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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) ILKOM Jurnal Ilmiah Jurnal Teknik Informatika (JUTIF) Emerging Information Science and Technology Internet of Things and Artificial Intelligence Journal
Adi, Puput Dani Prasetyo
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Design Human Object Detection Yolov4-Tiny Algorithm on ARM Cortex-A72 and A53 Muwardi, Rachmat; Faizin, Ahmad; Adi, Puput Dani Prasetyo; Rahmatullah, Rizky; Wang, Yanxi; Yunita, Mirna; Mahabror, Dendi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27402

Abstract

Currently, many object detection systems still use devices with large sizes, such as using PCs, as supporting devices, for object detection. This makes these devices challenging to use as a security system in public facilities based on human object detection. In contrast, many Mini PCs currently use ARM processors with high specifications. In this research, to detect human objects will use the Mini PC Nanopi M4V2 device that has a speed in processing with the support of CPU Dual-Core Cortex-A72 (up to 2.0 GHz) + Cortex A53 (Up to 2.0 GHz) and 4 Gb DDR4 Ram. In addition, for the human object detection system, the author uses the You Only Look Once (YOLO) method with the YoloV4-Tiny type, With these specifications and methods, the detection rate and FPS score are seen which are the feasibility values for use in detecting human objects. The simulation for human object recognition was carried out using recorded video, simulation obtained a detection rate of 0,9845 or 98% with FPS score of 3.81-5.55.  These results are the best when compared with the YOLOV4 and YOLOV5 models. With these results, it can be applied in various human detection applications and of course robustness testing is needed.
Analysis of IoT-LoRa to Improve LoRa Performance for Vaname Shrimp Farming Monitoring System Adi, Puput Dani Prasetyo; Ardi, Idil; Plamonia, Nicco; Wahyu, Yuyu; Mariana L, Angela; Novita, Hessy; Mahabror, Dendy; Zulkarnain, Riza; Wirawan, Adi; Prastiyono, Yudi; Waryanto, Waryanto; Susilo, Suhardi Atmoko Budi; Rahmatullah, Rizky; Kitagawa, Akio
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.27598

Abstract

Shrimp farming requires a touch that must be right on the side of water quality; water is a fundamental factor that must be met to achieve maximum yields. Many factors affect the quality of the water, but some things cause changes in water quality caused by external and internal factors causing death in shrimp. Disease conditions in shrimp can attack at any time, coupled with external factors such as extreme climate change, and cause changes in water components such as water pH, CaMg or hardness, and other factors that cause death in shrimp. Water turbidity oxygen demand (DO) in water determines the life of shrimp. It is coupled with microorganisms that must be maintained to maintain water quality for the growth of a Vaname shrimp. This research raises the Aquaculture System, specifically in the process of intelligent monitoring of water quality in shrimp nurseries to the shrimp harvest process, especially vaname shrimp from the results of observations use three sensors connected to LoRaWAN is able to provide real-time data from pond water and transmit it to LoRa Server or Internet Server, and the realtime data can be read through a Smartphone. This research analyzes in detail the ability of LoRaWAN to send multi-sensor data and Quality of Service LoRaWAN communication at different distances. This research also discusses how the LoRa antenna design can be developed to improve the performance of LoRa as transmitting devices or Radio Frequency 920-923 MHz for sending sensor data for Aquaculture.The contribution of this research is shown in the real-time monitoring system of the water environment, namely water pH, ammonia, turbidity, DO, salinity, water temperature, and nitrate in vaname shrimp ponds. The following contribution is the development of LoRaWAN with Tago IO servers capable of being used in Smart Aquaculture for contributions to The Things Network community or LoRaWAN Community.
Additive Links on Multiple Access (ALOHA) Method to LoRaWAN Satellite-based communication Adi, Puput Dani Prasetyo; Stekelorom, Kevin; Vasista, Tatapudi Gopikrishna
Emerging Information Science and Technology Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v6i2.26406

Abstract

LoRaWAN communication systems continue to improve with various advantages that continue to improve, for example, terrestrial LoRaWAN which increases satellite communication, and improves performance in terms of wider range, up to> 100 km. However several problems arise, including multiple end-node connection conditions. Not only point-to-point but already multi-point which causes several obstacles including data collisions, thus requiring several methods such as ADR (Adaptive Data Rate) or ALOHA (Additive Links on Multiple Access). The role of ALOHA is to be able to build an inter-node communication system that can prevent data collisions. In this case, ALOHA is used to improve the performance of IoT-LoRaWAN on satellite infrastructure. Some of the simulation components of this research lie in the data transmission protocol mechanism and collision management strategy, as well as spectrum efficiency in essential satellite-based LoRaWAN networks. The core of the research is how to reduce signal interference and optimize power consumption. Hopefully, ALOHA can be used as an effective method to build LoRaWAN Satellite-based IoT in the future. The future applications are for tracking, environmental monitoring, and disaster warning systems.
Early Fusion of CNN Features for Multimodal Biometric Authentication from ECG and Fingerprint Using MLP, LSTM, GCN, and GAT Priyatama, Muhammad Abdhi; Nugrahadi, Dodon Turianto; Budiman, Irwan; Farmadi, Andi; Faisal, Mohammad Reza; Purnama, Bedy; Adi, Puput Dani Prasetyo; Ngo, Luu Duc
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5299

Abstract

Traditional authentication methods such as PINs and passwords remain vulnerable to theft and hacking, demanding more secure alternatives. Biometric approaches address these weaknesses, yet unimodal systems like fingerprints or facial recognition are still prone to spoofing and environmental disturbances. This study aims to enhance biometric reliability through a multimodal framework integrating electrocardiogram (ECG) signals and fingerprint images. Fingerprint features were extracted using three deep convolutional networks—VGG16, ResNet50, and DenseNet121—while ECG signals were segmented around the first R-peak to produce feature vectors of varying dimensions. Both modalities were fused at the feature level using early fusion and classified with four deep learning algorithms: Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Graph Convolutional Network (GCN), and Graph Attention Network (GAT). Experimental results demonstrated that the combination of VGG16 + LSTM and ResNet50 + LSTM achieved the highest identification accuracy of 98.75 %, while DenseNet121 + MLP yielded comparable performance. MLP and LSTM consistently outperformed GCN and GAT, confirming the suitability of sequential and feed-forward models for fused feature embeddings. By employing R-peak-based ECG segmentation and CNN-driven fingerprint features, the proposed system significantly improves classification stability and robustness. This multimodal biometric design strengthens protection against spoofing and impersonation, providing a scalable and secure authentication solution for high-security applications such as digital payments, healthcare, and IoT devices.
An Exploration of the Work Performance of Educators in Transformative Schools: Leveraging Machine Learning for Performance Insights Maulidi, Rakhmad; Palandi, Jozua Ferjanus; Kristanto, Bagus Kristomoyo; Isyriyah, Laila; Rahmatullah, Rizky; Adi, Puput Dani Prasetyo; Kitagawa, Akio
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.2358.109-125

Abstract

Education has gone through various phases, and entered the transformative school mode which can be said to change the existing order of the previous schooling system or procedures, because many modes can be done in the transformative school, students can learn in school buildings or classes, or in the field or real industry or the real world of work, with the introduction of a wider and more complex world, this is one of them. This research tries to create and analyze transformative schools in 3 algorithms, namely regression algorithms, classification algorithms, and clustering algorithms that provide a detailed analysis of the results of the analysis of transformative schools currently promoted by the government. from the results of the analysis raises performance conclusions, and in this phase a conclusion can be drawn whether the Transformative school is able to provide answers about the performance of teachers, students, teacher education levels, school locations, number of students, learning methods, or any paramaters that can provide detailed and detailed answers to get performance analysis from Machine Learning, and Work Performance of teachers in Transformative schools with precision. Quantitatively, the value of performance is determined by innovation by 43.2%, followed by technological capabilities and collaboration, 27.9% and 17.2% respectively. and based on cluster level, cluster 3 is the best with 118 educators, cluster 0, 127 educators with high innovators, and cluster 2, 126 educators, and cluster 1 with 129 educators. and from the paradox of transformative practices 30.6% are high Adopters
Co-Authors Adi Wirawan Adiprabowo, Tjahjo Agusti, Nelly Ahmad Faizin, Ahmad Ajib Hanani Andi Farmadi Ardi, Idil Arief Budi Santiko, Arief Budi Atmoko, Suhardi Bagus Kristomoyo Kristanto Bedy Purnama Dodon Turianto Nugrahadi Ganis Chandra Puspitadewi Harry Soepandi Hsb, Elysa Rohayani Irawan, Bagus Bhakti Irmayani, Deci Irwan Budiman Johan Ericka Wahyu Prakasa Jozua Ferjanus Palandi Kitagawa, Akio KUNCORO, EKO Laila Isyriyah Lesmana, Yandri Lukman Hakim Mahabror, Dendi Mahabror, Dendy Mariana L, Angela Mirna Yunita Muhammad Reza Faisal, Muhammad Reza Muhammad Yassir Ngo, Luu Duc Novilda, Mustamu Elizabeth Novita, Hessy Nurdiana, Dian Pane, Rahmadani Plamonia, Nicco Prastiyono, Yudi Priyatama, Muhammad Abdhi Rachmat Muwardi Rahmatullah, Rizky Rakhmad Maulidi Rufiyanto, Arief Saharuna, Saharuna Sastya Hendri Wibowo Satyawan, Arief Suryadi Septiawan, Reza Shabajee, Ravindra Kumar Sihombing, Volvo siregar, Victor M.M. Stekelorom, Kevin Sulistya, Budi Susilo, Suhardi Atmoko Budi Trie Maya Kadarina Vasista, Tatapudi Gopikrishna Wang, Yanxi Waryanto, Waryanto Yulianto, I Putu Eka Yuyu Wahyu Zulkarnain, Riza