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PERANCANGAN MODEL PENDUKUNG KEPUTUSAN UNTUK PENENTUAN LOKASI INDUSTRI BERDASARKAN PROSES HIERARKI ANALITIK

MATEMATIKA Vol 9, No 1 (2006): JURNAL MATEMATIKA
Publisher : MATEMATIKA

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Abstract

The Analytic Hierarchy Process (AHP), a decision-making method based upon division of problem spaces into hierarchies. This paper looks at AHP as a tool used in determination of industrial location. Solution of AHP method finished with the iteration process of through at scheme of  Pascal computer program to assist the calculation process. From result of program device which have been made to be obtained the highest total priority value (TPV) was potential distribution and promotion track

Optimisation towards Latent Dirichlet Allocation: Its Topic Number and Collapsed Gibbs Sampling Inference Process

International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018 (Part I)
Publisher : Institute of Advanced Engineering and Science

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Abstract

Latent Dirichlet Allocation (LDA) is a probability model for grouping hidden topics in documents by the number of predefined topics. If conducted incorrectly, determining the amount of K topics will result in limited word correlation with topics. Too large or too small number of K topics causes inaccuracies in grouping topics in the formation of training models. This study aims to determine the optimal number of corpus topics in the LDA method using the maximum likelihood and Minimum Description Length (MDL) approach. The experimental process uses Indonesian news articles with the number of documents at 25, 50, 90, and 600; in each document, the numbers of words are 3898, 7760, 13005, and 4365. The results show that the maximum likelihood and MDL approach result in the same number of optimal topics. The optimal number of topics is influenced by alpha and beta parameters. In addition, the number of documents does not affect the computation times but the number of words does. Computational times for each of those datasets are 2.9721, 6.49637, 13.2967, and 3.7152 seconds. The optimisation model has resulted in many LDA topics as a classification model. This experiment shows that the highest average accuracy is 61% with alpha 0.1 and beta 0.001.

SISTEM INFORMASI VEGETASI MANGROVE (SIVM) BERBASIS WEB DI TAMAN NASIONAL KARIMUNJAWA, JEPARA, JAWA TENGAH

JURNAL MASYARAKAT INFORMATIKA Vol 1, No 1 (2010): Jurnal Masyarakat Informatika
Publisher : Jurusan Ilmu Komputer/Informatika UNDIP

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Abstract

Mangrove forest at Karimunjawa National Park that has been used for research, education source, and tourism, needs an information system accessible for global community. The information covers the species, morphology, and taxonomy of mangrove vegetations in Karimunjawa National Park. Human need for up to date and accurate information supported with modern technology motivates the researcher to construct mangrove vegetations information system web based. It is hoped that the information can be accessed by interest group. Methods to be used refers to the stage of system development method. Its name is FAST system. SIVM can be used for all social stratum because this output is easy to understand interesting and user friendly.   Keywords: information system web based, mangrove vegetations

Implementasi Teknik Sampling untuk Mengatasi Imbalanced Data pada Penentuan Status Gizi Balita dengan Menggunakan Learning Vector Quantization

IPTEK-KOM : Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi Vol 19, No 1 (2017): Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi)
Publisher : BPSDMP KOMNFO Yogyakarta, Kementerian Komunikasi dan Informatika

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Abstract

Balita memerlukan suatu pengawasan khusus karena pada masa-masa tersebut balita rentan terhadap serangan penyakit dan kekurangan gizi. Untuk itu penelitian ini bertujuan untuk menerapkan metode Learning Vector Quantization (LVQ) dalam proses klasifikasi status gizi balita ke dalam gizi lebih, gizi baik, gizi rentan, dan gizi kurang. Data yang digunakan dalam penelitian ini adalah data gizi balita sebanyak 612, terdiri dari 38 data gizi lebih, 491 data gizi baik, 63 gizi rentan, dan 20 data gizi kurang. Data tersebut disebut sebagai data tidak seimbang.  Selanjutnya, penelitian ini menerapkan teknik undersampling dan oversampling untuk mengatasi permasalahan tersebut. Hasil penelitian menunjukkan bahwa penerapan metode LVQ terhadap data tak seimbang menghasilkan nilai akurasi sebesar 84.15 %, tetapi nilai overall accuracy sebesar 43.27%. Sedangkan penerapan metode LVQ terhadap data yang seimbang menghasilkan nilai akurasi dan overall accuracy yang sama yakni sebesar 74.38%.

CIELab Color Moments: Alternative Descriptors for LANDSAT Images Classification System

INKOM Journal Vol 8, No 2 (2014)
Publisher : Pusat Penelitian Informatika - LIPI

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Abstract

This study compares the image classification system based on normalized difference vegetation index (NDVI) and Latent Dirichlet Allocation (LDA) using CIELab color moments as image descriptors.  It was implemented for LANDSAT images classification by evaluating the accuracy values of classification systems. The aim of this study is to evaluate whether the CIELab color moments can be used as an alternatif descriptor replacing NDVI when it is implemented using LDA-based classification model.  The result shows that the LDA-based image classification system using CIELab color moments provides better performance accuracy than the NDVI-based image classification system, i.e 87.43% and 86.25% for LDA-based and NDVI-based respectively.  Therefore, we conclude that the CIELab color moments which are implemented under the LDA-based image classification system can be assigned as alternative image descriptors for the remote sensing image classification systems with the limited data availability, especially when the data only available in true color composite images.

Color Space to Detect Skin Image: The Procedure and Implication

Scientific Journal of Informatics Vol 4, No 2 (2017): November 2017
Publisher : Universitas Negeri Semarang

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Abstract

Skin detection is one of the processes to detect the presence of pornographic elements in an image. The most suitable feature for skin detection is the color feature. To be able to represent the skin color properly, it is needed to be processed in the appropriate color space. This study examines some color spaces to determine the most appropriate color space in detecting skin color. The color spaces in this case are RGB, HSV, HSL, YIQ, YUV, YCbCr, YPbPr, YDbDr, CIE XYZ, CIE L*a*b*, CIE L*u* v*, and CIE L*ch. Based on the test results using 400 image data consisting of 200 skin images and 200 non-skin images, it is obtained that the most appropriate color space to detect the color is CIE L*u*v*.

Studi Perbandingan Algoritma Pencarian String dalam Metode Approximate String Matching untuk Identifikasi Kesalahan Pengetikan Teks

Jurnal Buana Informatika Vol 7, No 2 (2016): Jurnal Buana Informatika Volume 7 Nomor 2 April 2016
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Abstract. Error typing resulting in the change of standard words into non-standard words are often caused by misspelling. This can be addressed by developing a system to identify errors in typing. Approximate string matching is one method that is widely implemented to identify error typing by using several string search algorithms, i.e. Levenshtein Distance, Hamming Distance, Damerau Levenshtein Distance and Jaro Winkler Distance. However, there is no study that compares the performance of the four algorithms.  Therefore, this research aims to compare the performance between the four algorithms in order to identify which algorithm is the most accurate and precise in the search string based on various errors typing. Evaluation is performed by using users’ relevance judgments which produce the mean average precision (MAP) to determine the best algorithm. The result shows that Jaro Winkler Distance algorithm is the best in word-checking with 0.87 of MAP value when identifying the typing error of 50 incorrect words.Keywords: Errors typing, Levenshtein, Hamming, Damerau Levenshtein, Jaro Winkler Abstrak. Kesalahan pengetikan mengakibatkan kata baku berubah menjadi kata tidak baku karena ejaan yang digunakan tidak sesuai. Hal tersebut dapat ditangani dengan mengembangkan sistem untuk mengidentifikasi kesalahan pengetikan. Metode approximate string matching merupakan salah satu metode yang banyak diterapkan untuk mengidentifikasi kesalahan pengetikan dengan berbagai jenis algoritma pencarian string yaitu Levenshtein Distance, Hamming Distance, Damerau Levenshtein Distance dan Jaro Winkler Distance. Akan tetapi studi perbandingan kinerja dari keempat algoritma tersebut untuk Bahasa Indonesia belum pernah dilakukan. Oleh karena itu penelitian ini bertujuan untuk melakukan studi perbandingan kinerja dari keempat algoritma tersebut sehingga dapat diketahui algoritma mana yang lebih akurat dan tepat dalam pencarian string berdasarkan kesalahan penulisan yang bervariasi. Evaluasi yang dilakukan menggunakan user relevance judgement yang menghasilkan nilai mean average precision (MAP) untuk menentukan algoritma yang terbaik. Hasil penelitian terhadap 50 kata salah menunjukkan bahwa algoritma Jaro Winkler Distance terbaik dalam melakukan pengecekan kata dengan nilai MAP sebesar 0,87.Kata Kunci: Kesalahan pengetikan, Levenshtein, Hamming, Damerau Levenshtein, Jaro Winkler

Suitability analysis of rice varieties using learning vector quantization and remote sensing images

TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

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Abstract

Rice (Oryza Sativa) is the main food for Indonesian people, thus maintaining the stability of rice production in Indonesia becomes an important issue for further study. A strategy to overcome the issue is to apply precision agriculture (PA) using remote sensing images as a reference due to its effectiveness. The initial stage of PA is suitability analysis of rice varieties, including INPARA, INPARI, and INPAGO. While the representative features that can be extracted from remote sensing images and related to agriculture field are NDVI, NDWI, NDSI, and BI. Therefore, the aim of this study is to identify the best model for analyzing the most suitable superior rice varieties using Learning Vector Quantization. The results show that the best LVQ model is obtained at learning rate value of 0.001, epsilon value of 0.1, and the features combination of NDWI and BI values (in standard deviation). The architecture generates accuracy value of 56%.