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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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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 Kebijakan New Normal dengan Menggunakan Automated Lexicon Senti N-Gram Rifki Akbar Siregar; Yuita Arum Sari; Indriati Indriati
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 1: Februari 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20231015006

Abstract

Dalam menghadapi pandemi COVID-19 ini, pemerintah Indonesia mengeluarkan beberapa kebijakan di antaranya adalah Pembatasan Sosial Berskala Besar, dan New normal. Kebijakan New normal ini kemudian menjadi ramai diperbincangkan oleh masyarakat. Analisis sentimen dari opini yang beredar terkait isu tersebut dapat dilakukan sehingga pemerintah dapat mengevaluasi kebijakan tersebut. Dalam penelitian ini diusulkan menggunakan Lexicon Senti-N-Gram untuk analisis sentimen dengan tujuan untuk mengetahui pengaruh Lexicon Senti-N-Gram pada analisis sentimen Bahasa Indonesia. Adapun penelitian ini menggunakan data sebanyak 350 data tweet yang terbagi menjadi 229 tweet kelas positif dan 121 tweet kelas negatif. Hasil evaluasi yang diperoleh dengan menggunakan data dengan stemming lebih tinggi dibandingkan dengan data tanpa stemming. Hasil pengujian kinerja sistem terhadap lexicon Senti-N-Gram mendapatkan nilai accuracy sebesar 63,42%, precision sebesar 77%, recall sebesar 62,88%, dan f-measure sebesar 69,23% dengan nilai rata-rata kappa antar Annotator sebesar 0.5395 untuk data yang melalui proses stemming.  Berdasarkan hasil pengujian yang telah diperoleh dapat disimpulkan bahwa proses stemming serta proses translasi kata satu per satu yang dilakukan dapat memengaruhi kata berdasarkan konteksnya. AbstractIn dealing with the COVID-19 pandemic, the Indonesian government has issued several policies, including Large-Scale Social Restrictions and New normal. The New normal policy then became widely discussed by the public. Sentiment analysis of the opinions circulating on this issue can be carried out so that the government can evaluate the policy. In this study, it is proposed to use the Lexicon Senti-N-Gram for sentiment analysis in order to determine the effect of the Lexicon Senti-N-Gram on Indonesian sentiment analysis. The research used 350 tweets, which were divided into 229 positive class tweets and 121 negative class tweets. The evaluation results obtained using stemming data were higher than those without stemming. The results of the system performance test of the Lexicon Senti-N-Gram obtained an accuracy value of 63.42%, 77% precision, 62.88% recall, and 69.23% f-measure with an average kappa value between Annotators of 0.5395 for data that goes through the stemming process. Based on the test results that have been obtained, it can be concluded that the stemming process and the process of translating words one by one can affect words based on their context.
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 : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Brawijaya

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.
COMBINED CONTOUR DETECTION AND POINT CLOUD OF RGB-DEPTH IMAGE FOR FOOD VOLUME ESTIMATION Yuita Arum Sari
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 8 No. 1 (2025): MISI Januari 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v8i1.1408

Abstract

Assessing nutritional consumption entails a procedure that enables nutritionists and dietitians to track the eating habits of patients within healthcare settingsTraditionally, this measurement relies on manual observations by specialists utilizing visual analysis. However, this approach is prone to subjectivity due to the risk of expert fatigue, which can result in inaccuracies. Furthermore, the evaluations may differ among experts based on varying viewpoints. In a decision support system, a more objective analysis is necessary. Previous research has utilized the area captured in a food image to estimate the weight of food on a plate. Nonetheless, this technique still results in numerous prediction errors. To tackle this issue, we propose a novel method to calculate the volume of food from a camera image, which aims to provide a more accurate weight prediction. In this paper, we introduce a new approach that combines contour detection with a point cloud derived from RGB depth images to capture height information. The Root Mean Square Error (RMSE) for height prediction is 1.04 and 1.55 when viewed from the first and second sides, respectively, while the volume prediction reaches an RMSE of 45.08. This suggests that the differences between the predicted and actual values for volume and height are suitable for practical applications.
Sistem Deteksi Kualitas Susu Menggunakan Metode Gray Level Co-occurrence Matrix dan Random Forest Simangunsong, Bryan Nicholas Josephin Hotlando; Utaminingrum, Fitri; Sari, Yuita Arum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 9 No 8 (2025): Agustus 2025
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

Penjaminan mutu susu merupakan aspek penting dalam rantai pasok produk susu, terutama untuk memastikan keamanan dan kualitas konsumsi. Namun, di tingkat peternak kecil, implementasinya masih menghadapi tantangan dalam konsistensi dan keterbatasan sumber daya. Metode Total Plate Count (TPC) yang umum digunakan membutuhkan waktu lama dan fasilitas khusus, sehingga tidak efisien untuk pemeriksaan cepat. Penelitian ini bertujuan mengembangkan sistem klasifikasi kualitas susu berbasis citra digital menggunakan metode Gray Level Co-occurrence Matrix (GLCM) dan Random Forest, serta diimplementasikan pada Raspberry Pi 4, sebagai alternatif praktis terhadap metode TPC. GLCM digunakan untuk mengekstraksi lima fitur tekstur dari citra susu, sedangkan Random Forest melakukan klasifikasi ke dalam tiga kelas: Baik, Rusak, dan Rusak Berat. Hasil pengujian menunjukkan bahwa konfigurasi terbaik dicapai dengan 100 pohon keputusan, jarak GLCM 4 piksel, dan sudut 135°, menghasilkan akurasi validasi 84,65%. Pada pengujian akhir terhadap 75 sampel, sistem mencapai akurasi 86,66%, dengan akurasi 100% untuk kelas Baik, serta 80% untuk Rusak dan Rusak Berat. Sistem terbukti efisien dengan rata-rata waktu pelatihan 0,2835 detik dan klasifikasi 0,6651 detik. Hasil ini menunjukkan bahwa sistem mampu melakukan deteksi kualitas susu secara cepat dan akurat, serta berpotensi menjadi alternatif praktis dari metode konvensional.
Perancangan User Experience Aplikasi Perangkat Bergerak Buku Penghubung Daycare Menggunakan Pendekatan Human-Centered Design (Studi Kasus: Daycare Triple-C) Alayasi, Mutia; Widodo, Agus Wahyu; Sari, Yuita Arum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 9 No 9 (2025): September 2025
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

Tempat penitipan anak merupakan salah satu alternatif tempat penitipan sementara ketika orang tua bekerja seperti dual-career family. Buku penghubung merupakan sarana untuk saling bertukar informasi terkait pengasuhan anak antara orangtua dan tempat penitipan anak. Daycare Triple-C merupakan taman penitipan anak yang meningkatkan kemitraan dengan orangtua, salah satunya melalui buku penghubung menggunakan Whatsapp dan masih memiliki kekurangan terutama akses informasi stimulasi anak harian. Daycare Triple-C membutuhkan suatu aplikasi yang dapat memfasilitasi pertukaran informasi terkait stimulasi dan pengasuhan anak di tempat penitipan anak dan di rumah. Pendekatan yang digunakan dalam perancangan adalah Human-Centered Design. Keluaran yang dihasilkan adalah hi-fi prototype aplikasi perangkat bergergak buku penghubung untuk pengajar, orangtua, dan kepala. Fitur yang dihasilkan antara lain laporan stimulasi harian, kemandirian anak, dokumentasi, dan infografis pembelajaran, dan laporan akhir setiap semester. Hasil uji usability menggunakan Single Ease Question diperoleh 6,46 di atas target benchmark SEQ sebesar 5,5 yang menunjukkan bahwa aplikasi mudah digunakan. Hasil pengujian menggunakan User Experience Questionnaire memperoleh hasil Excellent pada semua aspek.
A Comparative Analysis of Color Channel-Based Feature Extraction using Machine Learning versus Deep Learning for Food Recognition Sari, Yuita Arum; Nugraha, Dwi Cahya Astria; Adinugroho, Sigit
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5001

Abstract

Automated Dietary Assessment Accurate food recognition is a big challenge in computer vision which is critical for developing Automated Dietary assessment and health monitoring systems. The key question it answered was whether traditional machine learning with feature engineering by hand can beat modern deep learning approaches? In this Context, this study serves as a comparative analysis of these two paradigms. The baseline method worked by extracting texture (LBP,GLCM) and color information from different channels of five colors spaces (RGB, HSV, LAB, YUV,YCbCr) followed by feeding these features into multiple classifiers such as Nearest Neighbor(NN), Decision Tree and Naïve Bayes. These were then compared to deep learning models (MobileNet_v2, ResNet18, ResNet50, EfficientNet_B0). The best traditional one can reach an accuracy of 93.33%, using texture features extracted from the UV channel and classified with a NN. Nevertheless, the deep learning models consistently presented higher performance and MobileNet_v2 reached up to 94.9% accuracy without requiring manual feature selection. In this paper, we show that end-to-end deep learning models are more powerful and error robust for food recognition. These results highlight their promise for constructing more effective and scalable real-world applications with less need for intricate, domain-specific feature engineering.
Co-Authors Achmad Arwan Adinugroho, Sigit Agus Wahyu Widodo, Agus Wahyu Agus Zainal Arifin Alayasi, Mutia Alfisyar Jefry Pranata Anggi Gustiningsih Hapsani 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 Hurriyatul Fitriyah Indriati Indriati 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 Mustika Mentari Nabila Nur’aini Natasya Eldha Oktaviana Novanto Yudistira Nugraha, Dwi Cahya Astria Nuraini, Nabila Putra Pandu Adikara Putra Pandu Adikara Putra Pandu Adikari Raditya Rinandyaswara Randy Cahya Wihandika Ratih Kartika Dewi Renaldi Primaswara Praetya Rifki Akbar Siregar Rizal Maulana, Rizal Rizky Maulana Iqbal Sigit Adinugroho Simangunsong, Bryan Nicholas Josephin Hotlando Siti Mutrofin Tesa Eranti Putri Utaminingrum, Fitri Wijaya Kurniawan Yusuf Gladiensyah Bihanda