This paper presents the results of the Competition on the Analysis of Handwritten Text in Images of Balinese Palm Leaf Manuscripts that was organized in the context of the 15 th International Conference on Frontiers in Handwriting Recognition (ICFHR-2016). This competition provides a suitable challenge for testing and evaluation of robustness for some methods, image features and descriptors which were already proposed for handwritten text analysis of document image. In this competition, three different challenges in document analysis of palm leaf manuscript images are proposed: Challenge 1: Binarization of Palm Leaf Manuscript Images, Challenge 2: Query-by-Example Word Spotting on Palm Leaf Manuscript Images, and Challenge 3: Isolated Character Recognition of Balinese Script in Palm Leaf Manuscript Images. The first handwritten Balinese palm leaf manuscript dataset, the AMADI_LontarSet, is used for performance evaluation. This paper describes the competition details including the dataset creation and the ground truth construction, the evaluation measures used, a short description of each participant as well as the performance of the all submitted methods.

AbstrakTujuan penelitian ini (1) Untuk mengimplementasikan hasil Pengembangan E-Modul Berbasis Project Based Learning Pada Mata Pelajaran Simulasi Digital Untuk Siswa Kelas X Studi Kasus Di SMK Negeri 2 Singaraja, (2) Untuk mengetahui respon siswa terhadap Pengembangan E-Modul Berbasis Project Based Learning Pada Mata Pelajaran Simulasi Digital Untuk Siswa Kelas X Studi Kasus Di SMK Negeri 2 Singaraja.Jenis penelitian yang digunakan dalam penelitian ini adalah Penelitian dan Pengembangan Research and Development (R & D) dengan model pengembangan ADDIE. Subjek penelitian ini yaitu siswa kelas X Tata Boga 5 SMK Negeri 2 Singaraja tahun ajaran 2015/2016. Untuk mengetahui respon siswa terhadap e-modul yang dikembangkan, diperoleh dengan menggunakan metode kuisioner dengan alat pengumpulan data berupa angket.Hasil analisis respon siswa menunjukkan bahwa persentase siswa yang memberikan respon sangat baik sebesar 60%, persentase siswa yang memberikan respon baik sebesar 40%, dan tidak ada siswa yang memberikan respon cukup, kurang maupun sangat kurang. Berdasarkan hasil penelitian terhadap pengembangan e-modul pada mata pelajaran simulasi digital secara keseluruhan persentase respon siswa menunjukkan angka 90,6 % dapat dikategorikan sangat baik. Kata kunci:  E-Modul, R&D, ADDIE, Respon, Simulasi Digital. AbstractThe purposes of this study were (1) to impelements design of the E-modul development by the Project Based Learning model in the Digital Simulation subject for X grade students of SMK Negeri 2 Singaraja, (2) To know the response of the students toward the E-modul development by the Project Based Learning model in the Digital Simulation subject for X grade students of SMK Negeri 2 Singaraja.The type of this study was using Research and Development (R&D) with applied the method ADDIE model. The subject of this study was class of X Tata Boga 5 SMK Negeri 2 Singaraja in study year 2015/2016. The response of the students toward the e-modul development, was obtained by using questionnaire method.The result of the student’s responses were indicated that the percentages were 60% for the Very Good-response by 40% of the Good-response. Overall on the result of this study, by the percentage of 90% can be categorized as Very Good. Keywords : e-modul, Research and development, ADDIE, response, simulasi digital

We present the AMADI_LontarSet, the first handwritten Balinese palm leaf manuscript dataset. It includes three components of dataset as follows: binarized images ground truth dataset, word annotated images dataset, and isolated character annotated images dataset. The dataset was constructed from a hundred pages of randomly selected collections of palm leaf manuscripts from Bali, Indonesia. The dataset is publicly available for scientific use.

As a very valuable cultural heritage, palm leaf manuscripts offer a new challenge in document analysis system due to the specific characteristics on physical support of the manuscript. With the aim of finding an optimal binarization method for palm leaf manuscript images, creating a new ground truth binarized image is a necessary step in document analysis of palm leaf manuscript. But, regarding to the human intervention in ground truthing process, an important remark about the subjectivity effect on the construction of ground truth binarized image has been analysed and reported. In this paper, we present an experiment in a real condition to analyse the existance of human subjectivity on the construction of ground truth binarized image of palm leaf manuscript images and to measure quantitatively the ground truth variability with several binarization evaluation metrics.

The bone fracture detection using X-rays or CTscan produces accurate images but has harmful effect radiation. This paper presented the use of ultrasonic waves (US) as an alternative to substitute those two instruments. This study used femur bovine and chicken bones in conditions with and without meat. The fractures are artificially made on transverse and oblique patterns. The scanning US probe produces twodimensional (2D) B-mode images. Fracture detection is done using five variations of the Convolutional Neural Network (CNN) architectural design, i.e., CNN1-CNN5. The results showed that the CNN4 is the best design of bone contour recognition and bone fracture classification compared to the other tested designs, with 95.3% accuracy, 95% sensitivity, and 96% specificity. The comparison with the Support Vector Machine (SVM) and k-NN classification methods indicate that CNN has superior performance in accuracy, sensitivity, and specificity. Intisari-Pendeteksian patah tulang dengan X-ray atau CTscan menghasilkan gambar yang akurat tetapi memiliki efek negatif radiasi yang berbahaya. Makalah ini memaparkan penggunaan gelombang ultrasonik (US) sebagai alternatif pengganti kedua instrumen tersebut. Makalah ini menggunakan tulang femur sapi dan ayam dalam kondisi dengan dan tanpa daging, dengan patahan dibuat secara manual dengan pola patah transverse dan oblique. Pemindaian probe US menghasilkan citra B-mode dua dimensi. Pendeteksian tulang patah dilakukan menggunakan lima variasi desain arsitektur Convolutional Neural Network (CNN), yaitu CNN1-CNN5. Hasil uji coba menunjukkan bahwa desain arsitektur CNN4 memberikan hasil pengenalan kontur tulang dan klasifikasi tulang patah yang paling bagus dibandingkan desain arsitektur lain yang diuji, dengan akurasi 95,3%, sensitivitas 95%, dan specificity 96%. Hasil perbandingan dengan metode klasifikasi Support Vector Machine (SVM) dan k-Neural Network (k-NN) menunjukkan bahwa CNN memiliki unjuk kerja yang lebih unggul baik dalam hal akurasi, sensitivitas, maupun specificity. Kata Kunci-Citra ultrasonik B-mode, Convolutional Neural Network, lapisan konvolusi, tulang femur.