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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Computer Science and Informatics Engineering (J-Cosine) Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Jurnal Teknik Informatika (JUTIF) Jurnal Begawe Teknologi Informasi
Ario Yudo Husodo, Ario Yudo
Departmet of Informatics Engineering, Engineering Faculty, Mataram University
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Development of a Convolutional Neural Network Method for Classifying Ripeness Levels of Servo Variety Tomatoes Rosalina, Rosalina; Husodo, Ario Yudo; Wijaya, I Gede Pasek Suta
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4168

Abstract

The distribution of tomatoes in Indonesia is huge, making it an important commodity in the agricultural sector. However, manual classification of tomato ripeness can lead to human error and decrease supply chain efficiency. Therefore, an automated system capable of classifying tomatoes quickly and accurately is needed, in order to reduce the potential for human error and improve supply chain efficiency. This research aims to develop the Convolutional Neural Network (CNN) method to improve the accuracy of tomato ripeness detection through modifications to the architecture, such as reducing several layers, adding batch normalization, and adding dropouts. The dataset used in this study consists of 500 images taken by the researcher himself which are divided into 5 classes, namely unriped, half-riped, riped, half-rotten, and rotten, with each class containing 100 images. There are 3 proposed CNN models, namely the standard model, as well as the addition of batch normalization and dropout in the architecture. The results showed that the proposed model 3 with the addition of dropout on several layers of its architecture is the optimal model with a parameter of 2.4 million and using a batch size of 16 resulting in an accuracy of 98%, as well as precision, recall, and F1-score values of 98%. With these results, the proposed CNN model is effective in identifying the ripeness level of tomato fruit. This research is expected to be applied in the agricultural industry to improve the efficiency of sorting and distributing tomato fruits according to the desired quality standards.
Enhanced Identity Recognition Through the Development of a Convolutional Neural Network Using Indonesian Palmprints Aprilla, Diah Mitha; Husodo, Ario Yudo; Wijaya, I Gede Pasek Suta
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4169

Abstract

The use of palmprint as an identification system has gained significant attention due to its potential in biometric authentication. However, existing models often face challenges related to computational complexity and the ability to scale with larger datasets. This research aims to develop an efficient Convolutional Neural Network (CNN) model for palmprint identity recognition, specifically tailored to address these challenges. A novel contribution of this study is the creation of an original palmprint dataset consisting of 700 images from 50 Indonesian college students, which serves as a foundation for future research in Southeast Asia. The dataset includes different scenarios with varying input sizes (32x32, 64x64, 96x96 pixels) and the number of classes (30, 40, 50) to assess the model's scalability and performance. Three CNN architectures were designed with varying layers, activation functions, and dropout strategies to capture the unique features of palmprints and improve model generalization. The results show that the best-performing model, Model 3, which incorporates dropout layers, achieved 95% accuracy, 96% precision, 95% recall, and 95% F1-score on 50 classes with 1.2 million parameters. Model 1 achieved 98% accuracy, 99% precision, 98% recall, and 98% F1-score on 40 classes with 1.7 million parameters. These findings demonstrate that the proposed CNN models not only achieve high accuracy but also maintain computational efficiency, offering promising solutions for real-time palmprint authentication systems. This research contributes to the advancement of biometric authentication systems, with significant implications for real- world applications in Southeast Asia.
Modification Of Yolov11 Nano And Small Architecture For Improved Accuracy In Motorcycle Riders Face Recognition Based On Eye Ardiansyah, Randy; Wirarama WW, I Gde Putu; Husodo, Ario Yudo
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4535

Abstract

Face recognition still faces challenges in identifying faces covered by masks and helmets with open visors, such as those commonly used by motorcyclists, especially when entering parking areas. To improve the accuracy of face recognition in these conditions, this study proposes nano and small versions of the YOLOv11 modification, which is an internal version. Modifications are made to the neck section and the DySample module is added in place of the UpSample module to improve the model's capabilities. Experiments were conducted using a self-generated dataset consisting of 50 classes. The results show that the modified nano version achieves 99.3% accuracy at the same mAP50 as YOLOv11n and YOLOv12n. At mAP50-95, it shows a 1.6% accuracy improvement compared to YOLOv11n and YOLOv12n with 75% accuracy. Meanwhile, the modified small version achieved an accuracy improvement of 1.3% and 1.2% compared to YOLOv11n and YOLOv12n, respectively, reaching 76.1% on mAP50-95, although the accuracy on mAP50 remained the same as YOLOv11n and 0.1% superior to YOLOv12n. However, recall and precision did not show significant improvement in both as well as the increase in model parameters. However, the model is still in the nano and small versions. Therefore, the model can be implemented on edge devices. This research is important for the field of computer vision, especially in the context of face recognition. The contribution of this research is the improvement of the accuracy of the mAP50-95 metric in eye-based face recognition, which is relevant for intelligent security systems with limited resources.
Pengujian Multiplatform pada Aplikasi NTB Mall: Multiplatform Testing on the NTB Mall Application Raihan, Muhammad Dzulhi; Agitha, Nadiyasari; Husodo, Ario Yudo; Bimantoro, Fitri; Rabbani, Budiman
Jurnal Begawe Teknologi Informasi (JBegaTI) Vol. 5 No. 1 (2024): JBegaTI
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbegati.v5i1.1176

Abstract

NTB Mall merupakan inovasi dari Dinas Perdagangan Provinsi NTB untuk memasarkan produk-produk asli yang dibuat oleh para UMKM di daerah pulau Lombok dan Sumbawa Aplikasi NTB Mall, sebagai sebuah sarana jual beli online berbasis aplikasi dan web tentunya harus kompatibel untuk Aplikasi NTB Mall adalah wujud nyata dari komitmen untuk memajukan perekonomian daerah ini. Aplikasi ini dirancang sebagai wadah digital yang memungkinkan para pelaku usaha di Lombok dan Sumbawa untuk memasarkan produk-produk mereka secara lebih luas, bahkan hingga ke seluruh penjuru Indonesia dan luar negeri. Dengan demikian, NTB Mall memberikan aksesibilitas yang lebih mudah bagi konsumen untuk menemukan, membeli, dan mendukung produk-produk lokal yang berkualitas tinggi dan berkarakter unik. Dalam pengembangan aplikasi NTB Mall ini perlu dilakukan beberapa tesiting pada aplikasinya. Pentingnya pengujian multi-platform dalam konteks ini tidak bisa diabaikan. Pengujian multiplatform dilakukan agar aplikasi dapat beradaptasi dengan kebutuhan pengguna khususnya yang merupakan Pedagang Kaki Lima (PKL) sehingga mereka dapat memanfaatkan aplikasi ini dengan sebaik mungkin. Berdasarkan pengujian yang telah dilakukan didapatkan hasil berupa pada sistem IOS dan Android mencapai kesesuaian 87.5% dan pada system Web Browser mendapatkan Tingkat kesesuaian sebesar 91.6%.
SOSIALISASI PEMASARAN DIGITAL BAGI PELAKU UMKM DI DESA JURIT, LOMBOK TIMUR, NTB: Digital Marketing Socialization for Small and Medium Enterprises on Jurit Village East lombok Bimantoro, Fitri; Wijaya, I Gede Pasek Suta; Dwiyansaputra, Ramaditia; Nugraha, Gibran Satya; Husodo, Ario Yudo; Hamidi, Mohammad Zaenuddin; Akhyar, Halil; Darmawan, Riski
Jurnal Begawe Teknologi Informasi (JBegaTI) Vol. 5 No. 2 (2024): JBegaTI
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbegati.v5i2.1272

Abstract

Terletak di sebelah Selatan kaki gunung Rinjani, desa Jurit yang terletak di Lombok Timur merupakan salah satu desa yang memiliki sumber daya yang melimpah. Desa Jurit dengan mayoritas petani memiliki produk unggulan berupa Nanas. Saat ini, dengan perkembangan teknologi yang begitu pesat, tentu penggunaan teknologi menjadi salah satu faktor yang mampu mendongkrak kualitas hidup Masyarakat pada umumnya. Tentu hal itu juga menjadi fokus utama pengabdian di desa Jurit, yakni akan menyoroti tentang penggunaan digital marketing sebagai alat untuk meningkatkan penjualan berbasis digital, tentunya harapannya dapat meningkatkan pendapatan para petani dan pelaku usaha kecil dan menengah yang ada di desa Jurit. Pada prosesnya, sosialisasi ini memperkenalkan dan melatih pelaku usaha untuk menggunakan media pemasaran seperti media sosial, e-commerce, dan aplikasi mobile lainnya, dengan tujuan pelaku usaha mampu memahami dan menggunakan strategi digital yang baik seperti pemasaran digital, search engine optimizer, penggunaan media sosial dan tentunya e-commerce. Sehingga pada praktiknya, kegiatan ini tidak hanya berfokus pada cara penggunaan teknolginya, namun juga bagaimana mengenalkan dan menanamkan mindset dan model bisnis digital yang akan membantu peningkatan dan keberlangsungan pelaku usaha pada masa depan.
Co-Authors Afwani, Royana Agitha, Nadiyasari Akhyar, Halil Aprilla, Diah Mitha Ardiansyah, Randy Budi Irmawati Darmawan, Riski Dwiyansaputra, Ramaditia Fitri Bimantoro Grendis, Nuraqilla Waidha Bintang Hamidi, Mohammad Zaenuddin Hanan, Dimas Afryzal I B K Widiartha I Gede Pasek Suta Wijaya I Wayan Agus Arimbawa, I Wayan Agus Ma'we, Hannatul Muhammad, David Arizaldi Muntari Muntari Murpratiwi, Santi Ika Nugraha, Gibran Satya Rabbani, Budiman Rahayu, Sefani Cahyo Auliya Raihan, Muhammad Dzulhi Rassy, Regania Pasca Rosalina Rosalina Sugianti, Novalia Dwirohmatun Wirarama WW, I Gde Putu