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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 Indonesian Journal of Applied Informatics 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) TIN: TERAPAN INFORMATIKA NUSANTARA 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 Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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PENERAPAN ASSOCIATION RULE TERHADAP DIAGNOSA PENYAKIT MENGGUNAKAN ALGORITMA FREQUENT PATTERN GROWTH Wahid, Ach. Nur Aqil; Avianto, Donny
NERO (Networking Engineering Research Operation) Vol 8, No 2 (2023): Nero - 2023
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v8i2.22566

Abstract

Penumpukan data terus terjadi berbanding lurus dengan waktu, pemanfaatan data dapat digunakan dalam berbagai cara. Seperti pada umumnya, teknik asosiasi normalnya diterapkan pada sekumpulan data transaksi dengan harapan menemukan korelasi antara itemset. Namun, pada penelitian kali ini penulis ingin mencoba untuk menerapkan teknik asosiasi terhadap dataset diagnosa penyakit pada pasien umum, melihat kesamaan pola dari data yang dapat ditemukan korelasinya dengan algoritma Fp-Growth. Diharapkan hasil korelasi antara diagnosa dapat menjadi benang merah dalam pemanfaatan, penelitian, serta pengembangan untuk mencapai sebuah pembaharuan. Algoritma Frequent Pattern Growth (FP-Growth) merupakan algoritma yang sesuai untuk menentukan kumpulan data yang paling sering muncul (frekuensi itemset) dalam menganalisa korelasi antara diagnosa penyakit dari pasien, dan berikutnya hasil dari penambangan data divisualisasikan dengan basis website dengan streamlit. Dengan terus mencari hasil yang optimal dengan trial and error, dan salah hasil dari salah satu aturan terdapat pada nilai threshold 0,6 yang diterapkan pada kecamatan masa lembu mendapatkan korelasi pada aturan ketiga yaitu Neoplasma Jinak berkorelasi dengan Ileus paralitik dan obstruksi dengan nilai support 0,8 dan confidence 1. Hasil dari aturan asosiasi diharapkan dapat dikembangkan dan dapat memberikan kontribusi lebih lanjut dalam menentukan keputusan yang lebih matang.Kata kunci: Teknologi, Big Data, Penambangan Data, Association Rules FP-Growth
CLASSIFICATION OF CUSTOMERS’ REPEAT ORDER PROBABILITY USING DECISION TREE, NAÏVE BAYES AND RANDOM FOREST Dewi, Amelia Citra; Hermawan, Arief; Avianto, Donny
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.5243

Abstract

Limited customer information in sales data on e-commerce in Indonesia hinders companies in determining targeted marketing strategies, especially in targeting groups of potential customers to make repeat purchases. Sales data in the form of customers' names and cellphone numbers has been hidden by e-commerce, and only data is available in the form of products purchased, number of purchases, and customer addresses. So far, the methods used to determine potential customers mostly use more complete data features. Research that uses limited e-commerce data to determine potential customers is scarce. Several algorithms for predicting repeat purchases in e-commerce also have been widely used. However, the comparison of the performance of these methods in the context of e-commerce in Indonesia with limited data has yet to be discovered. In this research, the Decision Tree, Naive Bayes, and Random Forest methods were compared to classify potential customers using Maschere brand sales data from two e-commerce sites, namely Tokopedia and Shopee. The research results show that the Decision Tree algorithm achieved an accuracy of 90.91%, Naive Bayes achieved an accuracy of 37.50%, and Random Forest achieved the best level of accuracy, namely 93.94%. These results show that the Random Forest method is the best method for classifying customers' probability of repeat purchases. In the future, the results of this research can be developed again as a decision-making system to determine potential customers.
Vision-based chicken meat freshness recognition system using RGB color moment features and support vector machine Sutarman, Sutarman; Avianto, Donny; Wibowo, Adityo Permana
Science in Information Technology Letters Vol 4, No 2 (2023): November 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i2.1230

Abstract

Chicken meat is a highly sought-after food product among various segments of the general population, known for its high nutritional value and easy accessibility. Presently, meat identification is primarily conducted manually, relying on visual inspection or tactile assessment of the meat's color and texture. However, this approach presents several limitations, particularly when consumers lack the discernment to differentiate the quality of chicken meat freshness. This research aims to identify the freshness level of chicken meat using the Support Vector Machine method, employing the extraction of RGB color moment features to determine the freshness of the meat. The feature extraction process involves calculating the percentage of intensity values for R (Red), G (Green), and B (Blue) in each chicken meat image. Based on the image processing results, the percentage of intensity values, particularly in the R and B parameters, can be used as determining factors. The study involves software testing using fresh and non-fresh chicken meat. The developed system can identify the freshness level of fresh chicken meat with an accuracy rate of 71.6% using the linear kernel SVM and 60.5% using the RBF kernel SVM.  This research represents a significant step toward the automation of chicken meat freshness assessment, potentially reducing food waste and enhancing food safety in the food industry. Further research and development could improve the system's accuracy and expand its applications in various food quality control settings.Chicken meat is a highly sought-after food product among various segments of the general population, known for its high nutritional value and easy accessibility. Presently, meat identification is primarily conducted manually, relying on visual inspection or tactile assessment of the meat's color and texture. However, this approach presents several limitations, particularly when consumers lack the discernment to differentiate the quality of chicken meat freshness. This research aims to identify the freshness level of chicken meat using the Support Vector Machine method, employing the extraction of RGB color moment features to determine the freshness of the meat. The feature extraction process involves calculating the percentage of intensity values for R (Red), G (Green), and B (Blue) in each chicken meat image. Based on the image processing results, the percentage of intensity values, particularly in the R and B parameters, can be used as determining factors. The study involves software testing using fresh and non-fresh chicken meat. The developed system can identify the freshness level of fresh chicken meat with an accuracy rate of 71.6% using the linear kernel SVM and 60.5% using the RBF kernel SVM.  This research represents a significant step toward the automation of chicken meat freshness assessment, potentially reducing food waste and enhancing food safety in the food industry. Further research and development could improve the system's accuracy and expand its applications in various food quality control settings.
Pengembangan Aplikasi Android Menggunakan REST API dengan Metode Waterfall Untuk Peningkatan Aksesibilitas Situs Repositori Setiawan, Muhhamad Ajun; Avianto, Donny
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7056

Abstract

The repository system offered by the campus to students to increase students' interest in reading, in fact, is still low in use even in an academic environment. This happens because the repository system offered is less convenient for students to use as a literacy tool. The result of the research is an application that is expected to be able to improve accessibility to repositories. A data filter feature and a new display update for the repository system will be added by the author in the implementation. Users of this service can filter their search results depending on the field of study, research specialization, and research period. Usability testing on the created application has shown that many students will start to be interested in using the repository system as their literacy material. This test used the System Usability Scale methodology, where the users of the program - in this case, university students - were given a questionnaire. According to the results obtained in the questionnaire, the usability value was quite high at 75.63 percent. The conclusion is that the repository application developed with the addition of this filtering feature can increase literacy interest in students.
Prediksi Burnout Pada Programmer Menggunakan Teknik Pengenalan Pola Untuk Identifikasi Dini Dan Intervensi Saputra, Candra Heru; Hermawan, Arief; Avianto, Donny
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 3: Juni 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.1138070

Abstract

Burnout atau kelelahan kerja merupakan sebuah fenomena yang sering dihadapi oleh profesional dalam berbagai bidang, termasuk programmer. Dampak negatif dari burnout mencakup penurunan kesejahteraan individu dan produktivitas kerja. Penelitian ini bertujuan untuk mengembangkan sebuah model prediktif untuk identifikasi dini dan intervensi burnout pada programmer menggunakan teknik pengenalan pola. Data yang digunakan dalam penelitian ini diperoleh dari kuesioner yang mencakup pertanyaan terkait pola kerja, kebiasaan individu, dan indikator burnout berdasarkan kriteria Maslach Burnout Inventory (MBI). Metodologi yang diterapkan melibatkan pengumpulan dan pra-pemrosesan data, ekstraksi fitur, dan aplikasi algoritma pengenalan pola untuk konstruksi model. Hasil penelitian menunjukkan bahwa model yang dikembangkan mampu mengidentifikasi risiko burnout dengan akurasi yang memadai, dan teknik pengenalan pola terbukti efektif dalam menggali pola dan insight yang relevan untuk identifikasi dan intervensi burnout pada programmer.
Penerapan Metode Neural Network Berbasis Web Dalam Prediksi Harga Telur Ayam Febiansyah Annaufal Ahnaf Fauzi; Sri Wulandari; Donny Avianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1865

Abstract

The demand for animal protein consumption is increasing in line with the development and growth of the livestock industry. Chicken eggs are one of the choices as a source of protein due to their abundant availability and affordable price. However, Yogyakarta Province experiences unstable egg price fluctuations, as indicated by the imbalance between high demand and limited production. To overcome this challenge, the authors developed the use of the Neural Network Backpropagation method to predict chicken egg prices. The selection of this method is based on its reputation for providing accurate predictions in this case. The implementation of this method resulted in an accuracy rate of 85%, which provides farmers with one of the useful tools to better manage risks and plan their production. This research is expected to make a significant contribution to the livestock industry, by providing farmers with a useful tool to manage risks and plan their production activities. In addition, this research is also expected to provide a better understanding of market behavior for stakeholders in Yogyakarta Province and the wider community. Thus, it is expected that this effort will not only improve the sustainability of the local economy but will also advance the livestock industry as a whole. With the results of this study, farmers are expected to optimize their strategies in adjusting production to the fluctuating market demand. In addition, stakeholders in Yogyakarta Province can use this information to develop more effective policies to support the growth of the livestock sector, especially in chicken egg farming
The Analisis Properti Prospek Dan Non-Prospek Berdasarkan Data Penjualan Properti Menggunakan Metode K-Means: Klasterisasi Maulana, Adha; Avianto, Donny
Jurnal Sains dan Teknologi (JSIT) Vol. 4 No. 3 (2024): September - Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v4i3.2256

Abstract

Sangat penting bagi kehidupan manusia untuk memiliki tanah untuk membangun rumah karena merupakan syarat dasar untuk infrastruktur, serta stabilitas dan keamanan bagi penghuni. Tanah juga menawarkan kontrol atas lingkungan sekitar dan merupakan pilihan investasi berisiko rendah. Perusahaan properti sangat penting dalam menyediakan tanah dengan cara yang sesuai dengan kebutuhan masyarakat dan lingkungan. Namun, akan ada kerugian jika lokasi yang dipilih tidak sesuai dengan pasar yang dimaksudkan atau memiliki fasilitas umum yang buruk. Studi ini bertujuan untuk membuat sistem prediksi pemetaan spot properti ke dalam dua kelompok—prospek dan non-prospek—dengan menggunakan metode K-Means Clustering. Studi ini berbeda dari studi sebelumnya yang lebih berkonsentrasi pada prediksi harga properti. Penelitian ini menggunakan data iklan, respons, survei, dan penjualan dari staf marketing perusahaan x dari periode (Januari 2023–Maret 2024). Hasil pengujian menggunakan data latih diambil dari periode (Januari 2023– Januari 2024), dan periode sisa Februari 2024 dan Maret 2024 menunjukkan akurasi 95,28% dan 98,11% dengan seleksi fitur, masing-masing.
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.
Co-Authors Adhitama, Satriya Adicahya, Bina Sukma Adityo Permana Wibowo 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 Bimantoro, Nazar Iqbal 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 Iin Rohmatika Aulia Ilmy Eka Handayani Imantoko Imantoko Indra Maulana Iqbal, Muhammad Izza Jagad Raya Ramadhan Kurniawan, Dimas Rizqi Kusban, Muhammad Kusumastuti, Asriana Dyah Maulana, Adha Muh Arifandi Muhammad Irsyad Indra Fata Muhammad Rizki Muhammad Rizki Nasmah Nur Amiroh Novaldy, Olwin Kirab Nur Widiastuti Nurazila, Siti Octavianus, Yonathan Perdana, Aziz Purba, Yurjaa Ghoniyyan Purnomo Pratama, Rizki Putra, Kristianto Pratama Dessan Rahma Nur Azizah 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