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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Siasat Bisnis Jurnal Buana Informatika JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Anny Kartika Sari
Universitas Gadjah Mada
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Social Sciences

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Content Based Image Retrieval Berdasarkan Fitur Low Level: Literature Review Rahmad Hidayat; Agus Harjoko; Anny Kartika Sari
Jurnal Buana Informatika Vol. 8 No. 2 (2017): Jurnal Buana Informatika Volume 8 Nomor 2 April 2017
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v8i2.1077

Abstract

Abstract. Content-based Image Retrieval (CBIR) is an image search process by comparing the image features sought by the images contained in the database. Low-level features in the image are commonly used in CBIR is the color, texture, and shape. This article conducts a review of journals related to CBIR, particularly research based on low-level features. The journals are then classified based on the color space, features and feature extraction methods. The results show that the color space often used is the RGB and HSV due to their compatibility with the hardware and human perception of color. The features most often used in CBIR is the color feature. This is due to the fact that color features can easily and quickly be extracted. The most often used method to extract the color feature is the color histogram, the most common method used to extract texture features is the gray level co-occurence matrix, and the method most widely used to extract the shape feature is canny edge.Keywords: CBIR, color, texture, shape. Abstrak. Content based Image Retrieval (CBIR) merupakan proses pencarian gambar dengan membandingkan fitur-fitur yang terdapat pada gambar yang dicari dengan gambar yang terdapat dalam basis data. Fitur-fitur low level pada gambar yang biasa digunakan dalam CBIR adalah warna, tekstur, dan bentuk Artikel ini melakukan tinjauan terhadap penelitian-penelitian yang berkaitan dengan CBIR, khususnya penelitian yang berbasis pada fitur low level. Penelitian-penelitian tersebut kemudian diklasifikasikan berdasarkan ruang warna, fitur dan metode ekstraksi fitur. Hasil tinjauan menunjukkan bahwa ruang warna yang sering digunakan adalah RGB dan HSV karena dianggap cocok dengan hardware dan persepsi manusia terhadap warna. Adapun fitur yang paling sering digunakan dalam CBIR adalah fitur warna. Hal ini disebabkan fitur warna mudah dan cepat diekstraksi. Metode yang paling sering digunakan untuk mengekstraksi fitur warna adalah histogram warna, metode yang paling sering digunakan untuk mengekstraksi fitur tekstur adalah gray level co-occurence matrix, dan metode yang paling banyak digunakan untuk, mengekstraksi fitur bentuk adalah canny edge.Kata kunci: CBIR, warna, tekstur, bentuk.
Ontology-based Complementary Breastfeeding Search Model Astrid Noviana Paradhita; Anny Kartika Sari; Agus Sihabuddin
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 3 (2022): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.71963

Abstract

Children's nutritional requirements differ from those of adults. The health ministry's Indonesian data shows that in 2017, there were 17.8% of malnourished children under five years old (toddlers), one of which was related to complementary breastfeeding problems. Complementary breastfeeding is given to babies starting at 6–24 months of age. This research aims to build a complementary breastfeeding search model and be able to present it as a treatment for malnourished babies. A search model is built to understand natural language input given by a user. Also, it can do reasoning by applying a set of rules to obtain implicit knowledge about the complementary breastfeeding menu recommended for babies. The methods used in this research are data collection, designing a search model, building an ontology model, building SWRL, natural language processing, and usability testing by users and nutritionists. This research succeeded in building an ontology-based complementary breastfeeding search model in the form of a semantic web. The testing result shows that the web can provide an alternative complementary breastfeeding menu according to the baby’s nutritional needs and has a high usability capability of 4.01 on a scale of 1 to 5.
Automatic Requirements Engineering: Activities, Methods, Tools, and Domains – A Systematic Literature Review Rosa Delima; Khabib Mustofa; Anny Kartika Sari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4924

Abstract

Requirements engineering (RE) is an initial activity in the software engineering process that involves many users. The involvement of various users in the RE process raises ambiguity and vagueness in requirements modeling. In addition, traditional RE is a time-consuming activity. Therefore various studies have been conducted to support process automation on RE. This paper conducts a systematic literature review (SLR) to obtain information about RE automation related to RE activities, methods/models, tools, and domains. SLR is done through 5 main stages: definition of research questions, conducting the search, screening for relevant papers, data extraction, mapping, and analysis. The data extraction and mapping are carried out on 155 relevant publications from 2016 to 2022. Based on the results from SLR, around 53% of the research focuses on RE automation in analysis and specifications, 40% focuses on elicitation, validation, and requirements management, and 7% focuses on requirements quality. NLP is the most used method in elicitation and specification, while for analysis, machine learning, NLP, and goal-oriented models are mostly used in automatic RE. Furthermore, many papers use specific models and methods for validation and requirements management. From the domain analysis results, it is obtained that more than half of the papers contribute directly to the RE domain, and some contribute to the development of RE automation in the software application domain.
Error Action Recognition on Playing The Erhu Musical Instrument Using Hybrid Classification Method with 3D-CNN and LSTM Aditya Permana; Timothy K. Shih; Aina Musdholifah; Anny Kartika Sari
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 3 (2023): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.76555

Abstract

Erhu is a stringed instrument originating from China. In playing this instrument, there are rules on how to position the player's body and hold the instrument correctly. Therefore, a system is needed that can detect every movement of the Erhu player. This study will discuss action recognition on video using the 3DCNN and LSTM methods. The 3D Convolutional Neural Network method is a method that has a CNN base. To improve the ability to capture every information stored in every movement, combining an LSTM layer in the 3D-CNN model is necessary. LSTM is capable of handling the vanishing gradient problem faced by RNN. This research uses RGB video as a dataset, and there are three main parts in preprocessing and feature extraction. The three main parts are the body, erhu pole, and bow. To perform preprocessing and feature extraction, this study uses a body landmark to perform preprocessing and feature extraction on the body segment. In contrast, the erhu and bow segments use the Hough Lines algorithm. Furthermore, for the classification process, we propose two algorithms, namely, traditional algorithm and deep learning algorithm. These two-classification algorithms will produce an error message output from every movement of the erhu player.
Co-Authors Abdul Karim Aditya Permana Adriana Sari Aryani Afiyati, Afiyati Agus Harjoko Agus Harjoko Agus Qomaruddin Munir Agus Sihabuddin Aina Musdholifah Aina Musdholifah Alfian Abdul Jalid Astrid Noviana Paradhita Azhari Azhari Clara Hetty Primasari Ida Bagus Gede Sarasvananda Ihwan Addin Mufaqih Ilham unggara Khabib Mustofa Nurul Indarti Retantyo Wardoyo Retantyo Wardoyo Rosa Delima Timothy K. Shih Wakhid Slamet Ciptono Yessi Yunitasari