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All Journal Teknika TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Ilmiah KOMPUTASI Jurnal Nasional Komputasi dan Teknologi Informasi IJISCS (International Journal Of Information System and Computer Science) JSR : Jaringan Sistem Informasi Robotik Jurnal Teknik Informatika (JUTIF) IndraTech TIERS Information Technology Journal SIENNA Seminar Nasional Teknologi dan Multidisiplin Ilmu IJISCS (International Journal of Information System and Computer Science)
Sriyanto, Sriyanto
Department Of Informatics Engineering, Institute Of Informatics And Business Darmajaya Lampung
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Neuroscience Social Sciences Transportation Other

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Journal : Jurnal Informatika

 1 
PENERAPAN DECISION TREE C4.5 SEBAGAI SELEKSI FITUR DAN SUPPORT VECTOR MACHINE (SVM) UNTUK DIAGNOSA KANKER PAYUDARA Pakarti Riswanto; RZ. Abdul Aziz; Sriyanto -
Jurnal Informatika Vol 19, No 1 (2019): Jurnal Informatika
Publisher : IIB Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/ji.v19i1.1442

Abstract

In the field of medicine, the use of data mining has a quite important and evolutionary role that can change the perspective of doctors, practitioners and health researchers in the process of detecting breast cancer in a patient. There are 2 classification applications in it, namely the process of diagnosing (diagnosing) cancer cells that distinguishes between tumors (benign cancer) or malignant cancer and prognosis (prognosis) to determine the possibility of reappearance of cancer cells in patients who have been operated on in the future. Data mining aims to describe new findings in the dataset and explain a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and related knowledge from the database.Classification with data mining can be done using several methods, namely Decision Tree, K-Nearest Neighbor, Naive Bayes, ID3, CART, Linear Discriminant Analysis, etc., which certainly have advantages and disadvantages of each. But in this study, the author focuses on the classification of data mining using the Support Vector Mechine and Deccision Tree algorithms.This study will analyze the Breast Cancer Wisconsin Original data set obtained from the UCI Machine Learning Repository (repository of research data) to classify breast cancer malignancies. This time the author correlates between the Decision Tree classifier algorithm which has good ability to process large databases as a feature selection, then with a proper and relevant SVM Method used in analyzing and diagnosing breast breast cancer patients because it has accurate results for existing problems and several bases . Keywords— Data Mining, diagnosis, Decision Tree, SVM Method
Identifikasi Penyakit Diabetes Millitus Menggunakan Jaringan Syaraf Tiruan Dengan Metode Perambatan-Balik (Backpropagation) Sriyanto Sriyanto; Sutedi Sutedi
Jurnal Informatika Vol 10, No 2 (2010): Jurnal Informatika
Publisher : IIB Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/ji.v10i2.163

Abstract

Diabetes Melitus (DM) is dangerous disease that affect many of the various layer of work society. This disease is not easy to accurately recognized by the general society. So we need to develop a system that can identify accurately. System is built using neural networks with backpropagation methods and the function activation sigmoid. Neural network architecture using 8 input layer, 2 output layer and 5 hidden layer. The results show that this methods succesfully clasifies data diabetics and non diabetics with near 100% accuracy rate. Keyword : Neural Network, Backpropagation, diabets
IMPLEMENTASI SISTEM PAKAR UNTUK MENDIAGNOSA KERUSAKAN PRINTER JENIS CANON BJC-2100SP chairani chairani; Sriyanto Sriyanto; Fitria .
Jurnal Informatika Vol 12, No 1 (2012): Jurnal Informatika
Publisher : IIB Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/ji.v12i1.135

Abstract

Printer as the printing tool is already dominate several field, such as office, shop, school, university, educational institution, etc. In certain time, there must be trouble in printer using, just like another machines, damages. So that, no matter how hi-tech the printing tool, it must need the expert to fix the damages.Expert system transform the knowledge of the expert(s) to be a computer system that help general people to do anything like an expert. In this case, expert system is built to diagnose the damage of printer type Canon BJC-2100SP. This expert system will increase time of effort to diagnose the damage of the printer type Canon BJC-2100SP for computer technician, specially for printer type Canon BJC-2100SP. Keyword : Production system, expert system, Rule, Knowledge, Inference Engineering.
MEMBANDINGKAN PERFORMA ANTARA HYPERLEDGER DAN MYSQL Riko Herwanto; Onno W. Purbo; Sriyanto Sriyanto
Jurnal Informatika Vol 20, No 1 (2020): Jurnal Informatika
Publisher : IIB Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/ji.v20i1.1816

Abstract

In this paper, we report the benchmarking results of Hyperledger, a Distributed Ledger, which is the derivation Blockchain Technology. Method to evaluate Hyperledger in a limited infrastructure is developed. The measured infrastructure consists of 8 nodes with a load of up to 20000 transactions/second.. The benchmarking of Hyperledger shows better than a database system in a high workload scenario. We found that the maximum size data volume in one transaction on the Hyperledger network is around ten (10) times of MySQL. Also, the time spent on processing a single transaction in the blockchain network is 80-200 times faster than MySQL. This initial analysis can provide an overview for practitioners in making decisions about the adoption of blockchain technology in their IT systems.
Co-Authors A. Toding Abdollah, Mohd Faizal Adhi Tristiyanto Agiska Ria Supriyatna Ahmad, Asmala Ahmed Jamal Abdullah Al-Gburi Arifin, Oki Arpan, Atika Asep Eryana Asep Eryana Ayu Brahdika Putri Aziz, RZ. Abdul Chairani Chairani Dewi Agushinta R. Firmansyah Firmansyah Yunialfi Fitria . Hartono Hartono Herwanto, Riko Irianto Irianto Irma Agustina Juwariyem Juwariyem Khodijah Khodijah Kurniawan Saputra M Agus Badruzaman Al Khoir M. Abu Mas'ud, Mohd. Zaki meilantika, Dian Mohammed Yousif Zeain Muhamad Fahrurrozi Nursiyanto Nurul Qomariyah Oktaf Rina Onno W. Purbo Onno W. Purbo Pakarti Riswanto Permata, Rizka R. Syahputri Suhendro Suhendro Sutedi Sutedi Widyawati, Dewi Kania Y. Suhendro Z. Zakaria Zuriati, Zuriati