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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknik ITS IPTEK Journal of Proceedings Series IPTEK The Journal for Technology and Science Semantik Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika EMITTER International Journal of Engineering Technology Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri INOVTEK Polbeng - Seri Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam JAVA Journal of Electrical and Electronics Engineering JAREE (Journal on Advanced Research in Electrical Engineering) Community Empowerment Jurnal Nasional Teknik Elektro dan Teknologi Informasi Makara Journal of Technology Sewagati Jurnal Rekayasa elektrika
I Ketut Eddy Purnama
Institut Teknologi Sepuluh Nopember
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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Penempatan Posisi Multi Kamera Berdasarkan Gaya Sutradara Berbasis Logika Fuzzy Junaedi, Hartarto; Pranata, Jaya; Hariadi, Mochamad; Purnama, I Ketut Eddy
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 6: Desember 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (4185.037 KB) | DOI: 10.25126/jtiik.2018561117

Abstract

Teknologi komputer saat ini telah banyak digunakan dalam pengembangan animasi atau permainan komputer. Salah satu teknologi itu adalah machinima yaitu suatu sistem yang menggunakan teknologi mesin grafik 3D untuk menghasilkan produk sinematik secara real time. Dalam proses pembuatan produk sinematik itu penempatan posisi kamera sangat memegang peranan penting. Penempatan posisi kamera ini tentu harus sesuai dengan kaidah-kaidah sinematografi. Penelitian ini akan mengusulkan sebuah pendekatan agen cerdas dengan multi perilaku untuk menempatkan kamera virtual dalam lingkungan virtual secara otomatis sesuai dengan gaya seorang sutradara. Setiap kamera virtual itu akan memiliki perilaku yang berbeda berdasarkan kaidah sinematografi sehingga memiliki Point of View (POV) yang berbeda. Untuk memberikan perilaku pada kamera virtual akan digunakan pendekatan berbasis logika fuzzy dengan menggunakan metode mamdani. Jumlah variabel masukan yang digunakan sejumlah tiga dan variabel keluaran sejumlah tiga dengan membership function antara tiga sampai lima. Penelitian ini akan menggunakan simulasi permainan komputer dengan tiga kamera virtual dengan perilaku yang berbeda untuk merekam adegan yang sama dan hasilnya akan divalidasi berdasarkan hasil pengamatan dengan komunitas juru foto.  Pada akhirnya dapat diambil kesimpulan bahwa pendekatan logika fuzzy dapat digunakan untuk memberikan sebuah perilaku atau gaya sutradara pada kamera virtual.AbstractComputer technology is has been used widely in the development of animation or computer games. One of the technologies is machinima, a system that uses reak time 3D graphics engine technology to produce cinematic products. In the process of develop a cinematic product, camera positioning is a very important component. The camera positioning must be comply with cinematography’s rule. This research will propose an intelligent multi agent behavior to positining a virtual camera in a virtual environment automatically according to the director’s style. Each virtual camera will have a different behavior based on cinematographic rules so that it has a different Point of View (POV). To assign a behavior on the virtual camera will be based on  fuzzy logic using the mamdani method. The number of input variables are three and the output variables are three with the number membership functions between three to five. This research will program  a computer game simulation with three multi behavior virtual cameras to capture some scene and the results will be validated based on observations with the photographer community. Finally it can be concluded that the fuzzy logic approach can be used to assign some behavior to a virtual camera.
Automated Breast Cancer Cell Counting: Comparing Multi-class Segmentation and Two-stage Classification Strategies Dzaky Hanif Arjuna; Edy Kurniawan; Reza Fuad Rachmadi; I Ketut Eddy Purnama
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2639

Abstract

The manual interpretation of Hematoxylin and Eosin (H&E) histopathology images for breast cancer diagnosis is hindered by time constraints and observer bias. This research aims to develop an automated system using deep learning for cell detection and classification by evaluating two key approaches: Multi-class Segmentation (single-stage) and Segmentation followed by Classification (two-stage). The U-Net architecture was employed for segmentation, while MobileNetV2 and VGG16 were used for classification. The models were evaluated on the public IHC4BC dataset and primary data from Airlangga University Hospital (RSUA). The study also evaluated the impact of Resizing and Tiling data processing strategies. Experimental results showed that although the MobileNetV2 and VGG16 classification models achieved a high testing accuracy of 98.80%, the integrated two-stage system exhibited a high counting error, with a Mean Absolute Error (MAE) of 119.87 for positive cells, primarily due to under-segmentation of overlapping cells. In contrast, the Multi-class Segmentation approach utilizing the Tiling strategy demonstrated superior performance. This model effectively preserved spatial resolution while distinguishing cell types simultaneously, achieving the lowest MAE of 18.46 for positive cells and 1.66 for negative cells. This study concludes that Multi-class Segmentation with the Tiling strategy is the most effective and accurate approach for automated cell counting in histopathology images.
Automatic 3D Cranial Landmark Positioning based onSurface Curvature Feature using Machine Learning Suputra, Putu Hendra; Sensusiati, Anggraini Dwi; Artaria, Myrtati Dyah; Verkerke, Gijsbertus Jacob; Yuniarno, Eko Mulyanto; Purnama, I Ketut Eddy
Knowledge Engineering and Data Science
Publisher : citeus

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

Abstract

Cranial anthropometric reference points (landmarks) play an important role in craniofacial reconstruction and identification. Knowledge to detect the position of landmarks is critical. This work aims to locate landmarks automatically. Landmarks positioning using Surface Curvature Feature (SCF) is inspired by conventional methods of finding landmarks based on morphometrical features. Each cranial landmark has a unique shape. With the appropriate 3D descriptors, the computer can draw associations between shapes and landmarks using machine learning. The challenge in classification and detection in three-dimensional space is to determine the model and data representation. Using three-dimensional raw data in machine learning is a serious volumetric issue. This work uses the Surface Curvature Feature as a three-dimensional descriptor. It extracts the local surface curvature shape into a projection sequential value (depth). A machine learning method is developed to determine the position of landmarks based on local surface shape characteristics. Classification is carried out from the top-n prediction probabilities for each landmark class, from a set of predictions, then filtered to get pinpoint accuracy. The landmark prediction points are hypothetically clustered in a particular area, so a cluster-based filter is appropriate to isolate them. The learning model successfully detected the landmarks, with the average distance between the prediction points and the ground truth being 0.0326 normalized units. The cluster-based filter is implemented to increase accuracy compared to the ground truth. Thus, SCF is suitable as a 3D descriptor of cranial landmarks.
Hybrid Residual UNet with Triplet Embedded Metric Learning for Low Light Tuberculosis Bacilli Segmentation Sari Ayu Wulandari; I Ketut Eddy Purnama; Eko Mulyanto Yuniarno; Mauridhi Hery Purnomo
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 1 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i1.112209

Abstract

Automated segmentation of Mycobacterium tuberculosis bacilli in Ziehl Neelsen stained sputum smears is essential for scalable tuberculosis (TB) screening, particularly in low resource settings where heterogeneous staining and poor illumination degrade image quality. Most encoder and decoder models such as Unet rely on overlap based supervision and lack embedding level discrimination, leading to feature confusion between bacilli and staining artifacts under low light conditions. To address this limitation, we propose a Hybrid Residual Unet with Triplet Embedded Metric Learning (RTL), which incorporates margin based metric supervision at the residual bottleneck using structured anchor, positive, and negative sampling and a joint Dice, binary cross entropy, and triplet objective. Evaluated on the DDS1 dataset with illumination stratified analysis, RTL outperformed Unet, Resunet, triplet based baselines, and Transunet, achieving higher Dice and mIoU, lower margin violation rates, and significantly improved embedding separability (p < 0.05). RTL also showed reduced performance variance across illumination subsets, indicating improved robustness to domain shift and more reliable bacilli delineation, which can support downstream components of automated TB microscopy workflows (detection, counting and slide level grading).
Co-Authors Abd Rahman Adhi Dharma Wibawa Adi Sutanto Ahmad Zaini Ahsan Ahsan Ait-Souar, Iliès Alamsyah Alamsyah - Andi Kurniawan Nugroho Arham Arham, Arham Arina Qona'ah Asayanda, Fikra Agha Rabbani Bernaridho Hutabarat, Bernaridho Boedinugroho, Hanny Budi Nur Iman Budi Santoso Catur Supriyanto Chastine Fatichah Dian Ratnawati Diana Purwitasari Dinar Mutiara Kusumo Nugraheni Dzaky Hanif Arjuna Edy Kurniawan Effendy Hadi Sutanto Eka Dwi Nurcahya Eko Mulyanto Yuniarno Eko Mulyanto Yuniarno Eko Mulyanto Yuniarno Elly Purwantini Endang Sri Rahayu Esther Irawati Setiawan Filiazsanti, Almira Firman Arifin Gijsbertus Jacob Verkerke Gijsbertus Jacob Verkerke Guruh Fajar Shidik Gusmaniarti, Gusmaniarti Handayeni, Ketut Dewi Martha Erli Hartarto Junaedi Hermawan, Norma Hernanda, Arta Kusuma Hidayat Arifin I Made Gede Sunarya Ida Hastuti Ima Kurniastuti Iman Fahruzi Ingrid Nurtanio Ismoyo Sunu Isturom Arif Jaya Pranata, Jaya Joko Priambodo Juanita, Safitri Khakim Ghozali Kristian, Yosi Kurniawan, Arief Lilik Anifah Lukman Affandhy Lukman Zaman Margareta Rinastiti Masy Ari Ulinuha Mauridhi Heri Purnomo Mauridhi Heri Purnomo Mauridhi Hery Purnomo Mauridhi Hery Purnomo Mauridhi Hery Purnomo Mira Candra Kirana Moch Hariadi Moch Hariadi Mochamad Hariadi Mochamad Yusuf Alsagaff Mochammad Hariadi Muhammad Anshari Muhammad Hariadi Muhammad Nur Alamsyah Muhtadin Muhtadin Muhtadin Mulyanto, Eko Munawir . Munawir Munawir Myrtati Dyah Artaria Myrtati Dyah Artaria, Myrtati Dyah Nazarrudin, Ahmad Ricky Nofiandri Setyasmara Nursalam . Pramunanto, Eko Priambodo, Joko Prioko, Kentani Langgalih Pulung Nurtantio Andono Putu Gde Ariastita Putu Hendra Suputra R Dimas Adityo Rachmadi, Reza Fuad Raihan, Muhammad Reza Fuad Rachmadi Ricardus Anggi Pramunendar Rifky Octavia Pradipta Rika Rokhana Rika Rokhana Rima Tri Wahyuningrum Rima Tri Wahyuningrum Robby Aldriyanto Raffly Rokhana, Rika Rumala, Dewinda Julianensi Saiful Bukhori Saiful Bukhori Sari Ayu Wulandari Sensusiati, Anggraini Dwi Setijadi, Eko Slamet Hartono Stevanus Hardiristanto Stevanus Hardiristanto Stevanus Hardiristanto, Stevanus Sugiyanto - Supeno Mardi Susiki Nugroho, Supeno Mardi Suryo, Yoedo Ageng Syahrul Munir Terawan Agus Putranto Tita Karlita Tita Karlita Tita Karlita Tomoko Hasegawa Tri Arief Sardjono Verkerke, Gijsbertus Jacob Wulandari, Ariani Dwi Yosi Kristian Yulis Setiya Dewi Zaimah Permatasari Zaman, Lukman