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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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Advancing Fruit Image Classification with State-of-the-Art Deep Learning Techniques Wijaya, Yunan Fauzi; Hindarto, Djarot
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13604

Abstract

Fruit image classification technology using deep learning is making significant contributions in the agriculture and food retail sectors, promising to increase efficiency and productivity. However, there is an identified knowledge gap in dealing with the considerable variation in fruit appearance caused by factors such as type, size, color, and lighting conditions, as well as the precise identification of damage or disease. This research focuses on applying the developed Convolutional Neural Network architecture to fill this gap by using it in an extensive and diverse dataset, covering 67,692 image files categorized into 131 fruit classes. The training process showed substantial accuracy improvement, with training accuracy reaching 98.39% and validation accuracy at 90%, while training loss decreased to 0.0430 and validation loss to 0.2991. In the advanced stage of training, the training accuracy peaked at 99.43% in the 59th epoch with a shallow loss of 0.0251. However, the validation loss showed variation, indicating room for improvement in model generalization. The findings provide insight into the potential and challenges of applying Convolutional Neural Network models and fruit image classification with improved fruit sorting accuracy. Contribution to the literature in the field of information technology and agriculture by showing deep learning models can be improved to address the issue of fruit image variability.
Edge Computing Architecture Sensor-based Flood Monitoring System: Design and Implementation Hindarto, Djarot
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13874

Abstract

The purpose of this research is to develop and execute a system for monitoring floods using sensors and edge computing architecture. The goal is to make flood detection and prediction more accurate and faster. The growing frequency and severity of flood disasters in different parts of the world has prompted the necessity for a better system to track these events. The primary goal of this study is to design a system that can reduce network load and latency by processing sensor data locally at edge devices before sending it to the cloud. To detect and anticipate flood events, the research method incorporates several environmental sensors that measure things like soil moisture, water level, and rainfall. These readings are subsequently processed by edge nodes using machine learning algorithms. Compared to more conventional methods that depend only on cloud computing, the results demonstrate that the system can deliver quicker and more accurate predictions. Results showed a detection and prediction accuracy of 98.95% for floods. Edge computing also succeeded in drastically cutting down on bandwidth consumption and communication latency. This research concludes that edge computing architecture based on sensors can effectively monitor floods and has excellent potential for use in many different areas prone to flooding. Improving the prediction algorithm and investigating its potential integration with a more thorough early warning system should be the focus of future research.
Android-manifest extraction and labeling method for malware compilation and dataset creation Hindarto, Djarot; Djajadi, Arko
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6568-6577

Abstract

Malware is a nuisance for smartphone users. The impact is detrimental to smartphone users if the smartphone is infected by malware. Malware identification is not an easy process for ordinary users due to its deeply concealed dangers in application package kit (APK) files available in the Android Play Store. In this paper, the challenges of creating malware datasets are discussed. Long before a malware classification process and model can be built, the need for datasets with representative features for most types of malwares has to be addressed systematically. Only after a quality data set is available can a quality classification model be obtained using machine learning (ML) or deep learning (DL) algorithms. The entire malware classification process is a full pipeline process and sub processes. The authors purposefully focus on the process of building quality malware datasets, not on ML itself, because implementing ML requires another effort after the reliable dataset is fully built. The overall step in creating the malware dataset starts with the extraction of the Android Manifest from the APK file set and ends with the labeling method for all the extracted APK files. The key contribution of this paper is on how to generate datasets systematically from any APK file.
Cybersecurity Integration in Enterprise Architecture for IoT Infrastructure in Steel Manufacturing Hindarto, Djarot
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4415

Abstract

As a result of the widespread adoption of Internet of Things technology in the steel manufacturing industry, there is an urgent requirement for the implementation of robust cybersecurity measures. The proliferation of IoT devices has caused a data explosion, which in turn has increased the risk of cyberattacks. The purpose of this research is to develop an enterprise architecture model that is capable of effectively managing cybersecurity risks on Internet of Things infrastructure in the steel manufacturing industry. This is a response to the urgent challenge that has been presented. The methodology utilized in this study is a rigorous qualitative approach, which involves the collection and analysis of data through interviews and literature reviews related to the topic. Following an in-depth analysis of the findings of the research, several important goals have been established. These goals include the identification of potential dangers, the reduction of potential risks, and the effective implementation of security controls. Within the context of the steel manufacturing industry, this research makes a significant contribution to the improvement of cybersecurity in Internet of Things infrastructure. In addition to identifying potential dangers and mitigating risks, the architecture model that has been proposed is about more than that. It offers a comprehensive and well-coordinated safety strategy, which guarantees a strong defense against cyber threats.
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.
Enhancing image quality using super-resolution residual network for small, blurry images Hindarto, Djarot; Wahyuddin, Mohammad Iwan; Andrianingsih, Andrianingsih; Komalasari, Ratih Titi; Handayani, Endah Tri Esti; Hariadi, Mochamad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4654-4666

Abstract

In the background, when low-resolution images are utilized, image identification tasks are frequently hampered. By employing the residual network super-resolution framework, super-resolution techniques are used to enhance image quality, specifically in the detection and identification of small and blurry objects. Improving resolution, decreasing blur, and enhancing object detail are the main goals of the suggested approach. The novelty of this research resides in its application of the activation exponential linear unit (ELU) to the super-resolution residual network (SR-ResNet) framework, which has been demonstrated to enhance image sharpness. The experimental findings demonstrate a substantial enhancement in the quality of the images, as evidenced by the training data's structural similarity index (SSIM) of 0.9989 and peak signal-to-noise ratio (PSNR) of 91.8455. Furthermore, the validation data demonstrated SSIM 0.9990 and PSNR 92.5520. The results of this study indicate that the implementation of SR-ResNet significantly enhances the capability of the detection system to detect and classify diminutive and opaque entities precisely. The expected and projected enhancement in image quality significantly influences image processing, especially in situations where accuracy and object differentiation are vital.
Use of RESNET-50 Neural Network in Diagnosing Diseases Mango Leaves 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.v6i1.3308

Abstract

Using a state-of-the-art convolutional neural network, specifically RESNET-50, for disease diagnosis on mango leaves is the focus of this research. The end goal is to develop a trustworthy method of mango plant disease detection using leaf image analysis. The approach used comprised gathering a sizable dataset encompassing a range of mango leaf diseases. Afterward, a classification system was developed by training the RESNET-50 model on image data. The system is able to learn extraordinarily intricate and profound visual patterns in pictures of mango leaves thanks to RESNET-50's deep and complicated architecture, which improves feature extraction. With a Test Accuracy of 99.16% and a Test Loss of only 0.4332, the results demonstrate a very reliable system. This impressive level of precision verifies that the system is capable of correctly distinguishing and categorizing mango leaf diseases. Consequently, this case demonstrates promising agricultural applications of the RESNET-50 model and offers a dependable and effective means of disease detection in mango plants. This study adds to the growing body of knowledge that can aid agricultural professionals and farmers in the early detection of disease symptoms on mango leaves, allowing for the prompt implementation of preventative measures. These findings also have broader implications, such as the potential for better agricultural productivity and management brought about by the use of comparable technologies for disease analysis in different crops.
Building Digital Platform for Property Marketing Sales with an Enterprise Architecture Approach Hindarto, Djarot; Putra, Tri Dharma
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.v6i1.3361

Abstract

Constructing a digital platform is an essential priority in an era where technology is the cornerstone of success in the real estate marketing and sales sector. Nevertheless, the advancement of these platforms is frequently impeded by obstacles pertaining to integration, security, and scalability, which stem from their inadequate establishment. Existing platforms' incapability to rapidly adapt to shifting market dynamics frequently impedes the development of innovative digital solutions for the real estate industry, which provided the impetus for this study. The fundamental objective of this study is to create a framework capable of accommodating scalable business expansion, enhancing data security, and overcoming integration obstacles. Utilizing Enterprise Architecture principles in the design and implementation of the platform, as well as conducting a comprehensive examination of the current IT infrastructure, stakeholder requirements, and mapping of pertinent business processes, will comprise the research methods. The results of this study are expected to contribute to a wholehearted comprehension of how the Enterprise Architecture approach can function as a resilient framework for the development of effective digital platforms. In the contemporary digital age, this platform is expected to furnish solutions capable of swiftly adjusting and reacting to evolving market dynamics and changes, thereby facilitating the marketing and sales requirements of real estate. Finally, this research endeavors to offer a comprehensive and practical perspective on constructing a robust and flexible digital infrastructure that can effectively cater to the demands of the real estate sector in the current era of digitalization.
Enterprise Architecture Design and Implementation for IoT Integration in Manufacturing Electrical Panels Hindarto, Djarot; Hendrata, Ferial; Wahyuddin, Mohammad Iwan; Wijanarko, Sigit
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.v6i1.3365

Abstract

Internet of Things technology has transformed manufacturing efficiency and optimization. Electrical panel manufacturing benefits from Internet of Things for better functionality, predictive maintenance, and smoother operations. This study examines the design and implementation of an Enterprise Architecture strategy for seamless Internet of Things integration in electrical panel manufacturing. This research aims to explain Enterprise Architecture and use it as a framework for Internet of Things integration in electrical panel manufacturing. This study examines the complex relationships between Internet of Things components, their connectivity, and a broad Enterprise Architecture framework needed to organize their functionality. This integration uses Enterprise Architecture principles to optimize resource use, reduce downtime, and improve manufacturing efficiency. This effort involves analyzing existing infrastructure, identifying Internet of Things deployment points, and creating an Enterprise Architecture plan that meets business goals. This research emphasizes the need for close IT-operations collaboration to achieve a unified vision and smooth Internet of Things integration. This research addresses Internet of Things implementation challenges in manufacturing, including security, data interoperability, and scalability. Strong governance and adaptable architecture are stressed to address these challenges within an Enterprise Architecture framework. This research aims to help electrical panel manufacturers harness the transformative power of the Internet of Things. Strategic Enterprise Architecture helps businesses navigate complexity, leverage Internet of Things, and create a more agile, connected, and optimized manufacturing landscape.
Enhancing Business: Incorporating Enterprise Architecture into Project Management in the Food Manufacturing Industry Hindarto, Djarot; Putra, Tri Dharma; Wahyuddin, Mohammad Iwan
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.v6i1.3368

Abstract

The present research examines the incorporation of Enterprise Architecture into project management in the food manufacturing sector with the aim of enhancing operational efficiency and corporate accountability. This study investigates the use of an integrated Enterprise Architecture strategy with project management in the food industry to improve production processes by using the crucial role of information technology. The aim of this approach is to enhance operational frameworks, customize information systems, and guarantee the congruence between business strategic aims and technology implementation. Within this framework, the analysis centers on the potential of integrating Enterprise Architecture to enhance transparency, interoperability, and scalability in the food manufacturing industry. Enterprise Architecture offers a comprehensive perspective on the technological infrastructure needed to ease effective and adaptable business operations. The implementation of Enterprise Architecture yields advantages in elucidating system architecture, enhancing coordination among diverse business components, and easing the more adaptable assimilation of modifications. Enterprise Architecture is crucial in project management as it eases improved decision-making and more efficient risk management. This study emphasizes the significance of incorporating Enterprise Architecture into the management of projects in the food industry as a strategic basis for ongoing operational advancement and enhancement while simultaneously prioritizing product quality, production efficiency, and responsiveness to evolving market demands.
Co-Authors Afarah, Sabrina Fajrul Afarini, Nihayah Alim, Syariful Alwi, Muchamad Nurdin Amalia, Nadia Amanda, Djaja Andrianingsih Anna Marina Arie Gunawan Arko Djajadi Dazki, Erick Desmana, Satriawan Djunarto Eko Hadianto, Eko Endah Tri Esti Handayani Fauziah Fauziah Fauziah Gustian, Yonathan Wijaya Handayani, Endah Tri Esti Haryono Haryono Hendrata, Ferial Indrajit, Eko ISWAHYUDI ISWAHYUDI Judijanto, Loso Mochamad Hariadi Nurfaizi, Kelfin Pamungkasari, Panca Dewi Prawira, Khairul Thamrin Putra, Tri Dharma R. Eko Indrajit Rahman, Ben Ramadhan, Duta Pramudya Ratih Titi Komalasari Ririn Ikana Desanti Santoso, Handri Saputra, Adi Dwifana Saputra, Adi Dwifana Saputra 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 Yulianto, Lili Dwi