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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 Pendidikan Informatika dan Sains 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 JURNAL REKAYASA TEKNOLOGI INFORMASI 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 JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terapan Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika JISICOM (Journal of Information System, Infomatics and Computing) 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 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 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 Jikom: Jurnal Informatika dan Komputer Journal of Data Science Theory and Application NERO (Networking Engineering Research Operation) SmartComp Jurnal Ilmu Komputer dan Sistem Informasi 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 Agriculture, Biological Sciences & Forestry Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Public Health Social Sciences Veterinary Other

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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.
Metode Neural Network Dalam Prediksi Jumlah Penumpang Kereta Api Berbasis Web Adicahya, Bina Sukma; Wulandari, Sri; Avianto, Donny
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6001

Abstract

The demand for railway transport in Java has increased along with the increasing population growth and increasingly complex mobility needs. Trains have become one of the main modes of transport due to their efficiency and reliability in travelling medium and long distances. However, there is a significant imbalance between ticket demand and train capacity availability, especially during holiday seasons, holidays, and weekends. To solve this problem, a simulation is needed to predict the number of passengers in the future using the Neural Network Backpropagation method. The implementation of this system resulted in a prediction accuracy rate of 80.33%, which provides PT Kereta Api Indonesia with an important tool to better manage schedules and capacity. It is hoped that this research can make a significant contribution to improving the company's operational efficiency, while providing a better experience for passengers. In addition, this research is also expected to provide stakeholders with greater insight into the dynamics of transport demand in Java, and help formulate more effective policies to support the growth of the public transport sector. With the results of this study, PT Kereta Api Indonesia is expected to develop optimal strategies in adjusting train capacity to the unstable passenger demand, while local governments can utilise this information to design policies that support the sustainability of transport services in Java.
PREDIKSI LONJAKAN PENJUALAN TOKO RETAIL ONLINE SAAT HARBOLNAS DENGAN MODEL SARIMA Putra, Kristianto Pratama Dessan; Hermawan, Arief; Avianto, Donny
Jurnal Khatulistiwa Informatika Vol 13, No 1 (2025): Periode Juni 2025
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jki.v13i1.25071

Abstract

Peralihan proses transaksi dari konvensional ke online telah merambah ke berbagai sektor, salah satunya toko retail. Banyak toko retail yang telah membangun sarana penjualan secara online dan setiap harinya jumlah transaksi mengalami peningkatan. Dengan semakin meningkatnya jumlah transaksi maka diperlukan peningkatan layanan server untuk mengakomodir kebutuhan pengguna. Selain itu, adanya tanggal-tanggal kembar yang dijadikan HARBOLNAS juga sering menyebabkan jumlah transaksi melonjak dari hari biasanya. Apabila lonjakan transaksi tidak diimbangi dengan spesifikasi server yang mumpuni maka akan terjadi “lost of sales”. Oleh karena itu, perlu adanya sistem prediksi untuk memperkirakan kenaikan ataupun lonjakan transaksi untuk hari mendatang guna antisipasi kebutuhan server. Dalam penelitian ini, metode prediksi yang digunakan adalah model SARIMA dengan dataset primer dari salah satu perusahaan retail online di Indonesia. SARIMA dipilih karena dataset memiliki bersifat musiman akibat adanya HARBOLNAS di setiap tanggal kembar. Hasilnya menunjukan bahwa model SARIMA sukses memproses dataset dan memprediksi lonjakan transaksi untuk 30 hari ke depan dengan nilai uji evaluasi MAPE di angka 15,05%. Lebih lanjut, penelitian juga menyediakan hasil uji evaluasi dengan metode lainnya sebagai pembanding untuk penelitian lanjutan dengan metode ataupun parameter yang berbeda.
Max Depth Impact on Heart Disease Classification: Decision Tree and Random Forest Rian Oktafiani; Arief Hermawan; Donny Avianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5574

Abstract

Results in heart disease classification that are inaccurate and have low accuracy can endanger the patient's life. Some parameters in the algorithm model also influence classification. This study compares the Decision Tree and Random Forest algorithms for heart disease. The influence of maximum depth on heart disease classification also has significant implications. If the maximum depth is not set correctly, the classification results can be inaccurate and lead to incorrect diagnoses. This study uses five data split schemes, namely 60%: 40%, 70%: 30%, 75%: 25%, 80%: 20%, 90%: 10% and tested with different max depth parameters, namely max depth = 3, 4, 5, 6, and 7. This research produces the best accuracy using the 90%:10% scheme and max depth = 7 with the best accuracy result using the Random Forest algorithm of 99.29% while the Decision Tree algorithm is 98.05%. Then the precision and recall value of the Random Forest algorithm is 99% while the Decision Tree is 98%. The results of computation time using Decision Tree are faster than using Random Forest with a computation time for training data of 0.0075 s, while the testing data are 0.009 s. In future research, research can be conducted on the effect of other parameters by testing using several data sets.
ANALISIS PROPERTI PROSPEK DAN NON-PROSPEK BERDASARKAN DATA IKLAN MENGGUNAKAN METODE K-MEANS DAN K-MEDOIDS Maulana, Adha; Avianto, Donny
Journal of Data Science Theory and Application Vol. 4 No. 1 (2025): JASTA
Publisher : LP3M Universitas Putra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32639/vg5bfb91

Abstract

The significant growth of the property industry market in the city of Yogyakarta. has attracted more attention from property companies. This is glanced at by property companies in bridging sales. However, property agency companies often experience losses in plotting advertising budgets. With this problem, the author offers a clustering system for prospective and non-prospective property spots using the K-Means and K-Medoids methods with the Forward Selection feature selection method. This study aims to allocate advertising budgets to targeted projects with great potential. The data used is primary data, namely IRSC data from company x in Yogyakarta with a range (January 2023–March 2024) totaling 212 records. Data processing uses the RapidMiner application with a data composition of 70% used for training data and 30% for testing data. This process produces a DBI value of 1.060 for the K-Medoids method without feature selection and 1.974 for the K-Medoids method using feature selection. The best method produced by the K-Means method using and without feature selection with a DBI value of 0.148.
Analisis Perbandingan Metode DES (Double Exponential Smoothing) dan WMA (Weighted Moving Average) dalam Peramalan Penjualan Laptop Gunawan, Asrul; Hermawan, Arief; Avianto, Donny
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.15314

Abstract

Rapid technological developments increase demand for electronic devices, especially laptops. Fluctuations in monthly sales are a challenge for companies in determining the optimal amount of inventory. The inability to predict market demand can disrupt inventory management and customer satisfaction. Therefore, accurate sales forecasting is essential for planning marketing and procurement strategies. This study compares two sales forecasting methods, namely Double Exponential Smoothing (DES) and Weighted Moving Average (WMA), to analyze the accuracy of each method. The results showed that the DES method has a better level of accuracy with an average MAPE value of 16.72%, compared to WMA which reached 21.22%. This study provides practical insights for companies in choosing the right forecasting method, in order to improve inventory management, product procurement strategies, and customer satisfaction
Co-Authors Adicahya, Bina Sukma Adityo Permana Wibowo Alwani, Adie G. Amalia Rizki Wulandari Andri Yudha Pratama Apriansyah, Ferryma Arba Ardiansyah, Diky Aribowo Aribowo Arief Hermawan Arieska Restu Harpian Dwika Ashari, Nadia Asrul Gunawan Aziz Perdana Baiq Nurul Azmi Bayu Tri Nugroho, Bayu Tri Bimantoro, Nazar Iqbal Bowo Hirwono Budiyanto, Irfan Cahaya Muzaddidah Dewi, Amelia Citra Dian Wijayanti Dimas Dwi Kurniawan Dimas Rizqi 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 Ida Kumala Sari Iin Rohmatika Aulia Ilmy Eka Handayani Imantoko Imantoko Indra Maulana Iqbal, Muhammad Izza Irfan Budiyanto Jagad Raya Ramadhan Khalifatur Rauf Kusumastuti, Asriana Dyah Laode Izat Trianto Haradin Lidya Nurmala Eva Maulana, Adha Muh Arifandi Muhammad Irsyad Indra Fata Muhammad Kusban Muhammad Rizki Muhammad Rizki Nasmah Nur Amiroh Novaldy, Olwin Kirab Nur Widiastuti Nurazila, Siti Octavianus, Yonathan Perdana, Aziz Purba, Yurjaa Ghoniyyan Putra, Kristianto Pratama Dessan Rahma Nur Azizah Reski Noviana Rian Oktafiani Rian Oktafiani Rianto Rianto Risnanto, Ari Rizarta, Rusma Eko Fiddy Rizki Purnomo Pratama Rizky Samudra Falasyfa Roselilie Simbulan Roy Fasti Rubangi Rubangi Rudi, Rudiono Rusma Eko Fiddy Rizarta Saputra, Candra Heru Satriya Adhitama 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 Wayan Praka