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All Journal Jurnal Jurusan Ilmu Keolahragaan Semantik Kepelatihan Olahraga E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Jurnal Teknologi Informasi Cyberku Jurnal Riset Rumpun Ilmu Kedokteran (JURRIKE) Journal of Student Research Community: Jurnal Pengabdian Pada Masyarakat Nusantara Sporta: Jurnal Pendidikan dan Ilmu Keolahragaan Journal Innovation In Education Scientific Journal of Informatics Mutiara Pendidikan dan Olahraga
Catur Supriyanto
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Journal : Jurnal Teknologi Informasi Cyberku

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METODE FASTICA UNTUK REDUKSI DATA DIMENSI TINGGI PADA ANALISIS SENTIMEN PARIWISATA KOTA SEMARANG MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE Mochamad Amry Assiva; Heru Agus Santoso; Catur Supriyanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 15 No 1 (2019): Jurnal Teknologi Informasi CyberKU Vol. 15, no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Some communities have a voice attractions via Twitter. The opinion can be used as sentiment analysis to determine the ratings of a tourist attraction. Results of sentiment analysis is expected to assist in the improvement and evaluation of the attraction. In related research sentiment analysis previously used linear dimension reduction method, but has the disadvantage produce a linear combination of all the features that will have difficulty if dealing with data that is non-linear. Therefore, in this study used methods of non-linear dimension reduction, namely FastICA in order to improve the accuracy of Support Vector Machine classifier that can handle high-dimensional and non-linear data. This study uses the Indonesian language text contained on the social networking site Twitter. Validation is done by using a 10-Fold Cross Validation. While the measurement accuracy is measured by the Confusion Matrix and ROC curves. Results application of dimension reduction FastICA gain accuracy of 92.90% and the AUC 0.9157 which means the accuracy of 0.95% better than on Support Vector Machine itself, is proven to increase the accuracy of the SVM algorithm on the non-linier tweet data of attractions in the city of Semarang that can be classified by both in positive and negative class.
PENGGABUNGAN METODE U-CONTROL CHART DAN METODE AUTOMATIC CLUSTERING DIFFERENTIAL EVOLUTION UNTUK PENENTUAN JUMLAH KLASTER PADA METODE K-MEANS Ahmad Ilham; Romi Satria Wahono; Catur Supriyanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 14 No 2 (2018): Jurnal Teknologi Informasi CyberKU Vol.14 no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Penentuan jumlah klaster K-Means adalah masalah utama yang paling popular di kalangan peneliti data mining karena sulitnya menentukan informasi dari data secara apriori akibatnya dimungkinkan hasil klaster tidak optimal dan cepat terjebak ke dalam minimum lokal. Metode pengklasteran otomatis dengan pendekatan evolutionary computation (EC) dapat menyelesaikan masalah K-Means. Metode automatic clustering differential evolution (ACDE) adalah salah satu metode pendekatan EC yang terkenal karena dapat menangani data berdimensi tinggi dan meningkatkan kinerja penglasteran K-Means dengan nilai validitas klaster yang rendah. Namun, proses penentuan ambang batas aktivasi k pada ACDE masih bergantung pada pertimbangan pengguna sehingga proses penentuan jumlah klaster K-Means belum efisien. Pada penelitian ini, masalah ACDE akan diperbaiki menggunakan metode u-control chart (UCC) yang terbukti efisien digunakan untuk mengatasi masalah penentuan jumlah klaster K-Means secara otomatis. Model yang diusulkan dievaluasi menggunakan kumpulan data terkini seperti data sintetik dan data real (iris, glass, wine, vowel, ruspini) dari repositori UCI serta menggunakan davies bouldin index (DBI) dan cosine similarity measure (CS) sebagai metode evaluasinya. Hasil dari penelitian ini menunjukkan bahwa metode UCC berhasil meningkatkan metode K-Means dengan nilai objektif DBI dan CS terendah masing-masing sebesar 0.470 dan 0.577. Nilai objektif DBI dan CS terendah adalah metode terbaik. Model yang diusulkan memiliki kinerja pengklasteran lebih unggul setelah dibandingkan dengan metode terkini lainnya seperti metode genetic clustering for unknown k (GCUK), dynamic clustering pso (DCPSO) dan automatic clustering approach based on differential evolution algorithm combining with K-Means for crisp clustering (ACDE) untuk hampir seluruh evaluasi fungsi objektif DBI dan CS. Dapat disimpulkan bahwa, metode UCC mampu memperbaiki kelemahan metode ACDE pada penentuan jumlah klaster K-Means dengan menentukan ambang batas aktivasi k secara otomatis.
OPTIMASI KLASIFIKASI STATUS GIZI BALITA BERDASARKAN INDEKS ANTROPOMETRI MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFICATION ADABOOST Achmad Ridwan; Catur Supriyanto; Pulung Nurtantio Andono
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 14 No 2 (2018): Jurnal Teknologi Informasi CyberKU Vol.14 no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Body Mass Index (BMI) is commonly used as a measure to assess the nutritional status of infants. If there are two babies whose weight and height are the same may have different nutritional status. If it happens then the use BMI to measure nutritional status less relevant. Anthropometric measurement tool to be very instrumental for determining the nutritional status. The guidelines for determining the nutritional status Anthropometric parameters are selected and recommended which includes an assessment of the age, weight, height. On the contrary, along with the development of technology, increasing the amount of data that requires some methods to process and draw conclusions from such data and information. NBC algorithm is an algorithm of decision tree method has good performance in dealing with the classification of Toddler Nutritional Status based index Anthropometry, but NBC has a weakness in the class imbalance. Adaboost one boosting methods that could reduce imbalances class by giving weight to the level of classification Error which may alter the distribution of data. The use of Adaboost with reason this method can improve the accuracy in the process of classification and prediction by means generate a combination of a model, select the model that has the greatest weight. These experiments will apply the NBC algorithm used for classification of Toddler Nutritional Status based index Anthropometry and will be increased again by Adaboost method for being able to overcome the imbalance class thus increasing the probability value of each class and improve accuracy, it also lowers Error Classification. While that would be classified are five classes: normal, fat, very fat, thin, or very thin. The results of the experiment were obtained from NBC method to an accuracy of 88.60% and a classification Error of 11.40%, while the method by Adaboost (NBC + Adaboost) to an accuracy of 88.84% and 11.16% of the classification Error. So we can conclude NBC with Adaboost algorithm implementation on the Classification of Toddler Nutritional Status based index Anthropometry proved capable of overcoming the class imbalance and improve accuracy also lowers Error Classification.
Co-Authors ABDUL ROHMAN Abdul Syukur Abu Salam Achmad Ridwan Achmad Widodo Ahmad Ilham Akhmad Khanif Zyen Amalia Amiril Mukminin Ananda Michael Firdaus Andun Sudijandoko Anindya Mar'atus Sholikhah Ardytha Luthfiarta Aris Tri Jaka Harjanta, Aris Tri Assiva, Mochamad Amry Brighaza Gayoeh Arsyanadi Cinantya Paramita Defri Kurniawan Desy Ika Puspitasari Dhita Aulia Octaviani Dwi Cahyo Kartiko Edi Noersasongko Eko Pudjianto Fajar Jaya Rosadi Fajriyanto Fajriyanto FATKUR ROHMAN KAFRAWI Faza Dhi’fan Wicaksono HERI WAHYUDI Heru Agus Santoso Himawan Wismanadi Indah Munitasri Iqbal Naufal Ahmad Irmantara Subagio Khalivio Rahmyanto Putra Khoirul Mawahib Lutfhi Abdil Khuddus Made Pramono Pudjiijuniarto Pudjiijuniarto Pulung Nurtantio Andono Purwanto Purwanto Ragil Ar Rasyid Ricardus Anggi Pramunendar Rochmatul Anjani Romi Satria Wahono ROY JANUARDI IRAWAN Rudy Donny Liklikwatil Sitti Munifah Soni Sulistyarto Stefanus Santosa Supriyadi Supriyadi syaifuddin syaifuddin Testa Adi Nugraha Utomo, Danang Wahyu Vincent Suhartono