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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Computer Networks, Architecture and High Performance Computing International Journal Software Engineering and Computer Science (IJSECS) Abdi Implementasi Pancasila: Jurnal Pengabdian kepada Masyarakat
Hindarto, Djarot
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Public Health Social Sciences

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Comparison Accuracy of CNN and VGG16 in Forest Fire Identification: A Case Study Hindarto, Djarot
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3371

Abstract

The current research aims to assess the precision of forest fire detection using CNN and VGG16 models, specifically in the context of fire identification. While both models have demonstrated significant promise in visual pattern recognition, a comprehensive analysis regarding their specific benefits in forest fire identification is still needed. The rationale behind this research stems from the significance of promptly identifying forest fires as a preemptive measure to mitigate their detrimental effects on the environment and society. The employed approach involves the application of transfer learning techniques on a diverse and extensive dataset encompassing different forest fire scenarios. The dataset was used to train both CNN and VGG16 models. The test results indicated that the CNN model achieved a forest fire detection accuracy of 96%, while VGG16 achieved 98% accuracy. The primary objective of this research is to enhance comprehension regarding the merits and demerits of each model in the context of forest fire identification scenarios. While VGG16 exhibits marginally superior performance in identifying forest fires, this discrepancy offers valuable insight into the practical applicability of these two models for fire detection in real-world scenarios. These findings establish a solid basis for the advancement of more dependable and efficient early detection technology in the prevention and management of forest fires in the future. This can be accomplished by capitalizing on the unique capabilities of each model to optimize their performance in practical scenarios.
Case Study: Gradient Boosting Machine vs Light GBM in Potential Landslide Detection Hindarto, Djarot
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3374

Abstract

An increasing demand for precise forecasts concerning the likelihood of landslides served as the impetus for this investigation. Human life, infrastructure, and the environment are all profoundly affected by this natural occasion. Constructing models capable of discerning intricate patterns among diverse factors that impact the likelihood of landslide occurrences constitutes the primary obstacle in landslide detection. Predicting potential landslides requires algorithms that are both accurate and efficient in their processing of vast quantities of data encompassing a variety of geographical, environmental, and ecological characteristics. An evaluation of the efficacy of both Gradient Boosting Machine and Light Gradient Boosting Machine in identifying patterns associated with landslides is accomplished by comparing their performance on a large and complex dataset. In the realm of potential landslide detection, the primary aim of this research endeavor is to assess the predictive precision, computation duration, and generalizability of Gradient Boosting Machine and Light Gradient Boosting Machine. This research aims to enhance comprehension regarding the comparative benefits of these two approaches in surmounting the obstacles associated with risk assessment and modeling pertaining to potential landslides, with a specific emphasis on efficiency and precision. The research findings are anticipated to serve as a valuable reference in the identification of more efficient approaches to reduce the likelihood of landslide-induced natural catastrophes. The accuracy of the GBM experiment reached 82% and LGBM reached 81%.
Optimizing Transportation Services: Using TOGAF for Efficiency and Quality Wedha, Bayu Yasa; Hindarto, Djarot
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3407

Abstract

In the rapidly expanding transportation industry, it is crucial to make focused and coordinated efforts to improve services with maximum efficiency. This paper seeks to explore the optimization of the Enterprise Architecture approach to effectively attain the primary objectives of the transportation industry, specifically the enhancement of service quality. The main emphasis is on implementing the enterprise architecture methodology of the open group architecture framework on a strategic basis. This paper examines how Enterprise Architecture can offer systematic and quantifiable solutions by identifying problems in infrastructure and operational processes. The research aims to provide comprehensive insights into how the Enterprise Architecture concept can optimize operational efficiency and streamline processes in the provision of transportation services. By implementing TOGAF, it is expected that the integration of systems will be seamless, technology usage will be optimized, and customer experiences will be improved. To summarize, this paper demonstrates the desire to improve transportation services. It explains how Enterprise Architecture methods, specifically within the TOGAF framework, can directly lead to advantages such as increased operational efficiency and improved service quality. This paper aims to be easily understood by a wide range of readers, including management, Information Technology professionals, and other stakeholders in the transportation industry. It avoids using overly technical language to ensure accessibility and comprehensibility.
PELATIHAN PENGEMBANGAN MATERI PEMBELAJARAN INTERAKTIF BERBASIS TEKNOLOGI Ningsih, Sari; Gunawan, Arie; Fauziah; Hindarto, Djarot; Yulianto, Lili Dwi; Desmana, Satriawan
Abdi Implementasi Pancasila:Jurnal Pengabdian kepada Masyarakat Vol 4 No 2 (2024): November
Publisher : Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/abdi.v4i2.7802

Abstract

Pelatihan pengembangan materi pembelajaran interaktif berbasis teknologi bertujuan untuk meningkatkan kompetensi guru MTS Asyafi’iyah 04 Jakarta dalam mengintegrasikan teknologi ke dalam proses pembelajaran. Kegiatan ini dilatarbelakangi oleh kebutuhan mendesak untuk mempersiapkan guru dalam menghadapi tantangan era digital dan memastikan pembelajaran yang relevan serta efektif bagi siswa. Metode yang digunakan dalam pelatihan ini meliputi workshop, simulasi, dan evaluasi. Workshop dirancang untuk memberikan pemahaman dasar mengenai teknologi pendidikan dan aplikasinya. Simulasi dilakukan untuk memberikan pengalaman langsung dalam mengembangkan dan menggunakan materi pembelajaran interaktif. Evaluasi dilakukan untuk menilai pemahaman dan kemampuan guru setelah mengikuti pelatihan. Hasil dari pelatihan ini menunjukkan peningkatan yang signifikan dalam kemampuan guru dalam menggunakan teknologi untuk membuat materi pembelajaran interaktif. Selain itu, terdapat peningkatan motivasi dan keterlibatan guru dalam proses pembelajaran. Pelatihan ini diharapkan dapat menjadi model bagi institusi pendidikan lainnya dalam upaya meningkatkan kualitas pembelajaran melalui integrasi teknologi.
Development of a Prototype for a Product Recommendation System Using Blockchain Technology Setiawan, Adrian Tri; Hindarto, Djarot
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.3855

Abstract

Blockchain technology ensures data security and transparency through decentralization and immutability. Smart contracts facilitate automation and foster trust by reducing dependence on intermediaries. Nevertheless, most existing recommendation systems remain centralized, leaving them susceptible to manipulation and security breaches. Although recommendation algorithms are widely used, their application within blockchain-based systems remains limited. By leveraging the Ethereum blockchain and smart contracts, the proposed system enhances transparency, security, and decision-making reliability. The algorithm ranks products based on price, appearance, quality, size, and availability, with results permanently recorded on the blockchain. Experimental findings indicate that the integration of TOPSIS and PROMETHEE II algorithms improves the recommendation process by systematically evaluating multiple criteria. Each product is assessed according to its proximity to both positive and negative ideal solutions, with the final ranking score calculated as the ratio of the negative distance to the total distance (positive plus negative). For example, Pocari Sweat achieved the highest preference score of 0.9619, indicating it is the top recommendation, while Coca-Cola 390ml scored 0.4182, reflecting a lower ranking. These results demonstrate the algorithms’ capacity to distinguish products objectively, supporting accurate and transparent recommendations. The study advances blockchain-based decision support systems by providing secure and transparent recommendation mechanisms. Additionally, the integration of TOPSIS and PROMETHEE II within a blockchain framework demonstrates both feasibility and effectiveness in decentralized environments.
PEMANFAATAN SOCIAL MEDIA MARKETING UNTUK PARA PELAKU BISNIS UMKM Ningsih, Sari; Fauziah, Fauziah; Pamungkasari, Panca Dewi; Hindarto, Djarot; Sholihati, Ira Diana; Handayani, Endah Tri Esti; Sari, Ratih Titi Komala
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 6 No. 4 (2025): Volume 6 No 4 Tahun 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v6i4.47652

Abstract

Usaha Mikro, Kecil, dan Menengah (UMKM) memegang peranan krusial dalam mendukung perekonomian nasional, termasuk di wilayah Cikarang Selatan. Dalam era persaingan bisnis yang semakin intens, pelaku UMKM perlu mengadopsi teknologi digital agar tetap mampu bersaing. Salah satu pendekatan yang dinilai efektif adalah pemanfaatan pemasaran melalui media sosial. Berbagai platform seperti Instagram, Facebook, dan Twitter memberikan peluang luas bagi UMKM untuk memperluas jangkauan pasar, meningkatkan kesadaran merek, serta membangun kedekatan dengan konsumen. Program   Pengabdian kepada Masyarakat (PKM) ini bertujuan mengidentifikasi sejauh mana pemanfaatan media sosial oleh pelaku UMKM di Cikarang Selatan serta dampaknya terhadap peningkatan penjualan dan pengenalan produk. Metode yang digunakan adalah survei kualitatif dan wawancara mendalam. Hasil yang diharapkan adalah peningkatan penjualan hingga 30% dalam enam bulan pertama pemanfaatan media sosial. Strategi yang diterapkan meliputi pembuatan konten menarik, penggunaan iklan berbayar, dan interaksi aktif dengan followers. Namun, pelaku UMKM menghadapi tantangan seperti keterbatasan pengetahuan tentang digital marketing dan keterbatasan waktu dalam mengelola akun. Oleh karena itu, pelatihan dan pendampingan diperlukan agar penggunaan social media marketing lebih optimal. Dengan pendekatan yang tepat, media sosial dapat menjadi alat efektif dalam mendukung pertumbuhan UMKM.
MCDM-based Fire Risk Mapping with Geospatial Visualization and Blockchain Paays, Emmanuel Abet Rossi; Hindarto, Djarot; Sani, Asrul
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.v9i4.15436

Abstract

Forest fires are among the most destructive environmental disasters in Indonesia, causing long-term ecological damage, health problems, and economic disruption. Increasing occurrences driven by climate anomalies, land clearing, and vegetation dryness highlight the need for intelligent and data-driven risk monitoring systems. This study introduces a hybrid analytical framework that integrates Multi-Criteria Decision-Making (MCDM) with blockchain-based data management and geospatial visualization to identify forest fire risk levels. The proposed model combines the Analytic Hierarchy Process (AHP), Weighted Sum Model (WSM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate multiple parameters, including temperature, humidity, rainfall, and the Normalized Difference Vegetation Index (NDVI). Environmental data were securely obtained from a private Ethereum blockchain using Ganache, Truffle, and MetaMask to ensure transparency, integrity, and immutability. Results were visualized through an interactive Leaflet.js interface, allowing real-time geospatial monitoring linked to blockchain transaction hashes. The AHP analysis revealed that temperature (0.36) and humidity (0.27) contributed 63% of the total decision weight, while TOPSIS identified high-risk zones consistent with historical records. Validation against BNPB data achieved 90.7% accuracy, confirming the model’s reliability. The integration of MCDM, GIS, and blockchain provides a transparent, decentralized, and verifiable approach for national-scale fire-risk management, enhancing the accuracy and credibility of environmental decision-making systems.
Hybrid Artificial Intelligence–Blockchain Approach for Landslide Risk Classification and Recommendation Indriawan, Rizal; Komalasari, Ratih Titi; Hindarto, Djarot; Sani, Asrul
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.v9i4.15465

Abstract

Increased rainfall intensity, steep topography, and changes in land use in Indonesia, particularly in Java, such as Garut Regency, have increased the risk of landslides that have a widespread impact on public safety and environmental stability. This study proposes a Hybrid Artificial Intelligence and Blockchain approach to develop an accurate, secure, and transparent landslide risk classification and recommendation system. The model integrates three Multi-Criteria Decision Making (MCDM) methods, namely Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). These three methods are used sequentially to determine criterion weights, calculate ideal solutions, and produce optimal compromise decisions based on geospatial factors. The dataset used consists of 766 geospatial observation data covering stability, rainfall, vegetation, river distance, slope, prediction, and ground truth parameters, obtained from satellite data and open geospatial repositories in the Java Island region. The research process included pre-processing, normalization, weighting analysis using AHP–TOPSIS–VIKOR, and integration of the results into the Ethereum Blockchain Smart Contract system with a Proof of Authority (PoA) consensus mechanism. The test results showed a 17.8% increase in classification accuracy and a 21.4% increase in data storage efficiency compared to conventional methods. This approach is expected to improve the reliability, security, and transparency of the analysis system and mitigate the risk of landslides based on smart technology in Indonesia.
Smart Contract Architecture for a Blockchain-Driven Multi Criteria DSS in Forest Fire Monitoring and Response Cahyo, Fajar Yusuf Nur; Hindarto, Djarot
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.15009

Abstract

The current centralized system is vulnerable to data manipulation due to the absence of independent verification mechanisms, thereby compromising the reliability of information. In addition, the inconsistency of formats and data silos across agencies exacerbates information fragmentation. Delays in data distribution hamper rapid response in emergency situations, while uneven communication infrastructure—especially in remote areas—reduces real-time monitoring capabilities. Lack of coordination among stakeholders—such as BNPB, forestry agencies, local communities, and the private sector—adds to the complexity of disaster management and often leads to overlapping tasks. The decision-making process is further complicated by competing criteria, such as priority areas, resource availability, dynamic weather conditions, and limited IoT sensor coverage. Additionally, high operational costs for system maintenance and limited audit trails make it difficult to track data history and ensure accountability. Therefore, the Multi-Criteria Decision Making (MCDM) method is necessary to handle uncertainty, combine different geospatial factors in an organized way, and make sure the decision-making process is reliable and clear. This research fills the technological gap by introducing a decentralized audit trail while facilitating cross-sector collaboration in fire mitigation decision-making and minimizing the risk of evidence-based data errors.
Comparative Performance Evaluation of MobileNetV3 and ResNet50 for Forest Fire Image Classification Hidayat, Muhammad Rizky Amirullah; Hindarto, Djarot; Sani, Asrul
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.v9i4.15415

Abstract

Indonesia is one of the countries with a high incidence of forest and land fires (karhutla), especially during the dry season, thus requiring a fast and efficient early detection system. This study aims to compare the performance of two popular deep learning architectures, namely MobileNetV3 (Large and Small variants) and ResNet50, in forest fire image classification tasks using a transfer learning-based approach. This study emphasizes the comparison between accuracy and computational efficiency in a CPU-only environment, which represents real-world conditions of use in the field without GPU support. The dataset used is a combination of local field images from the Puncak area, Bogor, and a curated public forest fire dataset to ensure the model's generalization ability to diverse geographical conditions. The results of the experiment show that ResNet50 provides the highest accuracy with a training accuracy value of 0.677 and a validation accuracy of 0.647, but requires longer training and inference times. Meanwhile, MobileNetV3-Large and MobileNetV3-Small showed better computational efficiency, with only slightly lower accuracy (0.635 and 0.61) and high training stability. These findings confirm that lightweight models such as MobileNetV3 strike an optimal balance between accuracy, speed, and resource consumption, making them an ideal solution for implementing edge computing-based early detection systems. Overall, this research contributes by providing an empirical comparative analysis that can serve as a reference for selecting deep learning architectures for efficient and adaptive forest fire detection systems that are constrained by hardware limitations.
Co-Authors Afarah, Sabrina Fajrul Afarini, Nihayah Alim, Syariful Alwi, Muchamad Nurdin Amalia, Nadia Amanda, Djaja Andrianingsih Anna Marina Arie Gunawan Arko Djajadi Asrul Sani Cahyo, Fajar Yusuf Nur Dazki, Erick Desmana, Satriawan Djunarto Eko Hadianto, Eko Endah Tri Esti Handayani Farahdinna, Frenda Fauziah Fauziah Fauziah Gustian, Yonathan Wijaya Handayani, Endah Tri Esti Haryono Haryono Hendrata, Ferial Hidayat, Muhammad Rizky Amirullah Indrajit, Eko Indriawan, Rizal ISWAHYUDI ISWAHYUDI Judijanto, Loso Mochamad Hariadi Nurfaizi, Kelfin Paays, Emmanuel Abet Rossi Pamungkasari, Panca Dewi Prawira, Khairul Thamrin Putra, Tri Dharma R. Eko Indrajit Rahman, Ben Ramadhan, Duta Pramudya Ramadhani, Nursetiaji Ratih Titi Komalasari Ririn Ikana Desanti Santoso, Handri Saputra, Adi Dwifana Saputra, Adi Dwifana Saputra Sari Ningsih Sari Ningsih Sentot Imam Wahjono Setiawan, Adrian Tri Sholihati, Ira Diana Sjah, William Surya Suherman, Endang Sze, Edward Usman, Sabrina Fajrul Ula Wahyuddin, Mohammad Iwan Wedha, Alessandro Benito Putra Bayu Wedha, bayu Yasa WIJANARKO, SIGIT Wijaya, Yunan Fauzi Wirayasa, I Ketut Adi Yudhakusuma, Dudi Yudistira, Hernan Yulianto, Lili Dwi