Garuda - Garba Rujukan Digital

Pengembangan Metode Klasterisasi Data Berbasis Hybrid Improved Artificial Bee Colony (IABC) dan K – Harmonic Means Musa, Saiful Bahri; Humaira, Fitrah Maharani; Widiartha, I Made; Herumurti, Darlis; Arifin, Agus Zainal; Fiqar, Tegar Palyus
Specta Journal Vol 2 No 3 (2018): SPECTA Journal of Technology
Publisher : Specta Journal

One of data grouping process method is k-harmonic clustering method (KHM) which has a relatively short and simple process. However, it has a weakness at cluster center point. Randomly formed cluster center point causes difficulty to converge solutions. One way to solve the problem at the cluster center point requires a method which has a global solution for KHM. The method is Improved artificial bee colony (IABC), improvement of artificial bee colony (ABC) method based on behavior patterns of honey bee colony in food searching process. Advantage of the IABC method is able to have more optimum global solution. This research proposes a new method of clustering using improved artificial bee colony and K-Harmonic means (IABC-KHM) to optimize the center point in clusters that lead to global solution. In this study, the IABC is functioned for finding the most optimum cluster center point for the data clustering process using KHM. Furthermore, the performance test of the IABC-KHM clustering method is compared with ABC and ABC-KHM methods on three different datasets. The result of mean value of best function of IABC-KHM method of Iris dataset is 152,87, Contraceptive Method Choice dataset is 918,54, and Wine dataset is 31,01. Moreover, the result of the average value of the best F-Measure method IABC-KHM Iris dataset is 0.90, the Contraceptive Method Choice dataset is 0.41, the Wine dataset is 0.95. To conclude, IABC-KHM method has successfully optimized the position of cluster center point that directs the cluster result which has global solution.

Pengembangan Metode Klasterisasi Data Berbasis Hybrid Improved Artificial Bee Colony (IABC) dan K â€“ Harmonic Means Fiqar, Tegar Palyus; Musa, Saiful Bahri; Humaira, Fitrah Maharani; Widiartha, I Made; Herumurti, Darlis; Arifin, Agus Zainal
SPECTA Journal of Technology Vol 2 No 3 (2018): SPECTA Journal of Technology
Publisher : LPPM ITK

One of data grouping process method is k-harmonic clustering method (KHM) which has a relatively short and simple process. However, it has a weakness at cluster center point. Randomly formed cluster center point causes difficulty to converge solutions. One way to solve the problem at the cluster center point requires a method which has a global solution for KHM. The method is Improved artificial bee colony (IABC), improvement of artificial bee colony (ABC) method based on behavior patterns of honey bee colony in food searching process. Advantage of the IABC method is able to have more optimum global solution. This research proposes a new method of clustering using improved artificial bee colony and K-Harmonic means (IABC-KHM) to optimize the center point in clusters that lead to global solution. In this study, the IABC is functioned for finding the most optimum cluster center point for the data clustering process using KHM. Furthermore, the performance test of the IABC-KHM clustering method is compared with ABC and ABC-KHM methods on three different datasets. The result of mean value of best function of IABC-KHM method of Iris dataset is 152,87, Contraceptive Method Choice dataset is 918,54, and Wine dataset is 31,01. Moreover, the result of the average value of the best F-Measure method IABC-KHM Iris dataset is 0.90, the Contraceptive Method Choice dataset is 0.41, the Wine dataset is 0.95. To conclude, IABC-KHM method has successfully optimized the position of cluster center point that directs the cluster result which has global solution.

DOCUMENT CLUSTERING BY DYNAMIC HIERARCHICAL ALGORITHM BASED ON FUZZY SET TYPE-II FROM FREQUENT ITEMSET Saiful Bahri Musa; Andi Baso Kaswar; Supria Supria; Susiana Sari
Jurnal Ilmu Komputer dan Informasi Vol 9, No 2 (2016): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

One of ways to facilitate process of information retrieval is by performing clustering toward collection of the existing documents. The existing text documents are often unstructured. The forms are varied and their groupings are ambiguous. This cases cause difficulty on information retrieval process. Moreover, every second new documents emerge and need to be clustered. Generally, static document clustering method performs clustering of document after whole documents are collected. However, performing re-clustering toward whole documents when new document arrives causes inefficient clustering process. In this paper, we proposed a new method for document clustering with dynamic hierarchy algorithm based on fuzzy set type - II from frequent itemset. To achieve the goals, there are three main phases, namely: determination of key-term, the extraction of candidates clusters and cluster hierarchical construction. Based on the experiment, it resulted the value of F-measure 0.40 for Newsgroup, 0.62 for Classic and 0.38 for Reuters. Meanwhile, time of computation when addition of new document is lower than to the previous static method. The result shows that this method is suitable to produce solution of clustering with hierarchy in dynamical environment effectively and efficiently. This method also gives accurate clustering result.

PARAMETER SIGMOID TRANSFORM CONTRAST ENHANCEMENT FOR DENTAL RADIOGRAPH CLASSIFICATION AND NUMBERING SYSTEM Andi Baso Kaswar; Saprina Mamase; Saiful Bahri Musa; Ahmad Mustofa Hadi; Anny Yuniarti; Agus Zainal Arifin
Jurnal Ilmu Komputer dan Informasi Vol 8, No 2 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Dental record is a method that is used to identify a person. The identification process needs a system that could recognize each individual tooth automatically. The similar intensity level between the teeth and the gums is one of the main problem in tooth identification in a dental radiograph. The intensity problem could influence the segmentation process of the system. In this paper, we proposed a new contrast enhancement by using parameter sigmoid transform to increase the segmentation accuracy. There are five main steps in this method. The first step is to fix the contrast of the image with the proposed method. The next steps are to segment the teeth using horizontal and vertical integral projection, feature extraction, and classification using Support Vector Machine (SVM). The last step is teeth numbering. The experiment result using the proposed method have an accuracy rate of 88% for classification and 73% for teeth numbering.

Analisis Fitur Sinyal Emosi EEG Berdasarkan Hybrid Decompotion Saiful Bahri Musa; Handayani Tjandrasa
Energy - Jurnal Ilmiah Ilmu-Ilmu Teknik Vol 7 No 1 (2017): Jurnal ENERGY Vol. 7 No. 1 Edisi Mei 2017
Publisher : Fakultas Teknik

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (640.107 KB)

Hasil rekaman EEG memiliki sejumlah channel atau elektroda dalam menentukan emosi manusia. Setiap Channel tersebut memilik frekuensi dengan kurun waktu yang sama, di mana masing-masing channel memiliki karakteristik frekuensi yang tidak sama. Hal tersebut dapat mempengaruhi proses komputasi cerdas dalam pengenalan emosi manusia. Dalam penelitian ini diusulkan proses penyaringan sinyal EEG berdasarkan metode Empirical Mode Decomposition (EMD) dan WaveletPacketDecomposition (WPD) dalam membentuk data fitur emosi manusia. Selanjutnya data fitur dianalisis berdasarkan grafik penyebaran dan karakteristik data dengan menggunakan analisis boxplot.Pada penelitian ini menggambarkan bahwa fungsi logaritmicstatistic dapat menghasilkan nilai fitur sinyal emosi manusia yang memiliki pola serta penyebaran data fituryang simetris.Kata kunci : empirical mode decomposition, waveletpacketdecomposition, analisis boxplot

Pelican Crossing System for Control a Green Man Light with Predicted Age Purnawarman Musa; Eri Prasetyo Wibowo; Saiful Bahri Musa; Iqbal Baihaqi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 21 No 2 (2022)
Publisher : LPPM Universitas Bumigora

Traffic lights are generally used to regulate the control flow of traffic at an intersection from all directions, including a pelican crossing system with traffic signals for pedestrians. There are two facilities for walker crossing, namely using a pedestrian bridge and a zebra cross. In general, the traffic signals of the pelican crossing system are a fixed time, whereas other pedestrians need "green man" traffic lights with duration time arrangement. Our research proposes a prototype intelligent pelican crossing system for somebody who crosses the road at zebra crossings, but the risk of falling while crossing is not expected, especially in the elderly age group or pedestrians who are pregnant or carrying children. On the other hand, the problem is that the average step length or stride length (distance in centimeter), cadence or walking rate (in steps per minute), and the possibility of accidents are very high for pedestrians to make sure do crossing during the lights “green man”. The new idea of our research aims to set the adaptive time arrangement on the pelican crossing intelligent system of the traffic lights “green man” based on the age of the pedestrians with artificial intelligence using two combined methods of the FaceNet and AgeNet. The resulting measure can predict the age of pedestrians of the training dataset of 66.67% and testing prototype in real-time with participants on the pelican crossing system of 73% (single face) and 76% (multi faces).

Title

Found 6 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 6 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 6 Documents
Search