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All Journal Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial IJCIT (Indonesian Journal on Computer and Information Technology) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Teknik Komputer AMIK BSI Paradigma Jurnal Pilar Nusa Mandiri JUTIM (Jurnal Teknik Informatika Musirawas) EVOLUSI : Jurnal Sains dan Manajemen IMTechno: Journal of Industrial Management and Technology Jurnal Abdimas Komunikasi dan Bahasa SPEED - Sentra Penelitian Engineering dan Edukasi Akademika
Evy Priyanti
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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IMPLEMENTASI NEURAL NETWORK PADA PREDIKSI PENDAPATAN RUMAH TANGGA Priyanti, Evy
Swabumi Vol 6, No 1 (2018): Volume 6 Nomor 1 Tahun 2018
Publisher : Universitas Bina Sarana Informatika Kota Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/swabumi.v6i1.3312

Abstract

ABSTRAK Pendapatan rumah tangga sangat penting dalam kehidupan sehari-hari, oleh karena itu, untuk memprediksi bagaimana pendapatan rumah tangga dapat ditingkatkan di sini, penulis menggunakan algoritma jaringan syaraf tiruan untuk memprediksi faktor-faktor yang dapat mempengaruhi pendapatan rumah tangga. Algoritma jaringan syaraf tiruan merupakan teknik peramalan yang paling umum digunakan, karena algoritma Neural Network dapat cepat dan akurat, banyak peneliti menggunakan jaringan syaraf tiruan untuk memecahkan masalah peramalan. Jaringan Syaraf Tiruan memiliki keunggulan bahwa jaringan syaraf tiruan dapat mengatasi masalah nonlinier, memiliki toleransi yang tinggi terhadap data yang mengandung noise dan mampu menangkap hubungan yang sangat kompleks antara variabel prediktor dan keluaran. Pada data pendapatan rumah tangga ini algoritma jaringan syaraf tiruan dapat memprediksi jumlah pendapatan dengan akurasi sebesar 83,62%. Nilai akurasi yang didapat sangat tinggi dan dapat membantu dalam menata keuangan di setiap rumah tangga, sehingga jaringan syaraf tiruan dapat memecahkan masalah dalam memprediksi pendapatan rumah tangga di berbagai negara di dunia sesuai dengan data dari UCI dataset dibandingkan menggunakan algoritma KNN yang nilai akurasinya sebesar 79.18%. Kata Kunci : Data Mining, Neural Network, Rumah Tangga ABSTRACT Household income is very important in everyday life, therefore, to predict how household incomes can be improved here, the authors use artificial neural network algorithms to predict factors that may affect household incomes. Artificial neural network algorithms are the most commonly used forecasting techniques, because the Neural Network algorithm can be fast and accurate, many researchers using artificial neural networks to solve forecasting problems. Artificial Neural Networks have the advantage that artificial neural networks can overcome nonlinear problems, have high tolerance to noise-containing data and be able to capture the very complex relationship between predictor and output variables. In this household income data artificial neural network algorithm can predict the amount of income with an accuracy of 83.62%. The accuracy of the value obtained is very high and can help in managing the finances in every household, so that neural networks can solve the problem in predicting household income in various countries in the world according to data from UCI dataset than using KNN algorithm whose accuracy value is 79.18%. Keyword : Data Mining, Household, Neural Network
PENERAPAN ALGORITMA NEURAL NETWORK UNTUK KLASIFIKASI KANKER PARU Evy Priyanti
Bianglala Informatika Vol 9, No 1 (2021): Bianglala Informatika 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (419.052 KB) | DOI: 10.31294/bi.v9i1.9989

Abstract

Neural Network Algorithm is an algorithm that is used to study the workings of the human brain which is applied to neurons connected to billions of network requirements and is able to work in many data learning processes, in this case the neural network algorithm will study the classification of lung cancer. Lung cancer is the third largest type of cancer in Indonesia. Lung cancer is divided into three classes of lung cancer pathologically. Class 1 consists of 9 observations, class 2 consists of 13 observations and class 3 consists of 10 observations. The results of lung cancer testing with this neural network algorithm produce an accuracy value of 75%, thus the neural network algorithm can be ascertained in classifying the pathology of lung cancer obtained from a survey by Stefan Aeberhard with a total sample size of 32 people and a total of 57 attributes. , one class label attribute and 56 attributes in the form of a nominal integer with a limit between 0-3 classes in the UCI Dataset.
PENERAPAN ALGORITMA NAÏVE BAYES UNTUK KLASIFIKASI BAKTERI GRAM-NEGATIF Evy Priyanti
JURNAL TEKNIK KOMPUTER AMIK BSI Vol 3, No 2 (2017): JURNAL TEKNIK KOMPUTER AMIK BSI
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1090.627 KB) | DOI: 10.31294/jtk.v3i2.1779

Abstract

The classification depends on the variety of bacteria that exist. The important feature to identify an organism of bacterial phenotype is the scheme that utilizes the morphology and staining properties of the bacteria itself, to classify the phenotype scheme is used Naïve Bayes algorithm that has proven to have a high degree of accuracy and high rate of speed when applied into E.coli dataset in E. coli dataset consisting of seven features are: mcg, gvh, lips, chg, aac, alm1, alm2, and proteins are classified into 8 classes: cytoplasmic (cp), an inner membrane without the signal sequence (im), perisplasm (pp), in the membrane with uncleavable signal sequence (IMU), outer membrane (oM), outer membrane lipoprotein (OML), the membrane lipoprotein (IML), an inner membrane with cleavable signal sequence (IMS) with an accuracy of 80.93%, with Naïve Bayes algorithm so it can be ascertained that the classification of gram-negative bacteria with E. coli phenotype datasets prove to be accurate.
Optimasi Naïve Bayes Dan Algoritma Genetika Untuk Prediksi Penerimaan Beasiswa Pendidikan Pada SMP Utama Nining Suryani; Evy Priyanti
JURNAL TEKNIK KOMPUTER Vol 5, No 2 (2019): JTK - Periode Agustus 2019
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (676.633 KB) | DOI: 10.31294/jtk.v5i2.5343

Abstract

Educational scholarships are one of the efforts to sustain students in getting a better education. Not a few students drop out in the middle of the road or cannot continue their education at the same level or higher level. Selection according to the criteria for scholarship recipients is important so that scholarships are on target. Similar to Depok Primary Middle School, educational scholarships are provided by schools based on 9 criteria for scholarship recipients, namely parent status, parent work, rented house, home appliances, vehicles, parents 'savings, parents' jewelry, cellphones and pocket money. With the number of prospective scholarship recipients there is an algorithm needed to accurately predict students who are entitled to scholarships. With the naïve bayes algorithm, accuracy is 77.50% in predicting scholarship recipients based on the criteria found in students. The use of genetic algorithms is done to get a more optimal level of accuracy. This is evidenced by the accuracy of 83.33%.
PENINGKATAN BACKWARD ELIMINATION DENGAN WINDOWED MOMENTUM UNTUK PREDIKSI KONTRASEPSI EVY PRIYANTI
Paradigma Vol 17, No 2 (2015): Periode September
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (510.25 KB) | DOI: 10.31294/p.v17i2.749

Abstract

Rapid population growth rate that can influence government policies on various aspects of life. It is necessary for the proper way to reduce the rate of population growth and create a safer contraceptive choice. Windowed momentum is a technique to improve the performance in backpropagation learning. But to ensure the accuracy of the momentum needed windowed performance computing methods such as neural networks to solve problems with the accuracy of data and not linear. Neural Network Optimization tested weeks to produce the best accuracy rate, applying Neural Network-based Backward Elimination aims to raise the accuracy produced by Neural Network. Experiments were performed to obtain the optimal architecture and generate increased accuracy. The results of the research is a confusion matrix to prove the accuracy of Neural Network before Backward Elimination is optimized by 54.64% and 57.03% after optimize. This proves estimate windowed momentum trials using neural network-based method Backward Elimination more accurate than the individual methods of neural network.
DIAGNOSIS KANKER PAYUDARA MENGGUNAKAN NEURAL NETWORK BERBASIS ALGORITMA GENETIKA Evy Priyanti - AMIK BSI Jakarta
Evolusi : Jurnal Sains dan Manajemen Vol 5, No 2 (2017): Jurnal Evolusi 2017
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/evolusi.v5i2.2596

Abstract

Abstract - Breast cancer is one type of cancer that continues to increase every year, especially in developing countries. Breast cancer there are two classes of benign and malignant cancer. Class cancer can be predicted by using a genetic algorithm-based neural network. Very high accuracy rate if breast cancer dataset is tested with algorithm of genetic algorithm based on genetic algorithm that is accurate equal to 96.85% compared with only neural network which get accurate value 95.42%, because neuron which has been chosen by neural network algorithm will next be done The process of chromosomal selection. These chromosomes will evolve in a sustainable manner called a generation. In each generation the chromosomes evaluated the success rate of the solution to the problem to be solved using a measure of fitness. To select a chromosome that is maintained for the next generation is a process called selection. The process of chromosome selection using Darwin's previously mentioned concept of Darwinian rule of thumb is that chromosomes with high fitness values will have a greater chance of being selected again in the next generation. Keyword: Neural Network, Genetic Algorithm, Breast Cancer Abstrak - Kanker payudara merupakan salah satu jenis kanker yang terus meningkat setiap tahunnya, terutama di negara berkembang. Kanker payudara ada dua golongan kanker jinak dan ganas. Kanker kelas dapat diprediksi dengan menggunakan jaringan syaraf berbasis algoritma genetika. Tingkat akurasi yang sangat tinggi jika dataset kanker payudara diuji dengan algoritma algoritma genetika berdasarkan algoritma genetika yang akurat sebesar 96,85% dibandingkan dengan hanya jaringan syaraf tiruan yang mendapat nilai akurat 95,42%, karena neuron yang telah dipilih oleh algoritma jaringan syaraf tiruan selanjutnya Proses seleksi kromosom dilakukan. Kromosom ini akan berkembang secara berkelanjutan yang disebut generasi. Pada setiap generasi, kromosom mengevaluasi tingkat keberhasilan pemecahan masalah yang harus diselesaikan dengan menggunakan ukuran kebugaran. Untuk memilih kromosom yang dipertahankan untuk generasi berikutnya adalah proses yang disebut seleksi. Proses pemilihan kromosom dengan menggunakan konsep Darwin yang sebelumnya telah disebutkan sebelumnya adalah bahwa kromosom dengan nilai fitness tinggi akan memiliki kesempatan lebih besar untuk dipilih lagi pada generasi berikutnya. Kata Kunci: Neural Network, Algoritma Genetika, Kanker Payudara
Deteksi Bakteri Pada Produk Makanan Kemasan Menggunakan Algoritma Naïve Bayes Evy Priyanti
IMTechno: Journal of Industrial Management and Technology Vol. 2 No. 1 (2021): Januari 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (206.286 KB) | DOI: 10.31294/imtechno.v2i1.147

Abstract

Produk kemasan merupakan salah satu hal penting yang harus diperhatikan. Dengan pengemasan yang baik maka produk yang ada akan terjaga kualitasnya. Kemasan produk yang baik akan membantu dalam proses pemasaran dan akan meningkatkan pembelian dari konsumen. Penerapan algoritma Naive bayes terbukti dapat mendeksi adanya bakteri dengan nilai akurasi sebesar 80,93%. Tingkat akurasi yang tinggi membuat algoritma naive bayes ini mampu dalam mengurangi kerugian dari produk baik bagi konsumen maupun bagi produsen. Bagi produsen dengan adanya kemasan produk yang baik maka akan menjaga kualitas dari produk dan menambah nilai dari produk itu sendiri dikarenakan akan adanya kepercayaan dari konsumen yang membuat loyalitas tersendiri. Bagi konsumen sendiri akan terhindar dari berbagai macam bakteri yang dapat menyebabkan berbagai macam penyakit yang tentunya tidak diharapkan apalagi untuk produk-produk yang tingkat resikonya besar seperti produk untuk bayi, anak-anak atau lansia yang memerlukan penanganan khusus dalam pengemasan dan pengontrolan produk kemasan yang dijual dipasaran secara bebas
Analisa Keputusan Penerapan Preventive Maintenance Pada Boyd Crusher di Perusahaan Penunjang Industri Pertambangan Girman Sihombing; Evy Priyanti; Bayu Nur Kuncoro; Sofyan Wahyu
IMTechno: Journal of Industrial Management and Technology Vol. 3 No. 1 (2022): Januari 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/imtechno.v3i1.1003

Abstract

Mining industry has the closed relation which is not separated from some types of company. Those service company are very important for mining activities. The support company is a company who give service for making the mining activities run well. Research or analysis service for solid or water mineral content is one of the support companies as state above. The purpose of this research is to know the often damage of spare parts of Boyd Crusher as one of main equipment of service company above. In this case, the coefficient value of the form and the time parameter must be known by using Weibull distribution, so that the maintenance method can be decided by applicating preventive maintenance concept. This research is done by using quantitative – qualitative method which based data around 1 year and the result is found that the Cylindrical Roller Bearing is the highest damage component. This finding is continued to be analysed by using fish bone diagram to know the main factor of those damages and the result is the less of routine lubrication and Lubricant excessive quantity for that component part where this part is made from metal material that has high friction.
PENINGKATAN NEURAL NETWORK DENGAN FEATURE SELECTION UNTUK PREDIKSI KANKER PAYUDARA Evy Priyanti
Swabumi Vol 4, No 1 (2016): Volume 4 Nomor 1 Tahun 2016
Publisher : Universitas Bina Sarana Informatika Kota Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/swabumi.v4i1.1016

Abstract

Breast cancer is increasing in everycountries in the world, especially in developing countries likeIndonesia. Neural network is able to solve problems with the accuracy of data and not linear. Neuralnetwork optimization tested weeks to produce the best accuracy value, applying neural network withfeature selection methods such as Wrapper with Backward Elimination to raise the accuracy produced byNeural Network. Experiments conducted to obtain optimal architecture and to increase the value ofaccuracy. Results of the research is a confusion matrix to prove the accuracy of Neural network beforeoptimized by Backward Elimination was 96.42% and 96.71% after becoming optimized. This proves theestimation of feature selection trials using neural network-based method Backward Elimination moreaccurate than the individual neural network method.
Peningkatan Algoritma Naïve Bayes Menggunakan Algoritma Genetika Pada Klasifikasi Bakteri Evy Priyanti
Swabumi Vol 9, No 2 (2021): Volume 9 Nomor 2 Tahun 2021
Publisher : Universitas Bina Sarana Informatika Kota Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/swabumi.v9i2.11217

Abstract

In previous studies, only using the nave Bayes algorithm and resulted in an accuracy value of 80.93% and currently the accuracy value will be increased by using a genetic algorithm. Learning patterns in genetic algorithms can inform new entrants or new classifications with a faster time. Difficulties in the configuration that exist in nave Bayes can be helped by this genetic algorithm in addition to being able to provide adequate modeling to describe the system. Bacteria consists of three classifications of bacteria: based on how they obtain food, based on gram staining, and based on their shape. In this bacterial data, which consists of numerical parameters containing the sequence name, Mcg, gvh, Lip, chg, aac, alm1, alm2 and a class distributron of 336 attributes, in the class distribution there are 8 protein classes classified, namely cytoplasm (cp), membrane inside without signal sequence (im), perisplasm (pp), inside membrane with uncleavable signal sequence (IMU), outer membrane (om), outer membrane lipoprotein (OML), inside membrane lipoprotein (IML), inner membrane with cleavable signal sequence ( STI). The results of this study resulted in an accuracy value of 81.19%. Thus, it is proven that the performance of the nave Bayes algorithm can be improved by using a genetic algorithm for bacterial classification
Co-Authors Annisa Rahmanita bayu Nur Bayu Nur Kuncoro Fachrul Ramadhani Fadhil Muhammad Abdurrahman Girman Sihombing Hidayat, Tino Huzaiful Yaman Kuncoro, Dwiyanto maulana rizki Nining Suryani Raden Mohammad Riezky Pahlevi Rahmad Budi Ansyah Rahmanita, Annisa Riezky Pahlevi, Raden Mohammad Sofyan Wahyu Tamrin Lanori Veti Apriana Veti Apriana, Veti