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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 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

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Peningkatan keterampilan komputer bagi Siswa SDN 1 Sinduadi Sleman Ratnawati, Dwi; Tri Untoro, Iwan Hartadi; Vivianti, Vivianti; Hardiyantari, Oktavia; Fatmawati, Soraya; Widodo, Tri; Avianto, Donny
KACANEGARA Jurnal Pengabdian pada Masyarakat Vol 6, No 3 (2023): Agustus
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/kacanegara.v6i3.1615

Abstract

Perkembangan teknologi informasi dan komunikasi mengusai semua bidang, terutama bidang Pendidikan. Kegiatan ujian berbasis paperless diwajibkan pemerintah dilakukan oleh semua sekolah. Banyak siswa yang belum memiliki kemampuan dalam komputer dasar sehingga mereka kesulitan dalam mengerjakan ujian dalam bentuk paperless. Permasalahan tersebut diselesaikan dengan mengadakan pelatihan Peningkatan Keterampilan Komputer bagi Siswa SD N 1 Sinduadi, Sleman yang dilakukan selama dua minggu. Pelaksanaan ini dilakukan dengan pendampingan dari tim dosen Universitas Teknologi Yogyakarta. Kegiatan ini dilakukan untuk mengatasi kesulitan siswa dalam mengoperasikan komputer dasar. Hasil dari pelaksaan ini adalah  78% siswa mampu meningkatkan kompetensi mengoperasikan komputer dasar dengan baik, dan 22% belum dapat menguasai komputer dasar dengan baik
Analyze Important Features of PIMA Indian Database For Diabetes Prediction Using KNN Perdana, Aziz; Hermawan, Arief; Avianto, Donny
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 1 (2023): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i1.1598

Abstract

Diabetes is a chronic, non-communicable disease, and a long-term health condition that affects how the body uses glucose, the type of sugar that gives energy. In Indonesia, diabetes ranks as the sixth highest cause of death, following conditions related to childbirth. In 2021, Indonesia has a total of 19.5 million diabetes patients, making it the fifth-highest in the world. Some machine learning research has used data from the PIDD (PIMA Indian Diabetes Dataset) to predict diabetes. In this research, in addition to prediction accuracy, data complexity is also important. This research analyzes important features in the PIMA Indian database using the KNN (k-nearest neighbor) method for classification. The results show that using KNN with k=22 value results in the highest accuracy of 83.12%. The analysis also found that the important features required by the KNN method to achieve high accuracy from the PIMA Indian database, in order of importance, are glucose, age, insulin, blood pressure, Body Mass Index, pregnancy, skin thickness, and diabetes pedigree function. However, when used in the KNN classification method, the diabetes pedigree function feature was found to be unnecessary, not relevant, and can be reduced. 
Klasifikasi Penyakit Antraknosa Pepaya California Menggunakan Convolutional Neural Network Nurazila, Siti; Avianto, Donny
JTERA (Jurnal Teknologi Rekayasa) Vol 8, No 1: June 2023
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v8.i1.2022.165-174

Abstract

Pepaya memiliki berbagai varian, salah satunya adalah pepaya California yang memiliki nilai jual tinggi di pasaran. Namun, petani sering kali mengalami gagal panen dikarenakan munculnya penyakit pada pepaya California. Salah satu penyakit yang menyerang pepaya adalah penyakit antraknosa. Kurangnya pengetahuan petani, apalagi petani baru sangat berpengaruh dengan kurangnya tindakan pencegahan penyakit antraknosa. Oleh karena itu, dibuatlah penelitian klasifikasi penyakit antraknosa pada pepaya California menggunakan Convolutional Neural Network (CNN). Data penelitian yang digunakan berjumlah 300 data citra dengan pembagian 150 data pepaya sehat dan 150 data pepaya antraknosa. Dalam proses pembangunan model CNN dataset akan dibagi menjadi dua bagian dengan perbandingan 80:20 antara data training dan data validation. Penelitian ini bertujuan untuk memberi informasi kepada petani baru tentang pengklasifikasian penyakit antraknosa pada pepaya. Hasil pengujian menunjukkan bahwa model terbaik dihasilkan menggunakan parameter optimizer Adam, epoch 20, dan loss binary cross-entropy. Model tersebut menghasilkan akurasi training 99,17% dan testing 99,58% dengan loss training 0,0239 dan loss validation 0,0177. Hasil penelitian menunjukkan bahwa algoritma CNN optimal dalam melakukan klasifikasi citra pepaya.
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.
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