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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Knowledge Engineering and Data Science Journal of Applied Engineering and Technological Science (JAETS)
Agus Wahyu Widodo
Fakultas Ilmu Komputer, Universitas Brawijaya
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Estimasi Sisa Makanan Otomatis pada Kotak Makan menggunakan Segmentasi Citra berbasis Clustering dengan Algoritme K-Means Haris Bahtiar Asidik; Yuita Arum Sari; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Food is a source of energy for living things, consuming food in standardized portions can help meet nutritional needs, but on the other hand, it will have a negative impact on the body. Foodservice in the hospital is a support system in accelerating the patient's healing process, the patient's leftover food reflects the patient's low acceptance of food which can increase the risk of malnutrition. Currently, the global volume of food waste is estimated at 1.6 billion tonnes with food waste carbon estimated at 3.3 billion tonnes of CO2 equivalent to greenhouse gases per year. It takes a tool to find out how much food a person has consumed. With the development of technology, the process of calculating the weight of food waste can be done through the image of the food before it is eaten and the image of the food after it is eaten. The food image in the lunch box is segmented to obtain food segments in the image through the clustering method using the K-means algorithm based on the blue color level in the image. The results of the Intersection over Union (IoU) segmentation accuracy on images with a white background reached 98.9%. Based on the segmentation results obtained, the weight of leftover food was predicted using the Elementary Leftover Food Estimation (EFLE) method. By using the Root Mean Square Error (RMSE), the experimental results reach the smallest error of 1.12. This indicates that the proposed method is capable to project the weight of the food residue.
Pengembangan Sistem Informasi Manajemen Aset Fakultas Ilmu Komputer Universitas Brawijaya berbasis Mobile Umar Zaki Izzuddin; Herman Tolle; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 8 (2022): Agustus 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The Faculty of Computer Science (FILKOM) has a work unit that is responsible for managing and maintaining infrastructure, namely the general and equipment work units. There are several problems that exist in this work unit. The first problem is that the process of reporting complaints from the academic community is still verbal so that it takes time to coordinate from the reporter to the staff of the infrastructure section until the improvement of infrastructure is carried out. The second problem is that the process of making daily activity reports made in the form of physical documents results in wasteful use of paper and file stacking. The third problem is in routine maintenance activities, making maintenance reports from activities already use google forms but there is a buildup of data on the limited capacity of the google drive. From the use of this google form, there are also problems in the form of incomplete maintenance data and mismatch of data from each infrastructure. This research seeks to develop a mobile-based asset management information system. This research adopts the waterfall method in system development. The stages of the waterfall method are requirements, design, implementation and testing. The requirements stage produced four actors, 28 functional needs, one functional need, and requirements modeling using use case diagrams and use case scenarios. Design stage results in modeling sequence diagrams, diagram classes, algorithm design, database design and interface design. The implementation stage is carried out by realizing the actual system of the results of the design that has been carried out. All functional requirements tests which are unit testing, integration testing, and validation testing result in valid status in all test cases. In the usability test, an average score of 87,5 was obtained, which means that the system is acceptable to users.
Deteksi Covid-19 dari Citra X-ray menggunakan Vision Transformer Javier Ardra Figo; Novanto Yudistira; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 3 (2023): Maret 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Corona Virus is a single stranded RNA virus that can infect human dan a few animal. X-ray Imaging can be one of the few way to check or monitor lungs condition such in the case of tuberculosis, pneumonia, and hernia. Combining X-ray Imaging with deep learning can be one of the solution to the covid-19 detection problem. Vision Tranformer is an architecture that inspired by transformer which is state of the art in the natural language processing realm. One of the few public dataset that contain x-ray image is covidX. CovidX can be breakdown into 3 classes which is pneumonia, covid-19, and normal with as few as 30,530 x-ray image available.the Dataset will processed with data augmentation gaussian blur and colorjitter. The vision transformer that will be used in this experiment is base, large, and huge. This architecture will be used with transfer learning and data augmentation. This experiment will use 40 Epoch, stochastic gradient descent Optimizer, WarmupCosine Scheduler, and Cross Entropy loss function. This experiment will test the effect of transfer learning toward accuracy, the effect of data augmentation toward accuracy, and then will be compared to other architecture. The best accuracy from this experiment is achieved by ResNet50 with transfer learning that achieve accuracy as high as 0.9617006 with validation data and 0.9548872 in test data. Based on this result, the model is overfitting.
Penentuan Mutu pada Citra Buah Jeruk Keprok menggunakan Metode Local Binary Pattern (LBP) Angelika Trivena Lodong; Agus Wahyu Widodo; Muh. Arif Rahman
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 4 (2023): April 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Pertumbuhan jumlah produksi jeruk di Indonesia terus meningkat setiap tahunnya. Data dari Kementerian Pertanian pada tahun 2014 menyatakan bahwa dari semua jenis jeruk yang ada di Indonesia, jeruk keprok memiliki hasil produksi yang paling banyak yaitu sekitar 92% dari total hasil produksi buah jeruk seluruhnya. Mutu dari hasil produksi buah jeruk keprok menjadi hal yang sangat penting, terutama dalam persaingan pasar. Pemanfaatan teknologi visual dapat digunakan dalam penentuan mutu jeruk keprok dan dapat menggantikan proses penentuan mutu secara manual oleh manusia, agar sesuai dengan standarisasi mutu buah jeruk keprok. Penelitian ini memanfaatkan hasil dari ekstraksi fitur Local Binary Pattern (LBP) citra jeruk keprok untuk penentuan mutu. Langkah awal dari penelitian ini yaitu mengambilan data citra jeruk keprok. Pada citra jeruk keprok, dilakukan pemotongan citra untuk mendapatkan setiap area yang akan diklasifikasikan menjadi kelas baik atau buruk, selanjutnya dilakukan proses pre-processing yang didalamnya terdapat proses mengubah citra berwarna menjadi citra grayscale. Kemudian dilakukan proses ekstraksi fitur Local Binary Pattern (LBP). Hasil ekstraksi fitur dari potongan citra tersebut akan diklasifikasikan menjadi kelas baik atau buruk. Setelah semua potongan citra telah diklasifikasikan, maka akan didapatkan jumlah potongan yang baik dan buruk dalam sebuah citra, sehingga dapat ditentukan Grade dari buah jeruk keprok. Mutu jeruk keprok dibagi menjadi 3 kelas yaitu, Grade Super, Grade A dan Grade B. Pada penelitian ini diperoleh hasil akurasi terbaik yaitu sebesar 80%, dengan ukuran dimensi citra sebesar 100x100 piksel dan jarak ketetanggaan atau nilai R=1.
Vision Transformer for Active Compound Function Classification Based on 2D Molecular Structures Dian Eka Ratnawati; Diva Kurnianingtyas; Agus Wahyu Widodo; Rekyan Regasari Mardi Putri
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9418

Abstract

Accurate classification of active compounds based on molecular structure is crucial for accelerating drug discovery while reducing laboratory costs and time. However, existing structure-based classification methods, particularly convolutional neural networks and graph-based models, often struggle to capture long-range dependencies or require large-scale datasets and extensive feature engineering. This study investigates the use of the Vision Transformer (ViT) model to classify 2D molecular structure images of compounds into cancer and cardiovascular therapy categories. A dataset containing 500 images, consisting of 250 per class, was obtained from the PubChem database, processed for consistency, and divided into 72% training, 20% testing, and 8% validation. To address the limited dataset size, careful preprocessing, regularization through weight decay, and systematic hyperparameter tuning were applied to reduce overfitting risks. The ViT model was trained with the Adam optimizer and a linear learning rate scheduler. Hyperparameters were systematically tuned to identify the optimal configuration. Results show that the best settings, with batch size 60, weight decay 0.1, learning rate 3.0×10⁻⁶, and 15 epochs, achieve an accuracy, F1 score, and loss of 80.0%, 79.9%, and 0.597, sequentially. These findings highlight the potential of ViT for small-scale cheminformatics tasks, offering an alternative to conventional methods while maintaining competitive performance.
Co-Authors Achmad Arwan Achmad Dewanto Aji Wibisono Adam Hendra Brata Adinugroho, Sigit Afrida Djulya Ika Pratiwi Aida Fitri Nur Amrina Ainun Najib Eka Christianto Aisha Laras Akmilatul Maghfiroh Al-Mar'atush Shoolihah Allifira Andara Hasna Ana Mariyam Puspitasari Andika Indra Kusuma Andreas Pardede Angelika Trivena Lodong Anggita Nurfadilla Mahardika Annisa Amalia Nur'aini Anto Satriyo Nugroho Ardiansyah Setiajati Arry Supriyanto Arya Agung Andika Aryu Hanifah Aji Asfie Nurjanah Ayu Anggrestianingsih Ayudiya Pramisti Regitha Ayustina Giusti Azizah Nurul Asri Bagas Laksono Bayu Rahayudi Beryl Labique Ahmadie Budi Darma Setiawan Budi Kurniawan Cahya Chaqiqi Candra Dewi Dani Devito Delischa Novia Sabilla Deo Hernando Dian Eka Ratnawati Diantarakita Diantarakita Diva Kurnianingtyas Dwi Retnoningrum Dyan Putri Mahardika Eko Wahyu Hidayat Erlyan Eka Pratiwi Faizatul Amalia Fajar Pangestu Fajar Pradana Fajri Eka Saputra Farizky Novanda Pramuditya Femilia Nopianti Feris Adi Kurnia Sadiva Fitri Dwi Astuti Fransiskus Cahyadi Putra Pranoto Grace Theresia Situmorang Gusti Ngurah Wisnu Paramartha Hafid Satrio Priambodo Hardyan Zalfi Haris Bahtiar Asidik Harits Abdurrohman Herman Tolle Imam Cholissodin Indriati Indriati Irwan Shofwan Javier Ardra Figo Jefri Hendra Prasetyo Kholifa'ul Khoirin Lailil Muflikhah Latifa Nabila Harfiya Laviana Agata M. Ali Fauzi Maharani Tri Hastuti Maria Sartika Tambun Miftahul Arifin Muh Arif Rahman Muh. Arif Rahman Muh. Arif Rahman Muh. Ihsan As Sauri Muhamad Rendra Husein Roisdiansyah Muhammad Dimas Setiawan Sanapiah Muhammad Fahmi Hidayatullah Muhammad Fahmi Wibawa Muhammad Faiz Abdul Hamif Muhammad Fajriansyah Muhammad Heryan Chaniago Muhammad Ikhsan Nur Muhammad Rafi Farhan Muhammad Tanzil Furqon Muhja Mufidah Afaf Amirah Nabilla Putri Sakinah Nanda Dwi Putra Miskarana Ade Natassa Anastasya Naufal Sakagraha Kuspinta Nelli Nur Rahma Ni'mah Firsta Cahya Susilo Ningsih Puji Rahayu Nizar Riftadhi Prabandaru Novanto Yudistira Nur Afifah Sugianto Nur Faiqoh Laely Ambarwati Nur Firra Hasjidla Nur Kholida Afkarina Nurudin Santoso Nurul Hidayat oktiyas muzaky Luthfi, oktiyas muzaky Olive Khoirul L.M.A. Puteri Aulia Indrasti Putra Pandu Adikara Putri Bunga Rahmalita Putu Satya Cahyani Rahma Juwita Sany Randy Cahya Wihandika Rekyan Regasari Mardhi Putri Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Restu Widodo Resya Futri Hadi Febryana Retno Dewi Anissa Revan Yosua Cornelius Sianturi Ridho Saputra Rinindya Nurtiara Puteri Rizka Husnun Zakiyyah Rizki Aziz Amanullah Rosi Afiqo Rr Dea Annisayanti Putri Ryan Iriany Satria Habiburrahman Fathul Hakim Sayyidah Karimah Sindy Erika Br Ginting Sri Rahadian Ramadhan Sakti Susiawan Hastomo Ajie Talitha Raissa Tusiarti Handayani Tusty Nadia Maghfira Umar Zaki Izzuddin Utaminingrum, Fitri Vriza Wahyu Saputra Wayan Firdaus Mahmudy Wayan Firdaus Mahmudy Wenny Ramadha Putri Willy Karunia Sandy Winda Cahyaningrum Winda Ika Praseptiyana Witriana Sumarni Yane Marita Febrianti Yosafat Vincent Saragih Yuita Arum Sari Yunita Kristanti Emilia