Article Per Year (5 Year)
p-Index From 2021 - 2026
9.275
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi POSITIF Edu Komputika Journal Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Computatio : Journal of Computer Science and Information Systems RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Khatulistiwa Informatika JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri JTERA (Jurnal Teknologi Rekayasa) Jurnal Sains dan Informatika INOVTEK Polbeng - Seri Informatika Matrix : Jurnal Manajemen Teknologi dan Informatika SINTECH (Science and Information Technology) Journal Jurnal Informatika Universitas Pamulang Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) KACANEGARA Jurnal Pengabdian pada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer KOMPUTIKA - Jurnal Sistem Komputer KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terapan Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika EVOLUSI : Jurnal Sains dan Manajemen Building of Informatics, Technology and Science JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknologi Informasi dan Multimedia Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) JISA (Jurnal Informatika dan Sains) International Journal of Engineering, Technology and Natural Sciences (IJETS) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Idealis : Indonesia Journal Information System Jurnal Teknik Informatika (JUTIF) Jurnal Digit : Digital of Information Technology Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Science in Information Technology Letters Journal of Soft Computing Exploration Jurnal Indonesia : Manajemen Informatika dan Komunikasi Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer International Journal Software Engineering and Computer Science (IJSECS) Jurnal Sains dan Teknologi International Journal Science and Technology (IJST) Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Scientific Research, Education, and Technology Journal of Data Science Theory and Application NERO (Networking Engineering Research Operation) SmartComp Jurnal Indonesia : Manajemen Informatika dan Komunikasi Emitor: Jurnal Teknik Elektro IJISCS (International Journal of Information System and Computer Science)
Donny Avianto
Universitas Teknologi Yogyakarta
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

Published : 81 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 81 Documents
Search

 4   5   6   7   8 
Klasifikasi Penyakit Pada Daun Kopi Robusta Menggunakan Arsitektur AlexNet dan Xception dengan Metode Convolutional Neural Network Ashari, Nadia; Avianto, Donny
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6109

Abstract

Diseases on the leaves of robusta coffee plants can have a significant impact on the growth and yield of robusta coffee plants. The leaves of the robusta coffee plant are susceptible to various types of diseases caused by fungi, bacteria or insects with symptoms such as brown, yellow or black patches and discoloration on the surface of the leaves of the robusta coffee plant. Early detection of diseases in robusta coffee leaf plants is very important to obtain effective control to maintain plant health. In this study, a disease classification model on the leaves of robusta coffee plants was made using the Convolutional Neural Network (CNN) architecture. The architecture used in this study is AlexNet and Xception. In this study, a dataset of images of robusta coffee leaves obtained through direct observation of robusta coffee plantations in Temanggung Regency was used. The number of datasets used was 1400 data which was divided into 4 classes, namely healthy, root down, leaf rust and red spider mites. The CNN model was tested by setting parameters consisting of batch size, drop out, learning rate, optimizer and the number of epochs that varied 35, 50 and 100. The results of this study show that the AlexNet architecture model with 50 epoch tests obtains the best accuracy of 98.57% and the Xception architecture obtains an accuracy of 100% in each epoch test. Overall, the use of AlexNet and Xception architectures is very effective in classifying diseases in robusta coffee leaves, but the Xception architecture is superior in the ability to classify complex datasets and higher accuracy.
Identifikasi Penyakit Daun pada Tanaman Solanaceae dan Rosaceae Menggunakan Deep Learning Faqih, Allan Bil; Avianto, Donny
Jurnal Teknologi Terpadu Vol 10 No 2 (2024): Desember, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i2.1440

Abstract

With a projected global population of 9.7 billion by 2050, agriculture faces significant challenges in ensuring food security. One major obstacle is plant diseases that reduce crop yields by 40% per year. Previous research is often limited to disease detection in a single plant species, thus poorly reflecting multi-species needs in real agricultural practices. This research aims to develop and evaluate deep learning-based plant disease detection system using Convolutional Neural Networks (CNN) applied to two plant families, Solanaceae and Rosaceae. The dataset used was PlantVillage, containing 54,306 leaf images in JPEG format downloaded from GitHub, with data outside two families discarded during pre-processing. Three deep learning models were tested: transfer learning with InceptionV3 architecture and two custom CNNs (DFE and LCNN). LCNN model showed the best performance with training, validation, and testing accuracies of 99%, 99%, and 95%, respectively. In contrast, InceptionV3 achieved 96% training, 98% validation, and 92% testing accuracy, while DFE with 86% training, 94% validation, and 82% testing accuracy. Confusion matrix analysis showed difficulty distinguishing between healthy potatoes and potatoes with late blight, as well as cedar apple rust. These results highlights importance of developing specific model architectures rather than complex models for accurate multi-crop disease detection.
Squeeze-and-Excitation networks and attention mechanism in automatic detection of coffee leaf diseases based on images Iqbal, Muhammad Izza; Avianto, Donny
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.490

Abstract

This research examines the effectiveness of Squeeze-and-Excitation Networks (SENet) combined with Attention Mechanism for automated detection of coffee leaf diseases. The integration of SENet and Attention Mechanism presents a promising technological opportunity as SENet has proven effective in improving CNN performance by modeling channel interdependencies, while Attention Mechanism enables focused feature extraction on crucial leaf areas - a combination that remains underexplored in coffee leaf disease detection. Using a combination of three datasets: Coffee Leaf Diseases, Disease and Pest in Coffee Leaves, and RoCoLe.Original, comprising 3,177 coffee leaf images divided into four classes (Healthy, Miner, Phoma, and Rust), this study compares the performance of SENet against other deep learning architectures such as InceptionV3, ResNet101V2, and MobileNet. Experiments were conducted with variations in epochs (15 and 30), three data split ratios, and three optimizer types. Results demonstrate that SENet with Attention mechanism performs, achieving a peak accuracy of 96% at 30 epochs with an 80:20 data ratio and RMSprop optimizer. InceptionV3 and MobileNet showed competitive performance with 93% accuracy, while ResNet101V2 achieved 81%. Class-wise analysis reveals SENet's proficiency in detecting various coffee leaf diseases, with F1-scores 91% for all classes.
PEMBENTUKAN POHON KEPUTUSAN UNTUK PENERIMA BANTUAN BERAS MISKIN MENGGUNAKAN ALGORITMA DECISION TREE C4.5 Avianto, Donny; Wibowo, Adityo Permana
NERO (Networking Engineering Research Operation) Vol 9, No 1 (2024): Nero - 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i1.28020

Abstract

Beras Miskin (Raskin) merupakan salah satu program pemerintah yang bertujuan untuk memberikan bantuan pangan pokok kepada masyarakat kurang mampu. Namun, tantangan besar dalam implementasi program ini adalah ketidaktepatan sasaran, di mana terdapat kasus di mana warga yang seharusnya menerima bantuan malah tidak mendapatkannya, sementara sebagian yang tidak memenuhi syarat justru menerima bantuan. Penelitian ini bertujuan menghasilkan model pohon keputusan yang dapat membantu proses klasifikasi penerima bantuan beras miskin secara lebih mudah dan akurat, sehingga penyaluran program Raskin menjadi lebih tepat sasaran. Pembuatan model dilakukan menggunakan aplikasi RapidMiner Studio versi 10.3 dengan menerapkan algoritma pembentuk Decision Tree C4.5. Dalam menentukan kelayakan penerima, aplikasi menggunakan tujuh kriteria utama: tingkat kesejahteraan, jumlah tanggungan, jenis pekerjaan, sarana sanitasi, sumber air, jenis atap, dan jenis lantai. Algoritma C4.5 pada penelitian ini dilatih menggunakan 100 data pelatihan dan diuji dengan 20 data uji, menghasilkan akurasi sebesar 79,17% dengan faktor yang paling menentukan dalam prediksi adalah jenis lantai. Penelitian ini juga memvisualisasikan pohon keputusan yang terbentuk secara utuh untuk memudahkan interpretasi hasil prediksi dan peluang peningkatan di masa depan.
Sistem Klasifikasi Kualitas Bunga Cengkeh Kering berbasis Website menggunakan Logika Fuzzy Metode Tsukamoto Fadhila, Arifa Farras; Avianto, Donny
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27983

Abstract

Indonesian cloves have strong competitiveness in the main market due to their economic benefits, such as being raw materials for kretek cigarettes, spices, and the perfume industry. However, high global competition demands improvements in product quality and consistency. The manual and subjective sorting of cloves often leads to inaccuracies and inconsistencies in quality, which can be detrimental to farmers, especially in smallholdings. The objective of our research is to develop a web-based system for classifying the quality of dried clove flowers using the Tsukamoto fuzzy logic method. The stages of system development using the waterfall method include system requirements analysis, architecture and interface design, website implementation with the Tsukamoto fuzzy method, and testing. The Tsukamoto fuzzy logic implementation method was chosen due to its ability to process uncertain data and produce consistent output. Our findings successfully produced a web-based system called 'Clove Tester', with an average sensitivity of 45.99% from sensitivity testing based on modifications to the membership function of condition and quality variables. These results indicate that the system has a good adaptability to variations in input data, making it suitable for application to data with a high level of uncertainty or ambiguity in this research.
The Implementation of a Body Mass Index (BMI) Calculator in an Android-Based Ideal Body Check and Nutrition Consultation Application Ardiansyah, Diky; Avianto, Donny
Jurnal Internasional Teknik, Teknologi dan Ilmu Pengetahuan Alam Vol 6 No 2 (2024): International Journal of Engineering, Technology and Natural Sciences
Publisher : Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46923/ijets.v6i2.366

Abstract

The rising prevalence of obesity necessitates the development of effective strategies for personalized nutritional guidance. This study aims to address common barriers to accessing nutritional advice, such as geographic distance, time constraints, and financial limitations, by introducing an innovative mobile application. The application incorporates a Body Mass Index (BMI) calculator for ideal weight estimation and a real-time online consultation feature with certified nutrition counselors. A user-centered design methodology was employed to ensure the app's usability, accessibility, and engagement. The findings reveal that the app effectively facilitates healthier lifestyle adoption by providing personalized nutritional recommendations and fostering user motivation through regular updates, reminders, and progress-tracking tools. Additionally, the application enhances community engagement by disseminating evidence-based nutritional practices at individual and societal levels. This research highlights the potential of the application as a scalable solution for bridging the gap between users and professional nutritional advice. By empowering individuals to make informed health decisions, the app contributes to obesity prevention and the promotion of a healthier society. Future studies should investigate its long-term effects on health outcomes and explore the integration of advanced features to further enhance its functionality and impact.
ENHANCING EFFICIENCY AND TRANSPARENCY IN COFFEE SUPPLY CHAIN THROUGH BLOCKCHAIN-INTEGRATED TRACEABILITY PLATFORM Jagad Raya Ramadhan; Donny Avianto
International Journal Science and Technology Vol. 3 No. 3 (2024): November: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v3i3.1686

Abstract

Coffee is a global commodity that plays a significant role in the economy of many countries, including Indonesia. As the world's fourth-largest coffee producer, Indonesia has a vast potential to increase its coffee exports. This economic impact is not only a source of foreign exchange but also a significant source of income for smallholder farmers. However, recent inefficiencies have led to declining exports and quality control issues. This issue is exacerbated by the lack of transparency and traceability in the coffee supply chain, which makes it difficult for stakeholders to monitor the movement of coffee beans from farm to market. Thus, this research aims to address these problems by developing a blockchain-integrated traceability platform enhanced with IoT technology. The platform connects all stakeholders in the coffee supply chain, including farmers, processors, distributors, sellers, and consumers, ensuring real-time monitoring and data transparency throughout the coffee supply chain. This benefitted not only the involved stakeholders but also the end consumers. The system's provided QR code allows consumers to access information about the coffee's origin, quality, and processing details, increasing customer awareness and trust in the product.
Implementasi Logika Fuzzy Tsukamoto untuk Optimasi Jumlah Produksi Es Batu Kemasan: Implementation of Fuzzy Logic Tsukamoto to Optimize the Quantity of Packaged Ice Cube Production Purba, Yurjaa Ghoniyyan; Avianto, Donny
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 1 (2025): MALCOM January 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i1.1736

Abstract

Menentukan jumlah produksi yang akurat merupakan hal penting dalam perencanaan produksi, terutama ketika menghadapi fluktuasi permintaan yang seringkali menjadi tantangan utama. Ketidakpastian dalam permintaan ini memerlukan optimasi agar jumlah produksi yang dihasilkan dapat memenuhi kebutuhan konsumen tanpa menyebabkan penumpukan stok berlebih. Salah satu pendekatan yang dapat digunakan untuk menentukan jumlah produksi adalah metode Logika Fuzzy, khususnya metode Tsukamoto, yang mempertimbangkan variabel permintaan dan persediaan. Penelitian ini bertujuan untuk menerapkan metode Tsukamoto dalam menentukan jumlah produksi es batu kemasan. Data penelitian diperoleh melalui wawancara dengan pemilik usaha dan mencakup data historis produksi, permintaan, serta persediaan es batu kemasan. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan menghasilkan Mean Absolute Percentage Error (MAPE) sebesar 1,86% dan akurasi prediksi sebesar 98,14%. Nilai MAPE yang berada di bawah 10% mengindikasikan bahwa sistem ini mampu memberikan prediksi jumlah produksi yang optimal dan efektif.
Modifikasi Arsitektur dalam Convolutional Neural Network untuk Klasifikasi Batik Lampung dan Batik Yogyakarta Octavianus, Yonathan; Avianto, Donny
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 1 (2025): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v6i1.1223

Abstract

Batik is one of the unique forms of Indonesian culture. On October 2, 2009 UNESCO (United Nations Educational, Scientific, and Cultural Organization) designated batik as a Masterpiece of the Oral and Intangible Heritage of Humanity. One of the batik heritages of our ancestors is Lampung Batik and Yogyakarta Batik, where both batik have their own differences and uniqueness, so that we as Indonesian people must maintain and preserve the cultural heritage of our ancestors by creating a system that can determine both batik without using instinct or based on recommendations from others which can still cause errors. In previous studies, classification using Multikernel SVM managed to achieve an accuracy of 100%. There are also those who use CNN(Convolutional Neural Network)-Sobel with an accuracy of 91,2% in the training process and 91,8% in the validation process. The problems experienced in previous studies were the limitations of the dataset and the model testing process which was still not optimal so that it did not get satisfactory results so that in this study the Convolutional Neural Network method will be used with 6 architectures, 3 of which are unModified architectures, namely MobileNetV2, DenseNet121, and Xception. And 3 Modified architectures, namely MobileNetV2 (Modified), DenseNet121 (Modified), and Xceptipn (Modified). The selection of the three architectures is because it has a very large number of layers so that it can calculate a very large amount of data and produce the appropriate output. The best results obtained in this study were the Modified architecture, namely Xception (Modified) with an accuracy of 100%, Precision 97%, Recall 94%. F1 Score 92%, and Loss 0,0066 in the 30th epoch experiment and learning rate 0,0001 so that Xception became the best model in the Modified architecture (Modified). This research is expected to be able to provide a renewable technology system to ordinary people who do not know Lampung Batik and Yogyakarta Batik to be able to distinguish between the two specifically so as to minimize errors in analyzing or when buying the desired Batik
Hyperparameter Optimization of CNN for Coffee Berry Disease Classification Using the Artificial Bee Colony Algorithm Fadilah, Faiz; Avianto, Donny
Journal of Scientific Research, Education, and Technology (JSRET) Vol. 3 No. 4 (2024): Vol. 3 No. 4 2024
Publisher : Kirana Publisher (KNPub)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58526/jsret.v3i4.605

Abstract

Indonesia is one of the world's largest coffee producers, with a significant contribution to the global market. However, extreme weather challenges, such as the El Nino phenomenon, have led to a decline in coffee production of up to 30%, affecting the quality and quantity of coffee beans. A major challenge in coffee cultivation is coffee berry diseases, such as the coffee berry borer and coffee berry damage, which can cause up to 60% crop loss. Early detection of these diseases is essential to reduce losses and preserve coffee quality. This study seeks to enhance the performance of a Convolutional Neural Network (CNN) model for coffee berry disease classification by optimizing hyperparameters using the Artificial Bee Colony (ABC) algorithm. The research dataset consists of 2100 images with three categories: Healthy Berry, Berry Borer, and Berry Damage. The research stages include data preprocessing, CNN model design, hyperparameter optimization, training, and model evaluation. The results showed that the application of the ABC algorithm succeeded in significantly improving the accuracy of the CNN model compared to the method without optimization. The accuracy result obtained is 97.14% with an architecture consisting of 3 convolutional layers and 3 fully connected layers. This finding makes a real contribution to the development of meta-heuristic-based optimization techniques for coffee fruit disease classification, as well as supporting efforts to improve coffee quality amid the challenges of global climate change.
Co-Authors Adhitama, Satriya Adicahya, Bina Sukma Adityo Permana Wibowo Alfin Syarifuddin Syahab Alwani, Adie G. Amalia Rizki Wulandari Apriansyah, Ferryma Arba Ardiansyah, Diky Aribowo Aribowo Arief Hermawan Arieska Restu Harpian Dwika Arif Hermawan, Arif Ashari, Nadia Aziz Perdana Baiq Nurul Azmi Bowo Hirwono Budiyanto, Irfan Dewi, Amelia Citra Dian Wijayanti Dimas Dwi Kurniawan Dwi Ratnawati, Dwi Edi Priyanto Enggar Novianto Enggar Novianto Erfin Nur Rohma Khakim Fadhila, Arifa Farras Fadilah, Faiz Fahri Putra Herlambang Fakharudin, Panji Rangga Adzan Fajar Faqih, Allan Bil Febiansyah Annaufal Ahnaf Fauzi Ferdinandus Edwin Penalun Gumilang, Muhammad Satrio Gunawan, Asrul Hanif, Rifqi Fadhlurrahman Hardiyantari, Oktavia Herdy Andriksen Ilmy Eka Handayani Imantoko Imantoko Indra Maulana Iqbal, Muhammad Izza Jagad Raya Ramadhan Kusban, Muhammad Kusumastuti, Asriana Dyah Maulana, Adha Muh Arifandi Muhammad Irsyad Indra Fata Muhammad Rizki Muhammad Rizki Muhammad Rizki Nasmah Nur Amiroh Nazar Iqbal Bimantoro Novaldy, Olwin Kirab Nur Widiastuti Nurazila, Siti Octavianus, Yonathan Perdana, Aziz Purba, Yurjaa Ghoniyyan Purnomo Pratama, Rizki Putra, Kristianto Pratama Dessan Reski Noviana Rian Oktafiani Rian Oktafiani Rianto Rianto Rizarta, Rusma Eko Fiddy Rizky Samudra Falasyfa Roy Fasti Rubangi Rubangi Rudi, Rudiono Rusma Eko Fiddy Rizarta Saputra, Candra Heru Setiawan, Muhhamad Ajun Siti Rokhanah Soraya Fatmawati Sri Wulandari SRI WULANDARI Sutarman Sutarman Syafrudin, Teguh Syahab, Alfin Syarifuddin Teguh Syafrudin Tri Untoro, Iwan Hartadi Tri Widodo Vivianti Wahid, Ach. Nur Aqil Widyastuti, Evi