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I Made Widiartha
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PENERAPAN MULTI LAYER PERCEPTRON DALAM ANOTASI IMAGE SECARA OTOMATIS Agus Muliantara; I Made Widiartha
Jurnal Ilmu Komputer Vol. 4, No. 2 September 2011
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

Penentuan anotasi terhadap suatu image (image annotation) merupakan topik penelitian yang berkembang pesat akhir-akhir ini. Permasalahan yang ada dalam menentukan anotasi sebuah image adalah dalam hal penentuan fitur dan metode yang digunakan agar hasil anotasi yang didapat sesuai dengan yang diharapkan oleh pengguna.Dalam penelitian ini akan diimplementasikan suatu model untuk memprediksi anotasi suatu image. Penentuan fitur suatu image dilakukan dengan menggunakan metode color quantization dan multi-level wavelet transform. Dalam melakukan prediksi anotasi suatu image, dilakukan dengan mengimplementasikan metode Multi Layer Perceptron (MLP).Untuk mengevaluasi performance dari model yang diimplementasikan digunakan data image sebanyak 453. Hasil penelitian yang telah dilakukan menunjukkan bahwa tingkat akurasi untuk prediksi anotasi oleh MLP adalah sebesar 81%.
PENERAPAN METODE ANT COLONY OPTIMZATION PADA METODE K-HARMONIC MEANS UNTUK KLASTERISASI DATA I Made Kunta Wicaksana; I Made Widiartha
Jurnal Ilmu Komputer Vol. 5, No. 1 April 2012
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

Data can be classified into several clusters, better known as Data Clustering using several methods, one of which is referred to as K-Means method (KM). It is one of the popular data clustering method. Its implementation is simple and can cope with a great number of data and the process is relatively short. However, KM has several weaknesses; the clustering result is sensitive to the initialization of the cluster center and leads to optimal local. It is the betterment of KM method referred to as K-Harmonic Means (KHM). Although it can minimize in the initialization, it could not overcome the problem of optimal local yet.Ant Colony Optimization (ACO) is an ant algorithm used to form a colony. ACO could avoid the problem of local optimal and was proved to have global solution. In this study, an algorithm was applied to clusterizing the ACO and KHM-based data referred to as ACOKHM. The performance of ACOKHM was compared to the algorithms of ACO and KHM using five data sets. The ACOKHM algorithm was proved to have better performance than ACO and KHM, in which ACOKHM could maximize the cluster center which directs to optimal global.
IMPLEMENTASI ALGORITMA C4.5 DAN K-MEANS PADA DIAGNOSIS PENYAKIT GINJAL KRONIS I Gede Aditya Mahardika Pratama; I Made Widiartha
Jurnal Teknologi Informasi dan Komputer Vol 7, No 4 (2021): Jurnal Teknologi Informasi dan Komputer
Publisher : LPPM Universitas Dhyana Pura

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ABSTRACTThe kidneys function to maintain the stability of the body by regulating the balance of electrolytes, body fluids and expenditure of metabolic products. Chronic kidney disease is a form of kidney disorder. This disease is a deadly disease, but it can be avoided with proper precautions. The prevalence of chronic kidney disease is increasing as a person ages. Classification technique is one technique that can be used in diagnosing chronic kidney disease. One of the machine learning algorithms that can be used for classification is the C4.5 algorithm. The C4.5 algorithm is one of the algorithms that can be used in the decision tree method (decision tree). The C4.5 algorithm can only use categorical data, so data of numerical type needs to be discretized. K-Means Clustering is one method that can be used in data discretization. Elbow method is used in determining the optimal number of k on K-Means by comparing the SSE value of each number of k. System testing was carried out using the confusion matrix and the values of accuracy, recall and precision were 97.92%, 94.44% and 100%.Keywords : Chronic Kidney Disease, Classification, C4.5, K-means, Discretization of Data, Elbow Method.ABSTRAKGinjal berfungsi untuk mempertahankan stabilitas tubuh dengan mengatur keseimbangan elektrolit, cairan tubuh dan pengeluaran hasil metabolisme. Penyakit ginjal kronis adalah salah satu bentuk gangguan pada ginjal. Penyakit ini merupakan penyakit yang mematikan, namun hal ini dapat dihindari dengan tindakan pencegahan yang tepat. Prevalensi penyakit ginjal kronis menjadi kian meningkat, seiring dengan bertambahnya umur seseorang. Teknik klasifikasi merupakan salah satu teknik yang dapat digunakan dalam mendiagnosis penyakit ginjal kronis. Salah satu algoritma machine learning yang dapat digunakan untuk klasifikasi adalah algoritma C4.5. Algoritma C4.5 adalah salah satu algoritma yang dapat digunakan dalam metode decision tree (pohon keputusan). Algoritma C4.5 hanya dapat menggunakan data kategorikal, sehingga data yang bertipe numerikal perlu dilakukan diskritisasi data. K-Means Clustering merupakan salah satu metode yang dapat digunakan dalam diskritisasi data. Metode elbow digunakan dalam penentuan jumlah k optimal pada K-Means dengan membandingkan nilai SSE masing-masing jumlah k. Pengujian sistem dilakukan dengan menggunakan confusion matrix dan didapatkan nilai accuracy, recall dan precision yaitu 97.92%, 94.44% dan 100%.Kata Kunci : Penyakit Ginjal Kronis, Klasifikasi, C4.5, K-Means, Diskritisasi Data, Metode Elbow
PERBANDINGAN KINERJA OPERATOR PARTIALLY MAPPED CROSSOVER, CYCLE CROSSOVER, DAN ORDER CROSSOVER DALAM ALGORITMA GENETIKA PADA PENCARIAN RUTE TERPENDEK PERJALANAN WISATA I Made Tangkas Wahyu Kencana Yuda; I Made Widiartha
Jurnal Teknologi Informasi dan Komputer Vol 6, No 3 (2020): Jurnal Teknologi Informasi dan Komputer
Publisher : LPPM Universitas Dhyana Pura

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Abstract

ABSTRACTTraveling Salesman Problem (TSP) or known as the Shortest Cross Search, is an optimizationproblem that has many important applications in finding the best results in order to find a solutionof values that are close to optimal. One of the TSP cases is determining the distance to tourismlocations in Bali Province. In general, tourists prefer to travel independently because they can freelydetermine their own travel destinations rather than using travel agents that require tourists to followthe travel packages provided. Therefore, this study discusses the Comparison of Operators'Performance Partially Mapped Crossover, Cycle Crossover, and Order Crossover in GeneticAlgorithms for Searching the Shortest Route of Travel Travel. The purpose of this research is to findthe best crossover technique so that it can optimize the process of distance traveled to tourismlocations in Bali Province. The route formation will be carried out by applying a genetic mutationalgorithm that is used is the inversion mutation in the optimum global search. The system built isexpected to optimize the process of tourist travel distance to every tourism location in Bali Province.In conducting this research, the authors used the Operator Partially Mapped Crossover, CycleCrossover, and Order Crossover method and used Inversion Mutation. Based on the researchconducted, it can be concluded that in this study, the optimal value is obtained by applying the CycleCrossover type crossover. With the results in the form of the shortest distance of 153.9 km and thecomputation time of 1.521929264 seconds for 8 cities. As well as the shortest distance of 229.1 kmwith a computing time of 1.915934801 seconds for 12 cities. This shows the average results arebetter when compared to 2 other types of crossovers, namely Partially Mapped Crossover and OrderCrossover.Keywords : Traveling Salesman Problem, Genetic Algorithm, Operator Partially MappedCrossover, Cycle Crossover, Order Crossover, Inversion Mutation.ABSTRAKTravelling Salesman Problem (TSP) atau disebut dengan Pencarian Lintas Terpendek, merupakanpermasalahan optimasi yang mempunyai banyak terapan penting dalam pencarian hasil yang terbaikdengan tujuan untuk mendapatkan solusi nilai-nilai yang mendekati optimal. Salah satu kasus TSPadalah menentukan jarak tempuh lokasi pariwisata di Provinsi Bali. Secara umum, wisatawan lebihsuka bepergian secara mandiri karena mereka dapat dengan bebas menentukan tujuan wisata merekasendiri daripada menggunakan agen perjalanan yang mengharuskan wisatawan untuk mengikutipaket perjalanan yang telah disediakan. Maka dari itu Penelitian ini membahas mengenaiPerbandingan Kinerja Operator Partially Mapped Crossover, Cycle Crossover, dan OrderCrossover dalam Algoritma Genetika pada Pencarian Rute Terpendek Perjalanan Wisata. Tujuandari penelitian ini adalah untuk menemukan teknik crossover yang paling baik sehingga dapatmengoptimalkan proses jarak tempuh lokasi pariwisata di Provinsi Bali. Pembentukan rute akandilakukan dengan menerapkan algoritma genetika mutasi yang digunakan adalah Mutasi Inversion320 Jurnal Teknologi Informasi dan Komputer, Volume 6, Nomor 3, Oktober 2020pada pencarian global optimum. Sistem yang dibangun diharapkan dapat mengoptimalkan proses jarak tempuh travel wisatawan ke setiap lokasi pariwisata di Provinsi Bali. Dalam melakukan penelitian ini, penulis menggunakan metode Operator Partially Mapped Crossover, Cycle Crossover, dan Order Crossover serta menggunakan Mutasi Inversion. Berdasarkan penelitian yang dilakukan dapat disimpulkan bahwa pada penelitian ini, nilai optimal diperoleh dengan menerapkan Crossover jenis Cycle Crossover. Dengan hasil berupa jarak terpendek 153.9 km dan waktu komputasi selama 1.521929264 detik untuk 8 kota. Serta jarak terpendek sebesar 229.1 km dengan waktu komputasi selama 1.915934801 detik untuk 12 kota. Hal ini menunjukan rata-rata Hasil yang lebih baik jika dibadingkan 2 buah jenis crossover yang lain yaitu Partially Mapped Crossover dan Order Crossover. Kata kunci— Travelling Salesman Problem, Algoritma Genetika, Operator Partially Mapped Crossover, Cycle Crossover, Order Crossover, Mutasi Inversion
PENERAPAN CROSSOVER PADA PERILAKU LEBAH SCOUT DALAM ALGORITMA ARTIFICIAL BEE COLONY UNTUK OPTIMASI VEHICLE ROUTING PROBLEM I Made Widiartha; Ngurah Agus Sanjaya ER; I Wayan Santiyasa
Jurnal Teknologi Informasi dan Komputer Vol 5, No 1 (2019): Jurnal Teknologi Informasi dan Komputer
Publisher : LPPM Universitas Dhyana Pura

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Abstract

ABSTRACT The economy in Indonesia has shown a relatively consistent increase from year to year where the growth rate at the end of 2017 is 5.5%. In terms of the trade balance, according to the Indonesian Central Bureau of Statistics (BPS) on January 15, 2018 it was said that Indonesia's trade balance had a surplus of 11.84 billion US dollars. The development of the trade sector in Indonesia is also inseparable from the distribution of goods. The distribution of goods is closely related to the distribution cost factor, because the longer the distribution distance, the longer the time and the greater the operational costs needed to distribute the goods. Therefore, it is necessary to determine the optimal distribution channel to obtain distribution efficiency. The problem of distributing this item to the world of computer science is known as the Vehicle Routing Problem (VRP). Along with the rapid development of technology and information, with the use of technology, research related to the distribution of goods has also been carried out. One method that has superior ability in determining the distribution route is artificial bee colony (ABC). Although it has superior performance, the ABC algorithm still has weaknesses where ABC requires a relatively long time to get an optimum solution. The main cause of this weakness is the bee scout technique in finding solutions (food sources). Looking at the weaknesses of the characteristics of bee scout in ABC, in this study the ABC algorithm was optimized by applying two crossover methods, namely Partially Mapped Crossover and Cycle Crossover on the solution search pattern by bee scout. Crossover is one of the optimization techniques aimed at finding the optimum solution in AG. This is the basis for the implementation of hybrid crossover in this vehicle routing problem. From the results of the study it was found that Cycle Crossover (CX) has a better performance than Partially Mapped Crossover (PMX) in optimizing the ABC algorithm, this can be seen from the CX solution produced for all datasets better than PMX. Besides having a better performance in terms of distance, CX also has afaster time performance than PMX.Keywords: artificial bee colony, vehicle routing problem, crossoverABSTRAKPerekonomian di Indonesia telah menunjukkan adanya peningkatan yang relatif konsisten dari tahun ke tahun dimana angka pertumbuhan pada penghujung tahun 2017 adalah 5,5 %. Dari sisi neraca perdagangan, menurut Badan Pusat Statistik (BPS) Indonesia pada tanggal 15 Januari 2018 dikatakan bahwa Neraca Perdagangan Indonesia mengalami surplus sebesar 11,84 milliar dolar AS. Perkembangan sektor perdagangan di Indonesia juga tidak terlepas dari faktor pendistribusian barang. Pendistribusian barang memiliki kaitan yang erat dengan faktor biaya distribusi, karena semakin jauh jarak pendistribusiannya maka semakin lama waktu dan semakin besar biaya operasional yang diperlukan dalam mendistribusikan barang tersebut. Maka dari itu, diperlukan penentuan jalur distribusi yang optimal untuk mendapatkan efisiensi pendistribusian. Permasalahan pendistribusian barang ini pada dunia ilmu komputer dikenal sebagai Vehicle Routing Problem (VRP). Seiring berkembangnya teknologi dan informasi yang pesat, dengan pemanfaatan teknologi, penelitian yang berkaitan dengan pendistribusian barang ini juga telah dilakukan. Salah satu metode yang memiliki kemampuan unggul dalam menentukan rute distribusi adalah artificial bee colony (ABC). Meski memiliki performa yang unggul, tetapi dalam algoritma ABC masih memiliki kelemahan dimana ABC membutuhkan waktu yang relatif lama untuk mendapatkan sebuah solusi optimum. Penyebab utama yang menyebabkan kelemahan ini adalah teknik lebah scout dalam mencari solusi (sumber makanan). Melihat kelemahan karakteristik lebah scout dalam ABC maka dalam penelitian ini dilakukan optimasi algoritma ABC dengan menerapkan dua metode crossover yaitu Partially Mapped Crossover dan Cycle Crossover pada pola pencarian solusi oleh lebah scout.Crossover merupakan salah satu teknik optimasi yang ditujukan untuk pencarian solusi optimumdalam AG. Hal ini menjadi dasar untuk penerapan hybrid crossover dalam permasalahan vehiclerouting problem ini. Dari hasil penelitian didapatkan bahwa Cycle Crossover (CX) memiliki kinerjayang lebih baik daripada Partially Mapped Crossover (PMX) dalam mengoptimasi algoritma ABC,hal ini terlihat dari solusi CX yang dihasilkan untuk semua dataset lebih baik dari PMX. Selainmemiliki kinerja yang lebih baik dalam hal jarak, CX juga memiliki kinerja waktu yang lebih cepatdaripada PMX.Kata Kunci: artificial bee colony, vehicle routing problem, crossover
Pengembangan Metode Klasterisasi Data Berbasis Hybrid Improved Artificial Bee Colony (IABC) dan K – Harmonic Means Tegar Palyus Fiqar; Saiful Bahri Musa; Fitrah Maharani Humaira; I Made Widiartha; Darlis Herumurti; Agus Zainal Arifin
SPECTA Journal of Technology Vol. 2 No. 3 (2018): SPECTA Journal of Technology
Publisher : LPPM ITK

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (466.517 KB) | DOI: 10.35718/specta.v2i3.3

Abstract

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.
Desain Aplikasi AMAN : Aplikasi Keselamatan untuk Wanita dan Anak – Anak Berbasis Mobile Alvin Wiraprathama; I Made Widiartha
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 2 (2023): JELIKU Volume 12 No 2, November 2023
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2023.v12.i02.p13

Abstract

Crime is one of the biggest threat and still often occurs easily in daily life in Indonesia until today. According to BPS ( Badan Pusat Statistik ) Indonesia, there are more than 200.000+ crime cases that happened every year. Crime can happen to anyone especially to women and children. This study aim to help solving women and children safety problem by taking the advantage of daily use of mobile phone and smart gadget. In order to solve the problem, the author create a design solution using prototyping method and model it in the form of a mobile application. In the prototype, user can send emergency request at anytime they need, call emergency contact or nearby authorities to help them. Hopefully this application can reduce the crime number and make a safer environment for women and children.
Penyisipan Digital Signature Ke Dalam Citra Digital Sebagai Keamanan Hak Cipta Dengan Metode DES dan BPCS Michael Tanaya; I Gede Arta Wibawa; I Made Widiartha; Luh Gede Astuti
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 13 No 1 (2024): JELIKU Volume 13 No 1, August 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

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Dalam era digital saat ini, keamanan hak cipta citra menjadi penting mengingat mudahnya distribusi dan reproduksi konten digital. Penyisipan tanda digital telah dikenal sebagai metode yang efektif untuk melindungi integritas dan keaslian citra digital. Penelitian ini mengusulkan pendekatan inovatif yang menggabungkan metode Data Encryption Standard (DES) dan Bit-Plane Complexity Segmentation (BPCS) untuk menyisipkan tanda digital ke dalam citra digital guna meningkatkan keamanan hak cipta. Pada tahap pertama, tanda digital dienkripsi menggunakan algoritma DES untuk memastikan kerahasiaan data sebelum penyisipan. Tahap berikutnya melibatkan penerapan metode BPCS, di mana tanda digital yang telah dienkripsi disisipkan ke dalam bit-bit rendah signifikan citra digital. Keunggulan metode BPCS terletak pada kemampuannya menyisipkan data tanpa mengorbankan kualitas visual citra secara signifikan.
Penerapan Metode Content Based Filtering Dan K-Nearest Neighbor Dalam Sistem Rekomendasi Musik I Made Teja Sarmandana; I Made Widiartha; Luh Arida Ayu Rahning Putri; I Gede Santi Astawa
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 13 No 1 (2024): JELIKU Volume 13 No 1, August 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

Current technological developments are able to change the way the younger generation enjoys music, where music can now be packaged in digital form, which is a new innovation in the music industry in Indonesia. Given the large amount of music data available on the internet, a system that provides services for users to search for their favorite music is really needed. The recommendation system will provide relevant information based on the preferences that the user wants to search for. Content Based Filtering recommends that users utilize the information contained in the data to use as parameters. The K-Nearest Neighbor (K-NN) algorithm is a method of classifying objects based on the closest training data to the object under test. In this study, accuracy testing techniques were used to measure the performance of the classification that has been carried out. The classification process that was created succeeded in obtaining the highest accuracy value at 90,49% with a value of k=9 which shows that the classification and recommendation process can run quite well. Keywords: Accuracy, Content Based Filtering, Classification, Recommendation System, K-Nearest Neighbor, Music
Analisis Serangan Cross Site Scripting (XSS) Pada Website OASE Menggunakan Metode OWASP Muhammad Arrysatrya Yusuf Putranda; I Komang Ari Mogi; I Gusti Ngurah Anom Cahyadi Putra; I Made Widiartha
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 13 No 1 (2024): JELIKU Volume 13 No 1, August 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

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Berkembangnya internet membuat majunya era teknologi digitalisasi, dimana hampir seluruh sektor kini dapat diakses secara digital, termasuk pendidikan. Salah satunya adalah Universitas Udayana yang memiliki LMS bernama Online Academic Service for Elearning (OASE) yang digunakan dalam proses pembelajaran di lingkungan Universitas Udayana. Namun perkembangan ini diikuti oleh potensi ancaman, dengan terwujudnya digitalisasi yang berarti hal tersebut dapat diakses oleh siapa saja, termasuk orang yang merusak suatu sistem. Salah satu jenis serangan yang banyak ditemukan adalah Cross Site Scripting (XSS). Untuk memastikan keamanan LMS OASE milik Universitas Udayana, perlu dilakukan Analisis Kerentanan terutama pada serangan XSS yang dilakukan dengan uji penetrasi menggunakan metode OWASP. Dari hasil pengujian ditemukan bahwa meskipun OASE memiliki beberapa potensi celah kerentanan, namun hanya satu fitur saja yang dikonfirmasi memiliki kerentanan, sedangkan fitur lainnya berhasil diproteksi dengan adanya fungsi filtering serta kontrol pada eksekusi script dari pengguna.
Co-Authors Agastia Maulana Akbar Agus Muliantara Agus Zainal Arifin Alvin Wiraprathama Bhisma Satwika Ari Priandana Cokorda Rai Adi Pramartha Darlis Herumurti Fitrah Maharani Humaira Hana Christine Octavia Hangralim, Ryan I Dewa Made Bayu Atmaja Darmawan I Gede Aditya Mahardika Pratama I Gede Arta Wibawa I Gede Rizki Heriana Prayoga I Gede Santi Astawa I Gusti Ngurah Anom Cahyadi Putra I Ketut Gede Suhartana I Komang Ari Mogi I Made Kunta Wicaksana I Made Tangkas Wahyu Kencana Yuda I Made Teja Sarmandana I Putu Gede Hendra Suputra I WAYAN SANTIYASA Ida Bagus Gede Dwidasmara Ida Bagus Made Mahendra Luh Arida Ayu Rahning Putri Luh Gede Astuti Mahandiri, Dewa Nyoman Agung Adipurwa Michael Tanaya Muhammad Arrysatrya Yusuf Putranda Muhammad Caesar Gilang Indrawan Ngurah Agus Sanjaya ER Ni Wayan Amanda Putri Astawa Putu Agus Dharma Kusuma Saiful Bahri Musa Tegar Palyus Fiqar Tri Adi Ningsih Wijaya, Partha