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All Journal International Journal of Electrical and Computer Engineering Indonesia Medicus Veterinus Perfecting a Video Game with Game Metrics Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Akbar, Saiful
Data And Software Engineering Research Group School Of Electrical Engineering And Informatics, Institut Teknologi Bandung
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Medicine & Pharmacology Veterinary

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Perbandingan Titer Antibodi Newcastle Disease pada Ayam Petelur Fase Layer I dan II Akbar, Saiful; Ardana, Ida Bagus Komang; Suardana, Ida Bagus Kade
Indonesia Medicus Veterinus Vol 6 (4) 2017
Publisher : Faculty of Veterinary Medicine, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (191.672 KB)

Abstract

Penelitian ini bertujuan untuk mengetahui titer antibodi terhadap penyakit Newcastle Disease (ND) pada ayam petelur fase layer I dan fase layer II pasca vaksinasi ND. Sampel penelitian ini adalah serum yang diambil dari tujuh peternakan pada lima desa di Kecamatan Penebel yaitu Desa Mangesta, Senganan, Babahan, Penebel, dan Jatiluwih. Total sampel adalah 131 sampel terdiri dari 78 sampel fase layer I dan 53 sampel fase layer II. Pengukuran titer antibodi ND dilakukan dengan uji Haemagglutination Inhibition (HI), kemudian hasilnya dianalisis secara statistik menggunakan Chi-square (X2) dan tabel kontingensi 2x2. Hasil penelitian ini menunjukkan vaksinasi ND pada ayam petelur fase layer I dan II di Kecamatan Penebel menunjukkan respon kebal yang protektif (99,24%) dengan nilai Geometric Mean Titre (GMT) 8,52. Kekebalan pada ayam petelur fase layer I (GMT 8,91) lebih besar daripada fase layer II (8,13). Namun, secara statistik kekebalan protektif pada ayam petelur fase layer I dan fase layer II tidak berbeda nyata (p>0,05). Analisis data menggunakan tabel kontingensi 2x2 menunjukkan nilai Odds Ratio (OR) adalah 0, ini berarti faktor tersebut adalah protektif.
Enhancing Performance in Medical Articles Summarization with Multi-Feature Selection Susetyo Bagas Bhaskoro; Saiful Akbar; Suhono Harso Supangkat
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 4: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (600.487 KB) | DOI: 10.11591/ijece.v8i4.pp2299-2309

Abstract

The research aimed at providing an outcome summary of extraordinary events information for public health surveillance systems based on the extraction of online medical articles. The data set used is 7,346 pieces. Characteristics possessed by online medical articles include paragraphs that comprise more than one and the core location of the story or important sentences scattered at the beginning, middle and end of a paragraph. Therefore, this study conducted a summary by maintaining important phrases related to the information of extraordinary events scattered in every paragraph in the medical article online. The summary method used is maximal marginal relevance with an n-best value of 0.7. While the multi feature selection in question is the use of features to improve the performance of the summary system. The first feature selection is the use of title and statistic number of word and noun occurrence, and weighting tf-idf. In addition, other features are word level category in medical content patterns to identify important sentences of each paragraph in the online medical article. The important sentences defined in this study are classified into three categories: core sentence, explanatory sentence, and supporting sentence. The system test in this study was divided into two categories, such as extrinsic and intrinsic test. Extrinsic test is comparing the summary results of the decisions made by the experts with the output resulting from the system. While intrinsic test compared three n-Best weighting value method, feature selection combination, and combined feature selection combination with word level category in medical content. The extrinsic evaluation result was 72%. While intrinsic evaluation result of feature selection combination merger method with word category in medical content was 91,6% for precision, 92,6% for recall and f-measure was 92,2%.
A New Strategy of Direct Access for Speaker Identification System Based on Classification Hery Heryanto; Saiful Akbar; Benhard Sitohang
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 4: December 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i4.2017

Abstract

In this paper, we present a new direct access strategy for speaker identification system. DAMClass is a method for direct access strategy that speeds up the identification process without decreasing the identification rate drastically. This proposed method uses speaker classification strategy based on human voice’s original characteristics, such as pitch, flatness, brightness, and roll off. DAMClass decomposes available dataset into smaller sub-datasets in the form of classes or buckets based on the similarity of speaker’s original characteristics. DAMClass builds speaker dataset index based on range-based indexing of direct access facility and uses Nearest Neighbor Search, Range-Based Searching, and Multiclass-SVM Mapping as its access method. Experiments show that the direct access strategy with Multiclass-SVM algorithm outperforms the indexing accuracy of Range-Based Indexing and Nearest Neighbor for one to nine percent. DAMClass is shown to speed up the identification process 16 times faster than sequential access method with 91.05% indexing accuracy.
Minimizing the Estimated Solution Cost with A* Search to Support Minimal Mapping Repair Inne Gartina Husein; Benhard Sitohang; Saiful Akbar
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1322.664 KB) | DOI: 10.11591/eecsi.v4.1080

Abstract

Incoherent alignment has been the main focus in the matching process since 2010.  Incoherent means that there is semantic or logic conflict in the alignment. This condition encouraged researches in ontology matching field to improve the alignment by repairing the incoherent alignment. Repair mapping will restore the incoherent to coherent mapping, by deleting unwanted mappings from the alignment. In order to minimize the impacts in the input alignment, repair process should be done as as minimal as possible. Definition of minimal could be (1) reducing the number of deleted mappings, or (2) reducing the total amount of deleted mappings’ confidence values. Repair process with new global technique conducted the repair with both minimal definitions. This technique could reduce the number of deleted mappings and total amount of confidence values at the same time. We proposed A * Search method to implement new global technique. This search method was capable to search the shortest path which representing the fewest number of deleted mappings, and also search the cheapest cost which representing the smallest total amount of deleted mappings’ confidence value. A* Search was both complete and optimal to minimize mapping repair size.
Automatic Grading System for Spreadsheet Formula Kurniandha Sukma Yunastrian; Saiful Akbar; Fitra Arifiansyah
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2086

Abstract

Spreadsheet is one of the tools that can be used to learn data analysis. Data analysis in spreadsheet can be done using formula. Spreadsheet tools can also be used for exams. For the assessment, there is a problem when the number of answers that need to be checked is large, that is it takes a long time to check all the answers. For this reason, an automatic grading system (autograder) that can evaluate formula in spreadsheet is needed. The method used in developing the autograder system is matching the answer key formula with the student's answer formula. The autograder system assesses the answer by calculating the similarity of the student's answer formula with the answer key formula. This paper explains how to build an autograder system that can evaluate the formula. At the end, an autograder system has been built successfully. It has been tested with 43 testcases and all of them are passed.
Identifikasi Hubungan Sebab-Akibat pada Artikel Kesehatan menggunakan Anotasi Elemen Medis dan Paragraf Susetyo Bagas Bhaskoro; Saiful Akbar; Suhono Harso Supangkat
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 2: Mei 2019
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (936.485 KB)

Abstract

This paper studies natural language processing on medical articles in Indonesian that aims to identify causal relationship and used as public health surveillance information monitoring system. This paper proposes selection-feature conformity, phrase annotation, paragraph annotation, and medical element annotation. System performance evaluation is carried out using intrinsic aprroach which compares supervised classification methods, i.e. naive bayes method and HMM. Results obtained for recall, precission, and f-measure are 0.905, 0.924, 0.910 and 0.706, 0.750, 0.720, respectively.
Folk Games Image Captioning using Object Attention Saiful Akbar; Benhard Sitohang; Jasman Pardede; Irfan Amal; Kurniandha Yunastrian; Marsa Ahmada; Anindya Prameswari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4708

Abstract

The result of a deep learning-based image captioning system with encoder-decoder framework relies heavily on the image feature extraction technique and the caption-based model. The accuracy of the model is heavily influenced by the proposed attention mechanism. The inability to distinguish between the output of the attention model and the input expectation of the decoder can cause the decoder to give incorrect results. In this paper, we proposed an object-attention mechanism using object detection. Object detection outputs a bounding box and an object category label, which is then used as an image input into VGG16 for feature extraction and into a caption-based LSTM model. The experimental results showed that the system with object attention performed better than the system without object attention. BLEU-1, BLEU-2, BLEU-3, BLEU-4, and CIDER scores for the image captioning system with object attention improved 12.48%, 17.39%, 24.06%, 36.37%, and 43.50% respectively compared to the system without object attention.
Co-Authors Anindya Prameswari Arifiansyah, Fitra Benhard Sitohang Benhard Sitohang Fitra Arifiansyah Hery Heryanto Ida Bagus Kade Suardana Ida Bagus Komang Ardana Inne Gartina Husein Irfan Amal Jasman Pardede Kurniandha Sukma Yunastrian Kurniandha Yunastrian Marsa Ahmada Suhono Harso Supangkat Susetyo Bagas Bhaskoro Susetyo Bagas Bhaskoro Yunastrian, Kurniandha Sukma