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All Journal Jurnal Teknik Elektro TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Lembaga Penelitian Universitas Mataram Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Computer Science and Informatics Engineering (J-Cosine) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Jurnal Abdi Insani JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknik Informatika (JUTIF) TIERS Information Technology Journal Jurnal Begawe Teknologi Informasi Edu Komputika Journal
I B K Widiartha
Program Studi Teknik Informatika Universitas Mataram
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics

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Incremental CNN-k-NN Hybrid Facial Recognition for Helmeted Facial Recognition in IoT-Enabled Smart Parking: A Case Study at Universitas Mataram Widiartha, Ida Bagus Ketut; Husodo, Ario Yudo; Thuy, Tran Thi Thanh; Murpratiwi, Santi Ika
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5447

Abstract

Helmeted rider identification challenges traditional facial recognition, especially in Indonesian campuses like UNRAM, where motorbike use is prevalent and theft risks are high. This study develops a hybrid CNN-k-NN system for secure parking access. The dataset contains 2,800 augmented images (Haar Cascade crop, 224x224 grayscale), with features extracted via VGG16/ResNet and classified using k-NN (k=1, Euclidean/Cosine). The system achieves 95.62% accuracy, with precision, recall, and F1 scores of 0.96. Incremental retraining reduces processing time to under 1 second, compared to 30 minutes for full retraining. The use of cosine similarity improves accuracy slightly over Euclidean distance. This solution enhances IoT-based smart campuses by enabling efficient, real-time identification and reducing theft by improving access control. It is adaptable to low-resource environments, supporting scalable deployments in smart parking and campus security systems.
Efficient Rice Leaf Disease Classification Using Enhanced CAE-CNN Architecture Suhada, Destia; Suta Wijaya, I Gede Pasek; Widiartha, Ida Bagus Ketut; Jo, Minho
TIERS Information Technology Journal Vol. 6 No. 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i2.7159

Abstract

This study introduces an enhanced Convolutional Autoencoder–Convolutional Neural Network (CAE–CNN) model designed for efficient and accurate classification of rice leaf diseases. This study aims to develop an architecture that achieves high accuracy while maintaining computational efficiency, serving as an integrative and applicative technical innovation for rice disease detection. The proposed architecture integrates a Squeeze and Excitation Block (SE-Block), Global Max Pooling (GMP), and Separable Convolution to improve feature extraction while reducing the number of parameters and inference time. A total of 7,430 labeled images from five rice disease classes were used for model training and evaluation. The model was optimized using Optuna-based hyperparameter tuning and validated through an ablation and comparative analysis to assess the impact of each component. Experimental results show that the proposed model achieves 99.39% accuracy with only 85,859 parameters, a compact size of 0.28 MB, and inference time at 0.06657 ms/image with 15,213 FPS. These findings demonstrate that the proposed CAE–CNN effectively combines high accuracy and low computational cost, making it highly suitable for real-time and edge-based rice disease classification systems.
Integration of Skyline Query with the PROMETHEE MCDM Method: A Case Study on Structural Official Selection Wijaya, Budiman; Wijayanto, Heri; Widiartha, Ida Bagus Ketut
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.29049

Abstract

The selection of structural officials within higher education institutions is a strategic and complex process that demands objectivity, transparency, and a data-driven approach. However, the increasing number of candidates and the diversity of evaluation criteria, such as years of service, rank, education, age, and performance, pose significant challenges in ensuring fair and efficient decision-making. Addressing this gap, this study proposes a hybrid method by integrating Skyline Query with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), offering a novel contribution to multi-criteria decision-making (MCDM) in public sector human resource selection. Skyline Query is employed as a preselection mechanism to eliminate 161 dominated candidates from an initial dataset of 228, allowing only the 67 most non-dominated candidates to advance to the ranking stage. PROMETHEE is then applied to generate rankings based on leaving and entering flow values. To evaluate the consistency and validity of this combined approach, the resulting rankings are compared with those from the pure PROMETHEE method using Spearman’s Rank Correlation. The analysis yields a high correlation coefficient of ρ = 0.967, indicating a very strong agreement between the two methods and confirming that the Skyline filtering does not distort ranking quality. The findings demonstrate that the Skyline+PROMETHEE integration significantly enhances the efficiency of the selection process by reducing computational complexity while preserving decision accuracy. Moreover, this approach strengthens the transparency and accountability of structural official selection, particularly in the context of the University of Mataram, and can be generalized to other institutional decision-making scenarios.
Co-Authors Afwani, Royana Agitha, Nadiyasari Al Qadri, Ramadhani Alamsyah, Noor Albar, Moh. Ali Albar, Mohammad Ali Anjarwani, Sri Endang Ario Yudo Husodo Ario Yudo Husodo, Ario Yudo Ariyan Zubaidi Arvini Anindhita Askit, Baiq Ratna Ayoedya, Jasmine Nabila Bimantari, Joselina Rizki Budi Irmawati Deo Marta Dipayana Dwi Ratnasari Erdian, Naufal Evandry Syahputra Fitri Bimantoro Gibran Satria Nugraha Harir, Zainul Heri Wijayanto Husna, Nabila Ummul Hwang, Jun-seok I Gede Pasek Suta Wijaya I Wayan Agus Arimbawa, I Wayan Agus Jayusman, Dirga Jo, Minho Kurrotaa'yun, Baiq Dwi Zulianti Kusuma, Pande Permadi Moh Ali Albar MUHAMMAD TAUFIK Munirah, Zahrahtun Murpratiwi, Santi Ika Mustianti Mustianti Ni Made Puspitayanti Oktariani Nurul Pratiwi Puja, Sena Rassy, Regania Pasca Rosika, Herliana Sari, Gita Mailand Septipalan, Mohammad Liyananta Sri Endang Anjarwani Sri Endang Arjarwani Sugianti, Novalia Dwirohmatun Suhada, Destia Thuy, Tran Thi Thanh Topan Khrisnanda Wijaya, Budiman Wulandari, Sagita Yoon, Hyoen-yeong Zubaidi, Ariyan