1,720,981 research outputs found
Modifikasi Distribusi Semut Pada Ant Colony Optimization Berdasarkan Gradient Untuk Deteksi Tepi Citra
Full text linkAnt Colony Optimization (ACO) merupakan algoritma optimasi yang terinspirasi oleh
tingkah laku semut dalam mencari makan. Karena keunggulan yang dimilikinya, ACO
banyak digunakan untuk menyelesaikan permasalahan non-polinomial yang sulit, salah
satunya adalah deteksi tepi pada citra. Penerapan ACO pada deteksi tepi telah terbukti
berhasil dengan baik, akan tetapi metode penyebaran semut pada ACO sangat
mempengaruhi tingkat akurasi.
Pada ACO tradisional semut awal disebarkan secara acak. Kondisi ini dapat
menyebabkan ketidakseimbangan distribusi semut yang kemudian mempengaruhi proses
penemuan jalur pada deteksi tepi. Berdasarkan permasalahan tersebut, modifikasi
distribusi semut pada ACO diusulkan untuk mengoptimalkan penyebaran semut
berdasarkan gradient. Nilai gradient digunakan untuk menentukan penempatan semut.
Metode yang diusulkan pada penelitian ini adalah dengan melakukan penyebaran semut
berdasarkan nilai gradient. Semut tidak disebar secara acak, akan tetapi ditempatkan di
gradient tertinggi. Cara ini diharapkan dapat digunakan untuk optimasi penemuan jalur.
Berdasarkan hasil uji coba, dengan menggunakan ACO modifikasi yang diusulkan dapat
diperoleh nilai rata-rata Peak Signal to Noise Ratio (PSNR) 12,884. Sedangkan,
menggunakan ACO tradisional diperoleh nilai rata-rata PSNR 11,665. Hasil ini
menunjukkan bahwa ACO modifikasi mampu menghasilkan citra keluaran yang lebih
baik dibandingkan ACO tradisional yang sebaran semut awalnya dilakukan secara acak.
=====================================================================================================
Ant Colony Optimization (ACO) is an optimization algorithm which is inspired by
the behavior of ants when finding foods. Because of its advantages, ACO is widely used
to solve the difficult non-polynomial problems, one of them is image edge detection.
Application of ACO on edge detection has been proven to work well, but the method of
spreading ants in ACO greatly affects the accuracy.
In traditional ACO, the initial ant randomly distributed. This condition can cause an
imbalance ants distribution which can affect the path discovery process on edge detection.
Based on this problem, a modified ant distribution in ACO is proposed to optimize the
deployment of ant based gradient. Gradient value is used to determine the placement of
the ants. Ants are not distributed randomly, but placed in the highest gradient. This
method is expected to be used for optimization path discovery. Based on the test results,
using the proposed ACO modification can be obtained average values of Peak Signal to
Noise Ratio (PSNR) 12.884. Meanwhile, using traditional ACO obtained an average
value of PSNR 11.665. These results indicate that the ACO modification capable of
generating output image better than traditional ACO which ants are initially distributed
randomly
Peramalan Nilai Saham BBCA Melalui Pendekatan Time Series Menggunakan Teknik Exponential Smoothing
No full textForecasting stock prices plays a crucial role in shaping investment strategies within the financial market. This article aims to predict the stock prices of Bank Central Asia (BBCA), a prominent entity in the Indonesian banking sector. Employing a time series methodology, this study utilizes the Exponential Smoothing technique to anticipate the fluctuations in BBCA\u27s share prices. Meanwhile, the dataset used is the BBCA share price data from April 2001 to early January 2023. The final error rate in this forecast is 10%
PEMANFAATAN HIERARCHICAL CLUSTERING UNTUK PENGELOMPOKKAN DAUN BERDASARKAN FITUR MOMENT INVARIANT
Full text linkAbstrakIlmu mengenai tanaman telah mengalami kemajuan yang pesat. Salah satunya cabang ilmu mengenai morfologi tanaman. Ilmu morfologi ini mempelajari susunan tubuh tanaman khususnya mengenai bentuk tepi daun. Pada penelitian ini akan dilakukan pengelompokkan daun berdasarkan bentuk tepi daun. Metode yang digunakan untuk melakukan pengelompokkan adalah metode Centroid Linkage Clustering yang merupakan bagian dari algoritma Hierarchical Clustering. Metode ini dikenal lebih memiliki beban komputasi yang relatif lebih ringan karena hanya cukup menghitung titik tengah antar cluster. Berdasarkan hasil uji coba yang dilakukan, penggunaan metode Centroid Linkage Clustering didapatkan nilai akurasi clustering sebesar 87%, sedangkan dengan menggunakan metode k-means didapatkan nilai akurasi clustering sebesar 81%. Hal ini menunjukkan bahwa kinerja metode Centroid Linkage Clustering lebih baik dibandingkan metode k-means. Kata Kunci: Morfologi, Centroid Linkage Clustering, Hierarchical Clustering, Cluster, K-mean
Image Retrival Pada Obyek Lingga Yoni Di Situs Peninggalan Sejarah Trowulan Mojokerto
Full text linkThis study contains about Image Retrieval system image on Lingga Yoni at historical sites Trowulan. In the area Trowulan is a legacy of work Majapahit era where the majority of people it is a Hindu, so many relics found in the form of Linga Yoni which serves as the worship of Lord Shiva. Data retrieval image Yoni Linga Linga Yoni as many as 50 images using a digital camera, and the image size Lingga Yoni 200 x 300 pixels in BMP file format. Stages Image Retrieval system on the study include segmentation stages: (1) Smoothing using the method Pas Low Filter to soften the image of the noise; (2) the extraction step texture by using Region Growing by altering the RGB color image is converted into to facilitate the HSL color groups; (3) Region Region Merging Growing did for the incorporation of color image corresponding to the object Linga Yoni; (4) to get to extraction stage form was originally looking for edge detection using Canny edge; (5) the image is converted into binary form to the morphology using opening and closing. At Stages Image Retrieval 50 Linga Yoni image texture extraction step performed using the 4 corners of each feature value GLCM with Different Inverse Moment IDM to revise the results of Image Retrieval using methods Precision and Recall
Pengenalan karakter angka menggunakan metode Integral Proyeksi
Full text link Saat ini dengan kemajuan teknologi membuat komputer memiliki kemampuan komputasi yang lebih tinggi untuk meningkatkan kemampuan dalam pengolahan data. Kemajuan teknologi ini juga berimbas pada kemampuan teknologi citra digital yang berhubungan dengan pengenalan karakter angka yang merupakan bagian dari pengenalan pola. Pengenalan karakter penting untuk pengolahan informasi yang memungkinkan proses identifikasi secara cepat dan otomatis. Pada penelitian ini dilakukan proses pengenalan karakter angka menggunakan metode Integral Proyeksi. Alasan menggunakan metode integral proyeksi karena mempunyai kelebihan pemrosesan yang sederhana dan cepat dalam mengidentifikasi suatu citra digital. Integral Proyeksi yang digunakan yaitu Integral Proyeksi vertikal dan Integral Proyeksi horisontal. Hasil penelitian menunjukkan pengenalan karakter angka mampu mengenali karakter dengan benar jika hasil praproses menghasilkan gambar yang baik. Pengenalan karakter angka akan kurang sempurna jika gambar yang diproses tidak baik, hal ini dikarenakan metode Integral Proyeksi bekerja dengan menghitung jumlah piksel tiap gambar untuk mengenai nilai gambar tersebut. Pengujian pengenalan karakater angka yang dilakukan terdapat 20 gambar uji menghasilkan nilai akurasi sebesar 65%. Nowadays with the advancement of technology makes computers have higher computing capabilities to improve the capability of data processing. Advances in technology have also affected the ability of digital image technology related to the introduction of alphanumeric characters that are part of pattern recognition. Character recognition is important for information processing that allows rapid identification process automatically. In this research, numeric character recognition process using integral projection method. Reasons for using integral projection method for processing has the advantage of a simple and quick in identifying a digital image. The integral projection used is vertical projection and horizontal projection. The results showed numeric character recognition could recognize the characters correctly if the results of preprocessing produce good images. The introduction of the characters will be less than perfect if the images are processed is not good, this is because the integral projection method works by counting the number of pixels for each image to the value of the image. Testing the result of recognition from 20 image which is on dataset has been built to get accuracy value about 65%
Forecasting Bitcoin using Double Exponential Smoothing Method Based on Mean Absolute Percentage Error
Full text linkAbstract— After being introduced in 2008, the rise in the price of bitcoin and the popularity of other cryptocurrencies triggered a growing discussion about how much energy was consumed during the production of this currency. Making cryptocurrency the most expensive and most popular, both the business world and the research community have begun to study the devel-opment of bitcoin. In this study bitcoin price predictions are performed using the double exponential smoothing method based on the mean absolute percentage error (MAPE). The MAPE value is used to find the best alpha (α) parameter as the basis for bitcoin price forecasting. The dataset used is the price of bitcoin from 2017 to 2019. The dataset was obtained from www.cryptocompare.com. As for the value of the alpha parameter (α), using a value of 0.1 to 0.9. Based on the test results using the double exponential smoothing method obtained the smallest MAPE value of 2.89%, with the best alpha (α) at 0.9. The prediction is done to see the price of bitcoin on January 1, 2020. The error rate generated on the predicted price of bitcoin uses an amount of 0.0373%. This shows that the system built can be used as a support for decision making when trading bitcoin
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
FUZZY K-NEAREST NEIGHBOR PADA KLASIFIKASI KEMATANGAN CABAI BERDASARKAN FITUR HSV CITRA
Full text linkCabai merupakan salah satu bahan masakan yang disukai masyarakat Indonesia. Salah satu cabai yang banyak dimanfaatkan sebagi bahan masakan yaitu cabai rawit. Pada umumnya identifikasi kematangan cabai dilakukan secara manual berdasarkan warna. Metode manual dilakukan dengan pengamatan secara visual. Cara ini membutuhkan tenaga lebih banyak dalam memilah kematangan cabai, padahal persepsi manusia bisa berbeda-beda, hal ini meninbulkan ketidakkonsistenan hasil yang diperoleh. Berdasarkan permasalahan tersebut, penelitian ini dilakukan untuk proses klasifikasi kematangan cabai rawit. Ekstraksi ciri yang digunakan pada penelitian ini dengan menggunakan nilai HSV. Nilai ini diperoleh dari perhitungan nilai RGB citra. Sedangkan proses klasifikasi menggunakan metode k-nearest neighbor yang ditambahkan fuzzy dalam mencari keanggotaan kelas hasil klasifikasi. Metode ini kemudian disebut Fuzzy K-Nearest Neighbor. Pengujian yang dilakukan terhadap 60 data cabai rawit. Berdasarkan pengujian dengan hasil sesuai klasifikasi kelas sesungguhnya yaitu 15 cabai matang, 15 cabai mentah, 15 cabai setengah matang,dan 7 cabai busuk. Sedangkan hasil klasifikasi yang salah yaitu 8 cabai busuk. Dari pengujian tersebut diperoleh 52 data dengan klasifikasi sesuai dengan kelas aslinya. Dari hasil tersebut diperoleh dengan akurasi sebesar 86,66%
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
- …
