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All Journal snimed Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI J I M P - Jurnal Informatika Merdeka Pasuruan PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat JISA (Jurnal Informatika dan Sains) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Sistem Informasi dan Sistem Komputer Djtechno: Jurnal Teknologi Informasi Bulletin of Computer Science Research JUTECH : Journal Education and Technology JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Artificial Intelligence and Digital Business Mekongga: Jurnal Pengabdian Masyarakat
Fadil Indra Sanjaya
Universitas Teknologi Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry 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 Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Klasifikasi Citra Biji Kopi Sangrai Arabika dan Robusta Menggunakan Convolutional Neural Network Al Firdaus, Muhammad Rafi; Mardhiyyah, Rodhiyah; Sanjaya, Fadil Indra
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8695

Abstract

Coffee is one of Indonesia's leading commodities, with two main varieties: Arabica and Robusta. The differences in characteristics between these two types of coffee, such as bean shape, color, and texture, are often difficult to distinguish visually, especially for the general public. This study aims to develop an automatic classification system capable of distinguishing Arabica and Robusta coffee beans using the Convolutional Neural Network (CNN) method with the application of transfer learning based on the MobileNetV2 architecture. The dataset used consists of 210 images of coffee beans taken using a smartphone camera with various positions and lighting, which were then divided into training data (60%), validation data (20%) and test data (20%). Before the training process, data augmentation such as rotation, zoom, flip, and brightness adjustment was performed to enrich image variation and reduce the risk of overfitting. Training was conducted with a learning rate of 0.0001, a batch size of 32, and an Adam optimizer. The results showed that the CNN model with MobileNetV2 transfer learning was able to achieve a training accuracy of 99.21% and a testing accuracy of 97.62%, with relatively low loss values of 0.0682 for training data and 0.1333 for validation data. The application of transfer learning contributes to improving the stability of the training process by utilizing the pre-trained weights from the ImageNet model. Based on these results, it can be concluded that the MobileN-based CNN method.
Klasifikasi Motif Batik Solo Menggunakan Convolutional Neural Network dengan Transfer Learning VGG16 Aditama, Daffa Ferdinan; Sejati, RR. Hajar Puji; Sanjaya, Fadil Indra
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8940

Abstract

Batik Solo has a rich variety of motifs with high philosophical value, but the process of identifying motifs is still largely done manually, making it subjective and prone to error. This study aims to develop an automatic classification system to distinguish four Batik Solo motifs, namely Parang, Kawung, Truntum, and Sekar Jagad, using the Convolutional Neural Network (CNN) method based on the VGG16 architecture with a transfer learning approach. The dataset used consists of 280 batik images divided evenly into four classes (70 images per class), where data limitations are overcome using ImageNet pre-trained weights, freezing all convolution layers, and applying data augmentation to reduce the risk of overfitting. The selection of VGG16 was based on the consideration that this study focused on evaluating feature extraction capabilities and analyzing the classification performance of Batik Solo visual patterns in depth, so VGG16 was used as a stable and interpretative baseline model, not for the purposes of computational efficiency or mobile implementation. The training process was carried out for 50 epochs with a data division of 60% training data, 20% validation data, and 20% test data, and the test results showed an accuracy of 85.71% with average precision, recall, and F1-score values of 0.88; 0.86; and 0.86, respectively, where the Sekar Jagad motif performed the best, while the Truntum motif was the most challenging class due to its smooth and repetitive texture characteristics.
Pengembangan Sistem Prediksi Saham Menggunakan Model Hybrid Gated Recurrent Unit–Long Short-Term Memory Berbasis Integrasi Indikator Teknikal Konvensional Aryanto, Fajar Hanggoro Dwi; Sejati, Rr. Hajar Puji; Sanjaya, Fadil Indra
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.8988

Abstract

Stock price prediction is a crucial aspect of investment decision-making in the Indonesian capital market. This study aims to design a hybrid Gated Recurrent Unit–Long Short-Term Memory (GRU–LSTM) model architecture integrated with technical indicators such as Moving Average Convergence Divergence, Moving Average, Exponential Moving Average, and Relative Strength Index to improve the accuracy and objectivity of predictions. Additionally, this study aims to optimize model performance through grid search and implement it into a Flask-based web application as a decision support system for investors. The system was developed using a research and development approach at the Yogyakarta University of Technology. Historical data on PT Bank Rakyat Indonesia (Persero) Tbk. (BBRI.JK) shares for the period from January 2, 2020, to October 17, 2025, was obtained through the Yahoo Finance API as the main dataset. The model was optimized to determine the best combination of hyperparameters. Evaluation was performed using the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The test results show that the model achieved MAE 0.0241, MSE 0.0012, RMSE 0.0346, and MAPE 2.7%, indicating a high level of accuracy. The web application provides interactive visualization dashboard features, model development, and educational documentation. These findings confirm that the integration of deep learning with technical indicators is an effective solution for more measurable and systematic stock analysis.
Integrasi Model Algoritma Genetika dan Constraint Satisfaction Problem pada Optimasi Penjadwalan Shift Karyawan UMKM Kuliner Syuhada, Arya Firgi; Mardhiyyah, Rodhiyah; Sanjaya, Fadil Indra
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.842

Abstract

Employee shift scheduling in the Micro, Small, and Medium Enterprises (MSMEs) sector is a complex problem because it must consider various aspects such as workforce availability, work hour restrictions, and individual preferences. At the Nasi Balap Cucun MSME which operates in the culinary field, the challenge is even greater because most of its employees are active students with diverse class schedules. The scheduling process is still done manually often takes a long time and results in an unbalanced division of labor. To overcome this, this study developed an automatic scheduling system based on Genetic Algorithms combined with Constraint Satisfaction Problems (CSP). The system was built using the Python programming language with the DEAP library, considering shift needs, employee schedule requests, and operational constraints. The implementation results show that the system is able to generate efficient weekly schedules with an increase in time efficiency of up to 80%. After testing the system, it was found that the scheduling results would appear less than 10 seconds after the user generated the schedule. In addition, the system showed an increase in fitness value from -1000 in the initial generation to 54 in the 50th generation, which means this system is able to reduce potential conflicts in scheduling. This approach can be an effective solution for MSMEs in optimizing human resource management intelligently.
Golden Goal Futsal Court Rental Mobile Application Using the First Come First Serve (FCFS) Algorithm and Payment Gateway Integration Sirajudin, Sirajudin; Sanjaya, Fadil Indra; Waluyo, Anita Fira
JISA(Jurnal Informatika dan Sains) Vol 8, No 2 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i2.2545

Abstract

The futsal pitch rental process at Golden Goal is still done manually through direct communication or text messages, which often results in various problems such as unstructured booking queues, schedule conflicts due to the lack of a clear queuing system, and late payments. These problems result in low operational efficiency and an increased potential for scheduling errors. This research aims to develop a mobile-based futsal pitch rental application equipped with the implementation of the First Come First Serve (FCFS) algorithm to ensure the booking process is carried out based on the user's arrival time in a fair and orderly manner. The system development method used is the Waterfall model, which includes requirements analysis, design, implementation, testing, and maintenance. The application was developed using the Flutter framework because it has the ability to produce Android and iOS applications with only a single codebase, faster development time, and stable and responsive interface performance. These advantages make Flutter suitable for building a real-time booking system that requires fast interaction and a consistent user experience. Furthermore, the application is integrated with Midtrans services as a payment gateway to facilitate automatic digital payment transactions. Testing results using the black-box method indicate that all key features, including schedule selection, FCFS-based queuing mechanism, payment processing, and rental data management, have run well as needed. The implementation of this system has proven to be able to reduce schedule conflicts, improve the accuracy of the booking process, and increase the efficiency of rental management at Golden Goal. Thus, this application can be an effective and modern solution to address the problem of futsal field rentals that have been handled manually.
Co-Authors Aditama, Daffa Ferdinan Adriansyah, M. Ridha Ansari Al Firdaus, Muhammad Rafi Anna Dina Kalifia Ar-Razi Ab, Fariddudin Aryanto, Fajar Hanggoro Dwi Astrianty, Ledy Elsera Baraputri, Jennie Nadia Dadang Heksaputra Dadang Heksaputra Dini Erlinawati Fajar Permana Putra Febri Liantoni Heksaputra, Dadang Jayanegara, Adi Prabu Kalifia, Anna Dina Khoeri, Elfan Fanhas Mabruri, Abdul Basith Mahardika, Setiawan Putra Muhammad Fachrie Mulia Sulistiyono Novianto, Amrizal Nugraha, Muhammad Satria Nurjaman, Muhammad Nuzula Afini Pramudwiatmoko, Arif Pratama, Mohammad Bayu Pratama, Rizki Purnomo Putra, Muhammad Ali Ali Pratama Qalam, Mouhammad Tariqoul Rahmatullah, Andi Ramadhan, Religius Ristianto, Andika Riyadi, Akhmad Zaqi Rodhiyah Mardhiyyah Rodhiyah Mardhiyyah Rr. Hajar Puji Sejati Sirajudin Sirajudin Sulistyo Dwi Sancoko Syuhada, Arya Firgi Taufik Hidayah Waluyo, Anita Fira Yuliana Rizki Ikhsanty Zahra Septa Hati