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Perancangan Penerimaan Calon Siswa Baru Berbasis Mobile Study Kasus Sekolah Menengah Kejurusan (SMK) Jakarta Pusat 1 Amelina, Amelina; Panca Saputra, Elin
Jurnal Teknik Informatika Vol. 4 No. 1 (2018): JTI Periode Februari 2018
Publisher : LPPM STMIK ANTAR BANGSA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51998/jti.v4i1.184

Abstract

Abstract-- One feature that is now trending from the development of mobile web-based. SMK Central Jakarta 1 is a private vocational high school in business and management. In managing the school information system SMK Central Jakarta 1 already has a website, just for the registration of new students this school is still implementing a system that is less efficient, from prospective students come to school carry file registration requirements and waiting to get the form, until the process of recording by the committee registration up to the storage of new student candidate files so that it is very time consuming. Registration of new mobile-based students is the best solution to solve the problems that exist in the school. The method used in the design of this program is the method of waterfall system development. Furthermore, the result of this design is generated a new student registration program based on mobile program at SMK Central Jakarta 1, so the computerized system is better than the previous system that can make it easier for the user..Intisari-- Salah satu fitur yang sekarang sedang tren dari perkembangan mobile berbasis web. SMK Jakarta Pusat 1 merupakan sekolah menengah kejuruan swasta dalam bidang bisnis dan manajemen. Dalam mengelola sistem informasi sekolah SMK Jakarta Pusat 1 sudah mempunyai website, hanya saja untuk kegiatan pendaftaran siswa baru sekolah ini masih menerapkan sistem yang kurang efisien, dari mulai calon siswa datang kesekolah membawa berkas persyaratan pendaftaran dan menunggu untuk mendapatkan formulir, sampai proses pencatatan oleh panitia pendaftaran hingga sampai penyimpanan berkas calon siswa baru sehingga dirasa sangat memakan waktu. Pendaftaran calon siswa baru berbasis mobile ini merupakan solusi yang terbaik untuk memecahkan permasalahan yang ada pada sekolah tersebut. Metode yang digunakan dalam perancangan program ini adalah metode pengembangan sistem waterfall. Selanjutnya hasil dari perancangan ini adalah dihasilkan suatu program sistem pendaftaran siswa baru berbasis mobile pada SMK Jakarta Pusat 1, sehingga sistem terkomputerisasi ini lebih baik dari sistem yang terdahulu yang dapat lebih memudahkan bagi pengguna.Kata kunci : Perancangan, Pendaftaran siswa, Mobile
Perancangan Penerimaan Calon Siswa Baru Berbasis Mobile Study Kasus Sekolah Menengah Kejurusan (SMK) Jakarta Pusat 1 Amelina, Amelina; Panca Saputra, Elin
Jurnal Teknik Informatika Vol 4 No 1 (2018): JTI Periode Februari 2018
Publisher : LPPM STMIK ANTAR BANGSA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51998/jti.v4i1.184

Abstract

Abstract-- One feature that is now trending from the development of mobile web-based. SMK Central Jakarta 1 is a private vocational high school in business and management. In managing the school information system SMK Central Jakarta 1 already has a website, just for the registration of new students this school is still implementing a system that is less efficient, from prospective students come to school carry file registration requirements and waiting to get the form, until the process of recording by the committee registration up to the storage of new student candidate files so that it is very time consuming. Registration of new mobile-based students is the best solution to solve the problems that exist in the school. The method used in the design of this program is the method of waterfall system development. Furthermore, the result of this design is generated a new student registration program based on mobile program at SMK Central Jakarta 1, so the computerized system is better than the previous system that can make it easier for the user..Intisari-- Salah satu fitur yang sekarang sedang tren dari perkembangan mobile berbasis web. SMK Jakarta Pusat 1 merupakan sekolah menengah kejuruan swasta dalam bidang bisnis dan manajemen. Dalam mengelola sistem informasi sekolah SMK Jakarta Pusat 1 sudah mempunyai website, hanya saja untuk kegiatan pendaftaran siswa baru sekolah ini masih menerapkan sistem yang kurang efisien, dari mulai calon siswa datang kesekolah membawa berkas persyaratan pendaftaran dan menunggu untuk mendapatkan formulir, sampai proses pencatatan oleh panitia pendaftaran hingga sampai penyimpanan berkas calon siswa baru sehingga dirasa sangat memakan waktu. Pendaftaran calon siswa baru berbasis mobile ini merupakan solusi yang terbaik untuk memecahkan permasalahan yang ada pada sekolah tersebut. Metode yang digunakan dalam perancangan program ini adalah metode pengembangan sistem waterfall. Selanjutnya hasil dari perancangan ini adalah dihasilkan suatu program sistem pendaftaran siswa baru berbasis mobile pada SMK Jakarta Pusat 1, sehingga sistem terkomputerisasi ini lebih baik dari sistem yang terdahulu yang dapat lebih memudahkan bagi pengguna.Kata kunci : Perancangan, Pendaftaran siswa, Mobile
IMPLEMENTASI INFORMATION RETRIEVAL SYSTEM MENGGUNAKAN TEKNIK VECTOR SPACE MODELS(VSM) UNTUK SISTEM PENGUJIAN KOMPUTER BERBASIS TEKS elin panca saputra; Sugiono .; Supriatiningsih .
SPIRIT Vol 11, No 2 (2019): SPIRIT
Publisher : STMIK YADIKA BANGIL

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (156.43 KB) | DOI: 10.53567/spirit.v11i2.124

Abstract

Ujian berbasis komputer merupakan suatu sistem ujian yang memanfaatkan teknologi informasi dalam pengelolaan jawaban serta penilaian dari hasil ujian yang telah dikerjakan oleh siswa. Pada umumnya ujian berbasis komputer masih bersifat multiple choice atau pilihan ganda, hal ini menyebabkan pola berfikir siswa kurang berkembang. Sejatinya ujian berbasis esay harus diterapkan untuk mengembangkan pola fikir siswa dalam memahami suatu bidang keilmuan. “Temu kembali infromasi” merupakan  ilmu yang mempelajari tentang prosedur dan metode yang digunakan untuk menemukan kembali informasi yang tersimpan dari berbagai sumber (resources) yang relevan atau koleksi sumber informasi yang dicari. Dengan tindakan  indeks (indexing), panggilan (searching), pemanggilan data kembali (penarikkaning). VSM(Vector Space Model) merupakan salah satu metode IR dengan cara mencari kesamaan (similarity) dari suatu dokumen berdasarkan query yang diinputkan. Pada penelitian ini metode VSM akan diterapkan kedalam permasalahan ujian komputer berbasis esay untuk mendapatkan tingkat similarity suatu jawaban dengan query yang telah ditentukan oleh guru. Studi kasus yang telah dilakukan dengan data berupa 1 soal esay yang dijawab oleh 4 siswa dengan masing-masing jawaban yang berbeda dapat menampilkan tingkat similaritas 0.14, 0.0, 0.10 dan 0.20. Sehingga dapat disimpulkan bahwa urutan rangking similarity jawaban terhadap query yaitu siswa ke-4, ke-1, ke-3 dan ke-2. 
Perancangan Aplikasi Sistem Pakar Diagnosa Awal Kanker Reproduksi Wanita Dengan Metode Certainty Factor Sukmawati Anggraeni Putri; Elin Panca Saputra
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 2, No 3 (2018): Juli 2018
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v2i3.659

Abstract

Until now, cancer is one that suffered by the people of Indonesia, especially in cervical cancer (cervix) suffered by Indonesian women. Not only cervical cancer suffered by Indonesian women, but also other diseases that attack the female reproductive organs. Such diseases, cervical cancer, ovarian cancer, endometrial cancer, vaginal cancer, ovarian cysts and myomas. To prevent the number of deaths of patients, of course the initial diagnosis as one of the solutions. As used in this study in development an early diagnosis system of female reproductive cancer. This expert system adds value to the technology to assist in the handling of an increasingly sophisticated information age. This Expert System Application generates an Update that enables patients who suffer from symptoms that are felt by the patient. This system is also a result of the necessity of women suffering from cancer experienced by patients. The amount of trust value is the result of calculation using Certainty Factor method.
Prediction of Successful Elearning Based on Activity Logs with Selection of Support Vector Machine based on Particle Swarm Optimization Elin Panca Saputra; Sukmawati Angreani Putri; Indriyanti Indriyanti
Indonesian Journal of Artificial Intelligence and Data Mining Vol 2, No 1 (2019): March 2019
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v2i1.6500

Abstract

Prediction is a systematic estimate that identifies past and future information, we predict the success of learning with elearning based on a log of student activities. In our current study we use the Support vector machine (SVM) method which is comparable with Particle Swarm Optimization. It is known that SVM has a very good generalization that can solve a problem. however, some of the attributes in the data can reduce accuracy and add complexity to the Support Vector Machine (SVM) algorithm. It is necessary for existing tribute selection, therefore using the Particle swarm optimization (PSO) method is applied to the right attribute selection in determining the success of elearning learning based on student activity logs, because with the Swarm Optimization (PSO) method can increase accuracy in determining selection of attributes.
Comparison Of Data Mining In E-Learning Learning Based On Log Aktivity On PSO-Based Nural Network Algorithms With PSO-Based SVM Elin Panca Saputra; Supriatiningsih Supriatiningsih; Indriyanti Indriyanti
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.10519

Abstract

The purpose of this research is to find a higher or better level of accuracy, we make a comparison between the Neural Network method based on Particle Swarm Optimization and the Particle Swarm Optimization-based support vector machine method, from evaluation on e-learning based learning systems is very important to determine the level. accuracy in learning.. In addition, the purpose of this study is to find the attributes of the highest predictive results of student learning who follow the e-learning learning system. The data we use are 641 users which are taken from the log of student learning activities from the LMS. The logs we use are Gender, Excercise, Forum, Chat, Diskusi, Upload An Assgmnt, Message, Excercise Quiz, dan Total Log. All logs will be recorded in the LMS. The data used in this study, the results of the tests we conducted, the results obtained using the PSO-based Neural Network (NN) method obtained an accuracy value of 97.35%, and the results of the AUC value were 98.60%. Then we did the second trial using the PSO-based support vectore machine (SVM) method to get an accuracy value of 88.47% and an AUC value of 93.80%. Then the conclusion is that using the neural network method is higher than using the spport vector machine method with an accuracy difference of 8.88% while the AUC accuracy value is a difference of 4.8%.
Classifications Using Artificial Neural Network Method In Protecting Credit Fitness Elin Panca Saputra; Indriyanti Indriyanti; Supriatiningsih Supriatiningsih
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 1 (2020): March 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i1.9442

Abstract

Classification is information that has the closest relationship with data, we make a prediction in providing customer eligibility to get a loan from a financial service institution. In this study, we use the Artificial Neural Network (NN) method in combination with the Particle Swarm Optimization method. It is known that the method has excellent generalizations to solve a problem in increasing accuracy. However, some of the attributes in the data can reduce accuracy and increase the complexity of the Artificial Neural Network (ANN) algorithm. Therefore, attribute selection is very necessary, the attribute selection method used in this study is the Particle swarm optimization (PSO) method. This method can be used for proper attribute selection in determining lending to customers, therefore the Particle Swarm Optimization (PSO) method can increase the value of higher accuracy weights in determining attribute selection.
PREDIKSI KEBERHASILAN TELEMARKETING BANK UNTUK MENCARI ALGORITMA DENGAN PERFORMA TERBAIK Elin Panca Saputra
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 2 No 2 (2017): JITK Issue February 2017
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (867.99 KB) | DOI: 10.33480/jitk.v2i2.385

Abstract

To find algorithms that have the best performance in predicting the success of telemarketing in banking courses researchers have conducted various material tests of several algorithms for data from the uci data set, and have as many as 17 attributes, some algorithms that have previously been tested in this study. to find the best performing algorithm using algorithm authors, among others, is to use an algorithm based on particle swarm optimization to optimize some attribute values ​​and to improve the accuracy of algorithms and higher data classification, and can produce even higher accuracy values. From the neural algorithm network (NN) based on PSO, the results are 91.80%, Support Vector Machine (SVM) to get an accuracy of 90.20%. Naif Bayes (NB) with an accuracy of 89.41%, and to use the Decision Tree (DT) algorithm with an accuracy of 90.93%. Then the PSO Neural Network based algorithm is clear resulting in higher accuracy than some algorithms tested with an accuracy of 91.80%. These results are classified as very good (very good classification).
PENERAPAN ALGORITMA SVM BERBASIS PSO UNTUK TINGKAT PELAYANAN MARKETING TERHADAP LOYALITAS PELANGGAN KARTU KREDIT Elin Panca Saputra
Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 12 No 2 (2015): TECHNO Periode September 2015
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1221.12 KB) | DOI: 10.33480/techno.v12i2.440

Abstract

This research will be used method of support vector machine and will do the selection of attributes by using particle swarm optimization to determine the level of service. After the test results obtained are support vector machine produces an accuracy value of 92.25%, 95.98% and a precision value AUC value of 0.976% then be selected attributes using particle swarm optimization attributes, amounting to 8 predictor variables selected two attributes used. The results showed higher accuracy value that is equal to 93.75%, 93.91% and a precision value AUC value of 0.973%. Thus increasing the accuracy of 1.5%, and increased the AUC of 0.006. With accuracy and AUC values, the algorithm of support vector machines based on particle swarm optimization in the category of classification is very good.
Grouping of Success Levels in E-Learning Learning Factors: Approaches with Machine Learning Algorithm Elin Panca Saputra; Sugiono; Indriyanti; Supriatiningsih; Hafis Nurdin
Jurnal Mantik Vol. 5 No. 1 (2021): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol5.2021.1271.pp78-85

Abstract

The purpose of this study is to obtain the results of the modeling process on grouping the results of student learning, and to produce student success rates, while to find the results of the accuracy level of student learning success based on E-Learning with the Support Vectore Machine (SVM) method. In this grouping, there are 5 clusters. Furthermore, the process of counting can be as many as 2 iterations, namely getting the final result in the form of Cluster-1 with a total of 10 students, cluster-2 with a total of 45 students, cluster-3 with a total of 22 students, cluster 4 with a total of 13 students, and the next is cluster-5 with a total of 19 students. Then the results of the resulting process with a total of 5 types of clusters, namely from students who get the highest results to the lowest. In addition, this study also looks for the level of accuracy in e-learning student learning processes using the Support Vectore Machine (SVM) method, the accuracy results obtained are 90.91%, while the AUC results are 82.81%. then the value of the calculated accuracy rate can be classified as accuracy with the predicate result that is good.