Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Inspiration JURNAL MEDIA INFORMATIKA BUDIDARMA
Kusrini Kusrini
Universitas Amikom, Yogyakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Jurnal Inspiration

 1 
Implementation Of C4.5 Algorithm For Predicting Late School Tuition Payments Using Python Victor Saputra Ginting; Kusrini Kusrini; Emha Taufiq
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol 10, No 1 (2020): Jurnal Inspiration Volume 10 Issue 1
Publisher : STMIK AKBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v10i1.2535

Abstract

The Donation of Educational Development (SPP) School is one of the important components in implementing education, because School SPP is one of the requirements in achieving educational goals. Research conducted by Muqorobin, 2019 with the title "Optimization of the Naive Bayes Method with Feature Selection Gain for Predicting Late School Fee Payments" with Object Research conducted at SMK Al-Islam Surakarta resulted in an accuracy rate of 90%. The research was conducted by using several variables such as the amount of income, family dependents, parents 'educational background and parents' age. The research that will be carried out later will predict the late payment of School Fees by using the Dataset from the research conducted by Muqorobin, 2019 and implemented into the form of programming using the python programming language to produce prediction results. The research results obtained get an accuracy rate of 73%.
Peningkatan Akurasi Klasifikasi Sentimen Ulasan Makanan Amazon dengan Bidirectional LSTM dan Bert Embedding David Junggu Manggala Pasaribu; Kusrini Kusrini; Sudarmawan Sudarmawan
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol 10, No 1 (2020): Jurnal Inspiration Volume 10 Issue 1
Publisher : STMIK AKBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v10i1.2568

Abstract

Sudah memasuki revolusi industri 4.0 dengan infrastuktur internet semakin memadai dan biaya lebih murah mengakibatkan banyak masyarakat menggunakan layanan pada internet. Sehingga organisasi bisnis terdisrupsi untuk merambah ke media online. Seperti Amazon perusahaan e-commerce meliputi Costumer to Costumer maupun Business to Business, salah satu produk yang dipasarkan adalah makanan. Untuk menaikkan pemasukannya maka perusahaan harus mengerti kebutuhan pembeli. Sehingga dilakukan analisis sentimen konsumen namun proses ini memerlukan waktu lama sehingga dibuat secara otomatis menggunakan metode kecerdasan buatan. Dalam hasil penelitian tentang analisis sentimen pada dataset Amazon Fine Food Review menggunakan metode deep learning Bidirectional Long Short-Term Memory dengan penghasil vektor kata Bidirectional Encoder Representations from Transformers mampu menghasilkan akurasi yang lebih baik daripada menggunakan smetode machine learning Logistic Regression dengan pembobotan kata Mutual Information dan Bag of Words serta model deep learning Convolutional Neural Network dan Long Short-Term Memory dengan penghasil vektor kata Word2Vec dan GloVe pada konfigurasi ukuran embedding dan jumlah dataset paling besar yaitu 300 dan 85.000 sebesar 93 %. 
Co-Authors David Junggu Manggala Pasaribu Emha Taufiq Henri Kurniawan Kusnawi Kusnawi Sudarmawan Sudarmawan Victor Saputra Ginting