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All Journal JEES (Journal of English Educators Society) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Media Elektro : Journal of Electrical Power, Informatics, Telecommunication, Electronics, Computer and Control System Jurnal Sisfokom (Sistem Informasi dan Komputer) Jiko (Jurnal Informatika dan komputer) Journal of Computer Networks, Architecture and High Performance Computing NERO (Networking Engineering Research Operation) JI-Tech Jurnal Komtika (Komputasi dan Informatika) Journal Information System and Computer Application
Cobantoro, Adi Fajaryanto
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media

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ANALISIS OPTIMASI QUEUE TYPE DALAM MIKROTIK ROUTER OS V7.15 PADA JARINGAN INTERNET DI SMP NEGERI 6 SUDIMORO Fadila, Daras; Cobantoro, Adi Fajaryanto
MEKAR : Journal Information System and Computer Application Vol. 1 No. 1 (2025): AGUSTUS
Publisher : PT Mekar Research and Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65475/6srksk04

Abstract

Dalam era digital, akses internet yang cepat dan stabil menjadi kebutuhan utama, khususnya di lingkungan pendidikan. SMP Negeri 6 Sudimoro menghadapi kendala dalam pengelolaan trafik jaringan internet, yang berdampak pada performa pembelajaran daring. Penelitian ini bertujuan untuk menganalisis pengaruh penerapan algoritma antrian queue type CoDel, FQ-CoDel, dan CAKE pada Mikrotik RouterOS v7.15 terhadap kualitas layanan jaringan (Quality of Service/QoS), serta menentukan algoritma yang paling optimal. Metode yang digunakan adalah pendekatan kuantitatif dengan pengujian parameter QoS seperti throughput, delay, jitter, dan packet loss berdasarkan standar TIPHON. Data dikumpulkan menggunakan aplikasi Wireshark dan dikonfigurasi melalui Winbox. Hasil pengujian menunjukkan bahwa algoritma FQ-CoDel memberikan performa terbaik dengan nilai throughput tertinggi tanpa peningkatan jitter maupun delay. Berdasarkan hasil tersebut, FQ-CoDel direkomendasikan sebagai queue type yang paling efektif untuk diterapkan pada jaringan internet sekolah. Implementasi algoritma ini diharapkan dapat meningkatkan efisiensi jaringan serta mendukung kelancaran kegiatan belajar mengajar berbasis digital.
Harnessing Remote Sensing for Soil Erosion Prediction: A Bibliometric Review of RUSLE Applications Cobantoro, Adi Fajaryanto; Wibowo, Mochamad Agung; Sanjaya, Ridwan
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2533

Abstract

This study examines recent advancements in soil erosion modeling using the Revised Universal Soil Loss Equation (RUSLE), integrated with remote sensing and artificial intelligence techniques. Adopting a Systematic Literature Review (SLR) and bibliometric analysis via Bibliometrix in R, 63 articles were analyzed from an initial 359 based on strict selection criteria. Findings reveal a sharp rise in publications since 2017, especially involving machine learning and Google Earth Engine (GEE) platforms. Co-authorship analysis highlights significant international collaboration, particularly between Asia and Europe. Concept maps and co-word analyses show a shift from traditional RUSLE applications toward AI and big data approaches. Thematic evolution further indicates a growing focus on climate change and the Sustainable Development Goals (SDGs). The review's primary contribution lies in its explicit identification of critical research priorities by pinpointing key gaps: the limited use of field validation, weak SDG integration, and fragmented international research networks. By highlighting these deficiencies, this study provides a clear roadmap for future investigations, steering the field toward more inclusive, data-driven, and validated approaches to address global land degradation and climate resilience. Overall, the study contributes to the development of more effective erosion mitigation models through technological integration and international collaboration.
CLASSIFICATION OF DURIAN LEAF IMAGES USING CNN (CONVOLUTIONAL NEURAL NETWORK) ALGORITHM Fitriani, Lely Mustikasari Mahardhika; Litanianda, Yovi; Cobantoro, Adi Fajaryanto
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8576

Abstract

This research investigates the classification of durian leaf images using Convolutional Neural Network (CNN) algorithms, specifically focusing on the architectures AlexNet, InceptionNetV3, and MobileNet. The study begins with the collection of a dataset comprising 1604 images for training, 201 images for validation, and 201 images for testing. The dataset includes five classes of durian leaves: Bawor, Duri Hitam, Malica, Montong, and Musang King, chosen for their varied characteristics such as taste, texture, and aroma. Data preprocessing involved several steps to ensure the images were suitable for model training. These steps included data augmentation to increase variability, pixel normalization to standardize the images, and resizing to 150x150 pixels to match the input requirements of the CNN models. After preprocessing, the CNN models were implemented and trained using deep learning frameworks such as TensorFlow and PyTorch. Model performance was evaluated using a Confusion Matrix, which provided detailed insights into classification accuracy, precision, sensitivity, specificity, and F-score. The results indicated that InceptionNetV3 and AlexNet achieved near-perfect classification accuracy, with no misclassifications, demonstrating their robustness and precision in identifying durian leaf images. The training accuracy for both models rapidly approached 100% within the first few epochs and stabilized, while the loss values decreased sharply, indicating effective learning without overfitting. In contrast, MobileNet, while showing high accuracy and low loss during training, exhibited several misclassifications across all classes. The training accuracy of MobileNet also approached 100%, but the presence of misclassifications suggested that further tuning and improvements were necessary. Specifically, MobileNet's Confusion Matrix revealed errors in correctly identifying samples from each class, indicating potential areas for enhancement in the model's architecture or preprocessing techniques. In conclusion, InceptionNetV3 and AlexNet proved to be highly efficient and accurate architectures for classifying durian leaf images, making them suitable for practical applications. MobileNet, although performing well, requires further refinement to achieve the same level of accuracy and reliability. This study highlights the importance of selecting appropriate CNN architectures and the need for thorough preprocessing to optimize model performance in image classification tasks.
Co-Authors Agustin, Nely Dwi Bambang Harmanto, Bambang Bonavidi, Rinjani Dyah, Dyah Mustikasari Elok Putri Nimasari, Elok Putri Elvina Dwi, Cahyani Fadila, Daras Faiz Giandra, Muhammad Fauzan Masykur Fitriani, Lely Mustikasari Mahardhika Karaman, Jamilah Mohammad Bhanu Setyawan Nurfitri, Khoiru Oktavianto, Hardiyan Permana Putra, Angga Dian Pratama, Aan Rachmatullah Ridwan Sanjaya Rizqi Rosyadi, Mohammad Sussolaikah, Kelik Velrinda Fahza'a, Risty Wibowo, Mochamad Agung Wicahyono, Fadhlullah Yoga Yovi Litanianda, Yovi Zulkarnain, Ismail Abdurrozaq Zulkarnain, Ismail Abdurrozzaq Zulkarnain, Ismail Abdurrozzaq