Article Per Year (5 Year)
p-Index From 2021 - 2026
4.818
P-Index
This Author published in this journals
All Journal Jurnal Simantec Jurnal Ilmiah Kursor TEKMAPRO Journal of Industrial Engineering and Management Scan : Jurnal Teknologi Informasi dan Komunikasi International Journal of Advances in Intelligent Informatics International Journal of Artificial Intelligence Research Teknika: Engineering and Sains Journal JIEET (Journal of Information Engineering and Educational Technology) IJEBD (International Journal Of Entrepreneurship And Business Development) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Penelitian SEINASI-KESI Journal of Information Systems and Informatics bit-Tech Journal of Robotics and Control (JRC) JATI (Jurnal Mahasiswa Teknik Informatika) e-NARODROID Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Journal of Applied Data Sciences International Journal of Data Science, Engineering, and Analytics (IJDASEA) Information Technology International Journal (ITIJ) Jurnal ilmiah teknologi informasi Asia Jurnal Pengabdian Masyarakat SENSASI Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
I Gede Susrama Mas Diyasa
Department Of Informatics, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Transportation

Published : 44 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 44 Documents
Search

 1   2   3   4   5 
Maintenance Scheduling for Buildings Using Fuzzy Logic Application I Nyoman Dita Pahang Putra; I Gede Susrama Mas Diyasa; Anak Agung Diah Parami Dewi; Bambang Trigunarsyah
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 16 No. 01 (2025): Vol.16, No. 01 April 2025
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2025.v16.i01.p05

Abstract

This research proposes an innovative approach to building maintenance scheduling using fuzzy logic. Fuzzy logic addresses uncertainty and complexity in decision-making processes concerning prioritizing and scheduling maintenance tasks. This study aims to enhance the efficiency of maintenance scheduling, reduce maintenance costs, and consider the variability in building conditions. Traditional methods, such as PERT (Program Evaluation and Review Technique) and CPM (Critical Path Method), have limitations in accurately predicting scheduling times. At the same time, fuzzy logic offers a more precise approach to overcoming uncertainty. Implementing a maintenance scheduling model based on fuzzy logic is expected to yield a more adaptive and responsive maintenance plan in response to changes in building conditions. The results of this research are expected to contribute positively to building maintenance management by leveraging the advantages of fuzzy logic in addressing the challenges of complexity and uncertainty in building maintenance management. By applying fuzzy logic-based maintenance scheduling, it is hoped that precise and efficient building maintenance scheduling can be achieved, thereby minimizing project completion time and assisting project managers. The fuzzy logic method can be employed for construction project scheduling according to the schedule determined by the contractor. This allows the contractor to use it as a consideration for the total duration, along with detailed timing in the project proposal. For the owner, it provides insights into the potential project completion time.
Convolutional layer exertion on few-shot learning for brain tumor classification Sunarko, Victor Immanuel; Puspaningrum, Eva Yulia; Widiastuty, Riana Retno; Hadi, Surjo; Awang, Mohd Khalid; Mas Diyasa, I Gede Susrama
Jurnal Ilmiah Kursor Vol. 13 No. 2 (2025)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i2.430

Abstract

Brain tumors, though relatively rare, pose a significant threat due to their critical location within the brain, impacting essential bodily functions. Accurate and timely diagnosis is vital, but traditional diagnostic methods are time-intensive and rely heavily on large labeled datasets. This study addresses these challenges by proposing a Few-Shot Learning (FSL) framework enhanced with Convolutional Neural Networks (CNNs) to classify brain tumors using MRI images. By employing the Matching Network architecture, the model leverages limited training data through an N-way-K-shot setup. Training results demonstrated accuracy levels of 71.58% (1-shot) and 82.89% (5-shot) for 1-layer CNNs, 66.65% (1-shot) and 84.03% (5-shot) for 3-layer CNNs, and 63.43% (1-shot) and 84.94% (5-shot) for 5-layer CNNs. However, validation accuracy revealed overfitting concerns, with the highest performance at 51.56% (1-layer, 1-shot). These results underscore the potential of FSL in medical imaging while highlighting the need for advanced augmentation and feature representation techniques to improve generalization.
Performance Comparison of Gaussian Mixture Model, Hierarchical Clustering, and K-Medoids in Passenger Data Clustering Thalita Syahlani Putri; I Gede Susrama Mas Diyasa; Achmad Junaidi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3013

Abstract

The rapid growth of urban populations and increasing reliance on public transportation in Indonesia present challenges in managing passenger demand effectively. In Surabaya, the steady rise in Suroboyo Bus passengers underscores the need for data-driven strategies to optimize fleet allocation, scheduling, and infrastructure development. Identifying passenger density patterns through clustering provides a systematic basis for decision-making. This study aims to address a local research gap by comparing three clustering algorithms Agglomerative Hierarchical Clustering (AHC), Gaussian Mixture Model (GMM), and K-Medoids on empirical passenger data. Unlike previous studies that emphasize route optimization or demand forecasting, this research highlights a comparative evaluation to determine the most effective method for handling fluctuating and outlier-prone transportation data. The dataset was obtained from the Surabaya City Transportation Office for the Purabaya–Perak route during a two-week period in 2024. Data preprocessing included attribute selection, transformation of time into numerical format, outlier detection using the Interquartile Range (IQR), and Z-Score normalization. Clustering results were assessed with the Silhouette Score and visualized using scatter plots and histograms. Findings show that K-Medoids achieved the highest Silhouette Score (0.4222), surpassing AHC (0.3657) and GMM (0.3024). K-Medoids produced more balanced clusters and stronger resilience to outliers, while AHC provided interpretable hierarchical structures, and GMM modeled complex patterns but with weaker separation. In conclusion, K-Medoids is recommended as the most suitable approach for passenger density clustering. Academically, this study contributes a comparative framework for clustering in transportation research, while practically offering insights to support data-driven public transport management in developing cities.
Comparison of SVM kernels in brain tumor image classification using GLCM feature extraction I Gede Susrama Mas Diyasa; Kraugusteeliana Kraugusteeliana; Hanif Nur Fadlilah; Yisti Vita Via; Anita Muliawati; Allan Ruhui Fatmah Sari; Erna Harfiani; Ni Made Ika Marini Mandenni; Deshinta Arrova Dewi
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The human brain plays a vital role in regulating bodily functions, and abnormal cell growth may lead to life-threatening brain tumors. Automated computer-aided diagnosis systems are therefore essential to support early detection from MRI images. This study investigates brain tumor classification using Gray Level Co-occurrence Matrix (GLCM) feature extraction combined with Support Vector Machine (SVM) classification. Unlike prior works that typically employ a single kernel configuration, this study conducts a systematic comparison of four SVM kernels linear, polynomial, radial basis function (RBF), and sigmoid under a consistent preprocessing pipeline and structured hyperparameter tuning framework. GLCM features including energy, contrast, correlation, and homogeneity were extracted at multiple distances and angles. Kernel performance was evaluated using controlled hyperparameter search procedures to ensure fair comparison across models. Experimental results on a binary MRI dataset consisting of 2,800 images demonstrate that the RBF kernel achieved the highest accuracy of 96% with C = 100 and gamma = 10, outperforming polynomial (74%), linear (72%), and sigmoid (71%) kernels. The findings highlight the importance of systematic kernel evaluation and parameter sensitivity analysis in texture-based medical image classification. The proposed GLCM–SVM framework provides a computationally efficient and interpretable approach that may support preliminary decision-aid systems for brain tumor screening.
Co-Authors Achmad Junaidi Achmad Junaidic Adiwidyatma, Afdhal Reshanda Ahmad Naufal Mumtaz Akmal, Mohammad Faizal Alfiatun Masrifah Alhamda, Denisa Septalian Allan Ruhui Fatmah Sari Amanullah , Nurkholis Anak Agung Diah Parami Dewi Anita Muliawati Ardianto, Taruna Ariyono Setiawan Aryananda, Rangga Laksana Aurelia, Cenditya Ayu Awaludin W., Moh. Haydir Awang, Mohd Khalid Azizah, Nabila Wafiqotul Bambang Trigunarsyah Bambang Trigunarsyah Budi Nugroho Cahyani Kuswardhani, Hajjar Ayu Deshinta Arrova Dewi Dewi, Deshinta Arrova Dewi, Deshinta Arrowa Dwi Arman Prasetya Dwi Kusuma, Irma Erma Suryani Erna Harfiani Etniko Siagian, Pangestu Sandya Eva Yulia Puspaningrum Fara Disa Durry Fatmah Sari, Allan Ruhui Firmansyah, Taufik Nur Firya Nadhira Gideon Setya Budiwitjaksono Gideon Setya Budiwitjaksono Gunawan, Ellexia Leonie Hadi, Surjo Hafidz Amarul Ma’rufi Halim, Christina Hamawi, Moch. Hawin Hanif Nur Fadlilah Humairah, Sayyidah I Nyoman Dita Pahang Putra I Nyoman Dita Pahang Putra Ilham Ade Widya Sampurno Ilham Ade Widya Sampurno Intan Yuniar Purbasari Jauharis Saputra, Wahyu Syaifullah Jojok Dwiridotjahjono Kraugusteeliana Kraugusteeliana Kraugusteeliana Kraugusteeliana Mandeni, Ni Made Ika Marinni Mandyartha, Eka Prakarsa Moch. Hatta Mohamad Nur Amin Mohammad Idhom Mohammad Rafka Mahendra A Mohammad Rafka Mahendra Ariefwan Mudjahidin Muhammad Rif'an Dzulqornain Mumtaz, Ahmad Naufal Munoto Mustika, Agung Nadhira, Firya Nahusuly, Barep J. A. I. Ni Made Ika Marini Mandenni Ni Made Ika Marini Mandenni Ni Made Ika Marini Mandenni NYOMAN DITA PAHANG PUTRA, NYOMAN Prabowo, Aris Prasetyo, Galih Novian Putri, Fitri Aulia Yuliandi Raditya, Askara Rangga Laksana A Rangga Laksana Aryananda Rheza Rizqi Ahmadi Ridho Syahdindo Rizal Harjo Utomo Sabrina Charya Floribunda Santoso, Sri Fuji Senny Meliyan Setiawan, Ariyono Setiawan, Ariyono Shodiq, Ja’far Slamet Winardi Sri Wibawani, Sri Sugeng Purwanto Sugiarto S Sugiarto Sugiarto Sugiarto, Sugiarto Sukri, Hanifudin Sulianto Bhirawa Sunarko, Victor Immanuel Suryani, Dedik Taruna Ardianto Terza Damaliana, Aviolla Thalita Syahlani Putri Trimono, Trimono Wafiqotul Azizah, Nabila Wahyu Caesarendra Wahyu Dwi Lestari Wahyu S.J. Saputra Wan Awang, Wan Suryani Wardhani, Naritha Cahya Widianto, Purwito Ridho Widiastuty, Riana Retno Wijaya, Pandu Ali Yisti Vita Via Yisti Vita Via