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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Journal of Environmental Engineering and Sustainable Technology Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Keteknikan Pertanian Tropis dan Biosistem Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Multimedia Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jiko (Jurnal Informatika dan komputer) Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Teknik Informatika (JUTIF) Jurnal Informatika dan Multimedia
Yuita Arum Sari
Brawijaya University, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Mechanical Engineering Social Sciences Other

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Hybrid Head Tracking for Wheelchair Control Using Haar Cascade Classifier and KCF Tracker Fitri Utaminingrum; Yuita Arum Sari; Putra Pandu Adikara; Dahnial Syauqy; Sigit Adinugroho
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.6595

Abstract

Disability may limit someone to move freely, especially when the severity of the disability is high. In order to help disabled people control their wheelchair, head movement-based control is preferred due to its reliability. This paper proposed a head direction detector framework which can be applied to wheelchair control. First, face and nose were detected from a video frame using Haar cascade classfier. Then, the detected bounding boxes were used to initialize Kernelized Correlation Filters tracker. Direction of a head was determined by relative position of the nose to the face, extracted from tracker’s bounding boxes. Results show that the method effectively detect head direction indicated by 82% accuracy and very low detection or tracking failure.
USER EMOTION IDENTIFICATION IN TWITTER USING SPECIFIC FEATURES: HASHTAG, EMOJI, EMOTICON, AND ADJECTIVE TERM Yuita Arum Sari; Evy Kamilah Ratnasari; Siti Mutrofin; Agus Zainal Arifin
Jurnal Ilmu Komputer dan Informasi Vol 7, No 1 (2014): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (271.587 KB) | DOI: 10.21609/jiki.v7i1.252

Abstract

Abstract Twitter is a social media application, which can give a sign for identifying user emotion. Identification of user emotion can be utilized in commercial domain, health, politic, and security problems. The problem of emotion identification in twit is the unstructured short text messages which lead the difficulty to figure out main features. In this paper, we propose a new framework for identifying the tendency of user emotions using specific features, i.e. hashtag, emoji, emoticon, and adjective term. Preprocessing is applied in the first phase, and then user emotions are identified by means of classification method using kNN. The proposed method can achieve good results, near ground truth, with accuracy of 92%.
Facial Expression Recognition using Residual Convnet with Image Augmentations Fadhil Yusuf Rahadika; Novanto Yudistira; Yuita Arum Sari
Jurnal Ilmu Komputer dan Informasi Vol 14, No 2 (2021): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v14i2.968

Abstract

During the COVID-19 pandemic, many offline activities are turned into online activities via video meetings to prevent the spread of the COVID 19 virus. In the online video meeting, some micro-interactions are missing when compared to direct social interactions. The use of machines to assist facial expression recognition in online video meetings is expected to increase understanding of the interactions among users. Many studies have shown that CNN-based neural networks are quite effective and accurate in image classification. In this study, some open facial expression datasets were used to train CNN-based neural networks with a total number of training data of 342,497 images. This study gets the best results using ResNet-50 architecture with Mish activation function and Accuracy Booster Plus block. This architecture is trained using the Ranger and Gradient Centralization optimization method for 60000 steps with a batch size of 256. The best results from the training result in accuracy of AffectNet validation data of 0.5972, FERPlus validation data of 0.8636, FERPlus test data of 0.8488, and RAF-DB test data of 0.8879. From this study, the proposed method outperformed plain ResNet in all test scenarios without transfer learning, and there is a potential for better performance with the pre-training model. The code is available at https://github.com/yusufrahadika-facial-expressions-essay.
Aplikasi Smart Nutrition Box dalam Identifikasi Kehilangan Zat Gizi (Loss of Nutrition) pada Limbah Makanan Kantin Nabila Nur’aini; Dhea Rahma Widyadhana; Yusuf Gladiensyah Bihanda; Yuita Arum Sari; Jaya Mahar Maligan
Jurnal Keteknikan Pertanian Tropis dan Biosistem Vol 8, No 3 (2020)
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jkptb.2020.008.03.09

Abstract

Kantin memiliki peran penting sebagai salah satu penyedia layanan konsumsi yaitu dengan menjual makanan yang dibutuhkan untuk pemenuhan zat gizi tersebut. Namun, perubahan budaya dan gaya hidup menimbulkan permasalahan baru terutama mengenai limbah makanan. Diperkirakan jumlah limbah makanan mencapai 1,3 Giga ton per tahun. Selain berdampak buruk bagi lingkungan, limbah makanan juga menandakan adanya sejumlah gizi yang terbuang dan tidak terkonsumsi oleh tubuh. Evaluasi gizi melalui sisa makanan menjadi salah satu cara yang dilakukan untuk mengetahui tingkat pelayanan gizi dan mengetahui jumlah gizi yang dikonsumsi masyarakat. Seiring dengan kemajuan teknologi, beberapa metode tersebut akhirnya diadaptasi dan dikembangkan ke dalam sebuah alat yaitu Smart Nutrition Box. Alat ini berfungsi untuk menghitung jumlah limbah makanan dengan metode image segmentation. Tujuan penelitian ini yaitu mengetahui aplikasi Smart Nutrition Box dalam mengidentifikasi kehilangan zat gizi pada limbah makanan kantin. Metode: Penelitian dilakukan melalui studi literatur yang berkaitan dengan aplikasi alat Smart Nutrition Box dan identifikasi kehilangan zat gizi. Jumlah sisa makanan dapat dihitung menggunakan Smart Nutrition Box. Adapun jumlah kehilangan zat gizi dilanjutkan dengan perhitungan menggunakan formulasi tertentu hingga didapatkan jumlah zat gizi yang hilang dari limbah makanan kantin. Aplikasi Smart Nutrition Box dalam mengestimasi jumlah limbah makanan kantin memiliki akurasi dengan nilai RMSE 2,37 dan identifikasi kehilangan zat gizi dapat dihitung dengan formulasi setelah data limbah makanan didapatkan.
Peningkatan Kompetensi Keilmuan IOT Melalui Pelatihan Pengontrolan Perangkat IOT dengan Menggunakan Smartphone untuk Siswa SMK dan SMA di Kota Malang Dahnial Syauqy; Yuita Arum Sari; Putra Pandu Adikara; Muhammad Aminul Akbar; Hurriyatul Fitriyah
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 3 (2020): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v4i3.3785

Abstract

The internet has encouraged the emergence of a new paradigm in the use of computing, marked by the emergence of devices that are connected to each other or known as the Internet of Things (IoT). One of the factors that contributed to the rapid emergence of "things" in the IoT network was the rapid development of chip technology and also increasingly varied wireless communication technology. With the emergence of the phenomenon of the development of IoT, especially those involving entrepreneurial opportunities in related fields, it is considered important to be introduced early on to students of vocational-high school equivalents in Indonesia. The activity was held in September 2019 at Filkom UB involving 41 participants from 6 schools of vocational-high school or equivalent. The activity succeeded in increasing the participant's understanding of the theory and also the practice of IoT where the participants' pre-test and post-test scores increased from an average of 61.6 to 92.9. The implementation of the activity was also declared successful from the results of the questionnaire. From a total of 425 answers, there were 17 STS answers, 39 TS answers, 205 S answers and 164 SS answers. Taking into account the positive and negative components, a recapitulation of 415 answers (98%) was found to be satisfied with the implementation of the activities.
Pengelompokan Dokumen Berita Berbahasa Indonesia Menggunakan Reduksi FiturInformation Gain dan Singular Value Decomposition dalam Fuzzy C-MeansClustering Tesa Eranti Putri; Yuita Arum Sari; Anggi Gustiningsih Hapsani
Jurnal Informatika dan Multimedia Vol. 10 No. 1 (2018): Jurnal Informatika dan Multimedia
Publisher : Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jim.v10i1.598

Abstract

Koran dan berita online merupakan media informasi digital saat ini yang proses pembaruan informasinya sangat mudah dan fleksibel. Kemudahan ini memungkinkan penulis berita untuk mengunggah informasi baru di waktu kapanpun dan dimanapun. Hal ini menyebabkan data dokumen berita sangat banyak dan tidak teratur sehingga perlu dilakukan pengelompokan berita sesuai dengan kontennya. Pengelompokanberita sesuai content dapat membantu pembaca untuk membaca berita dengan topiktertentu sesuai dengan minatnya. Proses pengelompokan informasi berita diimplementasikan denganbeberapa tahap, yaitu preprocessing dan pengelompokan dokumen. Preprocessing dilakukan dengan mengimplementasikan metode kombinasi reduksi fitur Document Frequency (DF) dan Information Gain (IG) Thresholding dalamSingular Value Decomposition (SVD). Algoritme SVD dipilih karena memiliki kemampuan untuk melakukan dekomposisi pada matriks dokumen-term, sehingga diperoleh matriks yang masih menyimpan informasi penting dengan ukuran dimensi yang lebih kecil.Pada tahap pengelompokan dokumen berita dilakukandengan algoritme Fuzzy C-Means. Hasil uji coba akurasipengelompokan dokumen berita menunjukkan bahwa pengelompokan yang dilakukan memberikan hasil pengkategorian yang cukup akurat dengan tingkat akurasi rata-rata 74,5 % (IG threshold 0.5, k = 5). Hal tersebut menunjukkan bahwa pengelompokan dokumen menggunakan IG dan SVD dengan FUZZY C-MEANS adalah sesuai dengan kebutuhan.
Analisis Sentimen pada Ulasan Hotel dengan Fitur Score Representation dan Identifikasi Aspek pada Ulasan Menggunakan K-Modes Muhammad Hafiz Azhar; Putra Pandu Adikari; Yuita Arum Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 9 (2018): September 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (946.476 KB)

Abstract

As the number of review is rising, there is a need to make a system that can do classify a review belong to which class, in this case there are positive and negative classes. Furthermore, we also need to know what aspect that commented in the review. In this research, sentiment analysis at aspect level, Bag of Nouns feature has been used for clustering to get aspect and sentiment classification with score representation feature to classify sentiment. With categorical attribute for Bag of Nouns feature, K-Modes is considered capable for clustering. In sentiment classification, score representation has been used for LVQ2 that can handle the correlation between attribute and also become alternative for another machine learning algorithm. Based on the evaluation with Silhouette Coefficient, the optimal number for clustering balanced data set is 7 and 5 for unbalanced data set. Based on the evaluation with precision, recall, and f1-score, the performance of the balanced data set are 89,2% for precision, 89,13% for recall, and 89,12% for f1-score. The evaluation for unbalanced data set are 87,38% for precision, 73,07% for recall, and 76,46% for f1-score. It can be concluded that score representation can be used for sentiment analysis.
NUTRITION ESTIMATION OF LEFTOVER USING IMPROVED FOOD IMAGE SEGMENTATION AND CONTOUR BASED CALCULATION ALGORITHM Adinugroho, Sigit; Sari, Yuita Arum; Maligan, Jaya Mahar; Sari, Kartika; Bihanda, Yusuf Gladiensyah; Nuraini, Nabila; Fatchurrahman, Danial
Journal of Environmental Engineering and Sustainable Technology Vol 9, No 01 (2022)
Publisher : Directorate of Research and Community Service (DRPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jeest.2022.009.01.5

Abstract

In pandemic conditions, awareness of keeping a healthy balance is necessary. One is considering food consumption and understanding its nutrition content to avert food waste. We have been developing a prototype to estimate the nutrition of leftover food, and the main problem lies in image segmentation. Therefore, we propose the Improved Food Image Segmentation (IFIS) and Contour Based Calculation (CBC) to measure the area of the segmented image instead of pixel-wise. First, the tray box image is acquired and broken down into compartments using an automated cropping algorithm. The first step of this proposed method is tray box image acquisition and dividing the compartment using an automatic cropping algorithm. Then each compartment is treated using IFIS, calculates the result of IFIS by CBC, measures the estimated leftover by Automatic Food Leftover Estimation (AFLE), and then predicts the nutritional content. The evaluation is applied by comparing the actual measurement from the Comstock method and leftover estimation by the proposed algorithm. The result shows that Root Square Means Error (RMSE) reaches 0.48 compared to the actual weighing scale and 96.67% accuracy compared to the Comstock method. Based on the results, the proposed algorithm is sufficient to be applied.
SPERM ABNORMALITY CLASSIFICATION USING MULTI-PURPOSE IMAGE EMBEDDING AND CLASSICAL MACHINE LEARNING Adinugroho, Sigit; Sari, Yuita Arum; Kurniawan, Wijaya; Arwan, Achmad
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.8938

Abstract

Since sperm cells have big impact for human welfare in terms of reproduction, there are many studies have been done. In this case, we are attracted to enrich the method in determining the morphological properties of them using machine learning. Most study about it is done using 2-steps action that are feature extraction which is continued by classification. In our work, we aimed to lower the complexity by using image embedding as a general-purpose feature extractor that requires no training. For feature extraction using image, it is found that RGB has better performance compared to grayscale if we want to use Support Vector Machine (SVM). Meanwhile, when a comparation is done between SVM, random forest, Multi-Layer Perceptron (MLP), Naïve Bayes, and k-Nearest Neighbour (kNN) for classification process, MLP shows the best performance among them which is around 85%. Moreover, our proposed method has low complexity indicated by the training time around one and a quarter minute s for the most accurate method, compared to hours of training time in similar methods.
A Novel RGB-Depth Imaging Technique for Food Volume Estimation Sari, Yuita Arum
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 1 (2025): Januari 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i1.1764

Abstract

Evaluating nutrient intake among patients in a hospital is crucial, as it can accelerate their recovery process. Estimating calorie intake can be achieved by monitoring the quantity of food consumed by patients both before and after their meals. This approach involves various methods, including the use of digital scales, the Comstock level, and digital imaging techniques. Nonetheless, these techniques have their own limitations, particularly the risk of subjective assessments. To reduce errors arising from human factors, an objective evaluation is proposed. This paper introduces a new technique for measuring volume using RGB-Depth images. The method incorporates image segmentation and edge detection in RGB images to correspond with the depth image. Subsequently, the segmented regions and boundaries from the depth images are converted into point clouds. The volume of interest is calculated by fitting the point cloud to an ellipsoid. The lowest Mean Average Percentage Error (MAPE) is 2.73. It indicates that the proposed method is sufficient to measure the food volume.
Co-Authors Achmad Arwan Adinugroho, Sigit Agus Wahyu Widodo, Agus Wahyu Agus Zainal Arifin Alayasi, Mutia Anggi Gustiningsih Hapsani Bihanda, Yusuf Gladiensyah Budi Darma Setiawan Chastine Fatichah Dahnial Syauqy Dewa Gede Trika Meranggi Dhea Rahma Widyadhana Edy Santoso Evy Kamilah Ratnasari Fadhil Yusuf Rahadika Fatchurrahman, Danial Hapsani, Anggi Gustiningsih Hapsani, Anggi Gustiningsih Hurriyatul Fitriyah Indriati Indriati Iqbal, Rizky Maulana Jaya Mahar Maligan Jaya Mahar Maligan Jayawarsa, A.A. Ketut Laksono Trisnantoro Lukman Hakim Luthfi Maulana, Luthfi M. Ali Fauzi Muhammad Aminul Akbar Muhammad Hafiz Azhar Muhammad Tanzil Furqon, Muhammad Tanzil Mustika Mentari Nabila Nur’aini Novanto Yudistira Nugraha, Dwi Cahya Astria Nuraini, Nabila Oktaviana, Natasya Eldha Pranata, Alfisyar Jefry Putra Pandu Adikara Putra Pandu Adikara Putra Pandu Adikari Randy Cahya Wihandika Ratih Kartika Dewi Renaldi Primaswara Praetya Rinandyaswara, Raditya Rizal Maulana, Rizal Sigit Adinugroho Simangunsong, Bryan Nicholas Josephin Hotlando Siregar, Rifki Akbar Siti Mutrofin Siti Mutrofin Tesa Eranti Putri Utaminingrum, Fitri Wijaya Kurniawan Yusuf Gladiensyah Bihanda