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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Sinergi ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JCES (Journal of Character Education Society) JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) MIND (Multimedia Artificial Intelligent Networking Database) Journal REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Society: Jurnal Pengabdian Masyarakat Nautical: Jurnal Ilmiah Multidisiplin Indonesia
KRISTIANA, LISA
Institut Teknologi Nasional Bandung
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences

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Autonomous Navigation onto Autodocking Drone System using Computer Vision KRISTIANA, LISA; MAULANA, KEINDRA BAGAS; FASYA, SHAFIRA KURNIA; DAFY, MUHAMMAD ZUFAR; JANUAR, MUKTIADI AKHMAD
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 13, No 1: Published January 2025
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v13i1.70

Abstract

Autodocking is a sophisticated technology that allows drones to land automatically at a predetermined docking station. Computer vision plays an important role in the drone autodocking system, allowing the drone to "see" the intended object. The problem with drone control is the precision of determining and placing objects, in this case docking, where there is still a difference or error between the desired docking set point. The solution proposed in this article is to use the PID Controller (Proportional-Integral-Derivative Controller) algorithm. By using a PID controller, the drone can regulate its movements more precisely, maintain stability, and ensure proper landing. The results achieved using this approach, reached a 90% success rate (precision) with control of several environmental parameters. first page. The abstract contains a summary of backgrounds, methods, and research results, with the maximum number of characters being 150.
Implementasi Game Pengenalan Binatang di TK Putra I Bandung Miftahuddin, Yusup; Kristiana, Lisa; Anindia, Hana Nathania; Sugiharto, Ariq Bagus; Adli, Muhammad Arkan; Nurrahmayanti, Fadhilah; Setiyawati, Setiyawati
Society : Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2025): Januari
Publisher : Edumedia Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55824/jpm.v4i1.511

Abstract

The animal recognition game application is designed to support interactive learning at TK Putra 1 Bandung, Arcamanik District, Bandung City. This application aims to provide an engaging learning experience where young children can learn about various animals, their habitats, sounds, and distinctive features. Interactive elements such as animal images, sounds, and videos are presented in an enjoyable way, allowing children to learn in an engaging and active manner. Features such as quizzes, clickable buttons, and animations support children’s engagement, enhance curiosity, and encourage active participation in the learning process. The application is also designed to make it easier for teachers and staff to deliver interactive content, ensuring that learning takes place in an enjoyable and effective manner.
Implementasi backbone CSPDarknet53 pada algoritma YOLOv4 sebagai sistem pendeteksi wajah manusia Rauf, Muhammad; Kristiana, Lisa
Nautical : Jurnal Ilmiah Multidisiplin Indonesia Vol. 2 No. 11 (2024): Nautical: Jurnal Ilmiah Multidisiplin Indonesia
Publisher : ARKA INSTITUTE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55904/nautical.v2i11.609

Abstract

Sistem pendeteksi wajah manusia merupakan salah satu teknologi yang banyak digunakan pada bidang komputer vision. Algoritma YOLOv4 merupakan algoritma yang dapat digunakan sebagai object detector. Algoritma YOLOv4 mampu mendeteksi secara realtime sebuah object benda termasuk pada wajah manusia. Algoritma YOLOv4 mempunyai beberapa struktur salah satunya adalah backbone yang akan digunakan pada penelitian ini. Backbone yang digunakan pada penelitian ini adalah CSPDarknet53. CSPDarknet53 merupakan struktur yang optimal sebagai ekstrasi fitur detector. Pada penelitian ini sistem pendeteksi wajah manusia dirancang menggunakan algoritma YOLOv4 dengan struktur backbone CSPDarknet53 yang dimana sistem ini diuji untuk mendapatkan nilai akurasi dan kecepatan respon deteksi dari jarak yang ditentukan. Hasil pengujian deteksi dan kecepatan respon mendapatkan nilai akurasi terbesar pada pengujian pendeteksian jarak 1meter dengan nilai akurasi sebesar 89.6% dan kecepatan respon sebesar 0.433 detik.
SISTEM KONTROL DAN KELISTRIKAN PADA ITEFUEL UNTUK PENGOLAHAN BIOGAS KOTORAN TERNAK MENJADI BIO-CNG Kristiana, Lisa; Miftahuddin, Yusup; Akbar, Deklan Malik; Dafy, Muhammad Zufar; Fasya, Shafira Kurnia
JCES (Journal of Character Education Society) Vol 9, No 1 (2026): Januari
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jces.v9i1.36898

Abstract

Abstrak: Pengelolaan limbah kotoran ternak di Desa Tanggulun, Kabupaten Garut, masih belum optimal dan berpotensi menimbulkan pencemaran lingkungan, bau tidak sedap, serta gangguan kesehatan masyarakat. Padahal, limbah tersebut memiliki potensi besar sebagai sumber energi terbarukan. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengembangkan dan mengimplementasikan sistem kontrol dan kelistrikan pada alat inovatif “ITEFUEL”, yaitu alat pengolah biogas dari kotoran ternak menjadi Bio-CNG (Compressed Natural Gas). Fokus utama kegiatan adalah pemurnian biogas hasil fermentasi anaerob menggunakan metode adsorpsi berbasis zeolit untuk meningkatkan kadar metana (CH₄), serta pengujian kinerja sistem kontrol dan keamanannya. Kegiatan dilaksanakan di Living Laboratorium AA Riverside, Garut. Hasil pelaksanaan menunjukkan bahwa sistem kontrol dan kelistrikan ITEFUEL berfungsi secara optimal sesuai rancangan, meliputi proses intake gas, pemurnian, penyimpanan, hingga dispensing Bio-CNG yang terkontrol dan aman. Selain menghasilkan prototipe alat ITEFUEL yang operasional, kegiatan ini juga mencakup sosialisasi dan pelatihan teknis kepada masyarakat, sehingga meningkatkan pemahaman dan kemandirian masyarakat dalam pemanfaatan energi alternatif berbasis limbah ternak.Abstract: The management of livestock manure waste in Tanggulun Village, Garut Regency, is still suboptimal and has the potential to cause environmental pollution, unpleasant odors, and public health issues. However, this waste has significant potential as a renewable energy source. This community service activity aims to develop and implement a control and electrical system for an innovative device called “ITEFUEL,” which processes biogas from livestock manure into Bio-CNG (Compressed Natural Gas). The activity focuses on purifying biogas produced through anaerobic fermentation using a simple zeolite-based adsorption method to increase methane (CH₄) concentration, as well as testing the performance, safety, and reliability of the control system. The program was conducted at the AA Riverside Living Laboratory in Garut. The results demonstrate that the ITEFUEL control and electrical system operates fully according to the design, successfully managing gas intake, purification, temporary storage, and dispensing processes in a controlled and safe manner. In addition to producing a functional ITEFUEL prototype, the activity included technical training and socialization sessions, which enhanced community knowledge and supported independent utilization of livestock waste–based renewable energy.
Real-time deep neural network-based waste detection and classification using a camera sensor Darlis, Arsyad Ramadhan; Lidyawati, Lita; Kristiana, Lisa; Hartati, Etih; Trisani, Faradilla Rizqi
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.019

Abstract

Waste generation is a growing environmental concern, with manual sorting methods often being inefficient and error-prone, particularly under varying lighting and environmental conditions. In Indonesia, waste is typically categorized into organic and nonorganic, yet existing automated classification systems lack real-time capabilities and robustness in dynamic settings. This study proposes a novel real-time waste detection and classification system using a deep neural network, implemented on the Jetson Nano platform with a camera sensor. The system utilizes the ResNet-18 convolutional neural network architecture and is developed using Python. It is designed to distinguish between organic and nonorganic waste in real-time. Training was conducted over 30 epochs, and the system was tested under various lighting conditions—morning, daytime, afternoon, and nighttime. Results show high accuracy: 95.24% in the morning, 95.24% during the day, 90.45% in the afternoon, and 86.90% at night, with an average accuracy of 91.96%. Performance was influenced by factors such as lighting intensity, distance, waste position, changes in organic waste, and occlusion by plastic. The proposed system offers a significant improvement over traditional and existing methods by enabling accurate, real-time waste classification under diverse conditions, contributing to more efficient and intelligent waste management.
Co-Authors ADITYA, HAFIDZ DAYU Adli, Muhammad Arkan AFRIZAL MAULANA MUHAMMAD Akbar, Deklan Malik Anindia, Hana Nathania ARCHANDHIKA, HEGAR REFALDY Arsyad Ramadhan Darlis ARSYAD RAMADHAN DARLIS Barmawi, Mira Musrini Dafy, Muhammad Zufar DANI RUSIRAWAN DEWI, IRMA AMELIA Dixy Abdul Rochman S DWIPUTRA, REZA FADILAH FASYA, SHAFIRA KURNIA Hartati, Etih Irma Amelia Dewi JANUAR, MUKTIADI AKHMAD LITA LIDYAWATI LUCIA JAMBOLA MANURUNG, AURALIUS Marisa Premitasari, Marisa MAULANA, KEINDRA BAGAS Miftahuddin, Yusup MIYANTO, DIAN MUHAMMAD RAFLY Narisha Kalya Mahsa Nurrahmayanti, Fadhilah RACHMA APRILIYANI RATNA SUSANA Rauf, Muhammad Rea Ramada Jayandanu Setiyawati, Setiyawati Sugiharto, Ariq Bagus Trisani, Faradilla Rizqi