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All Journal Jurnal Ilmu Komputer (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Jurnal Ilmiah Kursor JOIV : International Journal on Informatics Visualization Specta Journal of Technology Eduvest - Journal of Universal Studies Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
I Made Widiartha
Universitas Udayana
Aerospace Engineering Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Neuroscience Social Sciences Other

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Journal : Jurnal Ilmiah Kursor

 1 
FOREIGN TOURIST ARRIVAL FORECASTING TO BALI USING CASCADE FORWARD BACKPROPAGATION Ayu Nikki Asvikarani; I Made Widiartha; Made Agung Raharja
Jurnal Ilmiah Kursor Vol 10 No 4 (2020)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v10i4.252

Abstract

Bali has a recognized tourism potential in the world arena. In order to improve the quality and development of the tourism sector in the midst of global competition, it is necessary to formulate appropriate strategies by decision makers such as private parties and government. In support of more accurate decision making, the authors make a system of forecasting the number of foreign tourist visits to Bali Province using Cascade Forward Backpropagation (CFB) method with coverage of Australia, Japan, and United Kingdom which are the top 3 countries with the highest foreign tourist arrival to Bali in that years. Factors used as input in forecasting include the number of visits of foreign tourists the previous year, the population of countries of origin of foreign tourists, Gross Domestic Product at current prices of countries of origin of foreign tourists, and Relative Consumer Price Index Origin of foreign tourists. In this study, optimization of activation function parameters, hidden neurons, and learning rate to obtain forecasting results with the lowest error rate. Forecasting results using the CFB method produces a fairly good accuracy with MAPE range of 6 - 30% where the activation function tanh work better than sigmoid activation function.
RINDIK VOICE SYNTHESIS USING MODIFIED FREQUENCY MODULATION AS BALI CULTURAL PRESERVATION EFFORTS I Made Widiartha; Agus Muliantara
Jurnal Ilmiah Kursor Vol 8 No 3 (2016)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i3.90

Abstract

Gamelan bali is one aspect of the art highly favored by both domestik and foreign tourists. One type of popular typical Balinese gamelan is Rindik. Rindik is one of Balinese traditional musical instrument made of bamboo. Now days, the number of foreign culture and today’s lifestyle give some impacts on the declining interest of Balinese to interact with this type of conventional gamelan. The younger generation is now more inclined to like devices which are played through electronics/software component. To increase public interest towards traditional gamelan bali especially rindik we need a breakthrough to digitize gamelan rindik and presenting it in the form of rindik software application. Today's advanced technology has made a way to digitize a wide range of instruments including rindik into computerized form. For example we can use frequency modulation as a voice synthesis technique. This method was developed by researchers in the field of sound synthesis. In this research, we have done the sound synthesis process of rindik instrument into digital form using frequency modulation. The best results were obtained through the synthesis comparison of carrier signal frequency and modulator is 1:7. Outcomes of this research is a digitizing result which is presented in the form of a gamelan rindik package on desktop based software application.
ADAPTIVE DATA CLUSTERING METHOD BASED ON ARTIFICIAL BEE COLONY AND K-HARMONIC MEANS I Made Widiartha; Agus Zainal Arifin; Anny Yuniarti
Jurnal Ilmiah Kursor Vol 6 No 3 (2012)
Publisher : Universitas Trunojoyo Madura

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Abstract

ADAPTIVE DATA CLUSTERING METHOD BASED ON ARTIFICIAL BEE COLONY AND K-HARMONIC MEANS a I Made Widiartha, b Agus Zainal Arifin, c Anny Yuniarti a Jurusan Ilmu Komputer, FMIPA, Universitas Udayana Kampus Bukit, Gedung BJ Lt.I, Jimbaran Bali, b,c Informatics Department, Faculty of Information Technology Institute of Technology Sepuluh Nopember E-Mail: a imdewidiartha@cs.unud.ac.id Abstrak Berbagai metode telah dibuat untuk dapat melakukan klasterisasi data. Salah satu metode tersebut adalah K-Harmonic Means Clustering (KHM). KHM merupakan metode klasterisasi data yang menyempurnakan K-Means Clustering (KM). Metode KHM telah mampu mengurangi permasalahan KM dalam hal sensitifitas pada inisialisasi titik pusat awal, meskipun demikian dalam KHM masih terdapat kemungkinan solusi yang dihasilkan merupakan suatu lokal optimal. Permasalahan lokal optimal ini dapat diatasi dengan memanfaatkan suatu metode yang memiliki karakteristik pencarian solusi global ke dalam metode KHM. Artificial Bee Colony (ABC) merupakan suatu metode swarm yang berbasis pada perilaku mencari makan dari koloni lebah madu yang memiliki karakteristik untuk menghindari kemungkinan konvergensi terhadap lokal optimal. Dalam penelitian ini diusulkan sebuah metode baru untuk klasterisasi data yang berbasis pada metode ABC dan KHM (ABC-KHM). Kinerja metode ABC-KHM ini telah dibandingkan dengan metode KHM dan ABC dengan memanfaatkan lima dataset. Dari hasil penelitian didapatkan hasil dimana metode ABC-KHM ini telah berhasil mengoptimalkan posisi titik pusat klaster KHM yang mengarahkan hasil klaster menuju suatu solusi global. Kata kunci: K-Means Clustering, K-Harmonic Means Clustering, Artificial Bee Colony, ABC-KHM. Abstract Various methods have been made to cluster the data. One such method is K-Harmonic Means Clustering (KHM). KHM is a clustering method that improves K-Means Clustering (KM). KHM method was able to reduce the problem of KM in terms of sensitivity to the initialization of the initial center point nevertheless there is still a possibility that the result of KHM is a local optimum. The local optimal problem can be solved by utilizing a method that has characteristic of a global search into KHM method. Artificial Bee Colony (ABC) is a swarm method based on foraging behavior of honey bee colony that has characteristics to avoid the possibility of local optimum convergence. In this research, a new method for data clustering based on ABC and KHM (ABC-KHM) is proposed. The performance ABC-KHM method has been compared with ABC and KHM by using five datasets. The results show that ABCKHM method is able to optimize the position of the cluster center and directs the center to a global solution. Key words: K-Means Clustering, K-Harmonic Means Clustering, Artificial Bee Colony, ABC-KHM.
OPTIMIZATION OF K-MEANS CLUSTERING USING PARTICLE SWARM OPTIMIZATION ALGORITHM FOR GROUPING TRAVELER REVIEWS DATA ON TRIPADVISOR SITES I Made Satria Bimantara; I Made Widiartha
Jurnal Ilmiah Kursor Vol 12 No 1 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i01.269

Abstract

K-Means Algorithm can be used to group tourists based on reviews on tourist destination objects. This algorithm has a weakness that is sensitive to the determination of the initial centroid. The initial centroid that is determined at random will decreasing the level accuracy, often gets stuck at the local optimum, and gets a random solution. Optimization algorithms such as PSO can overcome this by determining the optimal initial centroid. The optimal number of clusters (K) will be determined using the Elbow method by calculating the SSE value of the resulting cluster. The average Silhouette Coefficient (SC) is used to measure the quality of the clusters produced by the K-Means Algorithm with and without the PSO Algorithm. This study uses secondary data obtained from the UCI Machine Learning Repository with the name Travel Reviews Data Set which consists of 980 records and 10 attributes. The test results show that K=2 is the optimal number of clusters. The K-Means and PSO Algorithm gives an average SC value of 0.300358 which is better than without the PSO Algorithm of 0.300076. The optimal PSO hyperparameter generated is the number of particles=30, \varphi_1=2.2, and {\ \varphi}_2=3 at maximum iteration of 100.
Co-Authors A A I N Karyawati Agus Muliantara Agus Zainal Arifin Agustiana, Ni Putu Arisya Alit Indrawan, I Gusti Ngurah Alvin Wiraprathama Anak Agung Gde Agung Pranandita Anak Agung Istri Ngurah Eka Karyawati Anggotra, Puspadevi Anny Yuniarti Apsari, Made Sri Ayu Ari Mogi, I Komang Arsa, Dewa Made Sri Astawa, Ni Wayan Amanda Putri Atmojo, Firman Ali Eka Ayu Nikki Asvikarani bratha, dede khausa bayu Darlis Herumurti Dewa Made Wiharta Firman Ali Eka Atmojo Gede Agung Aji Andar Sakti Gede Wisnu Bhaudhayana Gilang Indrawan, Muhammad Caesar Giri, I Nyoman Yusha Tresnatama Gst. Ayu Vida Mastrika Giri Humaira, Fitrah Maharani Humaira, Fitrah Maharani I Dewa Made Bayu Atmaja Darmawan, I Dewa Made Bayu I Gede Arta Wibawa I Gede Santi Astawa I Gusti Agung Gede Arya Kadyanan I Gusti Ngurah Anom Cahyadi Putra I Kadek Aldy Oka Ardita I Ketut Gede Suhartana I Made Eko Satria Wiguna I Made Nusa Yudiskara I Made Satria Bimantara I Putu Bayu Eka Pratama I Putu Gede Hendra Suputra I Putu Satwika I WAYAN SANTIYASA I Wayan Sugiana I Wayan Supriana Ida Bagus Gede Dwidasmara Ida Bagus Gede Dwidasmara Ida Bagus Made Mahendra Julianti, Syelvia Kadek Nanda Banyu Permana Ketut Ardha Chandra Kusuma, Putu Agus Dharma Luh Arida Ayu Rahning Putri Luh Gede Astuti Luh Gede Astuti Nathanael Richie Thomas Ngurah Agus Sanjaya ER Ni Made Elvina Aryadhika Putri Nyoman Putra Sastra Octavia, Hana Christine Panji Palguna, I Gusti Agung Ngurah Pijar Candra Mahatagandha Pramana, I Gst Bgs Bayu Adi PRATIWI, NI MADE DINDA Priandana, Bhisma Satwika Ari Purba, Kevin Joel Putra, I Gusti Ngurah Agung Widiaksa Raharja, Made Agung Ramadhan, Zhaqy Hikkammi Gullam Rukmi Sari Hartati Ryan, Ida Bagus Putu Saiful Bahri Musa Satria Wiguna, I Made Eko Satya, I Dewa Gede Rama Sitinjak, Anugrah Ignatius Tegar Palyus Fiqar Tristan Bey Kusuma Widnyana, I Kadek Agus Candra Wijaya, Partha Wikardiyan, Aditya Wiraprathama, Alvin Yande Pramana Yustika Pradeva