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All Journal International Journal of Electrical and Computer Engineering International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Seminar Nasional Informatika (SEMNASIF) JURNAL ISTEK Proceeding of the Electrical Engineering Computer Science and Informatics

Winarko, Edi

Computer Science And Electronics Department, Faculty Of Mathematics And Natural Sciences Universitas Gadjah Mada, Yogyakarta

Author-ID : 2379168

Computer Science & IT Electrical & Electronics Engineering

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Ontology-Based Sentence Extraction for Answering Why-Question Karyawati, A. A. I. N. Eka; Winarko, Edi; Azhari, Azhari; Harjoko, Agus
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Most studies on why-question answering system usuallyÂ Â usedÂ Â theÂ Â keyword-basedÂ Â approaches.Â Â TheyÂ Â rarely involved domain ontology in capturing the semantic of the document contents, especially in detecting the presence of the causal relations. Consequently, the word mismatch problem usually Â occurs Â andÂ the Â systemÂ oftenÂ retrieves Â not Â relevant answers. For solving this problem, we propose an answer extraction method by involving the semantic similarity measure, with selective causality detection. The selective causality detection is Â appliedÂ becauseÂ not Â all Â sentences Â belonging Â to Â an Â answer contain Â causality. Â Moreover,Â Â theÂ Â motivation Â of Â the Â use Â of semantic similarity measure in scoring function is to get more moderate results about the presence of the semantic annotations in a sentence, instead of 0/1. The semantic similarity measure employed is based on the shortest path and the maximum depth of the ontology graph. The evaluation is conducted by comparing the proposed method against the comparable ontology-based methods, i.e., the sentence extraction with Monge-Elkan with 0/1 internal similarity function. The proposed method shows the improvements in Â term of Â MRR (16%, 0.79-0.68), P@1 Â (15%, 0.76-0.66), P@5 (14%, 0.8-0.7), and Recall (19%, 0.86-0.72).

Mobile Content Based Image Retrieval Architectures Rahman, Arif; Winarko, Edi; Wibowo, Moh. Edi
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Mobile device features such as camera and other sensors are evolving rapidly nowadays. Supported by a reliable communications network, it raises new methods in information retrieval. Mobile devices can capture an image with its camera and pass it to the retrieval systems to get the information needed. This system, called Mobile Content-Based Image Retrieval (MCBIR), generally consists of two parts: Offline Database Construction, which create image features database and indexing structure, and Online Image Search, that search images in the database that similar to the user inputs. MCBIR system, based on its computational load and resource needs, can be categorized into three architectural models: client-side, client-server and distributed. These three models were analyzed in three aspects: scalability, latency, and resources. The results show that each architecture has its own characteristics in terms of these aspects and should be considered in the architecture selection phase for MCBIR development.

Ontology-based Why-Question Analysis Using Lexico-Syntactic Patterns A.A.I.N. Eka Karyawati; Edi Winarko; Azhari Azhari; Agus Harjoko
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 2: April 2015
Publisher : Institute of Advanced Engineering and Science

This research focuses on developing a method to analyze why-questions. Some previous researches on the why-question analysis usually used the morphological and the syntactical approach without considering the expected answer types. Moreover, they rarely involved domain ontology to capture the semantic or conceptualization of the content. Consequently, some semantic mismatches occurred and then resulting not appropriate answers. The proposed method considers the expected answer types and involves domain ontology. It adapts the simple, the bag-of-words like model, by using semantic entities (i.e., concepts/entities and relations) instead of words to represent a query. The proposed method expands the question by adding the additional semantic entities got by executing the constructed SPARQL query of the why-question over the domain ontology. The major contribution of this research is in developing an ontology-based why-question analysis method by considering the expected answer types. Some experiments have been conducted to evaluate each phase of the proposed method. The results show good performance for all performance measures used (i.e., precision, recall, undergeneration, and overgeneration). Furthermore, comparison against two baseline methods, the keyword-based ones (i.e., the term-based and the phrase-based method), shows that the proposed method obtained better performance results in terms of MRR and P@10 values.

Survey: Models and Prototypes of Schema Matching Edhy Sutanta; Retantyo Wardoyo; Khabib Mustofa; Edi Winarko
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 3: June 2016
Publisher : Institute of Advanced Engineering and Science

Schema matching is critical problem within many applications to integration of data/information, to achieve interoperability, and other cases caused by schematic heterogeneity. Schema matching evolved from manual way on a specific domain, leading to a new models and methods that are semi-automatic and more general, so it is able to effectively direct the user within generate a mapping among elements of two the schema or ontologies better. This paper is a summary of literature review on models and prototypes on schema matching within the last 25 years to describe the progress of and research chalenge and opportunities on a new models, methods, and/or prototypes.

Face Recognition Based on Symmetrical Half-Join Method using Stereo Vision Camera Edy Winarno; Agus Harjoko; Aniati Murni Arymurthy; Edi Winarko
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 6: December 2016
Publisher : Institute of Advanced Engineering and Science

The main problem in face recognition system based on half-face pattern is how to anticipate poses and illuminance variations to improve recognition rate. To solve this problem, we can use two lenses on stereo vision camera in face recognition system. Stereo vision camera has left and right lenses that can be used to produce a 2D image of each lens. Stereo vision camera in face recognition has capability to produce two of 2D face images with a different angle. Both angle of the face image will produce a detailed image of the face and better lighting levels on each of the left and right lenses. In this study, we proposed a face recognition technique, using 2 lens on a stereo vision camera namely symmetrical half-join. Symmetrical half-join is a method of normalizing the image of the face detection on each of the left and right lenses in stereo vision camera, then cropping and merging at each image. Tests on face recognition rate based on the variety of poses and variations in illumination shows that the symmetrical half-join method is able to provide a high accuracy of face recognition and can anticipate variations in given pose and illumination variations. The proposed model is able to produce 86% -97% recognition rate on a variety of poses and variations in angles between 0 °- 22.5 °. The variation of illuminance measured using a lux meter can result in 90% -100% recognition rate for the category of at least dim lighting levels (above 10 lux).

A Hybrid Model Schema Matching Using Constraint-Based and Instance-Based Edhy Sutanta; Retantyo Wardoyo; Khabib Mustofa; Edi Winarko
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 3: June 2016
Publisher : Institute of Advanced Engineering and Science

Schema matching is an important process in the Enterprise Information Integration (EII) which is at the level of the back end to solve the problems due to the schematic heterogeneity. This paper is a summary of preliminary result work of the model development stage as part of research on the development of models and prototype of hybrid schema matching that combines two methods, namely constraint-based and instance-based. The discussion includes a general description of the proposed models and the development of models, start from requirement analysis, data type conversion, matching mechanism, database support, constraints and instance extraction, matching and compute the similarity, preliminary result, user verification, verified result, dataset for testing, as well as the performance measurement. Based on result experiment on 36 datasets of heterogeneous RDBMS, it obtained the highest P value is 100.00% while the lowest is 71.43%; The highest R value is 100.00% while the lowest is 75.00%; and F-Measure highest value is 100.00% while the lowest is 81.48%. Unsuccessful matching on the model still happens, including use of an id attribute with data type as autoincrement; using codes that are defined in the same way but different meanings; and if encountered in common instance with the same definition but different meaning.

Optimal Solution of Minmax 0/1 Knapsack Problem using Dynamic Programming Ani Dijah Rahajoe; Edi Winarko
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 2, No 1: April 2013
Publisher : Institute of Advanced Engineering and Science

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Knapsack problem is a problem that occurs when looking for optimal selection of objects that will be put into a container with limited space and capacity. On the issue of loading goods into the container, optimal selection of objects or items to be sent must fulfilled to minimize the total weight of the capacity or volume limits without exceeding the maximum capacity of containers that have been determined. The types of knapsack that has been discussed so far is only to maximize the use not to exceed the limits specified capacity so it cannot be applied to the problem. This study aims to develop a dynamic programming algorithm to solve the MinMax 0/1 knapsack, which is an extension of the 0/1 knapsack with minimal and maximal constrain. The result study showed that application of the MinMax 0/1 knapsack is used to generate the optimal solution to the problem of loading system goods into the container to optimize container space available compared with the loading of goods by PT DFI.DOI: http://dx.doi.org/10.11591/ij-ict.v2i1.1299

Satellite imagery and machine learning for aridity disaster classification using vegetation indices Sri Yulianto Joko Prasetyo; Kristoko Dwi Hartomo; Mila Chrismawati Paseleng; Dian Widiyanto Chandra; Edi Winarko
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Central Java Province is one of provinces in Indonesia that has a high aridity risk index. Aridity disaster risk monitoring and detection can be done more accurately in larger areas and with lower costs if the vegetation index is extracted from the remote sensing imagery. This study aims to provide accurate aridity risk index information using spectral vegetation index data obtained from LANDSAT 8 OLI satellite. The classification of drought risk areas was carried out using k-nn with the Spatial Autocorrelation method. The spectral vegetation indices used in the study are NDVI, SAVI, VHI, TCI and VCI. The results show a positive correlation and trend between the spectral vegetation index influenced by seasonal dynamics and the characteristics of the High R.A. and Middle R.A. drought risk areas. The highest correlation coefficient is SAVI with a High R.A. amounted to 0.967 and Middle R.A. amounted to 0.951. The results of the Kappa accuracy test comparison show that SVM and k-nn have the same accuracy of 88.30. The result of spatial prediction using the IDW method shows that spectral vegetation index data that initially as an outlier, using the k-nn method, the spectral vegetation index data can be identified as data in the aridity classification. The spatial connectivity test among sub-districts that experience drought was done using Moran’s I Analysis.

KONSEP MULTICRITERIA COLLABORATIVE FILTERING UNTUK PERBAIKAN REKOMENDASI Wiranto Wiranto; Edi Winarko
Seminar Nasional Informatika (SEMNASIF) Vol 1, No 4 (2010): Intelligent System dan Application
Publisher : Jurusan Teknik Informatika

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Untuk membantu pencari informasi yang belum memiliki referensi diperlukan alat bantu recommender system. Pengembangan recommender system sebagian besar dilakukan dengan menggunakan pendekatan berbasis collaborative filtering. Sistem berbasis collaborative filtering akan bekerja dengan cara mempelajari kebiasaan para pencari informasi dan membangun profil pencari informasi, kemudian memberikan rekomendasi. Pendekatan collaborative filtering klasik diterapkan pada kasus pemilihan item yang hanya memiliki satu kriteria. Sementara itu, banyak kasus yang tidak bisa dimodelkan dengan satu kriteria. Oleh karena itu konsep collaborative filtering perlu dikembangkan untuk pemilihan item yang memiliki banyak kriteria agar rekomendasi yang dihasilkan memiliki kualitas lebih baik dan relevan dengan kebutuhan pengguna.

PENGGUNAAN KNN (K-NEARST NEIGHBOR) UNTUK KLASIFIKASI TEKS BERITA YANG TAK-TERKELOMPOKKAN PADA SAAT PENGKLASTERAN OLEH STC (SUFFIX TREE CLUSTERING) Jumadi Jumadi Jumadi; Edi Winarko
JURNAL ISTEK Vol 9, No 1 (2015): ISTEK
Publisher : JURNAL ISTEK

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Dokumen teks yang dipublikasi di internet dari hari ke hari semakin banyak jumlahnya. Salah satu teknologi internet yang paling sering terjadi proses pemuktahiran konten dokumen teks ini, adalah microblogging yang dijadikan sebagai sarana untuk membangun komunitas di dunia maya dan penyebar informasi yang praktis dan cepat. Salah satunya adalah Twitter yang merupakan salah satu social media dengan jumlah tweet yang dipublikasi dalam hitungan jam oleh para pemilik akun tersebut, khususnya para jurnalis. Berita-berita yang dipublikasi oleh para jurnalis melaui Twitter terkadang kurang nyaman untuk dibaca oleh para pembaca berita. Karena berita-berita tersebut ditampilkan secara tersusun beruntun ke bawah pada halaman web tersebut. Tetapi setelah tweet-tweet yang ada dikelompokkan secara tematik jadi semakin menarik karena pembaca dapat memilih berita-berita tertentu yang telah dikelompokkan oleh Algoritma Suffix Tree Clustering (STC). Tetapi pada algoritma ini, masih tetap menghasilkan dokumen-dokumen yang tidak memiliki kelompok. Pada Penelitian ini, dokumen-dokumen tersebut mencoba untuk di klasifikasikan ke dalam kelompok yang ada dengan menggunakan Algoritma K-Nearset Neighbor (KNN).

Co-Authors Agus Harjoko Agus Harjoko Anak Agung Istri Ngurah Eka Karyawati Ani Dijah Rahajoe Aniati Murni Arymurthy Arif Rahman Arif Rahman Azhari Azhari Azhari, Azhari Dian Widiyanto Chandra Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Edy Winarno Jumadi Jumadi Khabib Mustofa Khabis Mustofa Kristoko Dwi Hartomo Mila Chrismawati Paseleng Moh. Edi Wibowo Muhammad Davi Ramos Somya Retantyo Wardoyo Sigit Priyanta Sri Yulianto Joko Prasetyo Wibowo, Moh. Edi Wiranto Wiranto

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