Article Per Year (5 Year)
p-Index From 2019 - 2024
3.463
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Perfecting a Video Game with Game Metrics Computatio : Journal of Computer Science and Information Systems Petir Kilat Energi & Kelistrikan TEKNOLOGIA TERANG : Jurnal Pengabdian Pada Masyarakat Menerangi Negeri Indonesian Journal of Electrical Engineering and Computer Science Jurnal Ilmiah Teknik Elektro Pancaran Pendidikan
Riki Ruli A. Siregar, Riki Ruli A.
Institut Teknologi PLN
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics

Published : 35 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search
Journal : TEKNOLOGIA

 1 
ESTIMASI DAYA BEBAN LISTRIK PADA GARDU INDUK CENGKARENG DENGAN MENGGUNAKAN METODE TIME SERIES MODEL DEKOMPOSISI Tia Choirun Nisa; Riki Ruli A. Siregar; Widya Nita Suliyanti
JURNAL TEKNOLOGIA Vol 1 No 2 (2019): Teknologia
Publisher : Aliansi Perguruan Tinggi Badan Usaha Milik Negara (APERTI BUMN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (893.618 KB)

Abstract

In this research, Cengkareng substation serves the burden which in every year is increasing. So in this study a system of estimating the load power at the substation was made as a result of forecasting for the following year using the Time Series Decomposition model method which was then analyzed from the existing transformer capacity. The Time Series Decomposition model method is a method of combining Trend, Seasonal and Cycle to predict the electrical load power of a substation. In this study, the development of the system uses the CRISP-DM method so that the work becomes more ordered and the testing is done with MAD, MSE and MAPE to verify the error value in the forecasting results. From this study, it produces an error value of MAD and MAPE of 9.11%. These results, prove that the Time Series Decomposition model method can be used to assist in estimating the Power Load in Cengkareng Substation.
PENANGANAN GANGGUAN LISTRIK RUMAH TANGGA MENGGUNAKAN ALGORITMA GREEDY UNTUK PENENTUAN JARAK OPTIMAL Ayu Fadhilah Prianty; Riki Ruli A. Siregar; Rakhmat Arianto
JURNAL TEKNOLOGIA Vol 2 No 1 (2019): Jurnal Teknologia
Publisher : Aliansi Perguruan Tinggi Badan Usaha Milik Negara (APERTI BUMN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (8482.066 KB)

Abstract

Electricity is a necessity that can be said has become the basic needs of society today. By exploiting technological developments, it is expected to facilitate PLN electricity customers in reporting electrical disturbances easily so that officers can provide response handling disorders quickly. One of the most commonly utilized technologies is Internet technology that can easily access website applications. Technical Service Post, PLN Area Cengkareng and Kalideres not utilize it well, for that need an application that can do the calculation of the optimal distance from report point to nearest duty using Greedy Algorithm. In the optimization problem, the Greedy Algorithm can produce an optimal solution. With the process, customers can easily find the nearest PLN officers, monitor the status of the report so that expected information can be immediately responded to disruption and handling
Penerapan Algoritma Backpropagation Pada Pengenalan Tanda Nomor Kendaraan Bermotor Untuk Kartu Parkir Berbasis RFID Rizki Putra Pamungkas; Dwina Kuswardani; Riki Ruli A. Siregar
JURNAL TEKNOLOGIA Vol 3 No 1 (2020): Jurnal Teknologia
Publisher : Aliansi Perguruan Tinggi Badan Usaha Milik Negara (APERTI BUMN)

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

Abstract

This research was conducted to improve a parking system's security using Radio Frequency Identification (RFID) technology to open parking barriers. To open the parking barriers, you must use an RFID card that has been registered to the system. There is no license plate checking on the parking system so that the RFID card can take other vehicles from the parking lot. Therefore, the RFID card needs to be linked with the license plate data using pattern recognition with an Artificial Neural Network. In this parking system, the backpropagation algorithm will be applied to identify the characters from the license plate's image captured with the camera. Information on parked vehicles can be seen through a web- based application that the administrator can only access. Also, this application can view the history of previously parked vehicles. Based on the calculation of accuracy using the confusion matrix, the backpropagation algorithm to identify the characters in this parking system has an accuracy value of 85.7%.
Klasifikasi Untuk Memprediksi Pembayaran Kartu Kredit Macet Menggunakan Algoritma C4.5 Putri Ayu Mardhiyah; Riki Ruli A. Siregar; Pritasari Palupiningsih
JURNAL TEKNOLOGIA Vol 3 No 1 (2020): Jurnal Teknologia
Publisher : Aliansi Perguruan Tinggi Badan Usaha Milik Negara (APERTI BUMN)

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

Abstract

Many credit card issuing companies experience problems related to bill payments by their customers or also known as bad credit payments that are not according to the agreement so that they are detrimental to the company itself. In this case, there is still a pile of unclassified credit cardholder customer data and problem-solving patterns are found. The C4.5 algorithm is used to predict whether a customer is a credit default payment or not. This study uses a data set that has determining criteria, namely the amount of credit, status, age, and payment status for 1-3 months. From the results of research using 4199 customer data results in an evaluation that the C4.5 algorithm is applied accurately to predict whether or not customer credit card payments are bad with an accuracy level of 70.93%.
Implementasi Metode Naive Bayes Classifier (NBC) Pada Komentar Warga Sekolah Mengenai Pelaksanaan Pembelajaran Jarak Jauh (PJJ) Naomi Chatrina Siregar; Riki Ruli A. Siregar; M. Yoga Distra Sudirman
JURNAL TEKNOLOGIA Vol 3 No 1 (2020): Jurnal Teknologia
Publisher : Aliansi Perguruan Tinggi Badan Usaha Milik Negara (APERTI BUMN)

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

Abstract

Many credit card issuing companies experience problems related to bill payments by their customers or also known as bad credit payments that are not according to the agreement so that they are detrimental to the company itself. In this case, there is still a pile of unclassified credit cardholder customer data and problem-solving patterns are found. The C4.5 algorithm is used to predict whether a customer is a credit default payment or not. This study uses a data set that has determining criteria, namely the amount of credit, status, age, and payment status for 1-3 months. From the results of research using 4199 customer data results in an evaluation that the C4.5 algorithm is applied accurately to predict whether or not customer credit card payments are bad with an accuracy level of 70.93%.
Co-Authors Abdul Haris Abdurrasyid Abdurrasyid Abdurrasyid, Abdurrasyid Alwi Baraqbah Andika Prasatya Anugrah Danny Prasetyo Ayu Fadhilah Prianty Budi Prayitno Chairul G Irianto Darma Rusjdi Dine Tiara Kusuma Dwina Kuswardani Efy Yosrita Efy Yosrita Fera Amelia Sinaga Grace Sondakh Haris, Abdul Hengki Sikumbang Hermawan Prasetya dan Taufik Djatna Indrianto Indrianto Iriansyah BM Sangadji Irwansyah Saputra Jaka Mahardika Jaka Suprapto Karina Djunaidi Karina Djunaidi Luqman Luqman M. Yoga Distra Sudirman Meilia Nur Indah Susanti, Meilia Nur Naomi Chatrina Siregar Ningrum, Rahma Farah Novi Gusti Pahiyanti Nurfachri Wardana Nurul Dyah Budiana Nurul Ramadhanti Hikmiyah Palupiningsih, Pritasari Puji Catur Siswipraptini Puji Catur Siswipraptini Purwanto, Yudhi Purwati Putri J. Putri Ayu Mardhiyah Rahma Farah Ningrum Rahma Farah Ningrum Rakhmat Arianto Rakhmat Arianto Rakhmat Arianto Rifky Raymond Rio Jefri Pasaribu Rizki Pratama Putra Rizki Putra Pamungkas Rosida Nur Aziza Rosida Nur Aziza Rosida Nur Aziza Rosida Nur Aziza Rosida Nur Aziza Satrio Yudho Sergius Sarmose Manggara Putra Sarmose Manggara Putra Siswipraptini, Puji Catur Suyanto Suyanto Tia Choirun Nisa Trisma Juwita Widya Nita Suliyanti Zuhdiyyah Ulfah Siregar