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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of ICT Research and Applications Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JETT (Jurnal Elektro dan Telekomunikasi Terapan) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SMARTICS Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Indonesian Journal of Electrical Engineering and Computer Science Journal of Dinda : Data Science, Information Technology, and Data Analytics eProceedings of Engineering Jurnal INFOTEL Journal of Information Technology and its Utilization International Journal of Computing Science and Applied Mathematics-IJCSAM IJoICT (International Journal on Information and Communication Technology)
Aji Gautama Putrada
Universitas Telkom
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics

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Ant Colony Optimization for Low-Rank Factorization with DNN on People Counting IoT using Environmental Sensors Ganendra Zefanya Patty Patty; Muhammad Faris Fathoni, S.T., M.T., Ph.D. Fathoni; Aji Gautama Putrada Putrada
IJoICT (International Journal on Information and Communication Technology) Vol. 11 No. 1 (2025): Vol. 11 No. 1 Jun 2025
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v11i1.9102

Abstract

People counting Internet of Things (IoT), which plays a role in counting people indoors based on sensor values, is a vital part of smart buildings because it affects other IoT systems that regulate devices like lighting and air conditioning (AC), impacting efficiency. However, a lightweight solution is needed to perform people counting without threatening personal privacy. This study aims to develop an edge computing-based people counting system using environmental sensors and a Deep Neural Network (DNN) model optimized using the LRF technique. The system is designed to operate in real-time on edge devices with low latency and efficient resource consumption. In general, the system's work process is divided into three main stages, namely (1) data acquisition and pre-processing, (2) model development and optimization, and (3) overall system performance evaluation. The system runs automatically on edge devices and follows a cyclic workflow to detect the number of people continuously. This study also uses ant colony optimization (ACO) for hyperparameter tuning and obtains optimum hyperparameters. Experimental results support the claim that LRF significantly reduces model size while maintaining high prediction accuracy. ACO on hyperparameter tuning obtains the optimum hyperparameters: the number of neurons as many as 128 units, Adam learning rate of 0.005, and batch size of 8. Then DNN + ACO is proven to perform better than DNN without ACO and the state-of-the-art random forest model with accuracy, precision, recall, and F1-score of 0.98, 0.99, 0.94, and 0.97. This is while overcoming the imbalance problem in the dataset with recall for counts 0, 1, 2, and 3, of 1.00, 1.00, 1.00, and 0.78, respectively. Finally, we found that the optimum rank on LRF to reduce the number of parameters in DNN is 32, where at that rank the model size is reduced from 28.6 KB to 26.6 KB without significant accuracy loss.
Quantization-Aware Training for Man-in-the-Middle Attacks Detection in IoT Application Dyah Ayu Suci Ilhami; Aji Gautama Putrada; Farah Afianti
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 1 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v12i1.8786

Abstract

Issues in securing the resilience of Deep Learning models and operational problems related to model size and latency in the Internet of Things (IoT) environment, especially when applied to devices with limited resources, can be overcome by implementing a modified Quantization-Aware Training (QAT) model based on IDS and an autoencoder. This research proposes the use of QAT to achieve operational efficiency while strengthening the detection model’s resilience against data manipulation in MiTM attacks. By integrating QAT, the model not only becomes lighter for edge devices but also more capable of maintaining detection integrity even when model weights are compromised. The research was conducted using the CICIoT2023 dataset, and the output of the advanced QAT model was then applied to resource-constrained IoT devices, namely the Raspberry Pi 3. The methodology began with data collection, followed by data preprocessing, which was then normalized before being fed into the IDS and autoencoder techniques. After the autoencoder model was successfully created, the QAT model was developed so that it remained unchanged or even improved when implemented on the Raspberry Pi 3. With the application of QAT modifications, an 80.43% reduction in model size and an 11.2% increase in model inference speed were confirmed. Furthermore, when faced with QAT model weight corruption of up to 20%, the F1-score value remained stable at 100%. These results show that the QAT model is highly effective at improving model reliability and maintaining the resilience of deep learning models against MiTM attacks, even under limited conditions.
A Hybrid Genetic Algorithm-Random Forest Regression Method for Optimum Driver Selection in Online Food Delivery Aji Gautama Putrada; Nur Alamsyah; Ikke Dian Oktaviani; Mohamad Nurkamal Fauzan
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.v9i4.27014

Abstract

The online food delivery trend has become rapid due to the COVID-19 incident, which limited mobility, while the broader challenge in the online food delivery system is maximizing quality of service (QoS). However, studies show that driver selection and delivery time are important in customer satisfaction. The solution is our research aim, which is the selection of optimal drivers for online food delivery using random forest regression and the genetic algorithm (GA) method. Our research contribution is a novel approach to minimizing delivery time in online food delivery by combining a random forest regression model and genetic algorithms. We compare random forest regression with three other state-of-the-art regression models: linear regression, k-nearest neighbor (KNN), and adaptive boosting (AdaBoost) regression. We compare the four models with metrics including , mean squared error (MSE), root mean squared error (RMSE), mean total error (MAE), and mean absolute percentage error (MAPE). We use the optimum model as the fitness function in GA. The test results show that random forest performs better than linear, KNN, and AdaBoost regression, with an , RMSE, and MAE value of 0.98, 54.3, and 11, respectively. We leverage the optimum random forest regression model as the GA fitness function. The best efficiency is reducing the delivery time from 54 to 15 minutes, achieved through rigorous testing on various cases. In addition, by completing this research, we also achieve some practical implications, such as an increase in customer satisfaction, a reduction in cost, and a paramount finding in the field of data-driven decision-making. The first key finding is an optimum driver selection model in random forest regression, while the second is an optimum driver selection model in GA.
Analysis of the Effect of QUIC and TCP on Quality of Experience (QoE) during Handover Muhammad Adlan Hafizha; Aji Gautama Putrada; Ryan Lingga Wicaksono
IJoICT (International Journal on Information and Communication Technology) Vol. 12 No. 1 (2026): Vol.12 No.1 Jun 2026
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v12i1.10893

Abstract

Video streaming on mobile devices experiences interruptions when the wireless connection switches to a new network. This study aims to evaluate Quick UDP Internet Connections (QUIC) protocol’s performance as an alternative to Transmission Control Protocol (TCP) that oftentimes encounters performance setbacks during handovers. The simulation was performed using Mininet-WiFi emulator with a topology of two Access Points with various bandwidth capacities (10 Mbps to 1000 Mbps) and one mobile station (client). The simulations were repeated 30 times to ensure statistical validity for packet loss, latency, throughput, and buffering time metrics. Simulation results show that QUIC outperforms TCP in network metrics performance. QUIC’s Connection ID and 0-RTT initiation features reduce packet loss by up to 6.39%, maintain latency below 0.14 ms, and instantly restore throughput. However, an anomaly was found at high bandwidth (>500 Mbps) where buffering time of QUIC was longer due to the computational load of TLS 1.3 decryption in user space. Although QUIC is strong in wireless mobility, the CPU processing of the receiver’s device must be optimized on high-speed network.
MDI and PI XGBoost regression-based methods: regional best pricing prediction for logistics services Agus Purnomo; Aji Gautama Putrada; Roni Habibi; Syafrianita Syafrianita
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 5: October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i5.26037

Abstract

The logistics industry in Indonesia, with PT Pos Indonesia as the dominant player, is confronted with intense price competition. The challenge lies in establishing the most favorable price for regional logistics services in every region, with the aim of gaining a competitive edge and augmenting revenue. This intricate task encompasses local market conditions, competition, customer preferences, operational costs, and economic factors. To address this complexity, this study proposes the utilization of machine learning for price prediction. The price prediction model devised incorporates the extreme gradient boosting regression (XGBR), support vector machine (SVM), random forest, and logistics regression algorithms. This research contributes to the field by employing mean decrease in impurity (MDI) and permutation importance (PI) to elucidate how machine learning models facilitate optimal price predictions. The findings of this study can assist company management in enhancing their comprehension of how to make informed pricing decisions. The test results demonstrate values of 0.001, 0.005, 0.458, 0.009, and 0.9998. By employing machine learning techniques and explanatory models, PT Pos Indonesia can more accurately determine optimal prices in each region, bolster profits, and effectively compete in the expanding regional market.
Investigating Shallow Learning Methods for Optical Character Recognition of Indonesia’s Nusantara Scripts Mahmud Dwi Sulistiyo; Aji Gautama Putrada; Aditya Firman Ihsan; Prasti Eko Yunanto; Donny Richasdy; Hassan Rizky Putra Sailellah; Sabrina Adinda Sari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6648

Abstract

Indonesia has numerous regional scripts—or so-called Nusantara scripts—and recognizing them is important to preserve Indonesia's cultural heritage. The advances of AI and computer vision technologies make it possible for a machine to optically read the handwritten scripts through the Optical Character Recognition (OCR) technique. However, collecting some of the top OCR solutions and comprehensively investigating their performances on the Nusantara scripts is currently lacking. This study investigates and evaluates some shallow learning-based methods on our newly introduced datasets, consisting of more than 38,000-character images across 80 letter classes in total; here, we focus on three regional scripts: Javanese, Sundanese, and Balinese. The methods include Random Forest, SVM, Logistic Regression, and Gaussian Naïve Bayes, as well as boosting techniques such as XGBoost, Light GBM, and CatBoost. A 5-fold cross-validation approach assessed model performance based on accuracy, precision, recall, and F1-score. Based on the experimental results, the methods demonstrated their competitiveness in reaching the best models for scripts; in particular, XGBoost, Light GBM, and Random Forest-Gini were the winners for Javanese, Sundanese, and Balinese scripts, respectively. These findings demonstrate the effectiveness of ensemble learning methods for diverse handwritten scripts. Comparative analysis to prior deep learning studies is also discussed in this paper. In addition, this research also contributes to preserving Indonesian traditional scripts, as well as offers insights for future regional OCR in other countries.
Co-Authors Abdillah, Hilal Nabil Abiyan Bagus Baskoro Aditya Firman Ihsan Adrian Gusti Nurcahyo Agita Rachmad Muzakhir Agus Purnomo Algi Fajardi Alieja Muhammad Putrada Andrian Rakhmatsyah Angga Anjaini Sundawa Anita Auliani Argo Surya Adi Dewantoro Aziz Nurul Iman Baginda Achmad Fadillah Bambang Setia Nugroho Bayu Kusuma Belva Rabbani Driantama Bramantio Agung Prabowo Calvin M.T Manurung Catur Wirawan W Catur Wirawan Wijiutomo Daniel Arga Amallo Dawani, Febri Dicky Prasetiyo Dita Oktaria Doan Perdana Dodi W. Sudiharto Dodi Wisaksono Sudiharto Dody Qori Utama Donny Richasdy Dyah Ayu Suci Ilhami Endro Ariyanto Erwid Musthofa Jadied Fachrial Akbar Fadhlillah Fadhlillah Fadhlurahman Irwan Fairus Zuhair Azizy Atoir Fajar Maulana Kadir Fakhri Akbar Pratama Farah Afianti Farisah Adilia Fauzan Ramadhan Fauzan, Mohamad Nurkamal Fazmah Arif Yulianto Febrina Puspita Utari Fitra Ilham Gabe Dimas Wicaksana Ganendra Zefanya Patty Patty Gentur Cipto Tri Atmaja Hamman Aryo Bimmo Hanifa Zahra Dhiah Hilal Hudan Nuha Hirianinda Malsegianty S Ikbar Mahesa Ikke Dian Oktaviani Ikrimah Muiz Ilham Fadli Surbakti Imas Nur Tiarani Irfan Dwi Wijaya Irfan Nugraha Januar Triandy Nur Elsan Krisna Kristiandi Hartono Kurnia Wisuda Aji Mahmud Dwi Sulistiyo Mahmud Imroba Maman Abdurohman Maman Abdurrahman Mar Ayu Fotina Mas'ud Adhi Saputra Maya Ameliasari Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Muhamad Irsan Muhamad Nurkamal Fauzan Muhammad Adlan Hafizha Muhammad Al Makky Muhammad Alkahfi Khuzaimy Abdullah Muhammad Dafa Prima Aji Muhammad Fahmi Nur Fajri Muhammad Faris Fathoni Muhammad Ihsan Muhammad Kukuh Alif Lyano Muhammad Shibgah Aulia Muhhamad Affan Hasby Muhhamad Affan Hasby Muhtadu Syukur A Mulia Hanif Nando, Parlin Nando, Parlin Niken Cahyani Novian Anggis Suwastika Nuha, Hilal H Nur Alamsyah Nur Alamsyah NUR ALAMSYAH Nur Alamsyah, Nur Nur Ghaniaviyanto Ramadhan Nurkamal Fauzan, Mohamad Pahlevi , Rizka Reza Pamungkas, Rizaldi Ramdlani Parman Sukarno Prasti Eko Yunanto Putrada, Alieja Muhammad Putri Azanny Raden Muhamad Yuda Pradana Kusumah Rafie Afif Andika Rahmat Suryoputro Rahmat Yasirandi Randy Agustyo Raharjo Reynaldo Lino Haposan Pakpahan Rizki Jamilah Guci Roni Habibi Ryan Lingga Wicaksono Sabrina Adinda Sari Sailellah, Hassan Rizky Putra Seli Suhesti Sena Amarta Sidik Prabowo Siti Amatullah Karimah Subkhan Ibnu Aji Sulthan Kharisma Akmal Syafrial Fachri Pane Syafrianita Syafrianita Syafwan Almadani Azra Syiarul Amrullah, Muhammad Taufik Suyanto Vera Suryani Wanda Firdaus Yahya Ermaya Yuda Prasetia Zidni Fahmi Suryandaru