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All Journal KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Indonesian Journal on Software Engineering (IJSE) Jurnal Teknik Komputer AMIK BSI Jurnal Khatulistiwa Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Bina Insani ICT Journal Jurnal Pilar Nusa Mandiri JISICOM (Journal of Information System, Infomatics and Computing) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Infortech Jurnal Manajemen Informatika Jayakarta Computer Science (CO-SCIENCE) Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Journal of Accounting Information System
Dwi Yuni Utami
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Social Sciences

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Prediksi Penyakit Diabetes Melitus Menggunakan Metode Support Vector Machine dan Naive Bayes Maulidah, Nurlaelatul; Supriyadi, Riki; Utami, Dwi Yuni; Hasan, Fuad Nur; Fauzi, Ahmad; Christian, Ade
Indonesian Journal on Software Engineering (IJSE) Vol 7, No 1 (2021): IJSE 2021
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijse.v7i1.10279

Abstract

Diabetes melitus adalah penyakit metabolik yang ditandai terjadinya kenaikan gula darah yang disebabkan oleh terganggunya hormon insulin yang memiliki fungsi sebagai hormon dalam menjaga homeostatis tubuh menggunakan cara penurunan kadar gula darah (American Diabetes Association, 2017). World Health Organization (WHO) memperkirakan jumlah penderita diabetes melitus orang dewasa diatas 18 tahun dalam tahun 2014 berjumlah 422 juta (WHO, 2016:25). Prevalensi diabetes melitus Asia Tenggara sudah berkembang dalam tahun 1980 sebanyak 4,1% dan tahun 2014 menjadi sebanyak 8,6%. Menurut Riset Kementerian Kesehatan pada tahun 2018, Prevalensi diabetes Indonesia sebanyak 2,0%, sedangkan di Provinsi Jawa Timur sebanyak 2,6% pada penduduk umur diatas 15 tahun (KEMENKES RI, 2019). Penelitian ini dikembangkan melalui pengolahan data sekunder database kesehatan Dataset Diabetes yang diambil dari dataset Kaggle dan dapat diakses melalui https://www.kaggle.com/johndasilva/diabetes. Dimana datanya sendiri terdiri dari 2000 record dengan beberapa variabel prediktor medik (Pregnancies/Kehamilan, Glucose/Glukosa, BloodPressure/Tekanan Darah, SkinThickness/Ketebalan Kulit, Insulin, BMI/Indeks Masa Tubuh, DiabetesPedigreeFunction/Keturunan, Age/Umur and Outcome/Hasil). Kemudian data tersebut akan diolah dengan menggunakan metode Support Vector Machine dan metode Naive Bayes untuk mengetahui akurasi hasil diagnosa diabetes. Berdasarkan hasil dari penelitian yang sudah dilakukan metode Support Vector Machine memiliki nilai akurasi yang jauh lebih tinggi dibandingkan dengan menggunakan metode Naive Bayes. Nilai akurasi untuk model metode Support Vector Machine adalah 78,04% dan nilai akurasi untuk metode Naive Bayes 76,98%. Berdasarkan nilai ini, perbedaan akurasinya adalah 1,06%. Sehingga dapat disimpulkan bahwa penerapan metode Support Vector Machine mampu menghasilkan tingkat akurasi diagnosis diabetes yang lebih baik dibandingkan dengan menggunakan metode Naive Bayes.
Aplikasi Sistem Informasi Koperasi Simpan Pinjam Berbasis Web Pada PT. Mitraindo Sejahtera Utama Tangerang Ari Abdilah; Elah Nurlelah; Fuad Nur Hasan; Dwi Yuni Utami
Jurnal Teknik Komputer AMIK BSI Vol 8, No 1 (2022): JTK Periode Januari 2022
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (908.083 KB) | DOI: 10.31294/jtk.v8i1.11587

Abstract

Koperasi karyawan PT. Mitraindo Sejahtera Utama cara kerjanya masih bersifat manual dengan menggunakan aplikasi Microsoft Excel, sehingga informasi yang dihasilkan kurang akurat dan kemungkinan terjadi kesalahan dalam proses pendataan dan perhitungan. Tujuan penelitian ini untuk membuat sebuah aplikasi simpan pinjam berbasis web pada Koperasi karyawan PT. Mitraindo Sejahtera Utama. Dengan adanya aplikasi ini diharapkan dapat mengurangi kesalahan dalam pengolahan data dan dapat memperlancar jalanya proses simpan pinjam pada Koperasi karyawan PT. Mitraindo Sejahtera Utama, sehingga proses kinerja menjadi lebih cepat dan akurat. Metode perancangan perangkat lunak yang digunakan yaitu analisa sistem berjalan pada Koperasi karyawan PT. Mitraindo Sejahtera Utama meliputi desain sistem, desain database dan implementasi sistem. Laporan yang dihasilkan meliputi laporan data anggota, simpanan anggota, laporan pinjaman serta laporan angsuran pinjaman. Bahasa pemograman yang digunakan adalah PHP My SQL, perangkat lunak yang mendukung dalam pembuatan aplikasi adalah Dreamweaver CS6, Adobe photoshop dan Xampp.
PENERAPAN MODEL WATERFALL DALAM SISTEM INFORMASI PENGOLAHAN DATA NILAI SISWA PADA SMK BINA MANDIRI SUKABUMI Elah Nurlelah; Dwi Yuni Utami; Ari Abdillah
Jurnal Khatulistiwa Informatika Vol 8, No 1 (2020): Periode Juni 2020
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jki.v8i1.8186

Abstract

SMK Bina Mandiri Sukabumi merupakan instansi yang bergerak dalam bidang pendidikan. Pengolahan data kehadiran dan data nilai siswa di sekolah ini diolah secara konvensional yaitu dengan cara masing-masing guru bidang studi melakukan pengumpulan data absen, nilai UTS, nilai UAS, serta nilai tugas kedalam suatu lembaran kertas kemudian disetorkan kepada wali kelas dan wali kelas menyalinnya kembali nilai ke dalam buku yang disebut dengan raport. Hal ini menyebabkan keterlambatan, banyak waktu dan tenaga yang diperlukan dalam proses pengisian nilai raport, kurang efisien dalam pencarian nilai siswa, update nilai siswa, dan rekap nilai siswa. Metode penelitian yang digunakan dalam perancangan sistem informasi pengolahan data nilai siswa ini yaitu menggunakan metode Waterfall dan rancangan basis data menggunakan Entity Relationship Diagram (ERD) dan Logical Record Structure (LRS). Dengan berbasiskan web informasi dapat di akses kapan saja dan dimana saja dan dapat mempermudah tugas wali kelas dan guru bidang studi dalam pengolahan data nilai siswa dan dalam pencarian nilai siswa menjadi efektif dan efisien.
Comparison of Neural Network Algorithms, Naive Bayes and Logistic Regression to predict diabetes Dwi Yuni Utami; Elah Nurlelah; Fuad Nur Hasan
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 1 (2021): EDISI JULY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i1.5201

Abstract

Diabetes is a disease that affects many people with the characteristics of high blood sugar levels. The International Diabetic Federation (IDF) estimates the number of Indonesians aged 20 years and over, suffering from diabetes at 5.6 million people in 2001, and increasing to 8.2 million people in 2020. The problem that occurs is that many people do not know that they suffer from diabetes because they do not have basic knowledge about diabetes and the existing methods to detect diabetes are time consuming. In this study, three data mining methods were compared, namely the neural network algorithm, naïve Bayes, and logistic regression using the rapid miner application by applying the Confusion Matrix Evaluation (Accuracy) and the ROC Curve. The result of this research is that logistic regression method is a fairly good method in predicting early diagnosis of diabetes compared to the naïve Bayes method and the neural network. From the evaluation and validation, it is known that logistic regression has the highest accuracy and AUC values among the comparable methods, namely 75.78% and AUC 0.801, followed by the naïve Bayes algorithm which is 74.87% and AUC 0.799, and the neural network is 69.27% and AUC 0.736. has the lowest accuracy.
Perancangan Sistem Informasi Persediaan Barang menggunakan metode RUP (Studi Kasus PT Medical Device Indonesia) Noer Hikmah; Rendi Simon Lesmana; Leliyanah Leliyanah; Dwi yuni Utami
Computer Science (CO-SCIENCE) Vol. 1 No. 1 (2021): Januari 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v1i1.202

Abstract

PT Medical Device Indonesia is a company in the distribution of books and medical devices. In the data processing specially the inventory section it used manual way. It made owner and employees of PT. Medical Device Indonesia become overshelmed in data management, so there is a need for a system to simple the inventory process. The study aims to analyze and design a web based inventory information system, so that the data is more accurate and minimizes errors in data processing. The method used in system development is using the RUP (Rational Unified Process) methodology as making observations and interviews with the parties involved. For the system design method using the Object Oriented Analysis and Design method using UML such as usecase diagrams, activity diagrams, sequence diagrams. The results of this system are the users are able to manage product easily, perfoming complete transaction and process the reports completely.
Perancangan Jaringan Voice Over Internet Protokol (Voip) Menggunakan Platform Asterisknow Ahmad Fauzi; Ade Setiawan; Dwi Yuni Utami
Computer Science (CO-SCIENCE) Vol. 2 No. 1 (2022): Januari 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v2i1.887

Abstract

Communication is an important stage to support the running of a work program, without good communication it can cause misunderstandings between the two parties, the importance of the role of communication means several companies are growing rapidly in order to innovate in the manufacture of equipment in the telecommunications sector, which is commonly used in long-distance communication. far is the use of telephone services, both individual telephones and company-based telephones, for some companies that use high-enough telephone activity, this will result in soaring telephone bills that must be paid, thus to reduce the level of communication with high costs, we can use VoIP technology ( Voice Over Internet Protocol) which can be used by utilizing the TCP / IP protocol where the protocol without us realizing it is a protocol used for exchanging data, thus the protocol can also be accessed. used as voice voice data exchange using Open Source Software in the Asterisk application which is used as a substitute for PBX (Private Branch Exchange) devices, where in the application it is used to make the TCP / IP protocol as an intermediary for data exchange to communicate or Voice. The advantage of this application is that it is able to serve a maximum of 1000 online accounts on one server and 240 concurrent calls. This is of course suitable for companies who want to implement this technology based on open source applications.
SELEKSI ATRIBUT PADA ALGORITMA NEURAL NETWORK MENGGUNAKAN PARTICLE SWARM OPTIMIZATION UNTUK DIAGNOSIS PENYAKIT LIVER Elah Nurlelah; Dwi Yuni Utami
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 9, No 2 (2022)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v9i2.460

Abstract

The liver is a vital human organ that has complex and diverse functions, one of which is to maintain the needs of the organs in the body, especially the brain. One of the diseases that attack the liver is hepatitis or liver. According to WHO (World Health Organization) data, nearly 1.2 million people per year, especially in Southeast Asia and Africa, die from liver disease. The problem that usually occurs is that it is difficult to recognize liver disease early on, even when the disease has spread. From these problems, the researchers diagnosed liver disease using data mining using the Neural Network Algorithm and Particle Swarm Optimization (PSO)-based Neural Network Algorithm which was taken from secondary data from the UCI Machine Learning Repository (University of California Invene). Based on the results of the research, the accuracy value of the Neural Network algorithm is 66.83%, while the accuracy value of the Neural Network Optimization algorithm using PSO is 72.37% so that the difference in the accuracy value is 5.54%. So it can be concluded that the application of particle swarm optimization techniques is able to select attributes on the Neural Network, resulting in a better level of accuracy in the diagnosis of liver disease than using the individual method of the Neural Network algorithm. Keywords: Liver, Neural Network Algorithm, Particle Swarm Optimization (PSO)-based Neural Network Algorithm Hati  adalah  organ vital  manusia  yang memiliki   fungsi   kompleks   dan   beragam,   salah satunya  adalah  dengan  menjaga  kebutuhan  organ dalam  tubuh,  khususnya  otak. Salah satu penyakit yang menyerang hati adalah hepatitis atau liver. Menurut data WHO (World Health Organization) menunjukkan hampir 1,2 juta orang per tahun khususnya di Asia Tenggara dan Afrika mengalami kematian akibat terserang penyakit liver. Permasalahan yang biasanya terjadi adalah sulitnya mengenali penyakit liver sejak dini, bahkan ketika penyakit tersebut sudah menyebar. Dari permasalahan tersebut peneliti melakukan diagnosa penyakit liver dengan data mining menggunakan algoritma Neural Network dan Algoritma Neural Network dioptimasi dengan Particle Swarm Optimization (PSO) yang diambil dari data  sekunder Machine Learning Repository  UCI (Universitas California Invene). Berdasarkan hasil penelitian nilai akurasi algoritma Neural Network senilai 66,83%, sedangkan untuk nilai akurasi Optimasi algoritma Neural Network menggunakan PSO sebesar 72,37% dan tampak selisih nilai akurasi yaitu sebesar 5,54%. Sehingga dapat disimpulkan bahwa penerapan teknik optimasi particle swarm optimization mampu menyeleksi atribut pada Neural Network, sehingga menghasilkan tingkat akurasi diagnosis penyakit liver yang lebih baik dibanding dengan menggunakan metode individual algoritma Neural Network.Kata kunci: Liver, Algoritma Neural Network, Algoritma Neural Network berbasis Particle Swarm Optimization (PSO)
Implementasi Algoritma Apriori Terhadap Data Penjualan PT. Frasa Group Badariatul Lailiah; Dwi Yuni Utami; Ari Abdilah
Bahasa Indonesia Vol 9 No 2 (2022): Bina Insani ICT Journal (Desember) 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/biict.v9i2.2021

Abstract

PT. Frasa Group Ini adalah perusahaan yang memperluas segmentasi produsen makanan beku siap saji. Berfokus pada kebutuhan konsumen merupakan kesimpulan yang dapat mencakup niat individu konsumen untuk memanfaatkan nilai-nilai keselamatan dan standar etika yang tinggi terkait dengan keselamatan konsumen. Satu-satunya kelemahan dari pengembalian dana pos adalah pasokan juga merupakan satu kelemahan industri untuk bisnis mengingat pasokan suku cadang tidak dapat dihindari dalam jadwal pembayaran. Ini karena perangkat tidak dapat. Oleh karena itu, untuk menerapkan manajemen inventaris yang efektif, Anda perlu mengetahui realitas pasar. Oleh karena itu bagaimana mengimplementasikan algoritma apriori di PT. Kelompok frase dengan Tanagra untuk memungkinkan perusahaan menggunakan algoritma apriori untuk menentukan produk mana yang terlaris, menggunakan variabel yang memenuhi dukungan dan kepercayaan rendah. Hasil penelitian ini, asosiasi definitif diketahui, jika membeli YSA maka membeli YNAP support 39,02% dan confidence 86,74%, jika membeli YNAP maka membeli YSA support 39,02% dan confidence 80,89%, Dengan menggunakan hasil yang diperoleh, perusahaan dapat menggunakan algoritma apriori untuk meningkatkan strategi penjualan.
Prediksi Keberhasilan Pemasaran Layanan Jasa Perbankan Mengunnakan Algoritma Logistic Regreesion Sari Dewi; Hanggaro Aji Al Kautsar; Dwi Yuni Utami
Computer Science (CO-SCIENCE) Vol. 3 No. 2 (2023): Juli 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v3i2.1931

Abstract

Determining public interest in marketing banking services using data mining techniques. Prospect segmentation is one of the processes used in the marketing strategy of the banking industry. Data mining support plays an important role in classifying potential bank customers and evaluating the success of marketing their services. This is important to support the conclusion about the success rate of telemarketers in carrying out bank marketing tasks. a product whose way of working requires information about potential customers. This is a classification technique that is often used to classify prospects using logistic regression according to research maps supporting prospect data mining. Defining an accurate data mining classification algorithm to predict telemarketing success based on a 2010 experiment. In marketing banking service products, the results of the evaluation process of this algorithm are determined by cross-validation, Confusion Matrix, ROC curve and T-test. The logistic regression algorithm is more accurate with an accuracy of 92.32% and an AUC value of 0.962, so the algorithm used is included in the good classification group.
Attribute Selection in Naive Bayes Algorithm Using Genetic Algorithms and Bagging for Prediction of Liver Disease Utami, Dwi Yuni; Nurlelah, Elah; Hikmah, Noer
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 4 No. 1 (2020): ---> EDISI JULI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v4i1.3793

Abstract

Liver disease is an inflammatory disease of the liver and can cause the liver to be unable to function as usual and even cause death. According to WHO (World Health Organization) data, almost 1.2 million people per year, especially in Southeast Asia and Africa, have died from liver disease. The problem that usually occurs is the difficulty of recognizing liver disease early on, even when the disease has spread. This study aims to compare and evaluate Naive Bayes algorithm as a selected algorithm and Naive Bayes algorithm based on Genetic Algorithm (GA) and Bagging to find out which algorithm has a higher accuracy in predicting liver disease by processing a dataset taken from the UCI Machine Learning Repository database (GA). University of California Invene). From the results of testing by evaluating both the confusion matrix and the ROC curve, it was proven that the testing carried out by the Naive Bayes Optimization algorithm using Algortima Genetics and Bagging has a higher accuracy value than only using the Naive Bayes algorithm. The accuracy value for the Naive Bayes algorithm model is 66.66% and the accuracy value for the Naive Bayes model with attribute selection using Genetic Algorithms and Bagging is 72.02%. Based on this value, the difference in accuracy is 5.36%.Keywords: Liver Disease, Naïve Bayes, Genetic Agorithms, Bagging.
Co-Authors Ade Christian, Ade Ade Setiawan Ahmad Fauzi Ari Abdilah Ari Abdillah Badariatul Lailiah Baginda Oloan Lubis Elah Nurlelah Eri Riana, Eri Fauzi Ahmad Muda Firstianty Wahyuhening Fibriany Fuad Nur Hasan Hanggaro Aji Al Kautsar Hasan, Fuad Nur Hayatullah, Mohammad Rizky Hikmah, Noer Leliyanah Leliyanah Noer Hikmah Nurlaelatul Maulidah Nurlelah, Elah Rendi Simon Lesmana Riki Supriyadi Rizka, Muhammad Aditya Sari Dewi Sari Dewi Shidqi, Farsya Lisanush Vara, Regita Santo Wibowo, M.Dwi Saputra Yulianti Yulianti