Article Per Year (5 Year)
p-Index From 2020 - 2025
1.67
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Systemic: Information System and Informatics Journal
Rizal Setya Perdana
Fakultas Ilmu Komputer , Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 53 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 53 Documents
Search

 1   2   3   4   5 
Optimasi Model Segmentasi Citra Metode Fuzzy Divergence Pada Citra Luka Kronis Menggunakan Algoritma Genetika Ghenniy Rachmansyah; Wayan Firdaus Mahmudy; Rizal Setya Perdana
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 3 No 1: Maret 2016
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1433.971 KB) | DOI: 10.25126/jtiik.201631163

Abstract

AbstrakLuka kronis merupakan masalah yang masih terbilang berat dalam penanganan, memerlukan ketekunan, biaya mahal, tenaga terlatih dan terampil. Proses pengkajian luka masih dilakukan secara manual, membutuhkan waktu yang cukup lama dan menghasilkan hasil yang lebih subyektif. Dengan adanya permasalahan tersebut, maka dibutuhkan sistem yang dapat membantu pengkajian luka dengan pendekatan citra digital atau dikenal dengan istilah digital planimetry. Fokus permasalahan yang diselesaikan hanya sebatas pada penggolongan komposisi jaringan luka dengan pendekatan segmentasi citra. Pada task segmentasi citra, algoritma yang digunakan yaitu fuzzy divergence yang dioptimasi menggunakan algoritma genetika untuk pemilihan nilai threshold optimal. Pada algoritma genetika, representasi kromosom berupa real-coded, proses reproduksi meliputi operasi extended intermediate crossover dan random mutation, serta metode seleksi elit dengan penambahan mekanisme random injection. Metode yang diusulkan dapat digunakan untuk mengoptimasi model segmentasi citra multilevel thresholding dengan meminimalkan nilai fuzzy divergence dengan parameter algoritma genetika; meliputi ukuran populasi sebesar 60, kombinasi ukuran cr dan mr secara berturut-turut 0.6 dan 0.4, dan ukuran generasi sebesar 100. Kemudian, berdasarkan evaluasi hasil segmentasi citra menggunakan Standar Deviasi (SD), distribusi Gamma menghasilkan hasil segmentasi yang lebih baik.Kata kunci: luka kronis, digital planimetry, segmentasi citra, fuzzy divergence, algoritma genetikaAbstractChronic wounds are a problem that is still difficult in wound management, require persistence, high cost for treatment, and trained-skilled personnel. In wound management, the assessment process are still performed manually, however it’s very time-consuming and produce more subjective outcomes. Given these problems, there is a need for a system that helps wound assessment with the approach in measuring wound size using digital images, known as digital planimetry. In this work, the focus only on wound tissue classification using image segmentation. In image segmentation, the algorithm used is fuzzy divergence that optimized by using genetic algorithm for selecting optimal threshold. For genetic algorithm, the representation of chromosomes is real-coded, then reproduction process using the extended intermediate crossover and random mutation, and elitism selection with the addition of random injection mechanism. The proposed method can use to optimize image segmentation multilevel thresholding by minimizing the value of fuzzy divergence with genetic algorithm parameters which includes the size of the population is 60, the combination of size Cr and Mr respectively 0.6 and 0.4, and the size of generation is 100. Then, based on the evaluation result of image segmentation using Standard Deviation (SD), found that Gamma distribution leads better segmentation as compared to others.Keywords: chronic wounds, digital planimetry, image segmentation, fuzzy divergence, genetic algorithm
Klasifikasi Teks Bahasa Indonesia Pada Dokumen Pengaduan Sambat Online Menggunakan Metode K-Nearest Neighbors Dan Chi-square Claudio Fresta Suharno; M. Ali Fauzi; Rizal Setya Perdana
Systemic: Information System and Informatics Journal Vol. 3 No. 1 (2017): Agustus
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1093.934 KB) | DOI: 10.29080/systemic.v3i1.191

Abstract

K-Nearest Neighbors (K-NN) merupakan metode klasifikasi yang mudah untuk dipahami. Akan tetapi metode tersebut memiliki beberapa kekurangan, salah satunya dalam aspek komputasi perhitungan yang besar. Oleh karena itu, seleksi fitur digunakan sebagai salah satu cara untuk mengurangi besarnya komputasi adalah dengan mengurangi jumlah fitur yang tidak relevan dalam klasifikasi teks. Metode seleksi fitur yang digunakan adalah menggunakan metode Chi-Square untuk menghitung tingkat dependensi fitur. Proses yang dilakukan adalah mengumpulkan dokumen latih dan dokumen uji, melakukan tahap preprocessing dan seleksi fitur, kemudian dilakukan klasifikasi, dan pada tahap akhir dilakukan pengujian dan analisis terhadap hasil klasifikasi oleh sistem terkait nilai precision, recall, dan F-Measure. Dari penelitian ini dihasilkan bahwa seleksi fitur dapat meningkatkan nilai F-Measure dalam klasifikasi teks berbahasa Indonesia pada dokumen pengaduan SAMBAT Online dengan menggunakan metode klasifikasi K-Nearest Neighbors
Klasifikasi Teks Bahasa Indonesia Pada Dokumen Pengaduan Sambat Online Menggunakan Metode K-Nearest Neighbors (K-NN) dan Chi-Square Claudio Fresta Suharno; Mochammad Ali Fauzi; Rizal Setya Perdana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 10 (2017): Oktober 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (961.194 KB)

Abstract

K-Nearest Neighbors (K-NN) is one classification method that easy to learn. Although, this method has some drawbacks, one of them is this classification could provide a low accuracy casued by a large feature space with irrelevant features among them. Because of that drawback, feature selection is applied to reduce the feature space by reducing number of irrelevant features in text classification. Selection feature method that being used in this experiment is using Chi-Square method. Using Chi-Square method to select important features by measuring dependency level of each feature across classes and documents. The process including in this experiment is collecting training and testing documents, text preprocessing and feature selection, and classification. After classification is being done by the system, we make an observation and analysis towards classification result, including precision, recall, and F-Measure value. From 16 evaluations, the best precision and recall score obtained with 90% precision and 78% recall on k = 15 using 25% feature selection used. While the best F-Measure score obtained with 78% F-Measure on k = 15 and k = 5 using 25% feature selection used. From this experiment, its appear that feature selection take effect in increasing F-Measure value in text classification of SAMBAT Online complaint documents in bahasa using K-Nearest Neighbors classification method.
Implementasi Algoritma Modified K-Nearest Neighbor (MKNN) untuk Klasifikasi Penyakit Demam Fakihatin Wafiyah; Nurul Hidayat; Rizal Setya Perdana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 10 (2017): Oktober 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1192.322 KB)

Abstract

Fever is an early indicator for some diseases such as dengue fever, typhoid and malaria accompanied by similar symptoms, including muscle pain, indigestion, tongue condition and enlargement of the liver and spleen. Similar symptoms of each disease cause difficulties in getting anamnese (temporary diagnosis) so that patients get the inadequate initial treatment. Handling the problem, technology is needed to obtain a temporary diagnosis by applying one of the classification method of Modified K-Nearest Neighbor (MKNN). The method studied the pattern of previous examination data based on 15 symptoms of disease with eucledian distance calculation process, calculation of validity value and weighted voting calculation that the end result is used for class classification determination based on predetermined value of K. Testing of the value of K get the accuracy of 88.55%. The average value of accuracy obtained from testing of variation in the amount of training data is 92.42%. Testing the influence of the composition of train data get the average value of accuracy of 87.89%. Testing the influence of the composition of train data and test data get the average value of accuracy of 96.35%
Implementasi Metode Promethee II untuk Menentukan Pemenang Tender Proyek (Studi Kasus: Dinas Perhubungan dan LLAJ Provinsi Jawa Timur) Muhammad Wafi; Rizal Setya Perdana; Wijaya Kurniawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1140.472 KB)

Abstract

Implementation of duties by the Department of Transportation and LLAJ is done by disseminating information about project execution through Layanan Pengadaan Secara Elektronik (LPSE). The large number of bidders requires the Department of Transportation and LLAJ to make a good selection of winners. The winner's assessment can be done by applying the Preference Ranking Method Method of Organizational Methods for Enrichment Evaluation (PROMETHEE II) on the system to consider several alternatives and choose the best alternative based on administrative aspects, quality, price and qualification. The method of Promethee II performs calculations with several stages: weighting, multicriteria preference index calculation for 3 types of preferences, usual, level and quasi then continue by calculating of leaving flow, entering flow, and netflow. Based on the test, the highest accuracy of the system is 84.21% with usual criterion and quasi criterion type preferences. The lowest accuracy is 63.15% with level criterion type preference. The degree of accuracy in testing is influenced by the weighting conditions used for each of the criteria and the type of preferences used in the calculation process. Implementation of Promethee II method is expected to determine the winning bidder with a good assessment process by considering all criteria.
Sistem Pendukung Keputusan Pengurutan Berdasarkan Jenis Suara Anggota Baru Divisi Paduan Suara BIOS Menggunakan Metode Profile Matching (Studi Kasus: Logicio Choir FILKOM) Dinul Wikramaditya Syah; Edi Santoso; Rizal Setya Perdana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1025.972 KB)

Abstract

Faculty of Computer Science Choir's or commonly referred to with the Logicio Choir (LC) is a Division in the organization of Arts and Sports in an environment of Faculty of Computer Science called the BIOS (Badan Internal Olahraga dan Seni). The Division was used to accommodate FILKOM students and has talent in the field of the chorus. The participants emerged from the desire to improve the knowledge and skills of their music. To become a member of this choir performed need several stages of selection. The criteria assessed in the selection include the ability of auditory (hearing), the ability to read scores or not numbers, the ability to sing with good intonation, articulation and determination high and low tone of someone (ambitus tone). A large number of criteria in selecting a jury, at least as well as the results of the different criteria that each Member makes it difficult in reaching a decision. Profile Matching is one of the decision-making methods that is suitable for selecting the members objectively based on the criteria that are needed by the organization. Profile Matching is a decision-making mechanism to assume that there is an ideal level of predictor variables that must be owned by individuals, not the minimum level that must be passed. Assessment and calculation of the value gap based on 9 citeria: Ambitus, hearing 2 tone, hearing 3 tone, hearing 4 tone, hearing 5 tone, reading notes, reading block notes, singing mandatory song, singing free song. The result of the profile matching method implementation is it can be an efficient and effective solution in making a decision in the member admission. The system can be used to make a decision with the output of the system is in the form of urutan based on the final score from the highest score to the lowest with a total 61 test data with a percentage of validity 77.04%.
Analisis Sentimen Tentang Opini Pilkada DKI 2017 Pada Dokumen Twitter Berbahasa Indonesia Menggunakan Naive Bayes dan Pembobotan Emoji Agnes Rossi Trisna Lestari; Rizal Setya Perdana; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1072.223 KB)

Abstract

Sentiment analysis is a part of text mining, the main focus is to analysis text documents. Sometimes text documents contain non-textual elements, e.g. emojis. Emoji is an Unicode graphic Symbol representation using pictures to express a person's feelings. The algorithm used in this research is Naive Bayes with renewal in addition of non-textual weighting (emoticon). The results of normalised textual and non-textual weightings with Min-Max method will be combined with certain constant values that resulting in both positive and negative sentiments. Data taken from Twitter about 2017 DKI Jakarta elections as much as 900 data tweet. From the accuracy test results, 68,52% were obtained for textual weighting conditions, 74,81% for non-actual weighting, and 73,57% for merging conditions 0,5 for textual and 0,5 for non-textual. From the result of the examination non-textual weighting effect, can be conclude that the non-textual weighting had an effect on the accuracy and classification, with the best multiplier constants when α = 0,4 and β = 0,6 to α = 0,1 and β = 0,9.
Analisis Sentimen Tingkat Kepuasan Pengguna Penyedia Layanan Telekomunikasi Seluler Indonesia Pada Twitter Dengan Metode Support Vector Machine dan Lexicon Based Features Umi Rofiqoh; Rizal Setya Perdana; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1315.347 KB)

Abstract

Sentiment analysis is a part of research from Text Mining which is usefull to classify text documents contained opinion based on sentiment. Text document that is used in research comes from Twitter from people's opinion about cellular telecommunication service provider. The used method is Support Vector Machine with using Lexicon Based Features as its feature renewal instead of using TF-IDF features. The used data in this research is 300 data which divided into two types of data with ratio 70% for training data and 30% for testing data. The result of system accuracy that is obtained from sentiment analysis using Support Vector Machine and Lexicon Based Features method is 79% using degree value 2, constant learning rate value 0.0001, and maximum iteration is 50 times. While sentiment analysis system without using Lexicon Based Features is resulting accuracy at 84% with the same parameter values.
Analisis Sentimen Tentang Opini Film Pada Dokumen Twitter Berbahasa Indonesia Menggunakan Naive Bayes Dengan Perbaikan Kata Tidak Baku Prananda Antinasari; Rizal Setya Perdana; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1242.98 KB)

Abstract

The rapid growth of social media does not make Twitter left by its users. Twitter is one of the social media that allows user to interact each other, share information, or even to express feelings and opinions, including in expressing film opinions. Comments or Tweets about movies that exist on Twitter can be used as an evaluation in watching movies and increasing film production. To figure it out, sentiment analysis can be used to classify into negative or positive sentiments. In Tweets contain many languages ​​used in the form of non-standard languages ​​such as slang, word-outs, and misspellings. Therefore it takes special handling on Twitter comments. In this research used non-standard word dictionary and Levenshtein Distance normalization to improve non-standard word to standard word by classification Naive Bayes. Based on the result of the test, the highest accuracy, precision, recall, and f-measure value are 98.33%, 96.77%, 100%, and 98.36%.
Analisis Sentimen Terhadap Tayangan Televisi Berdasarkan Opini Masyarakat pada Media Sosial Twitter menggunakan Metode K-Nearest Neighbor dan Pembobotan Jumlah Retweet Winda Estu Nurjanah; Rizal Setya Perdana; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1275.886 KB)

Abstract

Twitter is a social media that attracts many internet users as a media for communication and getting information. The information covered on Twitter in the form of questions, opinions or comments, whether it is positive or negative. Sentiment analysis is a part of research from Text Mining that conducted the classification process on text documents. K-Nearest Neighbor was used as method of this research, by adding the quality of retweet (non-textual). The result of textual quality of the K-Nearest Neighbor classification and the non-textual quality from the sum of retweets would be combined using certain constants (α and β) to generate positive and negative sentiments. The data was used in the form of public opinion on the television show on twitter showed 400. From the test results of accuracy using non-textual quality obtained 82.50%, using 60% non-textual quality, and use the combination of both was 83.33% with the score k=3 and the exact multiplication constant α=0,8 and β=0.2.
Co-Authors Abdul Jabbar Agnes Rossi Trisna Lestari Arief Andy Soebroto Arina Indana Fahma Aryo Pinandito Bana Falakhi Budi Darma Setiawan Caesar Rio Anggina Toruan Claudio Fresta Suharno Claudio Fresta Suharno Dhony Lastiko Widyastomo Dian Eka Ratnawati Dinul Wikramaditya Syah Dwi Anggraeni Kuntjoro Dwi Rahayu Dwi Wahyu Puji Lestari Dyan Dyanmita Putri Edi Santoso Erma Rafliza Faisal Aji Prayoga Faiz Aulia Al Farisi Faiz Aulia Al Farisi Fakihatin Wafiyah Farid Syauqi Nirwan Fathor Rosi Fitra Abdurrachman Bachtiar Ghenniy Rachmansyah Grady Davinsyah I Made Budi Surya Darma I Putu Bagus Arya Pradnyana Imam Cholissodin Imanuel Juventius Todo Gurning Indriati Indriati Irma Ramadanti Fitriyani Ivan Fadilla Kukuh Haryobismoko Lailil Muflikhah M Yusron Syauqi Dirgantara M. Ali Fauzi Makrina Christy Ariestyani Marine Putri Dewi Yuliana Muhammad Abdurasyid Muhammad Fathur Rahman Khairul Muhammad Maulana Solihin Hidayatullah Muhammad Mishbahul Munir Muhammad Tanzil Furqon Muhammad Wafi Multohadi Hamzaturrazak Nana Nofiana Nirmala Fa'izah Saraswati Novanto Yudistira Nur Adli Ari Darmawand Nurul Hidayat Prananda Antinasari Putra Pandu Adikara Putu Amelia Vennanda Widyaswari Putu Rama Bena Putra Randy Cahya Wihandika Resti Febriana Riska Dewi Nurfarida Riyant Fajar Rizaldy Amsyar Umi Rofiqoh Wanda Athira Luqyana Wayan Firdaus Mahmudy Wijaya Kurniawan Winda Estu Nurjanah Yessivha Imanuela Claudy Yusuf Nurcahyo Zahra Asma Annisa Zahra Asma Annisa