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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 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) 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

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Detection of Abnormal Human Sperm Morphology Using Support Vector Machine (SVM) Classification Mas Diyasa, I Gede Susrama; Prasetya, Dwi Arman; Cahyani Kuswardhani, Hajjar Ayu; Halim, Christina
Information Technology International Journal Vol. 2 No. 2 (2024): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v2i2.36

Abstract

Abnormal sperm morphology is a key indicator of male infertility, making its accurate detection crucial for reproductive health assessments. This study explores the application of Support Vector Machine (SVM) classification to automatically detect abnormalities in human sperm morphology. A dataset of microscopic sperm images was collected and labelled based on normal and abnormal morphological features, including head shape, midpiece defects, and tail irregularities. Feature extraction techniques were employed to quantify key morphological characteristics, which were then used to train the SVM model. The proposed SVM-based approach demonstrated high accuracy in classifying normal versus abnormal sperm morphology, significantly reducing the time and error associated with manual analysis. This method provides an efficient, automated solution for andrology laboratories and fertility clinics, enhancing diagnostic consistency and reliability. By incorporating machine learning techniques, this system holds promise for improving the precision of sperm morphology analysis, ultimately contributing to better fertility treatments and outcomes
Analisis Sentimen Penggunaan Galon BPA Menggunakan Seleksi Fitur Chi-Square Dan Algoritma Support Vector Machine Aurelia, Cenditya Ayu; Trimono, Trimono; Mas Diyasa, I Gede Susrama Susrama
Jurnal Ilmiah Teknologi Informasi Asia Vol 18 No 2 (2024): Volume 18 nomor 2 2024 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

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

Abstract

Air Minum Dalam Kemasan (AMDK) menjadi elemen utama bagi keseimbangan tubuh. Adanya berita tentang bahaya galon yang mengandung BPA menimbulkan kekhawatiran di masyarakat terutama di platform media sosial Twitter sehingga menimbulkan keresahan masyarakat terhadap dampak negatif yang disebabkan dari penggunaan galon BPA. Hal tersebut menciptakan perdebatan antara dua pihak yang terdiri dari masyarakat yang mendukung penggunaan galon BPA dan masyarakat yang mendukung penggunaan galon non-BPA dari produk air minum tertentu. Penelitian ini melakukan analisis sentimen untuk mengelompokkan pendapat masyarakat terkait penggunaan galon menggunakan algoritma Support Vector Machine dan seleksi fitur Chi-Square. Hasil dari penelitian menunjukkan bahwa penerapan seleksi fitur Chi-Square meningkatkan akurasi hingga 0.95 pada kernel Linear dan RBF dengan 239 prediksi yang tepat dan 13 prediksi yang tidak tepat
Balinese Script Handwriting Recognition Using CNN and ELM Hybrid Algorithms Mas Diyasa, I Gede Susrama; Wijaya, Pandu Ali; via, Yisti Vita
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.87524

Abstract

One of the foundational scripts used in Balinese culture is the Balinese script, known as “Aksara Bali”. In its writing, Aksara Bali follows specific rules regarding distinctive stroke shapes that must be carefully maintained to preserve authenticity and readability. This study proposes the use of a hybrid algorithm combining Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) to recognize handwritten Balinese script characters. The preprocessing stage includes dataset splitting, rescaling, data augmentation, batch size adjustment, and visualization of class distribution. The training stage utilizes the Adam Optimizer to enhance model accuracy. Using 1,691 images of various Balinese script characters, the dataset is divided into an 80:10:10 ratio for training, validation, and testing. Experimental results show that the best accuracy achieved is 91%, indicating that the CNN-ELM hybrid model effectively recognizes Balinese script characters.
MOVIEMU : Sistem Rekomendasi Film Menggunakan Algoritma Cosine Similarity Rizal Harjo Utomo; I Gede Susrama Mas Diyasa
Jurnal Pengabdian Masyarakat SENSASI Vol. 4 No. 2 (2024): Jurnal Pengabdian Masyarakat SENSASI
Publisher : Faculty of Economics and Bussiness, UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/sensasi.v4i2.83

Abstract

Studi Independen adalah inisiatif pemerintah yang diluncurkan pada tahun 2020 oleh Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi (Kemendikbudristek) untuk memberikan mahasiswa kesempatan untuk memperdalam pengetahuan dan keterampilan di luar lingkungan perguruan tinggi melalui berbagai program inovatif. Salah satu program unggulan dalam MBKM adalah Magang dan Studi Independen Bersertifikat (MSIB), yang memungkinkan mahasiswa untuk terlibat dalam proyek nyata di industri dan mendapatkan sertifikat sebagai pengakuan atas pencapaian mereka. Penulis mengikuti program Studi Independen Bersertifikat (MSIB) di Bangkit Academy dengan learning path Machine Learning, Bangkit adalah mitra dalam program MSIB, yang bekerja sama dengan Google, didukung oleh perusahaan seperti GoTo, Traveloka, dan Kemendikbudristek RI. Dengan dukungan Kampus Merdeka, Bangkit menawarkan 9.000 tempat pada tahun 2024 bagi mahasiswa Indonesia untuk memastikan kecakapan yang sesuai dengan kebutuhan industri. Program ini dipandu oleh para ahli dari perusahaan teknologi dan startup Indonesia, dan peserta akan memperoleh keahlian dalam Machine Learning, Mobile Development, atau Cloud Computing bersama sertifikasi global dari Google. Untuk memperluas pengetahuan dan pengalamn dalam bidang pemrograman Machine Learning, penulis memutuskan untuk melakukan studi independen dengan mengikuti kelas machine learning dalam program Bangkit Academy dan menyelesaikan proyek dengan judul “MOVIEMU”. Di era digital saat ini, jumlah konten film yang tersedia untuk dinikmati konsumen meningkat secara eksponensial. Hal ini menciptakan tantangan bagi pengguna untuk menemukan film yang sesuai dengan preferensi mereka. Salah satu solusi yang dapat digunakan untuk mengatasi tantangan tersebut adalah dengan mengembangkan sistem rekomendasi film yang dapat membantu pengguna menemukan film yang sesuai dengan seleranya. “MOVIEMU” hadir sebagai solusi permasalahan tersebut dengan mengimplementasikan algoritma cosine kemiripan untuk memberikan rekomendasi film yang akurat dan relevan.
Implementation Of Hybrid EfficientNet V2 And Vision Transformer for Apple Leaf Diseases Classification Santoso, Sri Fuji; Hadi, Surjo; Nugroho, Budi; Mas Diyasa, I Gede Susrama
Information Technology International Journal Vol. 3 No. 1 (2025): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v3i1.42

Abstract

The apple farming industry faces challenges in managing apple leaf diseases. Current manual detection methods have limitations in expertise variability, time required, potential delays in identification leading to disease spread, and difficulty distinguishing diseases with similar visual symptoms. This research aims to develop an accurate, efficient, and automated apple leaf disease classification system using a hybrid approach that combines EfficientNet V2 architecture and Vision Transformer. The main objectives are to improve disease detection accuracy, reduce computational requirements, facilitate more effective plant management, and support modern agricultural practices in the apple industry. This research uses a hybrid deep learning model that integrates EfficientNet V2 and Vision Transformer components. Experiments were conducted on an apple leaf disease dataset to evaluate model performance. Results show the effectiveness of this method in classifying apple leaf diseases, achieving 98.56% accuracy and an F1 score of 0.9856 on test data. The proposed model has 15.6 million parameters, lighter than the original EfficientNetV2S model with 20 million parameters. Training time was reduced to 6 minutes 32 seconds compared to the original EfficientNetV2S model that required 8 minutes 41 seconds for 5 epochs on the same dataset.
Penerapan Gated Recurrent Unit dengan Bayesian Optimization dalam Prediksi Harga Saham Sektor FMCG Mas Diyasa, I Gede Susrama; Akmal, Mohammad Faizal; Junaidi, achmad
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n1.p36-41

Abstract

Peningkatan partisipasi investor muda terutama dari Generasi Z dan Milenial menciptakan kebutuhan mendesak untuk menggunakan metode prediksi yang lebih akurat guna meminimalkan risiko investasi. Penelitian ini bertujuan untuk mengembangkan model prediksi harga saham pada sektor Fast-Moving Consumer Goods (FMCG) di Indonesia dengan memanfaatkan algoritma Gated Recurrent Unit (GRU) yang dioptimalkan menggunakan teknik Bayesian Optimization. Metode penelitian ini dimulai dengan pembagian data saham PT Hanjaya Mandala Sampoerna Tbk (HMSP) dari tahun 2019 hingga 2025, yang dibagi menjadi data train (60%), data validation (20%), dan data test (20%). Selanjutnya, dilakukan preprocessing data berupa normalisasi dan sequencing untuk mempersiapkan data. Model GRU yang diterapkan diuji dengan menggunakan metrik evaluasi seperti Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), dan Mean Absolute Percentage Error (MAPE), yang menghasilkan akurasi prediksi yang tinggi dengan RMSE 17.07, MAE 11.50, dan MAPE 1.48%. Penelitian ini menunjukkan bahwa penerapan Bayesian Optimization dapat memberikan efektivitas pemilihan hyperparameter menghasilkan model yang lebih presisi dalam memprediksi harga saham FMCG di Indonesia dan memberikan panduan yang lebih andal bagi investor dalam pengambilan keputusan investasi
MRI image enhancement of the brain using U-NET Etniko Siagian, Pangestu Sandya; Puspaningrum, Eva Yulia; Wan Awang, Wan Suryani; Mas Diyasa, I Gede Susrama
Jurnal Simantec Vol 13, No 2 (2025): Jurnal Simantec Juni 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v13i2.29775

Abstract

The quality of Magnetic Resonance Imaging (MRI) images is often compromised by various types of noise, such as salt, pepper, salt-and-pepper, and speckle noise, caused by technical or environmental disturbances. This study aims to develop a brain MRI image denoising model based on the U-Net architecture, capable of effectively removing different types of noise. The methodology includes collecting normal brain MRI datasets, applying data augmentation to increase variability, and introducing artificial noise to simulate possible noise conditions. The U-Net model is trained and evaluated using the Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics. The novelty of this study lies in its combination of augmentation techniques, multi-intensity artificial noise variations, and its exclusive focus on normal brain MRI images. The results demonstrate that the U-Net model achieves optimal performance on salt-and-pepper noise at an intensity of 0.1, marked by the highest PSNR value of 37.2047 dB and the lowest MSE value of 0.000207. Conversely, the model shows the lowest performance on high-intensity speckle noise, indicating greater challenges in addressing multiplicative noise. This study contributes a systematic and empirically tested approach to improving the quality of brain MRI images with high efficiency, supporting the development of image-based diagnostic systems in the medical field.Keywords: Deep Learning, Denoising, Image Enhancement, Noise, U-Net.
Optimization of facial recognition authentication system using InceptionResNetV1 with Pretrained VGGFACE2 Gunawan, Ellexia Leonie; Mas Diyasa, I Gede Susrama; Jauharis Saputra, Wahyu Syaifullah
Jurnal Simantec Vol 13, No 2 (2025): Jurnal Simantec Juni 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v13i2.29776

Abstract

Face recognition as a biometric authentication method continues to evolve due to its high security and ease of use. However, training models from scratch faces challenges such as the need for large datasets and high computational resources. This study aims to optimize the face authentication system using the InceptionResNetV1 architecture with a transfer learning approach from the pretrained VGGFace2 model and to compare its performance with CASIA-WebFace. Face detection is conducted using YOLOv8, face embeddings are generated by InceptionResNetV1, and authentication is performed by calculating the Euclidean distance between embeddings. Face data were collected from university students and divided into training and testing datasets. Performance evaluation includes accuracy, precision, recall, F1-score, and the confusion matrix. The results show that the VGGFace2 model achieved an accuracy of 98.75%, a recall of 100%, and an F1-score of 99.26%, with no False Negatives, while CASIA-WebFace achieved an accuracy of 86.25% with a recall of 85.07%. The main contribution of this study is to demonstrate that the use of transfer learning with the pretrained VGGFace2 model can significantly improve the accuracy of face authentication systems and to show its effectiveness for developing systems with limited data and computational resources. This study contributes by highlighting the superiority of the pretrained VGGFace2 model in face authentication systems and emphasizing the effectiveness of transfer learning for implementing accurate systems under resource constraints.Keywords: Authentication System, InceptionResNetV1, Face Recognition, Transfer Learning, VGGFace2
Improving Classification Accuracy of Breast Ultrasound Images Using Wasserstein GAN for Synthetic Data Augmentation Mas Diyasa, I Gede Susrama; Humairah, Sayyidah; Puspaningrum, Eva Yulia; Durry, Fara Disa; Lestari, Wahyu Dwi; Caesarendra, Wahyu; Dewi, Deshinta Arrova; Aryananda, Rangga Laksana
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.25075

Abstract

Breast cancer remains one of the most prevalent cancers in Indonesia, and early detection plays a vital role in improving patient outcomes. Ultrasound imaging is a non-invasive and accessible technique used to classify breast conditions into normal, benign, or malignant categories. The advancement of deep learning, particularly Transfer Learning with Convolutional Neural Networks (CNNs), has significantly enhanced the performance of automated image classification. However, the effectiveness of CNNs heavily relies on large, balanced datasets—resources that are often limited and imbalanced in medical domains. To address this issue, this study explores the use of Wasserstein Generative Adversarial Networks (WGAN) for synthetic data augmentation. WGAN is capable of learning the underlying distribution of real ultrasound images and generating high-quality synthetic samples. The inclusion of the Wasserstein distance stabilizes training, with convergence observed around 2500–3000 epochs out of 5000. While synthetic data improves classifier performance, there remains a potential risk of overfitting, particularly when the synthetic images closely mirror the training data. Compared to traditional augmentation techniques such as rotation, flipping, and scaling, WGAN-generated data provides more diverse and realistic representations. Among the tested models, VGG16 achieved the highest accuracy of 83.33% after WGAN augmentation. Nonetheless, computational resource limitations posed challenges in training stability and duration. Furthermore, issues related to model generalizability, as well as ethical and patient privacy considerations in using synthetic medical data, must be addressed to ensure responsible deployment in real-world clinical settings.
Optimizing The XGBoost Model with Grid Search Hyperparameter Tuning for Maximum Temperature Forecasting Sugiarto, Sugiarto; Mas Diyasa, I Gede Susrama; Alhamda, Denisa Septalian; Aryananda, Rangga Laksana; Fatmah Sari, Allan Ruhui; Sukri, Hanifudin; Dewi, Deshinta Arrowa
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.885

Abstract

This study presents a novel comparative approach to maximum temperature forecasting in Surabaya, Indonesia, by integrating Extreme Gradient Boosting (XGBoost) with Grid Search Hyperparameter Tuning and benchmarking it against Autoregressive Integrated Moving Average (ARIMA) and Neural Prophet models. The main idea is to evaluate the capability of XGBoost in capturing nonlinear patterns in environmental time series data, which traditional models often fail to address. Using 15,388 historical daily maximum temperature records from the BMKG Juanda weather station spanning 1981–2022, the objective is to identify the most accurate predictive model for short- and medium-term forecasts. The modeling process involved four stages: data acquisition, preprocessing, training, and evaluation, with performance assessed using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings show that, after hyperparameter tuning, XGBoost achieved the best performance with MAE = 0.32 and RMSE = 0.65, outperforming ARIMA (MAE = 0.85, RMSE = 1.20) and Neural Prophet (MAE = 0.70, RMSE = 0.98). Prediction results for 2025 indicate peak maximum temperatures in January, October, and November, aligning with recent climate patterns. The contribution of this research lies in demonstrating the superiority of a tuned XGBoost model for complex environmental datasets, offering a practical tool for urban climate planning, agricultural scheduling, and heatwave risk mitigation. The novelty of this work is the systematic integration of Grid Search-based optimization with XGBoost for meteorological forecasting in a tropical urban context, producing higher accuracy than both classical statistical and modern hybrid time series methods. These results highlight the model’s adaptability and potential for broader climate-related applications, with future research recommended to incorporate additional meteorological variables such as humidity and wind speed for even greater predictive capability.
Co-Authors Achmad Junaidi Achmad Junaidic Adiwidyatma, Afdhal Reshanda Ahmad Naufal Mumtaz Akmal, Mohammad Faizal Alfiatun Masrifah Alhamda, Denisa Septalian Amanullah , Nurkholis Anak Agung Diah Parami Dewi 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 Dewi, Deshinta Arrova Dewi, Deshinta Arrowa Dwi Arman Prasetya Dwi Kusuma, Irma Erma Suryani 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 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 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 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 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