Article Per Year (5 Year)
p-Index From 2020 - 2025
0.983
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika Faktor Exacta Petir Minda Baharu Jurnal E-Komtek
Arvio, Yozika
Institut Teknologi PLN
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
Pendekatan Implementasi Model Substractive Clustering Dalam Memetakan Dan Klasifikasi Data Perilaku Konsumen Listrik Tegangan Rendah Studi Kasus : Pelanggan PT PLN (Persero) UP3 Cengkareng Yozika Arvio; Iriansyah BM Sangadji; Hengki Sikumbang; Meilinda Devi Anjarwati
PETIR Vol 12 No 2 (2019): PETIR (Jurnal Pengkajian Dan Penerapan Teknik Informatika)
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (608.608 KB) | DOI: 10.33322/petir.v12i2.553

Abstract

Electrical energy is one of the most important and vital human needs that cannot be released from daily needs. Customers also have begun to be critical of the purchase costs that must be paid every month. So by increasing electricity rates, improving efficiency in the use of electric power is a major consideration. The Advanced Measurement Infrastructure System (AMI) provides information on the use of granular energy for needs and customers. The IT system at AMI one of which uses EMS (Energy Management System) is an application to collect data from every smart meter installed in the customer, to store it in a database, and to connect the analysis and statistics of the data stored below. In this study aims to provide an analysis of electricity usage patterns by implementing AMI / Smart meters PT. PLN (Persero) by conducting a cluster of 1 phase and 3 phase electricity usage in customers of PT PLN (Persero) UP3 Cengkareng, namely the distribution booths DK60, TG70 and DK242 for 4 months, from November 2018 to February 2019. From the results of the study sought for customers 1 phase DK 60 with a radius of 0.5 produces 1 cluster that is stable every month depending on the customer at this substation is a household class customer, TG 70 requires stable and spending usage in December 2018, and DK 242 fix stable and use customers in the month December 2018 and January 2019, while for 3-phase DK 60 customers tend to be unstable because the customers of this apartment are different, starting from shops, production sites, and CV. TG 70 substations are predominantly places of worship, namely mosques and mosques, so the average use of mosques is higher, and for DK 242 3-phase customers need to be stable and use the highest in January 2019.AMI, System, EMS, Distribution Substation, Phase.
Inorganic Waste Detection Application Using Smart Computing Technology with YOLOv8 Method Arvio, Yozika; Kusuma, Dine Tiara; BM Sangadji, Iriansyah
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14117

Abstract

Waste and renewable energy are critical issues in Indonesia, with the government aiming for renewable energy (RE) to contribute 23% to the national energy mix by 2025. This research focuses on developing a waste processing system through the TOSS (Tempat Olah Sampah Setempat) method and Peuyeumisasi technique to convert waste into biomass, such as briquettes and pellets for fuel. However, manual waste sorting remains time-consuming, prompting the need for a real-time detection system. You Only Look Once (YOLO) is an object detection approach that utilizes Convolutional Neural Networks (CNN) for object detection, making it one of the applications of intelligent computing in the field of computer vision. the latest version of YOLO is YOLO v8 offering several improvements over the previous version, can be employed in a real-time detection system to separate organic and inorganic waste. In this study, the dataset used consists of 2.000 images comprising five classes of inorganic waste: plastic bottles, plastic, glass, cans, and Styrofoam. The study demonstrates that YOLOv8 performs exceptionally well in detecting inorganic waste, with an average accuracy of 98% based on direct testing, and model evaluation showing an average accuracy of 99.33%, precision of 99.63%, recall of 96.53%, and an f1-score of 98.03%. These results indicate that the YOLOv8 method can significantly accelerate and simplify the waste sorting process, thereby supporting the conversion of waste into renewable energy. This research is expected to provide a practical solution and serve as a reference for future studies.
Penerapan Metode Convolution Neural Network (CNN) Dalam Proses Pengolahan Citra Untuk Mendeteksi Cacat Produksi Pada Produk Masker Arvio, Yozika; Kusuma, Dine Tiara; Sangadji, Iriansyah BM; Dewantara, Erno Kurniawan
Faktor Exacta Vol 16, No 4 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i4.20073

Abstract

A mask manufacturer in Indonesia with a production of 4 million masks per day for various types of masks. However, in the production process there are still many defective and unsalable masks that enter the stock of goods to be sent, this is due to the quality control process that is still manual. So that to reduce product defects, it is necessary to mitigate by creating a system that can detect defective products, to facilitate the quality control process, an intelligent computing system is needed so that it is expected to reduce mask production defects to build this computational model will be carried out in several stages. The first stage will be a field study to obtain samples of defective and perfect products. The second stage builds a computational model, this model is built based on the Convolution Neural Network (CNN) method and the third stage builds a system that suits the needs in the field and tests the system against the company's needs. The purpose of this research is to produce a good and perfect defective product detection system so that it can be useful for reducing defective products that pass the quality control stage. From this research, if the process is run by entering existing data, it produces an accuracy percentage of 99% of the 750 data tested. While in real time testing, a percentage of 96.4% was obtained using 28 data.
EDUKASI LISTRIK SEHAT UNTUK MENCEGAH BAHAYA KEBAKARAN PADA PERUMAHAN PADAT PENDUDUK DI JAKARTA BARAT Makkulau, Andi; Pasra, Nurmiati; Fernandez, Alex; Mauriraya, Kartika Tesya; Afrianda, Rio; Suryana, Nana; Arvio, Yozika
MINDA BAHARU Vol 8, No 1 (2024): Minda Baharu
Publisher : Universitas Riau Kepulauan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33373/jmb.v8i1.5886

Abstract

Sering terjadinya kebakaran listrik khusunya pada perumahan padat penduduk, khususnya pada perumahan dijalan yang sulit dilalui kendaran roda empat. Salah satu bahaya utama kesalahan dalam instalasi listrik rumah tangga adalah korsleting listrik yang dapat diakibatkan oleh penggunaan peralatan listrik yang tidak tepat atau beban berlebihan pada suatu instalasi listrik rumah tangga. Kurangnya pemahaman masyarkat pada dalam hal instalasi listrik dan kurangnya pemahaman masyarakat dalam penggunaan kabel yang benar bisa menyebabkan korsleting listrik. Pengabdian ini bertujuan agar masyarakat dapat memahami pengamanan instalasi listrik dan cara mencegah kebakaran akibat korsleting listrik. Pengabdian ini melakukan edukasi kepada masyarakat dengan mempraktekkan langsung instalasi rumah tangga yang benar dengan penggunaan peralatan listrik sesuai standar, dan melakukan perbaikan instalasi rumah tangga. Hasil dari kegiatan ini masyarakat lebih memahami cara melakukan instalasi listrik yang benar. Masyarakat dapat memilih dan menggunakan kabel dan peralatan listrik lainnya dengan benar dalam instalasi listriknya. Daerah RT 08 / RW 07 Duri Kosambi melek teknologi dan tidak akan terjadi kebakaran akibat korsleting listrik
Implementation of YOLO V8 Algorithm in Organic and Anorganic Waste Detection Application for Waste to Energy Management Arvio, Yozika; Kusuma, Dine Tiara; Sangadji, Iriansyah BM
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 1 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2310

Abstract

Waste management in Indonesia is a major challenge, especially in the development of waste to energy (WTE). Accurate classification of organic and inorganic waste is required to optimise energy conversion. This research develops an automated waste detection system in temporary landfill sites (TPS) using the YOLOv8 algorithm, known for its high speed and accuracy. The research involved data collection, development of a YOLOv8-based computational model, and system construction and testing according to field requirements. The results show that YOLOv8 has high performance in detecting organic and inorganic waste, with 99.35% accuracy, 98.6% precision, 98.6% recall and 98.5% F1 score. This system can speed up the waste sorting process and has the potential to be used in domestic and public environments for the automatic detection of waste categories.
An efficient clustering approach in electrical energy consumption patterns Tiara Kusuma, Dine; Ahmad, Norashikin; Sakinah Syed Ahmad, Sharifah; BM Sangadji, Iriansyah; Arvio, Yozika
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8666

Abstract

A comprehensive understanding of electrical energy consumption patterns is essential for strategizing and monitoring the use of energy resources. Industry and business customers of electrical have energy consumption patterns that vary widely depending on the type of industry, business size, and operating hours. This research uses clustering analysis to obtain electrical energy consumption patterns in industrial and business electricity customer groups by grouping data into similar groups. The variables used in this research are daytime, active power (kW), apparent (kVa), and power factor (PF). The objective of this research is to determine the efficacy and benefits of each clustering technique employed in load profile analysis. The clustering algorithm approach used in this research is k-means and fuzzy subtractive clustering (FSC). The trials carried out on these two approaches provide valuable knowledge regarding the effectiveness and superiority of each algorithm in producing significant clusters from the data used in this research. The evaluation conducted using the Davies-Bouldin index (DBI) indicates that the quality value for FSC is 0.25 for business customers and 0.31 for industrial customers. On the other hand, the quality value for k-means is 0.55 for business customers and 0.56 for industrial customers.
Co-Authors Afrianda, Rio Ahmad, Norashikin BM Sangadji, Iriansyah Dewantara, Erno Kurniawan Fernandez, Alex Hengki Sikumbang Iriansyah BM Sangadji Iriansyah BM Sangadji Makkulau, Andi Mauriraya, Kartika Tesya Meilinda Devi Anjarwati Nana Suryana Pasra, Nurmiati Sakinah Syed Ahmad, Sharifah Sangadji, Iriansyah BM Tiara Kusuma, Dine