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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 Journal of Information Systems Engineering and Business Intelligence Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) JOIV : International Journal on Informatics Visualization 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 Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Data Science and Its Applications Jurnal Sistem Komputer dan Informatika (JSON) Journal of Applied Data Sciences Mosharafa: Jurnal Pendidikan Matematika Indonesian Journal of Social Science Research Open Science and Technology eProceedings of Engineering Innovative: Journal Of Social Science Research AMMA : Jurnal Pengabdian Masyarakat Jurnal INFOTEL
Adiwijaya
Universitas Telkom
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Deteksi Kanker Berdasarkan Klasifikasi Microarray Data Adiwijaya Adiwijaya
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 2, No 4 (2018): Oktober 2018
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v2i4.1043

Abstract

Cancer is one of the diseases that can cause human death in the world and become the biggest cause of death after heart disease. Therefore we need a DNA microarray technology which is used to examine how gene expression patterns change under different conditions, so that the technology is able to detect a person with cancer or not with accurate analysis. The size of the dimension in the microarray data can affect the gene expression analysis that is used to find informative genes, for that we need a good method of dimension reduction and classification so that it can get the best results and accuracy. Many techniques can be applied in DNA microarray, one of them is BPNN Back Propagation Neural Network as a classification and PCA as dimension reduction, where both have been tested in several previous studies. By applying BPNN and PCA on several types of cancer data, it was found that BPNN and PCA get more than 80% accuracy results with training time 0-4 seconds.
Deteksi Kanker Berdasarkan Data Microarray Menggunakan Metode Naïve Bayes dan Hybrid Feature Selection Bintang Peryoga; Adiwijaya Adiwijaya; Widi Astuti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 3 (2020): Juli 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i3.2096

Abstract

Cancer is a deadly disease that is responsible for 9.6 million death in 2018 based on WHO data so early cancer detection is needed so can be treated immediately and cancer deaths can be reduced. Microarray is technology that can monitor and analyze the expression of cancer genes in microarray data but has high data dimension and small sample so dimensional reductions are needed for the optimal classification process. Dimension reduction can reduce the use of features for the classification process by selecting some influential features. Hybrid method is one dimension reduction by combining Filter method with Wrapper so it gets the both advantage. In this case, researchers combined Naïve Bayes with Hybrid Feature Selection (Information Gain - Genetic Algorithm) on cancer data for microarray Lung Cancer, Ovarian Cancer, Breast Cancer, Colon Tumors, and Prostate Tumors. These data were obtained from Kent-Ridge Biomedical Dataset. The results showed that from 5 data used, 4 data obtained an accuracy between 87-100% while the prostate tumor data obtained the smallest accuracy of 61.14%. The implementation of the feature selection method and the classification of the 5 cancer data above only uses less than 63 features to obtain this accuracy
Analisis Sentimen Berbasis Aspek pada Review Female Daily Menggunakan TF-IDF dan Naïve Bayes Clarisa Hasya Yutika; Adiwijaya Adiwijaya; Said Al Faraby
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 2 (2021): April 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i2.2845

Abstract

The results of a product review will provide considerable benefits for producers or consumers. Female daily is a forum that discusses beauty products. There are many reviews that are obtained every day. Therefore a technique is needed to analyze the results of the review into valuable information. One of the techniques is aspect-based sentiment analysis. Aspect-based sentiment analysis will analyze each text to identify various aspects (attributes or components) then determine the level of sentiment (positive, negative, or neutral) that is appropriate for each aspect. From the results obtained, there are reviews that use multilingual languages. Then the steps taken are to translate the multilingual language into one language only, namely Indonesian. Before the review is processed, preprocessing will be carried out to make it easier to process. Then the word weighting is done using TF-IDF, and the method for classifying sentiments that will be used is Complement Naïve Bayes to overcome unbalanced data. From the test results obtained the best F1-Score of 62,81% for data translated into English and then into Indonesian and not using stopword removal
Pengklasifikasian Topik Hadits Terjemahan Bahasa Indonesia Menggunakan Latent Semantic Indexing dan Support Vector Machine Hafizh Fauzan; Adiwijaya Adiwijaya; Said Al-Faraby
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 2, No 4 (2018): Oktober 2018
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v2i4.948

Abstract

Hadith is used as the source of Islamic law othen than Qur’an, Ijma, Ijtihad and Qiyas, hadith is the second of Islamic law after the Qur’an. This study attempted to build a system than can classify shahih hadith of Bukhari in Indonesian Translation. This topic was chosen to help Muslims who want to understand from each hadith is in the form of informations, prohibitions or suggestion. Support Vector Machine was chosen because it can perform classification by providing good performance for dataset with a large number of features. Latent Semantic Indexing as a feature selection method was chosen because it can reduce features by eliminationg unimportant features (noise term). This study also using Bootstrap Aggregating (Bagging) method to improve accuracy of the classification system. The accuracy results show that by using Latent Semantic Indexing and Bootstrap Aggregating on Support Vector Machine classification single label system is 84% on polynomial kernel and 84.67% on RBF kernel
Klasifikasi Argument Pada Teks dengan Menggunakan Metode Multinomial Logistic Regression Terhadap Kasus Pemindahan Ibu Kota Indonesia di Twitter Mochammad Naufal Rizaldi; Adiwijaya Adiwijaya; Said Al Faraby
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 4 (2020): Oktober 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i4.2348

Abstract

Information on moving the Indonesian capital from Jakarta to East Kalimantan certainly raises the pros and cons conveyed by the Indonesian people through the Twitter social network. However, the pros and cons comments are of course varied, accompanied or not accompanied by arguments or even completely unrelated to the topic under discussion. User limitations in filtering out that information will certainly make it difficult for the public or even the government to analyze the information contained in the tweet. Therefore, a system was built that could classify tweets automatically into three classes, namely non-arbitration, argument and unknown. The method used in this research is Multinomial Logistic Regression (MLR). MLR is a generalization method of Logistic Regression and is used to classify 3 or more classes. Before the classification process is carried out, the tweet must be preprocessed in order to make the tweet clear of all existing noise. Feature extractions used in this study include unigram, bigram and trigram. In this study, there are 12 test scenarios and comparison methods, namely Artificial Neural Network (ANN). Of all the test scenarios the best results for the MLR method are SRU with an accuracy of 41,30%, while for the ANN method namely the RU scenario with an accuracy of 45,10%.
SISTEM KONTROL UMPAN BALIK UNTUK ALIRAN TCP PADA ROUTER SUATU JARINGAN KOMPUTER Adiwijaya Adiwijaya; Roberd Saragih; Bambang Riyanto T.
TEKTRIKA Vol 8 No 2 (2003)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v8i2.226

Abstract

Perkembangan jaringan komputer yang pesat saat ini disertai pula dengan berbagai permasalahannya. Salah satunya adalah masalah kongesti. TCP (Transmission Control Protocol) merupakan protokol yang dapat mendeteksi kehilangan paket dan menginterpretasikan kehilangan tersebut sebagai indikasi telah terjadi kongesti pada jaringan. Makalah ini membahas tentang penghindaran kongesti dengan menggunakan mekanisme Active Queue Management (AQM). Langkah pertama yang dilakukan adalah membangun model matematika perilaku aliran TCP. Selanjutnya, menganalisis model tersebut sehingga masalah AQM dapat dipandang sebagai masalah kontrol umpan balik. Akhirnya, dengan menggunakan aproksimasi Padé untuk masalah delay, kontrol umpan balik disimulasikan dengan menggunakan pengontrol proporsional–integral–turunan (PID) sebagai pengontrol AQM.Kata Kunci : kontrol umpan balik, AQM, kontrol kongesti end-to-end, aproksimasi Padé, pengontrol PID
KLASIFIKASI AYAT AL-QURAN TERJEMAHAN BAHASA INGGRIS MENGGUNAKAN K-NEAREST NEIGHBOR (KNN) DAN INFORMATION GAIN Timami Hertza Putrisanni; Adiwijaya Adiwijaya; Said Al Faraby
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 3, No 1 (2019): Smart Device, Mobile Computing, and Big Data Analysis
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v3i1.1614

Abstract

Al-Quran is a holy book that contains instructions and instructions for the life of Muslims. In the Al-Quran there are interpretations quoted from the previous verse and have an implied meaning, so to be able to obtain these verses textually and contextually it is necessary to classify the interpretation of the Al-Quran to facilitate Muslims in finding topics in theAl-Quran. In this study, it is proposed to classify the topic of Al-Quran verses in English translation which consists of three classifications, namely commands, prohibitions and others. In this research the system design is done by collecting datasets, preprocessing to get clean data, selecting features using gain information, classifying using the K-Nearest Neighbor (KNN) method, and testing the system. The results of the tests conducted resulted in a value 64,10% for accuracy, 63% for precision, and 62.68% for recall using the value of k = 17 and the dataset containing data testing and data training of 1:9, respectively.Keywords: classification, topics of Al-Quran, K-Nearest Neighbor, Information gain.
Klasifikasi Topik Multi Label pada Hadis Shahih Bukhari Menggunakan K-Nearest Neighbor dan Latent Semantic Analysis Dian Chusnul Hidayati; Said Al Faraby; Adiwijaya Adiwijaya
JURIKOM (Jurnal Riset Komputer) Vol 7, No 1 (2020): Februari 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (236.258 KB) | DOI: 10.30865/jurikom.v7i1.2013

Abstract

Hadith is the second source of Islamic law after Al-Quran, making it important to study. However, there are some difficulties in learning hadith, such as to determine which hadith belongs to the topic of suggestions, prohibitions, and information. This certainly obstructs the hadith learning process, especially for Muslims. Therefore, it is necessary to classify hadiths into the topic of suggestions, prohibitions, information, and a combination of the three topics which also called as multi-label topic. The classification can be done with the K-Nearest Neighbor, it is one of the best methods in the Vector Space Model and is the simplest but quite effective method. However, the KNN has a lack in dealing with high vector dimension, resulting in the long time computing classification. For that reason, it is necessary to classify Sahih Bukhari's Hadiths into the topic of recommendations, prohibitions, and information using the Latent-Semantic Analysis - K-nearest Neighbor (LSA-KNN) method. Binary Relevance method is also employed in this research to process the multi-label data. This research shows that the performance of LSA-KNN is 90.28% with the computation time is 19 minutes 21 seconds and the performance of KNN is 90.23% with the computation time is 37 minutes 06 seconds, which means that the LSA-KNN method has a better performance than KNN
Analisis Perbandingan Klasifikasi Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN) untuk Deteksi Kanker dengan Data Microarray Shidqi Aqil Naufal; Adiwijaya Adiwijaya; Widi Astuti
JURIKOM (Jurnal Riset Komputer) Vol 7, No 1 (2020): Februari 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (308.658 KB) | DOI: 10.30865/jurikom.v7i1.2014

Abstract

Cancer is a disease that can cause human death in various countries. According to WHO in 2018, cancer causes 9.6 million human deaths worldwide. Globally, about 1 in 6 deaths is due to cancer. Therefore, we need a technology that can be used for cancer detection with high acuration so that cancer can be detected early. Microarrays technique can predict certain tissues in humans and can be classified as cancer or not. However, microarray data has a problem with very large dimensions. To overcome this problem, in this study use one of the dimension reduction techniques, namely Partial Least Square(PLS) and use Support vector Machine (SVM) and K-Nearest Neighbors as a classification method, which will be used to compare which is better.The system built was able to reach 98.54% in leukemia data with PLS-KNN, 100% in lung data with KNN, 66.52% in breast data with PLS-KNN, and 85.60% in colon data with PLS- SVM. KNN is able to get the best in three data from four valued data.
Analisis Sentimen terhadap Ulasan Paris Van Java Resort Lifestyle Place di Kota Bandung Menggunakan Algoritma KNN Rizki Syafaat Amardita; Adiwijaya Adiwijaya; Mahendra Dwifebri Purbolaksono
JURIKOM (Jurnal Riset Komputer) Vol 9, No 1 (2022): Februari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i1.3793

Abstract

As the times move forward, technologies are following to develop with the times, especially internet technology. Nowadays, people can find out information about shopping center via the Internet. One of the best-known shopping centers in Bandung is Paris van Java resort lifestyle place. The person's consideration of going to the shopping center is based on the location and opinions of the person who once visited that shop. With a huge amount of opinion and an underlying amount of information that is of great value causes the information to become dangerous if the person misunderstood it. With all these problems, sentiment analysis one of the solution that can prevent the problems of these misunderstanding, in which sentiment analysis works to analyze each text to determine the level of positive or negative sentiment values. In this study, the sentiment analysis dataset goes first to the preprocessing stage, extraction of the Term Frequency-Inverse Document Frequency (TF-IDF) feature then classified using the K-Nearest Neighbor method, The K-Nearest Neighbor method was chosen because this method is one method that can classify text and data, besides K-Nearest neighbor method has a good classification accuracy where the classification process is easy and quite simple in its implementation. With a system that built using TF-IDF Unigram and Euclidean Distance, the best accuracy value is 88.29%.
Co-Authors A Rakha Ahmad Taufiq Abu Bakar, Muhammad Yuslan Ade Iriani Sapitri Ade Sumiahadi, Ade Adhitia Wiraguna Adhitia Wiraguna Aditya Arya Mahesa Adnan Imam Hidayat Adwin Rahmanto Afrian Hanafi Al Faraby, Said Al Mira Khonsa Izzaty Alfian Akbar Gozali Alvi Syah Amalya Citra Pradana Amir 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 Antika Putri Permata Wardani Aras Teguh Prakasa Astrid Frillya Septiany Astrima Manik Aziz, Muhammad Maulidan Azmi Hafizha Rahman Zainal Arifin Bambang Riyanto T. Bayu Julianto Bayu Munajat Bayu Munajat Bayu Rahmat Setiaji 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 Dwifebri, Mahendra Eko Darwiyanto Eliza Jasin Elza Oktaviana Elza Oktaviana Endro Ariyanto Ergon Rizky Perdana Purba F. A. Yulianto Fachri Pane, Syafrial Fahmi Salman Nurfikri 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 Firly Juanita Surahman 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 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 Imadudin, Fikri Rozan Imam Prayoga Indriani Indriani Irene Yulietha Irma Irma Irma Palupi Irwinda Famesa Iyon Priyono Jendral Muhamad Yusuf Zia Ul Haq Jenepte Wisudawati Simanullang K, Kasnaeny Kamal Hasan Mahmud Kemas Muslim Lhaksmana 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 Mahmud Dwi Sulistiyo 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 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 Nanda Prayuga Nida Mujahidah Azzahra Nida Mujahidah Azzahra Niken Dwi Wahyu Cahyani Novelty Octaviani Faomasi Daeli Novia Russelia Wassi Nuklianggraita, Tita Nurul Nur Ghaniaviyanto Ramadhan Oscar Ramadhan Pinem, Joshua Pratama Dwi Nugraha Preddy Desmon Purbalaksono, Mahendra Dwifebri Putri, Dinda Rahma Putri, Dita Julaika Raihana Salsabila Darma Wijaya Rendi Kustiawan Reynaldi Ananda Pane Riche Julianti Wibowo Riko Bintang Purnomoputra Riska Chairunisa Rizki Syafaat Amardita Rizky Pujianto Rizma Nurviarelda Roberd Saragih Rosyadi, Ramadhana Said Faraby Satria Mandala Sekar Kinasih Semeidi Husrin Sheila Annisa Shidqi Aqil Naufal Shuni’atul Ma’wa Sigit Bagus Setiawan St.Sukmawati S. Sugeng Hadi Wirasna Suriyanti Suriyanti Syafrial Fachri Pane, Syafrial Fachri Syahrizal Rizkiana Rusamsi Syam, Mukhlisah 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 Warih Maharani Widi Astuti 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