Article Per Year (5 Year)
p-Index From 2020 - 2025
6.18
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Charity : Jurnal Pengabdian Masyarakat E-JOINT (Electronica and Electrical Journal Of Innovation Technology) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Almufi Jurnal Pengabdian kepada Masyarakat eProceedings of Engineering Electrician : Jurnal Rekayasa dan Teknologi Elektro Journal of Computer Engineering: Progress, Application and Technology Jurnal Abdimas
Faisal Candrasyah Hasibuan
Universitas Telkom
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

Published : 47 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 47 Documents
Search

 1   2   3   4   5 
Identifikasi Persediaan Makanan di dalam Lemari Pendingin Berbasis Raspberry Pi dan Deep Learning Faisal Candrasyah Hasibuan; Andri Ulus Rahayu
E-JOINT (Electronica and Electrical Journal Of Innovation Technology) Vol. 2 No. 2: E-JOINT, Desember 2021
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/e-joint.v2i2.1046

Abstract

Sistem ini dibuat atas dasar permasalahan yang terjadi dalam kehidupan sehari-hari, salah satunya yaitu tidak terpantaunya persediaan bahan makanan di lemari pendingin. Ketika dibutuhkan suatu bahan makanan dari lemari pendingin dan ternyata tidak ada, maka akan menjadi masalah. Oleh karena itu, dibuatlah sebuah sistem yang mampu mengidentifikasi objek makanan di dalam lemari pendingin. Masukan dari sistem ini berupa foto objek makanan yang diambil menggunakan Raspberry Pi Camera dan terhubung langsung dengan Raspberry Pi di dalam lemari pendingin. Setelah diproses dengan algoritma pembelajaran mesin, maka keluaran yang dihasilkan berupa identifikasi objek makanan yang terdapat di dalam lemari pendingin tersebut. Objek makanan yang diuji berupa pisang, mentimun, brokoli, dan jeruk. Dari hasil pengujian, terlihat bahwa program mengidentifikasi objek dengan benar pada objek pisang dan jeruk yang ditunjukkan dengan confidence level tertinggi sebesar 56,98% dan 45,88%. Identifikasi objek mentimun dikenali sebagai zukini dengan confidence level tertinggi sebesar 78,61%. Adapun identifikasi objek paling rendah terdapat pada objek brokoli dengan confidence level kurang dari 1%.
Perancangan Aplikasi Deteksi Sifat Manusia Melalui Garis Tangan Menggunakan Metode Naive Bayes Dan Metode Probabilistic Neural Network Dengan Klasifikasi Citra Berbasis Android Ivan Rinaldhy Saputra; Budhi irawan; Faisal Candrasyah Hasibuan
eProceedings of Engineering Vol 7, No 1 (2020): April 2020
Publisher : eProceedings of Engineering

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

Abstract

Abstrak Deteksi melalui pola garis telapak tangan manusia (palmistry) dapat dilakukan dengan mudah apabila dibantu dengan software yang dibuat khusus untuk melakukan tugas tersebut. Input yang diperlukan berupa gambar telapak tangan objek dengan ukuran dan resolusi tertentu pada smartphone berbasis aplikasi android. Kemudian sistem akan melakukan pencocokkan pola garis tangan dari inputan dengan data terdapat pada database. Output dari system adalah berupa class terdekat atau class yang sesuai dari garis tangan pengguna ingin dikenali hasil dari analisa pola garis tangan pengguna dengan pola garis tangan yang ada di database berupa karakter dari pemilik pola garis tangan tersebut. Kata kunci : Ramalan Pola Garis Telapak Tangan, Naive Bayes Method, Probabilistic Neural Network, Sistem Pakar Abstract Detection through palmistry can be done easily when aided by software specifically designed to do the task. The required input is in the form of an object's palm image with a certain size and resolution on android based smarthone. Then the system will match the hand line pattern from input with the data contained in the database. The output of the system is in the form of the closest class or class corresponding to the user's hand line, the result of analyzing the pattern of the user's hand line with the hand pattern in the database in the form of characters from the owner of the hand pattern pattern. Keywords: Palmistry, Naive Bayes Method, Probabilistic Neural Network, Expert System
Prediction System on Electricity Consumption using Web-Based LSTM Algorithm Fathoni waseso jati; Komang Jaya Bhaskara; Faisal Candrasyah Hasibuan; Budhi Irawan
CEPAT Journal of Computer Engineering: Progress, Application and Technology Vol 1 No 02 (2022): August 2022
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cepat.v1i02.5227

Abstract

The technology development from year to year is increasing rapidly, especially in the electronics devices such as notebooks and smartphones. With the rapid development of technology, lifestyle habits have also changed. This can lead to an increase in the use of electrical energy. In addition, the negligence of electricity users in monitoring electricity usage at the place of the electricity meter also causes an increase in electrical energy. Monitoring the electricity meter in real time can limit the user from manage their electricity efficiently. This study aims to create a web-based electrical energy usage prediction system. This system can make it easier for users to manage and reduce waste of electrical energy. In the development of this system, it begins by collecting image data of remaining electricity which are processed manually into electrical energy consumption data. Then the data is pre-processed so that the data is clean and ready to use. The clean data is carried out by the process of making a Long-Short Term Memory (LSTM) model which was chosen because it can overcome Time Series and Non-Linear data types. LSTM model is designed to be able to predict the use of electrical energy. Then do the web application design as an interface on the predictive data. Based on the results of the test, the LSTM model can predict the use of electrical energy with a Loss Mean Square Error (MSE) value of 0.0071. While the results of website testing carried out with the alpha test get an accuracy of 100% and a beta test of 82.64%.
Random Forest Implementation in Prepaid Electric Meter Recognition Komang Jaya Bhaskara Mahatya; Fathoni Waseso Jati; Budhi Irawan; Faisal Candrasyah Hasibuan
CEPAT Journal of Computer Engineering: Progress, Application and Technology Vol 1 No 02 (2022): August 2022
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cepat.v1i02.5228

Abstract

While prepaid electricity services provide better flexibility, it comes with an additional step for the customer. Instead of paying a monthly bill based on electric usage, a prepaid system requires customers to actively predict their electricity usage before they pay for the correct electricity value. This presents a challenge because Underestimating electricity usage may lead to a power outage. Therefore, a system that monitors electricity can be developed to address this issue. There are two approaches to developing an electric monitoring system: designing the electric meter equipped with monitoring features or designing an external capturing device to work with the current electric meter. The first approach is costly and requires a meter disassembly. Thus, in this paper, the second approach is used. By utilizing image processing and a Random Forest machine learning algorithm, a monitoring device can be developed to read the digital meter's display. Although it may affect performance due to the low-power device, Raspberry Pi 3 and Raspberry Camera are used to provide automation. This method yields an accuracy of 97% using 375 images.
Empowering website based information and service quality: the role of backend developer in village digitalization Daffa Ahmadhan Khusumah; Agus Virgono; Faisal Candrasyah Hasibuan
CEPAT Journal of Computer Engineering: Progress, Application and Technology Vol 2 No 03 (2023): September 2023
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cepat.v2i03.6352

Abstract

In today's technology-driven era, this paper investigates the indispensable role of backend developers in empowering website-based information and service quality through digitization initiatives. As technology is critical in service delivery and information dissemination, backend developers are crucial in developing robust and efficient systems. This paper emphasizes the importance of their position by examining their primary responsibilities, skills required, challenges faced, and their impact on improving the quality of website-based services and information in the digitization process. By studying the realm of backend development, this paper highlights the vital contribution made by backend developers in achieving effective digital transformation.
Identifikasi Persediaan Makanan di dalam Lemari Pendingin Berbasis Raspberry Pi dan Deep Learning Faisal Candrasyah Hasibuan; Andri Ulus Rahayu
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 16 No. 1 (2022)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

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

Abstract

Intisari Sistem ini dibuat atas dasar permasalahan yang terjadi dalam kehidupan sehari-hari, salah satunya yaitu tidak terpantaunya persediaan bahan makanan di lemari pendingin. Ketika dibutuhkan suatu bahan makanan dari lemari pendingin dan ternyata tidak ada, maka akan menjadi masalah. Oleh karena itu, dibuatlah sebuah sistem yang mampu mengidentifikasi objek makanan di dalam lemari pendingin. Masukan dari sistem ini berupa foto objek makanan yang diambil menggunakan Raspberry Pi Camera dan terhubung langsung dengan Raspberry Pi di dalam lemari pendingin. Setelah diproses dengan algoritma pembelajaran mesin, maka keluaran yang dihasilkan berupa identifikasi objek makanan yang terdapat di dalam lemari pendingin tersebut. Objek makanan yang diuji berupa pisang, mentimun, brokoli, dan jeruk. Dari hasil pengujian, terlihat bahwa program mengidentifikasi objek dengan benar pada objek pisang dan jeruk yang ditunjukkan dengan confidence level tertinggi sebesar 56,98 persen dan 45,88 persen. Identifikasi objek mentimun dikenali sebagai zukini dengan confidence level tertinggi sebesar 78,61persen. Adapun identifikasi objek paling rendah terdapat pada objek brokoli dengan confidence level kurang dari 1persen. Kata kunci Deep Belief Network, Deep Learning, Lemari Pendingin, Machine learning, Raspberry Pi. Abstract — This system is made based on problems that occur in everyday life, one of which is the lack of monitoring of food supplies in the refrigerator. It will be a problem when there is a need for a food item from the refrigerator and it does not exist. Therefore, a system is made that can identify food objects in the refrigerator. The input from this system is in the form of photos of food objects taken using the Raspberry Pi Camera and connected directly to the Raspberry Pi in the refrigerator. After being processed with machine learning algorithms, the resulting output identifies food objects in the refrigerator. The food objects tested were bananas, cucumbers, broccoli, and oranges. The test results show that the program correctly identified the object on the banana and orange object, which was indicated by the highest confidence levels of 56.98 percents and 45.88 percents, respectively. The identification of the cucumber object was recognized as zucchini with the highest confidence level of 78.61 percents. The lowest object identification was found in broccoli, with a confidence level of less than 1 percents. Keywords Deep Belief Network, Deep Learning, Deep Belief Network, Machine learning, Raspberry Pi, Refrigerator.
Development of a User Interface (Front End) in a Website-Based Village Information System to Improve Service Quality and Community Empowerment Information: Sindangresmi Village Information System Titipan, Muhammad Raga; Virgono, Agus; Hasibuan, Faisal Candrasyah
CEPAT Journal of Computer Engineering: Progress, Application and Technology Vol 4 No 01 (2025): May 2025
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cepat.v2i04.6358

Abstract

Sindangresmi Village is one of 10 villages in the Jampang Tengah District, Sukabumi Regency. The village is the most accurate data source for searching population data. For this reason, the method of collecting population data, filling in data in a format, and data processing to present population data information to the public must be carried out effectively and efficiently so that the information can be conveyed quickly and accurately. For the case study in this village, the data collection process for services is still manual. Therefore, there are always errors and duplications of data. Because manual data collection is prone to data entry errors, duplication, or loss due to human error. In addition, the difficulty of accessing information is also a problem due to the need for an organized system for storing and managing village data. A website-based village information system has become a promising solution for improving service quality and access to information to empower rural communities. The programming language used to build this system is HTML (Hypertext Markup Language), with the framework PHP (Hypertext Preprocessor) and SQL (Structured Query Language) as the database. Keyword : Village Information System Digitalization Sindangresmi
Development of village information system website as a strategy for increasing the tourism experience in Sindangresmi village Pamungkas, Fajar Triaji; Virgono, Agus; Hasibuan, Faisal Candrasyah
CEPAT Journal of Computer Engineering: Progress, Application and Technology Vol 4 No 01 (2025): May 2025
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cepat.v2i04.6369

Abstract

Tourism is an important function in increasing the regional economy. In recent years, digitization has emerged as a transformative force in the tourism industry, providing new possibilities for enhancing the tourist experience and making development sustainable. Sindangresmi Village, located in Central Jampang, has a unique cultural heritage, natural landscapes, and culture attractive to tourists. However, to remain competitive in the current tourism scene, embodying virtual technologies to provide the land with immersive and engaging stories is important. Digital systems can facilitate the involvement and empowerment of networks in tourism development. Online networks and social media companies can serve as a channel of communication for residents, enabling them to share their stories, traditions, and entrepreneurship with tourists. This virtual connection fosters experiences of satisfaction and encourages sustainable tourism practices that maintain the cultural heritage and tourism of the village. The digitization of tourism provides a wide opportunity for Sindangresmi Village to enhance its tourism experience. By leveraging digital advertising and marketing techniques, immersive technologies, intelligent control structures, and internet engagement systems, villages can attract more visitors and enhance the tourism experience. Using digitalization is important for Sindangresmi Village to develop a dynamic tourism aspect while maintaining its distinctive identity and heritage.
Back-end website development for IoT-based automated water dissolved oxygen control Mahaasin, Habib Irfan; Kusuma, Purba Daru; Hasibuan, Faisal Candrasyah
CEPAT Journal of Computer Engineering: Progress, Application and Technology Vol 4 No 01 (2025): May 2025
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cepat.v2i04.6528

Abstract

The process of fish farming will never be separated from dissolved oxygen level control devices such as aerators so that the cultured fish can grow and develop optimally. This is quite troublesome for fish farmers, especially for people who often leave their cultivation sites, so they cannot monitor directly. Therefore, a platform for remote monitoring is needed. This journal focuses on designing and developing a back-end system, one of which functions as a data communication medium from the IOT antares platform to the website built so that the website can be used to monitor and control IOT devices using the waterfall development method and HTTP communication protocol. this method and communication protocol were chosen because they are currently quite popular and very flexible to use. The results of this research show that data communication carried out by the server with Antares using the GO programming language can run well and quite efficiently. The system built is also able to accommodate several requests from users simultaneously with an average response time of 60ms, while the exchange of IOT device data with the website has an average response time of 3 seconds.
Implementation of Self-Hosted IoT Ecosystem on NPK Soil Monitoring System Nugroho, Aditya Bakti; Hasibuan, Faisal Candrasyah; Perdana, Doan
CEPAT Journal of Computer Engineering: Progress, Application and Technology Vol 4 No 02 (2025): November 2025
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cepat.v3i01.6698

Abstract

In previous research, a tool has been made to detect soil NPK nutrient content with a screen display on the device. However, the reading results of the system cannot be monitored remotely. This research aims to develop a self-built IoT ecosystem to monitor the readings remotely. On the device side, a microcontroller can connect to the internet via a Wi-Fi network. The communication protocol selected is the MQTT protocol based on the pub-sub model. The software chosen to present the data is Node-RED. The service is self-hosted using a personal computer (PC). To be accessed from the internet, a tunneling service is used. The data presentation service obtained can be accessed remotely. Based on the test results, the MQTT protocol allows sending data only in the size of tens of bytes with an average delivery time of under one second. The data is presented in a dashboard that can be accessed via the internet with a browser.
Co-Authors Alfarrelwijaya, Dheandra Alfian, Devid Andreson, Enrico Andri Ulus Rahayu ANGGUNMEKA LUHUR PRASASTI Athallah, Raken Putra Bhaskara, Komang Jaya Bobby, Syahdika Budhi Irawan Daffa Ahmadhan Khusumah David, Axel Dhesvira Nurseha Putri Doan Perdana Fajri, Farhan Ulil Fakhrezi, Alfian Fathoni Waseso Jati Fathoni waseso jati Gantoro, Rasyid Abdi Hady , Muhammad Jibran Hariwidjaja, Valrama Wardhana Hartono, Aditya Firmansyah Herlambang, Yohanes Yogas Ivan Rinaldhy Saputra jati, Fathoni waseso Khusumah, Daffa Ahmadhan Komang Jaya Bhaskara Komang Jaya Bhaskara Mahatya Kusumo, Atallah Satrio L., Kemas Muslim Luthfi, Zaidan Mahaasin, Habib Irfan Mahatya, Komang Jaya Bhaskara Mahendra, Dimas Putra Mertu, Aidi Meta Kallista Muhammad Rafindha Aslam Novianty, Astri Nugroho, Aditya Bakti Nur, Alan Rezky Dwiputra Nur’ilham, Muhammad Fauzan Pamungkas, Fajar Triaji Prasetyo, Aldy Putra Purba Daru Kusuma Rahadyan , Rizkita Ilham Rahman, Muhammad Syaiful Randy Erfa Saputra Rifqi Muhammad Fikri Ruslan, Ramah Rinaldi Sakri, Annisa Aprilia Putri Setiady, R Rogers Dwiputra Shiddieqy, Hasbi Ash Silalahi, Panji Christoper Sulaeman, Ikhsar Taufiq, Hans Harison Titipan, Muhammad Raga Widodo, Muhammad Faiz Anindyo