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JURIKOM (Jurnal Riset Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 2407389X     EISSN : 27157393     DOI : http://dx.doi.org/10.30865/jurikom.v7i1.1927
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JURIKOM (Jurnal Riset Komputer) membahas ilmu dibidang Informatika, Sistem Informasi, Manajemen Informatika, DSS, AI, ES, Jaringan, sebagai wadah dalam menuangkan hasil penelitian baik secara konseptual maupun teknis yang berkaitan dengan Teknologi Informatika dan Komputer. Topik utama yang diterbitkan mencakup: 1. Teknik Informatika 2. Sistem Informasi 3. Sistem Pendukung Keputusan 4. Sistem Pakar 5. Kecerdasan Buatan 6. Manajemen Informasi 7. Data Mining 8. Big Data 9. Jaringan Komputer 10. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informati dan komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 26 Documents
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Search results for , issue "Vol 7, No 1 (2020): Februari 2020" : 26 Documents clear
Perencanaan dan Implementasi SAP pada PT XYZ dengan Menggunakan Metode Accelerated SAP (ASAP) Maulidina, Rahma; Rizki, Nur Anisa; Dewi, Renny Sari
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 (340.68 KB) | DOI: 10.30865/jurikom.v7i1.1856

Abstract

An integrated information system is a need to help solve problems in managing business transactions. SAP application is one application that is able to provide solutions to these problems. The purpose of writing this research is to plan running business processes, plan implementation of the current SAP system related to the implementation and maintenance of SAP applications and provide development suggestions based on the results of planning obtained, in terms of business processes, performance, and financial. For the analysis, the IT Balanced Scorecard method is used, while for system development the ASAP (Accelerated SAP) methodology is used. This research produces an analysis of the planning of the company's business processes in implementing the implementation of the SAP financial and controlling ERP.
Estimasi Biaya Software FAS (Financing Analysis System) Menggunakan Metode Function Point (Studi Kasus Pada PT BPRS Lantabur Tebuireng) Juliana Kristi; Siti Nur Aisah; Renny Sari Dewi
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 (338.335 KB) | DOI: 10.30865/jurikom.v7i1.1891

Abstract

Software cost estimation is the process of predicting software development efforts. The basic input of software cost estimation is the measurement metric. Projects often experience delays, over-budget, and are not completed due to failure to estimate software development costs. PT BPRS (Bank Perkreditan Rakyat Syariah) Lanatabur Tebuireng determines the estimated cost based on the amount of human resources, features needed, and funds owned. This study explains the estimated costs of the FAS (Financing Analysis System) software at PT BPRS Lantabur Tebuireng using the Function Point method. Function Point is a method of measuring software functionality based on the type of user function that is External Input, External Output, External Inquire, Internal Logic File, and External Interface File as well as technical calculations of software development. The final of the FAS (Financing Analysis System) study cost around IDR 94,797,120.
Penerapan Data Mining Untuk Memprediksi Pemesanan Bibit Pohon Dengan Regresi Linear Berganda Devi Sari Oktavia Panggabean; Efori Buulolo; Natalia Silalahi
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 (391.531 KB) | DOI: 10.30865/jurikom.v7i1.1947

Abstract

Data mining, often also called knowledge discovery in database (KDD), is an activity that includes collecting, using historical data to find order, patterns or relationships in large data sets. Outputs from data mining can be used to improve future decision making. Problems that often occur in BPDASHL are estimation problems such as weather, difficulties in planting, lack of labor, lack of experience in tree nurseries, and different soil conditions. Another problem that is found in agencies is that they do not have a system to predict estimated tree seedlings orders every year so that a method is needed, namely the Multiple Linear Regression Algorithm. So with this was made the Application of Data Mining To Predict Ordering Tree Seeds With Multiple Linear Regression. Multiple Linear Regression Algorithms which are methods that support estimating or predicting order targets for the coming period. Algorithm testing is done using SPSS software. From the results of the research that has been done, it can help BPDASHL to make it easier to predict the ordering of seeds using SPSS Software 
Template Khusus Untuk Menulis Notasi Angka: Modifikasi Fungsi Tools Program Aplikasi Musik Finale Branckly Egbert Picanussa
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 (348.557 KB) | DOI: 10.30865/jurikom.v7i1.1981

Abstract

The numerical notation is one of musical notation which is very popular for most of the Indonesian people, especially composers or music practitioners who have not learned music in formal education. This article is one of the researches about tools modification of music application program of Finale to get a special template to write numerical notation. The method which is used in this research is action research. With this method, the researcher use Finale Music Program tools to get a special template to write numerical notation. The result of this research is a special template to write numerical notation.
Data Mining untuk Klasifikasi Penderita Kanker Payudara Berdasarkan Data dari University Medical Center Menggunakan Algoritma Naïve Bayes Ibnu Ramadhan; Kurniawati Kurniawati
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 | DOI: 10.30865/jurikom.v7i1.1755

Abstract

The breast cancer sufferers data from the University Medical Center is data about patients suffering from breast cancer based on certain characteristics. This data has abundant information so that data mining can be done with the aim of digging deeper information which of course can be useful in the future. The data class itself is divided into 2 groups: recurrence and non-relapse classes. The technique used in this study is classification using the Naive Bayes algorithm. Naive Bayes is a simple probabilistic prediction technique based on the implementation of Bayes rules with a strong assumption of independence on features. The tool used to find accuracy values is RapidMiner 9.3. Data attributes consist of Class, Age, Menopause, Tumor-Size, Inv-Nodes, Node-Caps, Deg-Malig, Breast, Breast-Quad and Irradiant. In terms of methods, this study uses the CRISP-DM (Cross Industry Standard Process for Data Mining) method. This research is used as information in making decisions to determine policies taken in dealing with patients with breast cancer
Uji Akurasi Dataset Pasien Pasca Operasi Menggunakan Algoritma Naïve Bayes Menggunakan Weka Tools Muhammad Rahmadi; Fazriyanor Kaurie; Tuti Susanti
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 | DOI: 10.30865/jurikom.v7i1.1761

Abstract

Postoperative patient data sets taken for testing of this data are sourced from the UCI repository on the website https://archive.ics.uci.edu/ml/datasets/Post-Operative+Patient. Based on the website address, the study was conducted by Sharon Summers, School of Nursing, University of Kansas, Medical Center, Kansas City, KS 66160 and Linda Woolery, School of Nursing, University of Missouri, Columbia, MO 6521. Number of attributes from this data set there are 8 and 1 class, the attributes in question include; L-CORE (patient's internal temperature in C), L-SURF (patient's surface temperature in C), L-O2 (oxygen saturation in%), L-BP (last measurement of blood pressure), SURF-STBL (stability of the patient's surface temperature ), CORE-STBL (stability of the patient), BP-STBL (stability of the patient's blood pressure), COMFORT (perceived comfort of the patient at discharge, measured as an integer between 0 and 20) and ADM-DECS decision class / patient exit decision with information (I = patient sent to intensive care unit, S = patient ready to go home, A = patient sent to general hospital floor).

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