway. In this system, YOLO is first used for vehicle detection and combined with a vehicle-counting method to calculate traffic flow. Then, two effective models (CFNN and Vector CFNN) along with a network mapping fusion method are proposed for vehicle classification. In our experiments, the proposed methods achieved an accuracy of 90.45% on a public dataset. On this dataset, the average precision and F-measure (F1) of the proposed YOLO-CFNN and YOLO-VCFNN vehicle classification methods reached 99%, outperforming other approaches. On real highways, the proposed YOLO-CFNN and YOLO-VCFNN methods not only attained high F1-scores for vehicle classification but also demonstrated remarkable accuracy in vehicle counting. Furthermore, the system maintained a detection speed of over 30 frames per second. Thus, the proposed intelligent traffic monitoring system is well-suited for real-time vehicle classification and counting in real-world environments.
Co-Authors Anugrah, Harun Misbahul Munir Putri, Marsiska Ariesta Riyono