Article Per Year (5 Year)
p-Index From 2020 - 2025
3.266
P-Index
This Author published in this journals
All Journal JUITA : Jurnal Informatika Journal of Information System Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Paradigma Jurnal Pilar Nusa Mandiri Informatics for Educators and Professional : Journal of Informatics J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) Jurnal Riset Informatika REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer IJISTECH Jurnal AbdiMas Nusa Mandiri J-SAKTI (Jurnal Sains Komputer dan Informatika) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Insan Digital Transformation Technology (Digitech) J-Icon : Jurnal Komputer dan Informatika Jurnal Informatika dan Bisnis Pengabdian Kepada Masyarakat Indonesia SEAN (ABDIMAS SEAN)
Fintri Indriyani
Universitas Bina Sarana Informatika
Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Mathematics Nursing Physics Public Health Social Sciences Other

Published : 29 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : Jurnal Riset Informatika

 1 
COMPARATION OF DECISION TREE MODEL AND SUPPORT VERCTOR MACHINE IN SENTIMENT ANALYSIS OF REVIEW DATASET SAMSUNG SSD 850 EVO AT NEW EGG SHOP Muhammad Fahmi Julianto; Yesni Malau; Wahyutama Fitri Hidayat; Wawan Nugroho; Fintri Indriyani
Jurnal Riset Informatika Vol 3 No 4 (2021): Period of September 2021
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (704.421 KB) | DOI: 10.34288/jri.v3i4.278

Abstract

The development of information technology is currently growing very rapidly, including the impact on the hardware used. This can be exemplified in the use of hard drives that are starting to switch to SSDs. The process of selecting an SSD product to be used cannot be separated from the sources of information found on the internet. Through the internet, every user can provide reviews, both positive and negative reviews. With the many reviews regarding the review of the Samsung 850 Evo SSD on the NewEgg Store, the author uses it to be processed into information, which will have new knowledge. Based on that, the author makes research, in the form of opinion classification by analyzing sentiment through a text mining approach. In this study, two classification models were used, namely Decision Tree and Support Vector Machine. The results of this study are in the form of a comparison of the 2 models used based on the accuracy and AUC values. Based on research, the Support Vector Machine model is better than the Decision Tree model. This conclusion can be proven by the accuracy value of the Support Vector Machine model resulting in a value of 0.87 or 87% while the accuracy value of the Decision Tree model produces a value of 0.82 or 82%. In addition, the AUC value of the Support Vector Machine model produces a value of 0.87 and the Decision Tree mode produces a value of 0.82 or it can be said that the AUC value of the Support Vector Machine model is better than the Decision Tree model.
PENERAPAN METODE PROFILE MATCHING SEBAGAI PENDUKUNG KEPUTUSAN PEMILIHAN JURUSAN PADA SMK AL HIDAYAH Fintri Indriyani
Jurnal Riset Informatika Vol. 1 No. 2 (2019): Periode Maret 2019
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pemilihan jurusan pada SMK Al Hidayah dilakukan pada saat siswa/siswi mendaftar di sekolah tersebut, dengan acuan nilai minimal tiga mata pelajaran memenuhi standard minimal nilai yang telah ditetapkan pihak sekolah untuk tiap jurusan. Penempatan jurusan dilakukan secara manual oleh panitia penerimaan siswa baru dengan melihat data nilai siswa dan minat siswa. Hal ini memiliki kelemahan dari sisi waktu tidak efisien dan juga memungkinkan kesalahan input oleh panitia penerimaan siswa baru. Sehingga diperlukan sebuah sistem berbasis komputer untuk membantu penentuan jurusan yang sesuai dengan kompetensi calon siswa. Metode Profile matching dalam penelitian ini digunakan untuk membantu mempercepat proses analisa kriteria nilai dan minat siswa disesuaikan dengan standard dari pihak sekolah. Kriteria nilai meliputi nilai matematika, bahasa inggris dan bahasa Indonesia, sedangkan pilihan jurusan ada akuntansi, administrasi perkantoran dan pemasaran. Hasil penelitian adalah rekomendasi jurusan sehingga dapat mempercepat proses penjurusan sesuai dengan profile masing-masing siswa.
ANALISIS KEPUASAN PENGGUNA APLIKASI OJEK ONLINE MENGGUNAKAN METODE TECHNOLOGY ACCEPTANCE MODEL (TAM) Novian Putra Utama; Fintri Indriyani
Jurnal Riset Informatika Vol. 1 No. 3 (2019): Periode Juni 2019
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kemacetan di Jakarta adalah suatu fenomena yang sudah menjadi momok masyarakat, kemacetan terjadi karena semakin banyaknya kendaraan bermotor yang ada di Jakarta, masih kurangnya kesadaran masyarakat berkendaraan dan berlalu lintas dan kurang nya tingkat kesadaran masyarakat akan penggunaan transportasi umum yang menjadi salah satu solusi untuk mengurangi kemacetan di Jakarta. Berdasarkan uraian pada latar belakang masalah, maka dapat di idetifikasikan beberapa masalah, diantaranya 1) Apakah kemudahan persepsian (Percived Ease Of Use) berpengaruh positif terhadap penerimaan sistem aplikasi ojek online di Indonesia? 2) Apakah kemanfaatan persepsian (Percived Usefulness) berpengaruh positif terhadap penerimaan aplikasi ojek online di Indonesia?. Tujuan penelitian ini untuk menganalisis dan menguji apakah kemudahan persepsian (Percived Ease Of Use) berpengaruh positif terhadap penerimaan sistem aplikasi ojek online di Indonesia, dan untuk menganalisis dan menguji apakah kemanfaatan persepsian (Percived Usefulness) berpengaruh positif terhadap penerimaan aplikasi ojek online di Indonesia.
COMPARATION OF DECISION TREE MODEL AND SUPPORT VERCTOR MACHINE IN SENTIMENT ANALYSIS OF REVIEW DATASET SAMSUNG SSD 850 EVO AT NEW EGG SHOP Muhammad Fahmi Julianto; Yesni Malau; Wahyutama Fitri Hidayat; Wawan Nugroho; Fintri Indriyani
Jurnal Riset Informatika Vol. 3 No. 4 (2021): September 2021 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v3i4.97

Abstract

The development of information technology is currently growing very rapidly, including the impact on the hardware used. This can be exemplified in the use of hard drives that are starting to switch to SSDs. The process of selecting an SSD product to be used cannot be separated from the sources of information found on the internet. Through the internet, every user can provide reviews, both positive and negative reviews. With the many reviews regarding the review of the Samsung 850 Evo SSD on the NewEgg Store, the author uses it to be processed into information, which will have new knowledge. Based on that, the author makes research, in the form of opinion classification by analyzing sentiment through a text mining approach. In this study, two classification models were used, namely Decision Tree and Support Vector Machine. The results of this study are in the form of a comparison of the 2 models used based on the accuracy and AUC values. Based on research, the Support Vector Machine model is better than the Decision Tree model. This conclusion can be proven by the accuracy value of the Support Vector Machine model resulting in a value of 0.87 or 87% while the accuracy value of the Decision Tree model produces a value of 0.82 or 82%. In addition, the AUC value of the Support Vector Machine model produces a value of 0.87 and the Decision Tree mode produces a value of 0.82 or it can be said that the AUC value of the Support Vector Machine model is better than the Decision Tree model.
Co-Authors Agam, Alfadh Saifullah Agra Syahputra Ali Haidir Anisa Rahma, Anisa Annisa Dwi Wijayanti Eni Irfiani Fatmah Citra R Firmansyah, Naufal Hendro Priyon Hendro Priyono Heny Sumarno, Heny I Gusti Bagus Wiksuana Indra Riyana Rahadjeng Khoirunnisa, Tasya Ramadhinta Kurniasih R Sihite Linda Maulida Maulida, Linda Muhammad Fahmi Julianto Mukhlis Fahmi Asidiqi Nining Suryani Novian Putra Utama Priyono, Hendro Purwoko, Agus S Susliansyah Sanjaya, Yanuar Rizki Susliansyah, Susliansyah Syaiful Anwar Tommi Alfian Armawan Sandi Wahyu, Abdullah Guntur Wahyu, Abdullah Guntur Wahyutama Fitri Hidayat Wawan Nugroho Yahdi Kusnadi, Yahdi Yesni Malau