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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi E-Jurnal Matematika Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) YUME : Journal of Management TEKNOLOGIA TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Journal of Data Science and Its Applications Jurnal Sistem Komputer dan Informatika (JSON) eProceedings of Engineering Jurnal INFOTEL
Adiwijaya
Universitas Telkom
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics

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Dekomposisi Nilai Singular dan Discrete Fourier Transform untuk Noise Filtering pada Citra Digital Adiwijaya Adiwijaya; D. R. Suryandari; F. A. Yulianto
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2009
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Penggunaan citra digital pada saat ini telah menjadi suatu trend tersendiri. Namun, ketika dilakukan prosespengambilan gambar, seringkali terdapat noise yang masuk ke dalam citra, sehingga menyebabkan timbulnyabercak-bercak yang tidak beraturan. Jika hal ini terjadi, maka proses pengolahan citra yang akan dilakukantidak akan memberikan hasil yang optimal. Oleh karena itu, diperlukan suatu proses noise filtering untukmengurangi noise yang terdapat padanya. Pada makalah ini digunakan SVD (Singular Value Decomposition)dengan bantuan DFT (Discrete Fourier Transform) untuk mengurangi noise yang terdapat pada citra digital.Noise yang dibangkitkan untuk simulasi adalah additive Gaussian noise dan additive Laplacian noise. Denganmetode ini, matriks yang merepresentasikan citra ter-noise akan diuraikan, sehingga dapat diketahuikomponen-komponen matriks yang terpengaruh oleh noise tersebut. Dari hasil penelitian, dapat diketahuibahwa SVD dengan bantuan DFT dapat digunakan untuk mengurangi noise pada citra digital, dan ketikaparameter input yang digunakan optimal, maka kualitas citra hasil filtering yang diberikan pun lebih baikdibandingkan dengan SVD tanpa bantuan DFT.Kata Kunci: Gaussian noise, Laplacian noise, noise filtering, blok SVD, DFT
Analisis Performansi Aqm Routers yang Mendukung Aliran TCP dengan Menggunakan Pengontrol Proportional-Integral-Derivative (PID) I Kadek Haddy W.; Hafidudin Hafidudin; Adiwijaya Adiwijaya
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2006
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Active Queue Management (AQM) adalah proses penandaan source TCP dari pusat router dengan mempertimbangkan penggunaan queue dan delay. Penggunaan AQM pada router akan memegang peranan penting dalam peningkatan kinerja aplikasi-aplikasi internet. Seperti aplikasi yang termasuk didalamnya voice over IP (VoIP), class of service (CoS) dan video streaming di mana besar paket dan durasinya menunjukkan variasi yang sangat signifikan. Hal ini sesungguhnya merupakan permasalahan kontroling. Didasarkan pada pembuatan model dinamik dari TCP’s congestion-avoidance terdapat beberapa hal penting yang perlu mendapatkan perhatian, pertama parameter kunci network seperti TCP load, link capacity, dan round-trip-time yang menjadi penyebab utama masalah kontroling. Model standar AQM yang ada sekarang ini yaitu Random Early Detection (RED) sementara ini memang mampu mengatasi permasalahan TCP congestion.Dalam penelitian ini dianalisis dan disimulasikan suatu metoda AQM alternatif dengan menggunakan pengontrol Proportional-Integral-Derivative (PID). Membandingkan pengontrol Proportional-Integral-Derivative (PID) dengan metoda Random Early Detection (RED) dan Proportional Integral (PI) dengan menggunakan simulasi Network Simulator (NS) dan menunjukkan hasil bahwa pengontrol Proportional-Integral-Derivative (PID) lebih baik dalam hal throughput, paket loss dan index fairness.Kata kunci: AQM, RED, PID, PI, throughput, paket loss dan index fairness
Klasifikasi Teks Hadis Bukhari Terjemahan Indonesia Menggunakan Recurrent Convolutional Neural Network (CRNN) Muhammad Yuslan Abu Bakar; Adiwijaya Adiwijaya
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 5: Oktober 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021853750

Abstract

Hadis merupakan sumber hukum dan pedoman kedua bagi umat Islam setelah Al-Qur’an dan banyak sekali hadis yang telah diriwayatkan oleh para ahli hadis selama ini. Penelitian ini membangun sebuah sistem yang dapat melakukan klasifikasi teks hadis Bukhari terjemahan berbahasa Indonesia. Topik ini diangkat untuk memenuhi kebutuhan umat Islam dalam mengetahui apa saja informasi mengenai anjuran dan larangan yang terdapat dalam suatu hadis. Klasifikasi teks memiliki tantangannya tersendiri terkait dengan jumlah fitur yang sangat banyak (dimensi sangat besar) sehingga waktu komputasi menjadi besar dan mengakibatkan sulitnya mendapatkan hasil yang optimal. Pada penelitian ini, digunakan salah satu metode hibrid dalam dunia deep learning dengan menggabungkan Convolutional Neural Network dan Recurrent Neural Network, yaitu Convolutional Recurrent Neural Network (CRNN). Convolutional Neural Network dipilih sebagai metode seleksi dan reduksi data dikarenakan dapat menangkap informasi spasial yang saling berhubungan dan berkorelasi. Sementara Recurrent Neural Network digunakan sebagai metode klasifikasi dengan mengusung kemampuan utamanya yaitu dapat menangkap informasi kontekstual yang sangat panjang khususnya pada data sekuens seperti data teks dengan mengandalkan ‘memori’ yang dimilikinya. Hasil penelitian menyajikan beberapa hasil klasifikasi menggunakan deep learning, dimana hasil akurasi terbaik diberikan oleh Convolutional Recurrent Neural Network (CRNN), yakni sebesar 80.79%. Abstract Hadith is a source of law and guidance for Muslims after the Qur'an and many hadith have been narrated by hadith experts so far. This research builds a system that can classify Bukhari hadith in Indonesian translations. This topic was raised to meet the needs of Muslims in knowing what information about the suggestions and prohibitions that exist in a hadith. Text classification has its own challenges related to several features whose dimensions are very large so that it increases computing time and causes difficulties in getting optimal results. This research uses a hybrid method in deep learning by combining a Convolutional Neural Network and a Recurrent Neural Network, namely Convolutional Recurrent Neural Network (CRNN). Convolutional Neural Network was chosen as a method of selecting and reducing data that can be determined as spatial information that is interrelated and correlated. While Recurrent Neural Networks are used as a classification method by carrying out capabilities that can be used as very long contextual information specifically on sequential data such as text data by relying on the ‘memory’ it has. This research presents several classification results using deep learning, where the best accuracy results are given by the Convolutional Recurrent Neural Network (CRNN), which is equal to 80.79%.
Modified balanced random forest for improving imbalanced data prediction Zahra Putri Agusta; Adiwijaya Adiwijaya
International Journal of Advances in Intelligent Informatics Vol 5, No 1 (2019): March 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i1.255

Abstract

This paper proposes a Modified Balanced Random Forest (MBRF) algorithm as a classification technique to address imbalanced data. The MBRF process changes the process in a Balanced Random Forest by applying an under-sampling strategy based on clustering techniques for each data bootstrap decision tree in the Random Forest algorithm. To find the optimal performance of our proposed method compared with four clustering techniques, like: K-MEANS, Spectral Clustering, Agglomerative Clustering, and Ward Hierarchical Clustering. The experimental result show the Ward Hierarchical Clustering Technique achieved optimal performance, also the proposed MBRF method yielded better performance compared to the Balanced Random Forest (BRF) and Random Forest (RF) algorithms, with a sensitivity value or true positive rate (TPR) of 93.42%, a specificity or true negative rate (TNR) of 93.60%, and the best AUC accuracy value of 93.51%. Moreover, MBRF also reduced process running time.
ANALISIS REDUKSI DIMENSI PADA KLASIFIKASI MICROARRAY MENGGUNAKAN MBP POWELL BEALE ANDI FUTRI HAFSAH MUNZIR; , ADIWIJAYA; ANNISA ADITSANIA
E-Jurnal Matematika Vol 7 No 1 (2018)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2018.v07.i01.p179

Abstract

Cancer is the second leading cause of death in the world based on World Health Organization (WHO) survey in 2015. It took DNA microarray technology to analyze and diagnose cancer. DNA microarray has large dimensions so it influences the process of cancer ‘s classification. GA and PCA are used as reduction method and MBP Powell Beale as classification method. The testing of MBP classification without dimension reduction results 70, 59% ? 100% accuracy. MBP+PCA results 76, 47% ? 100% accuracy. MBP + GA results 76, 47% ? 92, 31% accuracy.
Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait Annas Wahyu Ramadhan; Didit Adytia; Deni Saepudin; Semeidi Husrin; Adiwijaya Adiwijaya
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 12 No 3 (2021): Vol. 12, No. 03 December 2021
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2021.v12.i03.p01

Abstract

Sea-level forecasting is essential for coastal development planning and minimizing their signi?cantconsequences in coastal operations, such as naval engineering and navigation. Conventional sealevel predictions, such as tidal harmonic analysis, do not consider the in?uence of non-tidal elementsand require long-term historical sea level data. In this paper, two deep learning approachesare applied to forecast sea level. The ?rst deep learning is Recurrent Neural Network (RNN), andthe second is Long Short Term Memory (LSTM). Sea level data was obtained from IDSL (InexpensiveDevice for Sea Level Measurement) at Sebesi, Sunda Strait, Indonesia. We trained themodel for forecasting 3, 5, 7, 10, and 14 days using three months of hourly data in 2020 from 1stMay to 1st August. We compared forecasting results with RNN and LSTM with the results of theconventional method, namely tidal harmonic analysis. The LSTM’s results showed better performancethan the RNN and the tidal harmonic analysis, with a correlation coef?cient of R2 0.97 andan RMSE value of 0.036 for the 14 days prediction. Moreover, RNN and LSTM can accommodatenon-tidal harmonic data such as sea level anomalies.
Classification of Electrocardiogram Signals using Principal Component Analysis and Levenberg Marquardt Backpropagation for Detection Ventricular Tachyarrhythmia Astrima Manik; Adiwijaya Adiwijaya; Dody Qori Utama
Journal of Data Science and Its Applications Vol 2 No 1 (2019): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/jdsa.2019.2.12

Abstract

Abstract Ventricular Tachyarrhythmia (VT) are the primary arrhythmias which are cause of sudden death. For someone who already has symptoms of VT should immediately perform an examination of one of them by using an electrocardiogram (ECG). An electrocardiogram is a recording of the heart's electrical results in a waveform. However, limited ability in analysis and diagnosis of ECG reading is still difficult to do. Therefore, the classification of ECG signals is needed to detect a person, especially those with VT or not. In this research focuses on the classification of VT heartbeats from ECG signals by using median filter method in preprocessing, Principal Component Analysis (PCA) as feature extraction and modified Backpropagation (MBP) as classification. This research used machine learning method that is a neural network with backpropagation modification that is Levenberg Marquardt to speed up network training process. The best VT detection performance results were based on the average accuracy of the overall scheme of 91.67% with the best parameters that principal component=10 and 20, hidden neuron=4, and µ value=0.001 as well training time 1 seconds with a comparison of train data and test data that is 80:20 percent. Keywords: Electrocardiogram, Levenberg Marquardt Backpropagation, Median filter, Principal Component Analysis, and Ventricular Tachyarrhythmia
Sentiment Analysis on Movie Reviews using Information Gain and K-Nearest Neighbor Novelty Octaviani Faomasi Daeli; Adiwijaya Adiwijaya
Journal of Data Science and Its Applications Vol 3 No 1 (2020): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/jdsa.2020.3.22

Abstract

The huge resources need effectiveness and efficiency, it can be processed by machine learning. There have been many studies conducted using machine learning method and produced quite good performance in sentiment analysis. Some machine learning methods that are often used in general are Naive bayes (NB), K-nearest neighbor (KNN), Support vector machine (SVM), and Random forest methods. Mostly, KNN did not achieve better performance than other machine learning methods in sentiment analysis. In this study, the Polarity v2.0 from Cornell movie review dataset will be used to test KNN with Information gain features selection in order to achieve good performance. The purpose of this research are to find the optimum K for KNN and compare KNN with other methods. KNN with the help of Information gain feature selection becomes the best performance method with 96.8% accuracy compared to the NB, SVM, and Random forest while the optimum K is 3.
Sentiment Analysis of Movie Review using Naïve Bayes Method with Gini Index Feature Selection Riko Bintang Purnomoputra; Adiwijaya Adiwijaya; Untari Novia Wisesty
Journal of Data Science and Its Applications Vol 2 No 2 (2019): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/jdsa.2019.2.36

Abstract

In movie reviews, there is information that determines whether the movie is good or bad. Sentiment analysis is used to process information to determine the polarity of the sentence. With unstructured reviews and a lot of data attributes so that it requires much time and computational capabilities that become a problem in the classification process. To process a lot of data selection features becomes a solution to reduce dimensions so it accelerate the classification process and reduce the occurrence of misclassification. The first Gini Index Text feature selection used to classify documents and successfully enhanced the classifier performance. Multinomial Naïve Bayes (MNNB) is a popular classifier used for document classification however, will the Gini Index Text feature selection able to improve MNNB classification performance. Therefore in this study the author aims to use the Gini Index Text (GIT) for text feature selection with MNNB classifier to classify movie review into positive and negative classes. The data used is IMDB dataset that contains reviews in English sentences, the data will be divided into two parts, training data is 90% and data testing is 10%. The test results prove that the Gini index as a selection feature can increase accuracy where accuracy without feature selection is 56% and with feature selection of 59.54% with an increase of 3.54%.
Cancer Detection based on Microarray Data Classification Using Principal Component Analysis and Functional Link Neural Network Iyon Priyono; Adiwijaya Adiwijaya; Annisa Aditsania
Journal of Data Science and Its Applications Vol 3 No 2 (2020): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/jdsa.2020.3.52

Abstract

Cancer is a deadly disease caused by abnormal growth of tissue cells that are not controlled in the body. In 2018, according to Globocan data, the number of cancer sufferers has increased from the previous years which was 18.1 million people, with a mortality rate of 9.6 million. In recent years, cancer prediction using DNA microarrays data can help medical experts in analyzing whether a person has cancer or not. DNA microarray data have very large and complex gene expression, therefore a dimensional reduction method is needed. Then, the dimension reduction results will be used for classification into types of cancer or not. In this paper, Principal Component Analysis (PCA) is used as a feature extraction to reduce dimension and Functional Link Neural Network as a classifier. Based on the simulation, the average of accuracy using the FLNN and PCA about 76.08%. Keywords: cancer detection, Microarray data, Functional Link Neural Network, Principal Component Analysis.
Co-Authors A Rakha Ahmad Taufiq Ade Iriani Sapitri Adhitia Wiraguna Adhitia Wiraguna Aditya Arya Mahesa Adnan Imam Hidayat Adwin Rahmanto Afrian Hanafi Al Faraby, Said Al Mira Khonsa Izzaty Alvi Syah Amalya Citra Pradana Andi Ahmad Irfa ANDI FUTRI HAFSAH MUNZIR Andina Kusumaningrum Andri Saputra Andrian Fakhri Andriyan B Suksmono Anggitha Yohana Clara Aniq Atiqi Aniq Atiqi Rohmawati Anisa Salama Annas Wahyu Ramadhan Annisa Adistania Annisa Aditsania Aras Teguh Prakasa Astrid Frillya Septiany Astrima Manik Azmi Hafizha Rahman Zainal Arifin Bambang Riyanto T. Bayu Julianto Bayu Munajat Bayu Munajat Bernadus Seno Aji Bernadus Seno Aji Bintang Peryoga Bisma Pradana Brama Hendra Mahendra Chiara Janetra Cakravania Clarisa Hasya Yutika D. R. Suryandari Dana Sulistiyo Kusumo Danang Triantoro Danang Triantoro Murdiansyah Daniel Tanta Christopher Sirait Dany Dwi Prayoga Dany Dwi Prayoga Della Alfarydy Akbar Deni Saepudin Denny Alriza Pratama Desi Sitompul Dewangga, Dhiya Ulhaq Dian Chusnul Hidayati Didi Rosiyadi Didit Adytia Dinda Karlia Destiani Dody Qori Utama Dody Qory Utama Dwi Yanita Apriliyana Dwi Yanita Apriliyana Eliza Jasin Elza Oktaviana Elza Oktaviana Ergon Rizky Perdana Purba F. A. Yulianto Faris Alfa Mauludy Faris Alfa Mauludy Farudi Erwanda Farudi Erwanda Fathur Rohman Fathurrohman Elkusnandi Fhira Nhita Fikri Rozan Imadudin Firda A. Ma’ruf Firdausi Nuzula Zamzami Fuad Ash Shiddiq Gde Agung Brahmana Suryanegara Ghozy Ghulamul Afif Gia Septiana Gia Septiana Gia Septiana Gilang Rachman Perdana Gilang Rachman Perdana Gilang Titah Ramadhani Grace Tika Guntoro Guntoro Guntoro Guntoro Guntoro Guntoro Hadyan Arif Hafidudin . Hafizh Fauzan Hendro Prasetyo Henri Tantyoko Honakan Honakan I Kadek Haddy W. I Made Riartha Prawira I.G.N.P.Vasu Geramona Ilham Kurnia Syuriadi Ilham Yunirakhman Indriani Indriani Irene Yulietha Irma Irma Irwinda Famesa Iyon Priyono Jendral Muhamad Yusuf Zia Ul Haq Jenepte Wisudawati Simanullang Kamal Hasan Mahmud Kemas Muslim Lhaksmana, Kemas Muslim Kemas Rahmat Saleh Raharja Kemas Rahmat Saleh Wiharja Kurnia C Widiastuti Kurniawan W. Handito Laila Putri Lalu Gias Irham Lisa Marianah Lisa Marianah Luke Manuel Daely Mahendra Dwifebri P Mahendra Dwifebri Purbolaksono Melanida Tagari Melanida Tagari Michael Sianturi Milah Sarmilah Moc. Arif Bijaksana Mochamad Agusta Naofal Hakim Mochammad Naufal Rizaldi Mohamad Irwan Afandi Mohamad Mubarok Mohamad Syahrul Mubarok Mohammad Syahrul Mubarok Monica Triyani Muhammad Afianto Muhammad Enzi Muzakki Muhammad Fauzan Muhammad Feridiansyah Muhammad Ghufran Muhammad Irvan Tantowi Muhammad Kenzi Muhammad Mubarok Muhammad Mujaddid Muhammad Naufal Mukhbit Amrullah Muhammad Nurjaman Muhammad Shiddiq Azis Muhammad Shiddiq Azis Muhammad Surya Asriadie Muhammad Syahrul Mubarok Muhammad Yuslan Abu Bakar Muhammad Yuslan Abu Bakar Nanda Prayuga Nida Mujahidah Azzahra Nida Mujahidah Azzahra Niken Dwi Wahyu Cahyani Novelty Octaviani Faomasi Daeli Novia Russelia Wassi Nuklianggraita, Tita Nurul Oscar Ramadhan Pratama Dwi Nugraha Preddy Desmon Putri, Dinda Rahma Raihana Salsabila Darma Wijaya Reynaldi Ananda Pane Riche Julianti Wibowo Riko Bintang Purnomoputra Riska Chairunisa Rizki Syafaat Amardita Rizky Pujianto Rizma Nurviarelda Roberd Saragih Rosyadi, Ramadhana Said Al-Faraby Said Faraby Sekar Kinasih Semeidi Husrin Sheila Annisa Shidqi Aqil Naufal Shuni’atul Ma’wa Sigit Bagus Setiawan Sugeng Hadi Wirasna Suriyanti Suriyanti Syahrizal Rizkiana Rusamsi Syifa Khairunnisa Talitha Kayla Amory Tati LR Mengko Tesha Tasmalaila Hanif Timami Hertza Putrisanni Tita Nurul Nuklianggraita Triyani, Monica Try Moloharto Untari Novia Wisesty Untari Wisesty Untari. N. Wisesty Untary Novia Wisesty Vina Mutiara Purnama Widi Astuti Widi Astuti Winda Christina Widyaningtyas Wisnu Adhi Pradana Yana Meinitra Wati Yoga Widi Pamungkas Yuliant Sibaroni Zahra Putri Agusta Zakia Firdha Razak Zulfikar Fauzi