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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jupiter Jurnal INKOM PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal technoscientia Jurnal Intelektualita: Keislaman, Sosial, dan Sains POSITIF Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) SMATIKA KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOIN (Jurnal Online Informatika) Jurnal Ilmiah KOMPUTASI Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Syntax Literate: Jurnal Ilmiah Indonesia CogITo Smart Journal Jurnal Ilmiah Matrik INOVTEK Polbeng - Seri Informatika Jusikom : Jurnal Sistem Komputer Musirawas JURNAL INSTEK (Informatika Sains dan Teknologi) IRJE (Indonesian Research Journal in Education) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Informatika Universitas Pamulang Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Informasi MURA Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Ilmiah Media Sisfo J-SAKTI (Jurnal Sains Komputer dan Informatika) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Informatika Global EDUMATIC: Jurnal Pendidikan Informatika JUSIM (Jurnal Sistem Informasi Musirawas) Jurnal Tekno Kompak Jurnal Mantik Jurnal Muara Ilmu Ekonomi dan Bisnis Journal of Information Systems and Informatics Zonasi: Jurnal Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknologi Informatika dan Komputer JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Djtechno: Jurnal Teknologi Informasi KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian kepada Masyarakat Bina Darma Jurnal Locus Penelitian dan Pengabdian Jurnal Bina Komputer Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) Jurnal Ilmiah Ilmu Terapan Universitas Jambi International Journal of Advanced Science Computing and Engineering Innovative: Journal Of Social Science Research Bulletin of Social Informatics Theory and Application Jurnal Teknologi Informasi Mura Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Edi Surya Negara
Universitas Bina Darma
Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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IMPLEMENTASI MANAGEMENT NETWORK SECURITY PADA LABORATORIUM CISCO UNIVERSITAS BINA DARMA Negara , Edi Surya
JURNAL ILMIAH MATRIK MATRIK Vol.16 No.1 April 2014
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (383.527 KB)

Abstract

The development of computer network technology as a medium for data communications to this increase. The need for the use of shared resources on the network both software and hardware has resulted in the emergence of a variety of network technology development itself. Along with the high level of need and the increasing number of network users who want some form of network that can deliver maximum results, in terms of both efficiency and increase network security itself, the improvement efforts continue to be made by the various parties. One measure of the quality of the computer network management and network security is good. By it because they needed a real action to create a system management and network security is good. So with the management and security of the network that will either guide to good computer network.
KAJIAN TERHADAP TOOLS DAN FRAMEWORK SOCIAL MEDIA ANALYTICS UNTUK PEMANFAATAN DATA SOCIAL MEDIA DALAM PENELITIAN ILMU SOSIAL Negara, Edy Surya
Jurnal Teknologi Technoscientia Vol 9, No 2 (2017): Vol 9 No 2 Februari 2017
Publisher : IST AKPRIND YOGYAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The increasing of using social media in Indonesia has the impact to the available of data about society perception toward many issues of life, even though it comes as member/personality of live and citizen in the society. These phenomena have given an opportunity for stakeholders, such as government and private that need information about the society perception or social attitudes in their social life to improve quality and quantity of information through the using of the social media. The well understanding about technology of internet, social media, databases, data structure, theories of information, data mining, machine learning, and the technique for visualization data and information are needed to get and analyze the data to determine certain information which will be used by the users (stakeholders of the information). The target of this research is to master technology of social media analytic and develop prototype of software that will be used as tools of collecting data in social media, therefore, the users of the data can focus to get comprehension/understanding about social phenomena and take decision without looking for techniques for collecting and data analysis issues. This research is designed into explanatory with focus to the understanding technology through basis of social media, moreover, it is used to review advantages and disadvantages of techniques currently used in social media and media analytic researches. The result of this research is the tools and framework that benefit for media social analysis.
Analisis Data Twitter: Ekstraksi dan Analisis Data G eospasial Negara, Edi Surya; Andryani, Ria; Saksono, Prihambodo Hendro
INKOM Journal Vol 10, No 1 (2016)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (601.355 KB) | DOI: 10.14203/j.inkom.433

Abstract

Data geospasial pada media sosial Twitter dapat dimanfaatkan untuk mengetahui informasi spasial (lokasi) yang merupakan lokasi sumber munculnya persepsi publik terhadap sebuah isu di media sosial. Besarnya produksi data geospasial yang dihasilkan oleh Twitter memberikan peluang besar untuk dapat dimanfaatkan oleh berbagai pihak sehingga menghasilkan informasi yang lebih bernilai melalui proses Twitter Data Analytics. Proses pemanfaatan data geospasial Twitter dimulai dengan melakukan proses ekstraksi terhadap informasi spatial berupa titik koordinat pengguna Twitter. Titik koordinat pengguna Twitter didapatkan dari sharing location yang dilakukan oleh pengguna Twitter. Untuk mengekstrak dan menganalisis data geospasial pada Twitter dibutuhkan pengetahuan dan kerangka kerja tentang social media analytics (SMA). Pada penelitian ini dilakukan ekstraksi dan analisis data geospasial Twitter terhadap suatu isu publik yang sedang berkembang dan mengembangakan prototipe perangkat lunak yang digunakan untuk mendapatkan data geospasial yang ada pada Twitter. Proses ekstraksi dan analisis dilakukan melalui empat tahapan yaitu: proses penarikan data (crawling), penyimpanan (storing), analisis (analyzing), dan visualisasi (vizualizing). Penelitian ini bersifat exploratory yang terfokus pada pengembangan teknik ekstrasi dan analisis terhadap data geospasial twitter
Analysis and Implementation Machine Learning for YouTube Data Classification by Comparing the Performance of Classification Algorithms Amanda, Riyan; Negara, Edi Surya
JOIN (Jurnal Online Informatika) Vol 5, No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.505

Abstract

Every day, people around the world upload 1.2 million videos to YouTube or more than 100 hours per minute, and this number is increasing. The condition of this continuous data will be useless if not utilized again. To dig up information on large-scale data, a technique called data mining can be a solution. One of the techniques in data mining is classification. For most YouTube users, when searching for video titles do not match the desired video category. Therefore, this research was conducted to classify YouTube data based on its search text. This article focuses on comparing three algorithms for the classification of YouTube data into the Kesenian and Sains category. Data collection in this study uses scraping techniques taken from the YouTube website in the form of links, titles, descriptions, and searches. The method used in this research is an experimental method by conducting data collection, data processing, proposed models, testing, and evaluating models. The models applied are Random Forest, SVM, Naive Bayes. The results showed that the accuracy rate of the random forest model was better by 0.004%, with the label encoder not being applied to the target class, and the label encoder had no effect on the accuracy of the classification models. The most appropriate model for YouTube data classification from data taken in this study is Naïve Bayes, with an accuracy rate of 88% and an average precision of 90%.
Pengembangan Model Untuk Prediksi Tingkat Kelulusan Mahasiswa Tepat Waktu dengan Metode Naïve Bayes Qisthiano, M Riski; Kurniawan, Tri Basuki; Negara, Edi Surya; Akbar, Muhammad
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3030

Abstract

Many parameters affect the timeliness of student graduation, starting from the student's interest in certain majors, the type of class chosen, to the grades for each semester obtained. This is a determining factor in how students can graduate on time or not at the end of their education. So a model is needed to predict student graduation rates on time, using alumni data whose data is obtained from several universities in Palembang City. The model used is a Naïve Bayes algorithm which serves as a model for classification. The dataset used is alumni data that has been collected from several universities, while the attributes used are the Department, College, Class Type, Temporary IP Value from semester 1 to 4, graduation year, and college generation. Then from the attributes and models used, the researcher used the Python 3 programming language and the Jupyter Notebook tools to process the prepared dataset. Furthermore, the distribution of the dataset is divided by 70% for training data and 30% for testing data. To test the algorithmic process used by researchers using K-Fold Validation. The results of this study are the accuracy of the prediction model carried out, where the accuracy results obtained from the Python 3 programming language and the Naïve Bayes algorithm are 0.8103.
Analisa Rekam Medis Elektronik Untuk Menentukan Diagnosa Medis Dalam Kategori Bab ICD 10 Menggunakan Machine Learning Amin, Zulius Akbar; Cholil, Widya; Herdiansyah, M. Izman; Negara, Edi Surya
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 7 No 2 (2021): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v7i2.1140

Abstract

Based on observations of the business process flow at the Siti Fatimah Hospital, the background for this study was the medical record document and ICD-10 code which was carried out manual diagnosis, making it difficult for the medical record section in the proper and fast CHAPTER arrangement of the ICD-10 code. The International Statistical Classification of Diseases and Related Health Problems (ICD) can be used to calculate or record a valid patient history of hospitalization. The Cross-Industry Standard Process For Data Mining (CRISP-DM) method is used in this study to become a strategy to describe the problem in general from the domain or research unit. While the machine learning algorithm for multiclass classification uses the Naïve Bayes algorithm, Support Vector Machine, Logistic Regression to create a diagnostic model for medical action. This study predicts ICD-10 chapter categories from medical action records from electronic medical records. With this research, it is hoped that machine learning can facilitate the medical record section in predicting the ICD-10 chapter category by analyzing electronic medical record data using the Chapter ICD-10 Decision Support System information system
A comparison between deep learning, naïve bayes and random forest for the application of data mining on the admission of new students Nurhachita Nurhachita; Edi Surya Negara
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp324-331

Abstract

The process of admitting new students at Universitas Islam Negeri Raden Fatah each year produces a lot of new student data. So that there is an accumulation of student data continuously. The purpose of this study is to compare deep learning, naïve bayes, and random forest on the admission of new students as well as being one of the bases for making decisions to determine the promotion strategy of each study program. The data mining method used is knowledge discovery in database (KDD). The tools used are rapid miner. The attributes used are student ID number, name, program study, faculty, gender, place of birth, date of birth, year of entry, school origin, national examination, type of payment, and nominal payment. The new student data used from 2016 to 2019 was an 18.930 item. The results of this study used deep learning bayes results resulted in an accuracy value of 52.65%, naïve bayes results resulted in an accuracy value of 99.79%, and random forest results resulted in an accuracy value of 44.65%.
ANALISIS LAYANAN TI PADA DOMAIN SERVICE OPERATION DENGAN MENGGUNAKAN FRAMEWORK ITIL V3 Winata Nugraha; Edi Surya Negara
JUSIM (Jurnal Sistem Informasi Musirawas) Vol 6 No 2 (2021): JUSIM (Jurnal Sistem Informasi Musirawas) DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (597.037 KB) | DOI: 10.32767/jusim.v6i2.1476

Abstract

Operasional bisnis PT. PLN (Persero) ULP sudah memanfaatkan TI dalam meberikan pelayanannya yang berupa sistem informasi berbasis website, layanan yang diberikannya seperti pelayanan online pemasangan listrik baru, penambahan daya listrik dan penyambungan sementara. Namun dalam penerapan operasional layanan TI yang berjalan belum sepenuhnya mengarah pada satu pengelolaan yang mengacu pada pedoman manajemen TI. Untuk memaksimalkan kinerja layanan TI, implementasi dari manajemen insiden dan masalah dengan kerangka kerja Information Technology Infrastructure Library (ITIL) merupakan salah satu solusi yang dibutuhkan untuk meningkatkan kualitas layanan TI di PT. PLN (Persero) ULP Lubuklinggau. Domian pada framerwork ITIL V3 yang digunakan dalam penelitian ini adalah domain service operation. Hasil yang didapatkan menunjukkan bahwa tingkat kematangan dari proses event management, incident management, dan problem management berada pada level 3 atau Defined serta request fulfillment berada pada level 2 atau Repeatable. Dengan nilai 3,06 untuk event management, nilai 3,12 untuk incident management, nilai 2,54 untuk request fulfillment, nilai 3,24 untuk problem management.
EVALUASI PEMANFAATAN INTERNET DESA DI KABUPATEN MUSI RAWAS (STUDI KASUS DESA NGADIREJO) Muhammad Cahyono; Dedy Syamsuar; Linda Atika; Edi Surya Negara; Yessi Novaria Kunang
JUSIM (Jurnal Sistem Informasi Musirawas) Vol 6 No 2 (2021): JUSIM (Jurnal Sistem Informasi Musirawas) DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (321.857 KB) | DOI: 10.32767/jusim.v6i2.1478

Abstract

Desa Ngadirejo merupakan salah satu desa yang mendapat bantuan program Desa Broadband dari Kementerian Kominfo, desa Ngadirejo mendapat bantuan internet melalui Program Desa Broadband Terpadu (DBT) dengan Kapasitas Bandwidth 5 mbps karena menjuarai lomba Desa Wisata Tingkat Nasional pada Tahun 2016, tetapi pada kenyataannya keberadaan program DBT di Desa Ngadirejo masih belum sesuai dengan harapan, pemanfaatan internet yang disediakan masih kurang maksimal serta keberadaan DBT belum maksimal dalam mendukung kinerja pemerintahan desa dalam memberikan pelayanan masyarakat hal ini karena bandwidth yang diberikan tersebut masih belum mencukupi, sehingga sebagian besar masyarakat belum bisa menikmati keberadaan DBT, selain itu masalah lainnya adalah jangkauan sinyal masih terpusat pada lokasi tertentu saja. Maka dari itu tujuan dari penelitian ini adalah untuk melakukan suatu kajian yang lebih mendalam tentang pemanfaatan jaringan internet desa yang ada di Desa Ngadirejo. Metode penelitian yang digunakan dalam penelitian ini adalah metode deksriptif, metode deskriptif adalah pencarian fakta dengan interpretasi yang tepat, adapun fakta-fakta dalam penelitian ini dilihat dari aspek efektivitas, kecukupan, perataan, responsivitas dan ketepatan. Hasil penelitian menunjukkan bahwa pemanfaatan internet desa di Desa Ngadirejo belum sesuai dengan tujuan diadakannya program internet desa.
BITCOIN-USD TRADING USING SVM TO DETECT THE CURRENT DAY’S TREND IN THE MARKET Ferdiansyah Ferdiansyah; Edi Surya Negara; Yeni Widyanti
Journal of Information System and Informatics Vol 1 No 1 (2019): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/journalisi.v1i1.7

Abstract

Bitcoin is a kind of Cryptocurrency and now is one of type of investment in the stock market. Stock markets are influenced by many risks of factor. And bitcoin is one kind of cryptocurrency that keep rising in recent few years, and sometimes fall without knowing influence behind it, on stock market. Because it’s fluctuations, there’s a need Automated tool to prediction of bitcoin on stock market. However, because of its volatility, there’s a need for a prediction tool for investors to help them consider investment decisions for bitcoin or another cryptocurrency trade. The predict methods will be used on this research is regime prediction to develop model to predict trend at the opening of market using SVM.
Co-Authors AA Sudharmawan, AA Adam Prasetya Ade Putra Adi Wijaya Adila, Nia Aditya, Ferdi Agam, Padel Mohammad Ahmad Ghiffari Ahmad Rusli Ahmad Syazili Akhiruddin, Deddy Rezano Amanda, Riyan Amin, Zulius Akbar Andreean Dharma Arisandi Andry Meylani andryani, ade Andryani, Ria Andryani, Ria Ari Hardiyantoro Susanto Arjun, Jennifer Axel Natanael Salim Azhiman, Fauzan Bhagaskara, - Bhianta Wijaya Chairul Mukmin Damayanti, Nita Rosa damayanti, selvia Dasmen, Rahmat Novrianda Deddy Rezano Akhiruddin Dedek Julian Dedi Irawan Dedy Syamsuar Dedy Syamsuar Dendi Triadi Dendi Triadi Deni Erlansyah Deris Stiawan Destarina, Nova Desy Arisandy Diana Donan, Hendri ENDRI ENDRI ERIENE DHEANDA evariani, evariani Fajarino, Aldo Fatoni Ferdiansyah Ferdiansyah Fernandy Jupiter Firdaus Firdaus Hastari Mayrita Hendra Marta Yudha Herdiansyah, Izman Herdiansyah, M. Izman Herdiansyah, M. Izman Indah, Mayang Puspa Jepri Yandi Juminovario Juminovario Junisti, Alfina Wulan KENI KENI Kiki Rizky Nova Wardani Kisworo, Marsudi Wahyu Kurniawan, Tri Basuki Latius Hermawan Linda Atika Linda Atika Liza Fahreni M Izman Herdiansyah Maria Ulfa Meilinda Meilinda Mery Sintia Mochammad Imron Awalludin Mohamad Farozi Muhamad Akbar Muhammad Cahyono MUHAMMAD FAHMI Muhammad Izman Herdiansyah Muhammad Izman Herdiansyah Muhammad Marzuki Muhammad Marzuki Muhammad Qurhanul Rizqie Muhammad Raihan Muhammad Wahyudi Nanda Tri Haryati Nico Michael Bryan Novaria Kunang, Yesi Novita Anggraini Novrianda, Rahmat Nurhachita Nurhachita Nurhachita Nurhachita Oktariansyah Oktariansyah, Oktariansyah Pratiwi, Ayu Okta Prihambodo Hendro Saksono Purnama Dharmawan Puspita Dewi Setyadi Putra, Yusuf Andi Putri Armilia Prayesy Qisthiano, M Riski Rahmad Kartolo Rahmat Gernowo Rahmat Ramadan Rahmat Ramadan Raihan, Muhammad Ramadani Ramayanti, Indri Rasmila, Rasmila REZA PAHLEVI Reza Pahlevi Reza Vidi Aditama Rezki Syaputra Ria Andriani Ria Andryani Rianda, M. Rianda Rifan Fadilah Rivaldi, Ahmad Riyan Amanda Rizma Adlia Syakurah RR. Ella Evrita Hestiandari Saksono, Prihambodo Hendro Sari, Yulia Permata Saro, Dewi Novita Sunda Ariana, Sunda Supratman, Edi Suryayusra Syaputra, Rezki Tata Sutabri Tri Basuki Kurniawan Triadi , Dendi Triyunsari, Desra Usman Ependi Wawan Setiawan Widya Cholil Winata Nugraha Winoto Chandra Yeni Widyanti Yepi Kusmeta Yesi Novaria Kunang Yessi Novaria Kunang Yuni Amrina Yuranda, Rezky Yusuf Andi Putra Yusuf, Abi daud Zulius Akbar Amin