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All Journal E-Dimas: Jurnal Pengabdian kepada Masyarakat Prosiding Seminar Nasional Teknoka Jurnal Nasional Komputasi dan Teknologi Informasi STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Jurnal ABDIKU Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer JRKT (Jurnal Rekayasa Komputasi Terapan) Kapas: Kumpulan Artikel Pengabdian Masyarakat
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Informatika, Universitas Indraprasta PGRI
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Journal : STRING (Satuan Tulisan Riset dan Inovasi Teknologi)

 1 
Perancangan Sistem Pendukung Keputusan dalam Membaca Cepat untuk Menemukan Ide Pokok Paragraf Nunu Kustian; Wanti Rahayu; Retna Ningsih
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 2, No 2 (2017)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (399.942 KB) | DOI: 10.30998/string.v2i2.2108

Abstract

Decision Support Systems (DSS) is a system that can assess the alternatives in order to help with the decision-making. Indonesian language lesson in Junior HighSchool, especially the topic of finding the main idea ina paragraph is still a focusIndonesian teachers keep as many students still have difficulty in understandinghow to find the main idea in a paragraph. Systems designed by using the method of Simple Additive Weighting (SAW) will perform to determine the selected speed reading techniques in finding the main idea in a paragraph. The data are analyzedbyevaluating the questionnaires in the form of questions given to population consisting of 36 students. The built systems help to resolve and speed up the selected techniques for finding a main idea in a paragraph.
Pemetaan Tabel Relationship dalam Visualisasi Diagram Relasi untuk Eksplorasi Data Pada Database Siti Julaeha; Nunu Kustian; Dudi Parulian
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 5, No 2 (2020)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (742.433 KB) | DOI: 10.30998/string.v5i2.6653

Abstract

Visualization is a tool that makes it easy to understand the information presented in charts and graphs. The purpose is to facilitate data exploration. Therefore, the authors use relationship diagrams as Mathematical Modeling to explore data that has a relationship between tables. The purpose of the study is to map the dataset, which found out a possibility that some errors when entering data. In the mapping process, we use the composition of relations, thus making the number of possible relationships in the mapping. Because of the problem, the table mapping use into a binary relationship diagram based on the number of attributes in a table. The conclusion obtained when using a binary relation diagram is that the more column in a table, the more mapping relationship diagrams must be drawn. Although the relationship diagram becomes very complex, it handled if the tables are correctly normalized.
Pemetaan Tabel Relationship dalam Visualisasi Diagram Relasi untuk Eksplorasi Data Pada Database Siti Julaeha; Nunu Kustian; Dudi Parulian
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 5, No 2 (2020)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (742.433 KB) | DOI: 10.30998/string.v5i2.6653

Abstract

Visualization is a tool that makes it easy to understand the information presented in charts and graphs. The purpose is to facilitate data exploration. Therefore, the authors use relationship diagrams as Mathematical Modeling to explore data that has a relationship between tables. The purpose of the study is to map the dataset, which found out a possibility that some errors when entering data. In the mapping process, we use the composition of relations, thus making the number of possible relationships in the mapping. Because of the problem, the table mapping use into a binary relationship diagram based on the number of attributes in a table. The conclusion obtained when using a binary relation diagram is that the more column in a table, the more mapping relationship diagrams must be drawn. Although the relationship diagram becomes very complex, it handled if the tables are correctly normalized.
Principal Component Analysis untuk Sistem Pengenalan Wajah dengan Menggunakan Metode Eigenface Nunu Kustian
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 1, No 2 (2016)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (426.17 KB) | DOI: 10.30998/string.v1i2.1042

Abstract

Pengenalan wajah (Face Recognition) adalah merupakan suatu pengenalan pola (pattern recognition) yang khusus untuk kasus wajah. Ini dapat dideskripsikan sebagai pengklasifikasian suatu wajah apakah dikenali (known) atau tidak dikenali (unknown), dimana setelah dibandingkan setelah kemudian disimpan secara tersendiri. Proses pengenalan wajah yang dilakukan oleh komputer tidak semudah dan secepat dibandingkan dengan proses pengenalan yang dilakukan oleh manusia. Manusia dengan mudah dapat mengenali wajah seseorang dengan sangat cepat tanpa rasanya harus berfikir. Input yang diperlukan pada aplikasi ini adalah berupa citra wajah dengan ukuran dan resolusi yang sama. Output aplikasi ini adalah berupa class terdekat dari wajah yang ingin dikenali. Aplikasi ini dibuat menggunakan MATLAB yang cukup handal dan mudah dalam perhitungan matematik dan bekerja dalam konsep matrik serta mempunyai fungsi visualisasi yang bervariasi. Salah satu metode pendekatan yang digunakan adalah Eigenface, sebuah metode yang dikemukakan oleh Turk dan Pentland. Metode ini melibatkan sebuah set wajah yang pada dasarnya melibatkan proses analisis komponen utama (Principal Component Analysis). Dalam metode ini citra wajah akan diproyeksikan dalam sebuah ruang fitur yang menonjolkan variasi yang signifikan di antara citra wajah yang diketahui. Fitur signifikan inilah yang disebut dengan Eigenface karena fitur-fitur tersebut adalah komponen utama dari suatu set citra wajah untuk pelatihan. Hal yang perlu diingat adalah fitur-fitur ini tidak berarti berhubungan dengan fitur-fitur yang terdapat pada wajah, seperti mata, hidung, mulut, dan telinga. Eigenface hanya akan menangkap point-point pada citra yang menyebabkan variasi yang signifikan antara wajah-wajah dalam database yang membuat mereka dapat dibedakan.Kata Kunci : Eigenface, Citra, Wajah, PCA , Matlab
Implementasi Data Mining Menggunakan Model SVM untuk Prediksi Kepuasan Pengunjung Taman Tabebuya Agus Darmawan; Nunu Kustian; Wanti Rahayu
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 2, No 3 (2018)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (787.719 KB) | DOI: 10.30998/string.v2i3.2439

Abstract

Park is an area built in hard and soft materials supporting each other and deliberately designed and created by people as an outdoor or indoor refreshment place. The Tabebuya Park in Jagakarsa, South Jakarta is a tourist attraction flocked by visitors on regular days and holidays. The place is very beautiful and can give a sensation different to the one in our daily activities. One of the ways to improve visitor’s satisfaction during their visit is by improving the park’s service quality. This research aims to predict the satisfaction of Tabebuya Park visitors by applying SVM (Support Vector Machine) algorithm method in which the experiments in the model are evaluated and validated using the Confusion Matrix and AUC (Area Under the Curve) with ROC (Receiver Operating Characteristic). From the results of the evaluation and validation, it can be concluded the average accuracy and performance of algorithm SVM is 86.00% with AUC value of 0.947.
Co-Authors Aan Risdiana Agus Darmawan Dudi Parulian Ek Ajeng Rahmi Pinahayu Erlin Windia Ambarsari Frasdica Fujianto Nugraha Irfan Ismunandar Mardiana Mardiana Mochamad Rizki Adityo Muhamad Salman Alfarisi Parulian, Dudi Rahayu, Wanti Reko Syarif Hidayatullah Retna Ningsih Retna Ningsih Risma Nurul Auliya Rizki Ridwan Rizki Ridwan Rudi Prasetya Siti Julaeha Siti Julaeha Wanti Rahayu Wanti Rahayu