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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Buletin Udayana Mengabdi Jurnal SPEKTRUM Jurnal Pendidikan Teknologi dan Kejuruan Jurnal Sains dan Teknologi PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Syntax Literate: Jurnal Ilmiah Indonesia SINTECH (Science and Information Technology) Journal Jurnal RESISTOR (Rekayasa Sistem Komputer) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Teknologi Informasi dan Pendidikan International Journal of Engineering and Emerging Technology Jurnal Ekonomi Manajemen Sistem Informasi Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Industri Terintegrasi (JUTIN) Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Teknik Informatika (JUTIF) Jurnal Riset Rumpun Ilmu Teknik (JURRITEK) Jurnal Teknik Elektro Journal of Electrical, Electronics and Informatics
Dewa Made Wiharta
Electrical Engineering Universitas Udayana
Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Other

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Potensi Metode Regresi Kuat dalam Pengukuran Skew Jam Setiawan, Putu Ayu Citra; Saputra, Komang Oka; Wiharta, Dewa Made
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 1 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i1.962

Abstract

Clock skew, defined as the difference in clock rates between digital devices, serves as a unique and stable fingerprint for device identification and authentication, particularly in distributed network environments. Traditional clock skew estimation techniques, such as linear regression, are effective under stable conditions but often fail in the presence of data disturbances, such as latency, jitter, and asymmetric delays, which introduce outliers. This study explores the application of robust regression methods to enhance the accuracy and stability of clock skew estimation under such conditions. Three robust techniques are comparatively analyzed: Least Median of Squares (LMedS), Random Sample Consensus (RANSAC), and S-Estimators. LMedS offers high resistance to outliers by minimizing the median of squared residuals, though it is computationally demanding for large datasets. RANSAC achieves a practical balance between robustness and efficiency through iterative model fitting and inlier maximization, while S-Estimators provide strong statistical resistance to both outliers and high-leverage points, albeit with increased implementation complexity. The comparative evaluation considers key parameters such as estimation accuracy, computational cost, and robustness to anomalies. Results indicate that RANSAC is generally preferred for clock skew measurement in distributed systems due to its efficient performance and explicit outlier detection capabilities. However, LMedS and S-Estimators remain valuable in scenarios with more complex anomaly structures or higher noise levels. This study contributes to the selection of appropriate robust regression methods for reliable clock skew estimation in dynamic and error-prone network environments.
Optimizing traffic lights at unbalanced intersections using deep reinforcement learning Khrisne, Duman Care; Sudarma, Made; Giriantari, Ida Ayu Dwi; Wiharta, Dewa Made
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2991-3002

Abstract

Unbalanced intersectional traffic flow increases vehicle delays, fuel consumption, and pollution. This study investigates the application of deep reinforcement learning (DRL) to optimize traffic signal timing at the Pamelisan intersection in Denpasar, Indonesia. Real-world traffic data were incorporated into a SUMO microsimulation environment to train DRL agents using the deep Q-network (DQN) algorithm. Experimental results show that DRL-based optimization reduced the average vehicle waiting time from 594.49 seconds (static control) to 169.44 seconds and 173.10 seconds for agents trained without and with noise, respectively. The average vehicle speed remained stable at 5.6–5.97 m/s across all scenarios, indicating enhanced traffic efficiency without adverse effects. The findings underscore the effectiveness and adaptability of DRL in addressing traffic inefficiencies, optimizing them, and offering a robust solution for dynamic traffic management at unbalanced traffic intersections in urban areas.
Multi-Document Summarization Using Tuna Swarm Optimization and Markov Clustering Widiartha, I Made; Hartati, Rukmi Sari; Wiharta, Dewa Made; Sastra, Nyoman Putra; Astuti, Luh Gede
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3365

Abstract

The Internet contains a large number of documents from various sources with similar content. The contents of documents that are almost identical will lead to news redundancy, making it difficult for readers to distinguish between factual information and opinions. Multi-document summarization has been designed to enable readers to easily understand the meaning of news documents without needing to read multiple documents. Multi-document summarization aims to extract information from several texts written about the same topic. The resulting summary report enables users to obtain a single piece of information from multiple similar pieces of information sourced from various locations. Various approaches have been used in creating multi-document summaries. Issues regarding accuracy and redundancy are still a significant focus of research. In this paper, a new multi-document summarization model was built using Tuna Swarm Optimization (TSO) and Markov Clustering (MCL) methods. The dataset of this research is Indonesian language news from various online media sources. Based on hyperparameter tuning using training data, the best TSO model performance was obtained at variable values a = 0.7, z = 0.9, and the optimal number of tuna fish > 80. From the research results, it was found that TSO outperformed other swarm intelligence methods. The use of MCL has proven to be effective, as evidenced by the performance results, where TSO achieved an average ROUGE value 7.95% higher when MCL was applied. In this performance test, four standard evaluation metrics of the ROUGE toolkit were used.
Real-Time Web-Based Ship Collision Risk Detection Using AIS Data and Collision Risk Index (CRI) Asana, I Made Dwi Putra; Widyantara, I Made Oka; Linawati, Linawati; Wiharta, Dewa Made; Wikananda, I Gusti Ngurah Satya
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15106

Abstract

The high density of maritime traffic in Indonesian waters, particularly in the Lombok Strait and Nusa Penida region, increases the risk of ship collisions, especially among vessels lacking adequate navigation systems. This study presents the development of a web-based system for real-time ship monitoring and collision risk assessment using Automatic Identification System (AIS) data. The system integrates a backend powered by FastAPI and MongoDB with a frontend built using React JS. AIS data is collected from a base station and processed to detect ship encounters using the DBSCAN clustering algorithm combined with Haversine distance to identify encounter detection. The risk assessment applies the Collision Risk Index (CRI) method by calculating DCPA (Distance to Closest Point of Approach) and TCPA (Time to Closest Point of Approach), allowing for graded risk categorization. Real-time risk notifications are delivered via WebSocket, and the interface includes interactive maps, ship detail views, and maritime weather information from the BMKG API. The system achieved high responsiveness, with an average detection time of 0.0075 seconds per ship and an end-to-end response time of approximately 61 milliseconds. Functional and usability tests show that the system effectively supports early detection of collision risks and improves maritime situational awareness. The proposed solution is scalable and applicable for maritime safety monitoring in busy sea routes, contributing to safer navigation and proactive decision-making.
GPS-Based Rocket Payload Position Tracking System Wiharta, Dewa Made; Sastra, Nyoman Putra; Putra, A.A.B. Rama Windhu
JST (Jurnal Sains dan Teknologi) Vol. 12 No. 1 (2023): April
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v12i1.55069

Abstract

The Ministry of Research, Technology and Higher Education Indonesia routinely hosts a competition of payload tracking system placed on a rocket with a Ground Control Station (GCS), a competition known as Kompetisi Muatan Roket dan Roket Indonesia. Fixed GCS antenna causes some problems, including poor communication between payload and GCS, and position detection of the payload. This research was conducted to create a tracking system capable of moving the GCS antenna towards the payload position. In this research, we use two servos to move the antenna. This payload position tracking system works by calculating the azimuth angle of GCS coordinate and of the payload, then converting the azimuth value into the servo angle value. The calculation performs by Arduino Mega 2560 which then commands both the horizontal and vertical  servos to direct the antenna towards the payload position. The experiments are performed with three main tests that are of tracking payload position based on GPS data, of antenna direction movement with servo horizontal motion direction, and of antenna direction movement with servo vertical motion direction. Testing are carried out by laying the payload on a drone and adjust the position and height of the drone manually. Experimental results show that the largest angular difference between the tracking system and the payload is 8 degrees azimuth. The mean angle difference is 4.7 degrees. This angle deviation occurs because the servo angle instruction can only be with an integer value.
Rancang Bangun Sistem Informasi Berbasis Web untuk Mengevaluasi Indeks Keandalan Loss of Load Probability (LOLP) Pembangkit Listrik Naradhiya, Gede; Jasa, Lie; Wiharta, Dewa Made
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 8 No. 4 (2025): October
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v8i4.50619

Abstract

Electrical energy is a vital need that continues to increase every year, thus the reliability of its supply needs to be monitored so that electricity distribution is in accordance with the established standards. The calculation of the reliability of power plants in supplying electricity is called the reliability index. This study aims to evaluate the reliability index of power plants using a web-based information system designed and built by the author. The evaluation of the reliability index was carried out using the LOLP (Loss of Load Probability) method based on the PLN 2016-2025 RUPTL standard. The accuracy of the information system performance was assessed using the MAPE (Mean Absolute Percentage Error) method by comparing the results of the information system calculations to three reference studies. The calculation results showed a MAPE value of the information system of 2.4233%, which is classified as "Excellent" in the MAPE results classification. 
Implementasi Aplikasi Mobile Augmented Reality Untuk Pengenalan Materi Bangun Ruang Widyantara, I Made Oka; Wiharta, Dewa Made; Widiadnyana, Putu
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 2: April 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022925032

Abstract

Proses interaksi pembelajaran yang awalnya tatap muka sekarang beralih ke daring karena adanya pandemi COVID-19. Selama proses pembelajaran daring, siswa mengalami kesulitan dalam memahami konsep dan mengembangkan konsep sampai refleksi. Penelitian ini memiliki tujuan yaitu mengembangkan dan menerapkan media pembelajaran yang dapat menarik minat siswa dan membantu pemahaman materi bangun ruang. Pengembangan media pembelajaran menggunakan teknologi augmented reality sebagai aplikasi mobile untuk memvisualisasikan materi bangun ruang dalam bentuk 3D yang diproyeksikan pada smartphone. Model waterfall dipilih sebagai acuan dalam pengembangan aplikasi. Penerapan aplikasi augmented reality dalam pembelajaran daring menggunakan strategi REACT untuk memaksimalkan penggunaan aplikasi. Hasil penelitian ini yaitu aplikasi berbasis Android dengan menggunakan model waterfall dengan hasil valid melalui uji black box, hasil penilaian kuesioner untuk penggunaan aplikasi mendapatkan rata-rata 82.44% dengan indikator kategori ”Baik”. AbstractThe learning interaction process that was originally face-to-face is now turning online due to the COVID-19 pandemic. During the online learning process, students have difficulty in understanding concepts and developing concepts until reflection. This research aims to develop and implement learning media that can attract students and help them understand the material of solid figures. The development of learning media uses augmented reality technology as a mobile application to visualize solid figure materials in 3D projected on smartphones. The waterfall model was chosen as a reference in application development. The adoption of augmented reality applications in online learning uses REACT strategies to maximize application usage. The results of this study are Android-based applications using waterfall models with valid results through black-box tests, questionnaire assessment results for application usage get an average of 82.44% with the category indicator "Good" 
Optimizing CDN Modeling with API Integration Using Time To- Live (TTL) Caching Technique Hendri, Hendri; Rukmi Sari Hartati; Linawati Linawati; Dewa Made Wiharta
Jurnal Ekonomi Manajemen Sistem Informasi Vol. 6 No. 2 (2024): Jurnal Ekonomi Manajemen Sistem Informasi (November - Desember 2024)
Publisher : Dinasti Review

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jemsi.v6i2.3236

Abstract

This research examines the implementation of Time-To-Live (TTL) caching within a Content Delivery Network (CDN) model that incorporates API integration, structured to simulate a hierarchical configuration of CDN edge servers across Indonesia's administrative tiers. The analysis centers on the influence of TTL configurations on critical performance metrics—namely latency, cache hit ratio, throughput, and bandwidth consumption. Special focus is placed on scenarios in which a 1 MB data object originating from the Central Government (Level 1) is primarily accessed through edge servers positioned at the village level (Level 5). The simulation envisions a CDN architecture where in the Central Government functions as the Main Server/Origin Server, with edge servers extending across 38 provinces (Level 2), 514 regencies (Level 3), 7,277 districts (Level 4), and 83,763 villages (Level 5).
Ship Trajectory Prediction Based on Spatial-temporal Data Using Long Short-Term Memory Setiawan, Widyadi; Linawati, Linawati; Widyantara, I Made Oka; Wiharta, Dewa Made; Asri, Sri Andriati; Pawana, I Wayan Adi Juliawan
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3353

Abstract

The frequent exploitation of shipping lines by passengers increased traffic and exposed it to more significant dangers. Precise predictions for ship trajectory conditions at sea must be available to ensure safe navigation across the oceans. This article presents a trajectory prediction approach based on Long Short-Term Memory (LSTM) neural networks applied to time series Automatic Identification System (AIS) position data, expressed in spatial-temporal form. LSTM is highly suitable for ship trajectory predictions as it can capture long-term dependencies and spatial-temporal patterns existing in AIS data, since LSTM is targeted toward sequential data. The proposed model extracts ship trajectories from AIS data and utilizes an LSTM (Long Short-Term Memory) model to predict future ship movements based on historical patterns. The experiments demonstrate that it is effective in predicting where ships to navigate next, providing a valuable tool for enhancing traffic flow and improving navigation safety. The model with LSTM unit 500, tested on 3,478 ship trajectories, showed a median RMSE prediction error ranging from 0.0720 to 0.0841, with prediction M=8 coordinate a head having the highest error (0.0841) and M=2 and M=9 having the lowest (0.0720); the interquartile range (IQR) spanned from 0.0571 to 0.1006, and M=2 had the most outliers (302) while M=8 had the fewest (171), indicating varying prediction stability across different points. Despite these results, challenges remain in maintaining prediction stability across all points. Further optimization could enhance the model's performance and address these limitations by incorporating more complex spatial-temporal features or hybrid techniques.
Adaptation in IoT-Fog Data Transmission: SLR and Future Perspectives on Dynamic Frequency Control Firmansyah Maulana Sugiartana Nursuwars; Linawati; Nyoman Putra Sastra; Wiharta, Dewa Made; Aceng Sambas
Jurnal Teknik Elektro Vol. 17 No. 1 (2025)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v17i1.30564

Abstract

The advancement of Internet of Things (IoT) and fog computing technologies has created significant opportunities for more efficient, faster, and proximity-based data management. However, IoT-Fog systems face considerable challenges related to device heterogeneity, traffic dynamics, and the complexity of network topologies that continuously change. This study conducts a Systematic Literature Review (SLR) of various research works covering dynamic scheduling, routing, context-aware data flow, offloading, and IoT-Fog systems without adaptive mechanisms. The findings indicate that most existing approaches still rely on relatively static topology assumptions, rendering them insufficiently adaptive to real-time changes in network conditions. One area identified as a research gap is dynamic frequency control, an adaptive mechanism capable of dynamically adjusting data transmission intensity based on network conditions. The main conclusion of this study emphasizes the necessity for developing adaptive systems that are topology-agnostic and supported by dynamic frequency control to maintain optimal performance even under significant topology changes. Such systems are anticipated to become a crucial foundation for future IoT-Fog applications, including smart cities, Industry 4.0, and intelligent healthcare services, which demand high reliability and low latency.
Co-Authors Aceng Sambas Adinda Hermawan, Salsabila Agus Permana Putra Agus Riki Gunawan Agus Supranartha Anak Agung Bagus Rama Windhu Putra Anak Agung Kompiang Oka Sudana Anggreni, Ni Komang Ayu Sri Anjeli Sitanggang, Feybe Anugrah Br. Ginting, Putri Arda Narendra, I Gusti Lanang Ari Wijaya I Kadek Asana, I Made Dwi Putra Bayu Bimantara Putra Bhaskara, I Made Adi Binti Dona, Sufiana Christanto Nadeak, Yobel Dd Hassel Putra Q Diafari Djuni H, I G A K Diafari, G A K dian krisnandari Doni Helmahera Duman Care Khrisne Eginna Gresia Br Purba Estry Nurya Savitri firmansyah maualana sugiartana nursuwars Frederik Nixon Gamantyo Hendrantoro Gede Sukadarmika Hendri Hendri I Gede Primanata I Gede Sudiantara I Gede Wira Darma I Gusti Ayu Garnita Darma Putri I Gusti Ayu Garnita Darmaputri I Gusti Ngurah Agung Jaya Sasmita I Kadek Dwi Gandika Supartha I Komang Leo Puja Artana I Made Adi Bhaskara I Made Agung Pranata I Made Arsa Suyadnya I Made Artana I Made Kris Widiantara I Made Oka Widyantara I Made Sastra Dwikiarta I Made Suartika I Made Widiartha I Nyoman Putra Maharddhika I Putu Ardana I Wayan Adi Juliawan Pawana I Wayan Krisna Saputra Ida Ayu Dwi Giriantari Ida Ayu Kaniya Pradnya Paramitha Ida Bagus Vidananda Agastya IGN. Agung Dwi Jaya Putra Ilham Ammarul Aziz Jayantari, Made Widya Kadek Teguh Purwanto Komang Budiarta Komang Oka Saputra Komang Sri Utami Komang Tania Paramecwari Lely Meilina Lie Jasa Linawati Linawati Linawati Luh Gede Astuti Made Sudarma Made Sudarma Mahardhika Tirta Naradhiya, Gede Naufal Muhajir Abidin Ngurah Indra ER Ni Kadek Diah Parwati Ni Komang Ayu Sri Anggreni Ni Made Ary Esta Dewi Wirastuti Ni Putu Diah Arista Ningsih Nicko Satrio Pambudi Nyoman Arun Wiratama Nyoman Putra Sastra Pawana P., I Gusti Ngurah Agung Pawana, I Wayan Adi Juliawan Putra, A.A.B. Rama Windhu Putra, Agus Permana Putra, Rio Juniyantara Putu Andhika Kurniawijaya Putu Ardana Putu Arya Mertasana Putu Dhiko Pradnyana Putu Krisna Adi, I Gusti Ngurah Putu Wirya Kastawan Rahadi, Putu Suta Adya Dharma Rio Juniyantara Putra Rukmi Sari Hartati Rukmi Sari Hartati Saputra, Aggry Saputra, Komang Oka Sari Dewi Hartanto, Dessy Ratna Setiawan, Putu Ayu Citra Solly Aryza Sri Andriati Asri Sri Andriati Asri, Sri Andriati Widiadnyana, Putu Widyadi Setiawan Wikananda, I Gusti Ngurah Satya Wirawan Wirawan Yohanes Hendra Nugroho Yohanes Pracoyo Widi Prasetyo