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All Journal International Journal of Electrical and Computer Engineering 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)
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 Health Professions Public Health

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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.
PELATIHAN PENGGUNAAN MEDIA SOSIAL UNTUK OPTIMALISASI PENYEBARAN INFORMASI INSTITUSI PENDIDIKAN Ningsih, Sari; Gunawan, Arie; Hindarto, Djarot
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 4 (2024): Volume 5 No. 4 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i4.31227

Abstract

Pelatihan penggunaan media sosial untuk optimalisasi penyebaran informasi institusi pendidikan adalah kesempatan untuk meningkatkan keterampilan dan pengetahuan peserta tentang cara terbaik untuk menggunakan media sosial untuk meningkatkan penyebaran informasi oleh institusi pendidikan. Pelatihan ini akan membahas berbagai media sosial seperti Facebook, Twitter, YouTube, Instagram, dan WhatsApp. Pelatihan ini akan mencakup penggunaan media sosial untuk meningkatkan efektivitas penyebaran informasi, menentukan strategi media sosial yang tepat untuk tujuan tertentu, membuat konten yang menarik bagi audiens, serta memahami cara terbaik untuk mengelola dan memonitor media sosial. Tujuan  dari  pelatihan  ini  adalah untuk  membantu  peserta memaksimalkan efektivitas penyebaran informasi melalui media sosial oleh institusi pendidikan
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.
Co-Authors Afarah, Sabrina Fajrul Afarini, Nihayah Alim, Syariful Alwi, Muchamad Nurdin Amalia, Nadia Amanda, Djaja Anna Marina Arie Gunawan Arko Djajadi Dazki, Erick Djunarto Eko Hadianto, Eko Gustian, Yonathan Wijaya Handayani, Endah Tri Esti Haryono Haryono Hendrata, Ferial Indrajit, Eko ISWAHYUDI ISWAHYUDI Judijanto, Loso Nurfaizi, Kelfin Prawira, Khairul Thamrin R. Eko Indrajit Rahman, Ben Ramadhan, Duta Pramudya Ratih Titi Komala Sari, Ratih Titi Komala Ririn Ikana Desanti Santoso, Handri Saputra, Adi Dwifana Saputra, Adi Dwifana Saputra Sari Ningsih Sentot Imam Wahjono Sjah, William Surya Suherman, Endang Sze, Edward Usman, Sabrina Fajrul Ula Wahyuddin, Mohammad Iwan Wedha, Alessandro Benito Putra Bayu Wedha, bayu Yasa Wijaya, Yunan Fauzi Wirayasa, I Ketut Adi Yudhakusuma, Dudi