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All Journal Jurnal Sistem Komputer Techno.Com: Jurnal Teknologi Informasi JUITA : Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sains dan Informatika Matrix : Jurnal Manajemen Teknologi dan Informatika Jurnal Teknoinfo ILKOM Jurnal Ilmiah Compiler KACANEGARA Jurnal Pengabdian pada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer INTEK: Informatika dan Teknologi Informasi Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terapan Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknologi Informasi dan Multimedia Jurnal Informatika Ekonomi Bisnis Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Didaktikum Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Infotech: Jurnal Informatika & Teknologi Journal of Engineering and Applied Technology KLIK: Kajian Ilmiah Informatika dan Komputer JUSTIN (Jurnal Sistem dan Teknologi Informasi) Algoritme Jurnal Mahasiswa Teknik Informatika Explore JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Arief Hermawan
Universitas Tekonologi Yogyakarta
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Other

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Cultural Introduction Application in Lembata Regency, NTT Using Android-Based Augmented Reality Bagus Wibowo Kusumo; Arief Hermawan
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 1 (March 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i1.844

Abstract

There are a lot of ways to conserve the Indonesian cultures, like using traditional things to create clothes, traditional houses, cultures, traditional dances, etc. The newest way to introduce Indonesia cultures is by making an application that can introduce the traditional culture. This research will focus in introducing the culture of Lembata Regency in Nusa Tenggara Timur using augmented reality technology. The application will use augmented reality technology based on android to show 3D product. This idea occurs because the limitation of information that shared by the government of Lembata Regency through its website. The understanding of local culture can give new experience to citizen and pushed them to filter the foreign culture that entering Indonesia by using virtual understanding. The Indonesian culture that localized in Lembata Regency NTT started to pushing back and threatened to be replaced by the foreign culture. That will make the citizen of Lembata Regency will loses their identity. The lack of information that provide by the local government, like the unavailability of local website that can be used to introduce the local culture is become the main idea for this research. The outcome of this research is an augmented reality application that can turned picture into 3D form. This application is mainly use to attract young generation awareness of the local culture.
Optimizing Windowing Techniques to Improve the Accuracy of Artificial Neural Networks in Predicting Outpatient Visits Nurcakhyadi, Fredianto; Hermawan, Arief
ILKOM Jurnal Ilmiah Vol 16, No 2 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i2.2254.172-183

Abstract

Hospitals are healthcare institutions that play an important role in providing health services to the community. To optimize the service, hospitals need to predict the number of outpatient visits. The objectives of this research are (1) determine the effect of window size on the accuracy of predicting the number of outpatient visits, and (2) identify the best window size and accuracy of neural networks in predicting the daily number of outpatient visits. To achieve the research objectives, the following steps were undertaken: data collection of outpatient visits at RSUD dr. Soedirman Kebumen from 2018 to 2023, preprocessing, applying different window sizes, modeling neural networks, and testing by calculating the RMSE value for each window size. The test results show that the lowest RMSE for 2018 was 1.267 with a window size of 34, for 2019 was 1.262 with a window size of 34, for 2020 was 1.515 with a window size of 17, for 2021 was 1.81 with a window size of 18, for 2022 was 1.282 with a window size of 20, and for 2023 was 1.263 with a window size of 29. These window sizes indicate the cycle of outpatient visits each year. By understanding these visit cycles, the number of outpatient visits can be predicted at any time.
Penerapan Metode JST Menggunakan Fitur GLCM pada Identifikasi Penyakit Tumbuhan Stroberi Wardaya, Imanuel Puspa; Hermawan, Arief
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 12, No 3: Desember 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v12i3.1606

Abstract

In recent years, strawberry farmers have experienced crop failures caused by diseases in strawberry plants. The lack of knowledge of strawberry farmers about the signs of strawberry plant disease results in more severe crop failure. This research aims to create a strawberry plant disease identification system with the artificial neural network method and compare the accuracy level of the application of the artificial neural network method to the strawberry plant disease detection system. One of the methods in artificial neural networks that can be used to identify strawberry plant diseases is backpropagation algorithm. The research was conducted by comparing the activation used between ReLU, Sigmoid, and Tanh. The best accuracy was obtained using ReLU activation with an accuracy of 83.74% and a model evaluation accuracy of 50%. This system can be used by strawberry farmers to identify diseases quickly so that they can anticipate crop failure due to disease attacks.Keywords: Artificial Neural Network; Backpropagation; Grey Level Co-occurrence Matrix; Strawberry Plant Disease AbstrakDalam beberapa tahun terakhir, petani buah stroberi mengalami gagal panen yang diakibatkan oleh penyakit pada tumbuhan stroberi. Kurangnya pengetahuan petani buah stroberi tentang tanda penyakit tumbuhan stroberi mengakibatkan gagal panen yang lebih parah. Penelitian ini bertujuan untuk menciptakan sistem identifikasi penyakit tumbuhan stroberi dengan metode jaringan syaraf tiruan dan membandingkan tingkat akurasi penerapan metode jaringan syaraf tiruan terhadap sistem pendeteksi penyakit tumbuhan stroberi. Salah satu metode dalam jaringan syaraf tiruan yang dapat digunakan untuk mengidentifikasi penyakit tumbuhan stroberi adalah algoritma backpropagation. Penelitian dilakukan dengan membandingkan aktivasi yang digunakan antara ReLU, Sigmoid, dan Tanh. Akurasi terbaik didapatkan menggunakan aktivasi ReLU dengan akurasi sebesar 83,74% dan akurasi evaluasi model sebesar 50%. Sistem ini dapat digunakan petani tanaman stroberi untuk mengidentifikasi penyakit dengan cepat sehingga dapat mengantisipasi terjadinya gagal panen akibat serangan penyakit. 
Analisis Perbandingan Model CNN Terhadap Klasifikasi Citra Komponen Elektronika Arrosyid, Muhammad Zydane; Hermawan, Arief; ., Sutarman
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.10259

Abstract

This study compares various Convolutional Neural Network (CNN) models in classifying electronic component images. The background of this research stems from the need to automatically identify and classify components in environments with limited computational resources. The data used in this research was collected through image scraping from the internet, supplemented by direct image acquisition using a camera. The data was then processed and trained using several CNN models, including MobileNet, NASNetLarge, VGG16, and others, as well as a custom CNN model developed by the researcher. The results show that NASNetLarge achieved the highest test accuracy of 79.31%, while MobileNet demonstrated high efficiency in computational resource usage. This study highlights that model size does not always correlate with accuracy, and models with fewer parameters can provide effective solutions for resource-constrained conditions.
Sistem Penjadwalan Kuliah Berbasis Click and Drag (Studi Kasus di Fakultas Sains & Teknologi Universitas Teknologi Yogyakarta) Shoffan Saifullah; Arief Hermawan
Jurnal Teknik Informatika dan Sistem Informasi Vol 3 No 1 (2017): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v3i1.652

Abstract

Utilization of technology in education is very important, especially at the University of Technology in Yogyakarta, which is one of the private universities in Yogyakarta with several objectives, one which is utilizing the maximum potential of technology to improve the effectiveness and efficiency of learning and dissemination of science and technology. One factor that can improve academic services are scheduling a lecture. Making a schedule of lectures is not an activity that is easy to do, because in doing scheduling not only arrange the schedule between subjects, time, lecturers and rooms. College scheduling systems currently running is less effective, because it requires a long time, and often clashes at student schedules. In this research, system development, scheduling lectures at the Faculty of Science and Technology, University of Technology Yogyakarta, where the scheduling system, click and drag more easily and to minimize clashes class schedules.
Rancang Bangun Sistem Penyewaan di ESGO Media SMK Negeri 1 Godean menggunakan ISO 25010 M. Royani Sopia Al Hasan; Arief Hermawan
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 2 (2025): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i2.566

Abstract

State Vocational High School 1 Godean is a school with a center of excellence that has various facilities and activities that support learning activities for students. One way is by using the teaching factory method. One of the teaching factory methods at State Vocational High School 1 Godean is that there is a rental service for multimedia equipment and goods intended for students and general users outside State Vocational High School 1 Godean called ESGO Media. There are problems experienced by ESGO Media in rental activities, one of which is the occurrence of human errors such as data input errors which make the data invalid and cause problems when you want to use it. The aim of this research is to develop a Web-based rental information system at ESGO Media State Vocational High School 1 Godean and test the information system with the ISO 25010 standard. The development method used is the Research and Development method with an Agile model with design through data flow diagrams. This research produced a Web-Based Rental Information System at ESGO Media with the results of obtaining a score of 100% in the functional suitability aspect with a very good category for use, 87.65% in the usability aspect and getting a very suitable for use category. In the reliability aspect, it gets an average test score of 77%, has a performance score of 70.48% and is categorized as grade C in the performance efficiency aspect, and is categorized as a system that is easy to maintain based on the maintainability aspect with a maintainability index value of 39.82 . It is hoped that this information system can improve performance and make it easier for ESGO Media managers to carry out rental activities at ESGO Media and become a basis for developing information systems in the future.
Pengaruh Cahaya dan Kualitas Citra dalam Klasifikasi Kematangan Pisang Cavendish Berdasarkan Ciri Warna Menggunakan Artificial Neural Network Aditya Dwi Putro; Arief Hermawan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i1.1396

Abstract

Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu buah pisang. Tujuan dari penelitian ini adalah menganalisa pengaruh cahaya dan kualitas citra dalam mengklasifikasikan tingkat kematangan buah pisang berdasarkan ciri warna buah pisang di Kebun Pisang Cavendish kabupaten banyumas jawa tengah sesuai dengan SNI 7422:2009[1]. Pisang yang terdapat di Kebun Pisang Cavendish ini beraneka ragam kualitas, sebagai buah lokal yang memiliki nilai ekonomi tinggi dan memiliki potensi pasar yang masih terbuka luas, pisang menjadi salah satu komoditas buah-buahan yang dapat diandalkan. Permasalahan yang sering ditemukan selain resource dan ketelitian yakni kurang tepatnya dan kurang pengetahuannya karyawan dalam membedakan tingkat kematangan pisang terutama karyawan baru. Artificial Neural Network digunakan sebagai metode dalam proses pengklasifikasian. Dataset pada penelitian ini adalah 80 citra buah pisang yang diambil per tandan terdiri dari 40 tandan citra pisang Cavendish yang diambil di pagi hari dengan kualitas citra bagus 20 dan kualitas citra tidak bagus 20, 40 tandan citra pisang Cavendish yang diambil di sore hari dengan kualitas citra bagus 20 dan kualitas citra tidak bagus 20. Tingkat kematangan pisang pada penelitian ini yaitu mentah dan matang. pengujian menghasilkan Akurasi tertinggi dalam proses klasifikasi kategori buah pisang cavendish menggunakan epoch 5000, goal 0.0001 dan learning rate 0.1 dengan jumlah akurasi sebesar 100% dengan model trainlm dan waktu 1.6 detik.
The Improvement of Artificial Neural Network Accuracy Using Principle Component Analysis Approach Arief Hermawan; Adityo Permana Wibowo; Akmal Setiawan Wijaya
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1880

Abstract

An important problem in a classification system is how to get good accuracy results. A way to increase the accuracy of a classifier system is to improve the number of input data attributes. Improving the number of input data attributes can be done using the Principal Component Analysis (PCA) method. The aim of this research is to reduce the number of input data attributes to increase the accuracy in a mushroom classification system. The research method used in this study started from collecting datasets from Kaggle.com related to mushroom-classification, then the data visualization process was carried out using pie charts then a dimension reduction process was carried out to reduce the number of variables using the PCA method. The next step is the training and testing of the artificial neural network. The architecture of artificial neural network used is backward error propagation with the number of hidden layers as much as 2 layers with the number of cells as many as 3 and 2. The training data used is 80%, while the testing data is 20%. Based on the test results, obtained an accuracy of 100% with 150,000 iterations and using 11 input variables from 22 existing input variables. By adding Principal Component Analysis part of the development that can improve the accuracy and performance of Artificial Neural Networks
Learning Accuracy with Particle Swarm Optimization for Music Genre Classification Using Recurrent Neural Networks Muhammad Rizki; Arief Hermawan; Donny Avianto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3037

Abstract

Deep learning has revolutionized many fields, but its success often depends on optimal selection hyperparameters, this research aims to compare two sets of learning rates, namely the learning set rates from previous research and rates optimized for Particle Swarm Optimization. Particle Swarm Optimization is learned by mimicking the collective foraging behavior of a swarm of particles, and repeatedly adjusting to improve performance. The results show that the level of Particle Swarm Optimization is better previous level, achieving the highest accuracy of 0.955 compared to the previous best accuracy level of 0.933. In particular, specific levels generated by Particle Swarm Optimization, for example, 0.00163064, achieving competitive accuracy of 0.942-0.945 with shorter computing time compared to the previous rate. These findings underscore the importance of choosing the right learning rate for optimizing the accuracy of Recurrent Neural Networks and demonstrating the potential of Particle Swarm Optimization to exceed existing research benchmarks. Future work will explore comparative analysis different optimization algorithms to obtain the learning rate and assess their computational efficiency. These further investigations promise to improve the performance optimization of Recurrent Neural Networks goes beyond the limitations of previous research.
Co-Authors Achmad Safruddin Aditya Dwi Putro Adityo Permana Adityo Permana Wibowo Akmal Setiawan Wijaya Aldi Tri Wijaya Arrosyid, Muhammad Zydane Bagus Wibowo Kusumo Baiq Nurul Azmi Bowo Hirwono Cahyo Prakoso Donny Avianto Edi Priyanto Enggar Novianto Ferdinandus Edwin Penalun Gunawan Wibisono Ilham Rafiedhia Pramutighna Imantoko Imantoko Intan Setiawati M. Royani Sopia Al Hasan Muh Arifandi Muhammad Aulia Muhammad Rizki Muhammad Rizki Muhammad Rousydi Hunafa Nur Hidayati Nur Widiastuti Nurcakhyadi, Fredianto Panji Al Muqsith Prasetyo Reski Noviana Rian Oktafiani Rianto Rianto Rizky Diar Panuntun Saifullah, Shoffan Shoffan Saifullah Sutarman . Tri Waluyo Tri Widodo Triyas Hevianto Saputro Wardaya, Imanuel Puspa