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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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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.
TOMATO CLASSIFICATION BASED ON VARIETY WITH RGB FEATURE EXTRACTION AND NAÏVE BAYES ALGORITHM Widyastuti, Evi; Hermawan, Arif; Avianto, Donny
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i1.3370

Abstract

Tomato is a fruit-category vegetable plant that is easy to cultivate in various locations. The diversity of tomato varieties, such as Red Zebra Tomato, Green Zebra Tomato, and Kumato, often makes rapid and accurate variety identification challenging. Misclassification can impact the selection of environmental conditions and pest or disease management, ultimately leading to suboptimal cultivation results. Currently, research primarily focuses on tomato shape, diseases, and ripeness levels, while cultivar classification based on color characteristics remains limited. This study aims to develop a method for classifying tomato cultivars based on RGB color features using the Naïve Bayes algorithm. The research was conducted by collecting 45 tomato images with similar shapes but different colors (red, green, and dark red). The research stages include RGB feature extraction, data rounding, splitting training and test data with a 70:30 ratio, and classification using Naïve Bayes. A re-evaluation was performed by removing specific color attributes to assess their impact on accuracy. This study is expected to support rapid and accurate tomato variety identification, enhance efficiency in modern agriculture, and expand the application of technology in the agricultural industry to achieve advanced, self-sufficient, and modern farming. The results show that the RGB feature extraction method and the Naïve Bayes algorithm can classify tomato cultivars with an accuracy of up to 78.57%. The RG color attributes have the most significant impact on accuracy, reaching 85.71%.
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.
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