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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Simetris Jurnal Pengembangan Kota Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Sisfokom (Sistem Informasi dan Komputer) JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Krea-TIF: Jurnal Teknik Informatika Jurnal Riset Informatika Progresif: Jurnal Ilmiah Komputer Jurnal Informatika: Jurnal Pengembangan IT
Arif Dwi Laksito
Universitas Amikom Yogyakata
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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PREDIKSI PENGUNDURAN DIRI MAHASISWA UNIVERSITAS AMIKOM YOGYAKARTA MENGGUNAKAN METODE NAIVE BAYES Mahanggara, Andika; Laksito, Arif Dwi
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 10, No 1 (2019): JURNAL SIMETRIS VOLUME 10 NO 1 TAHUN 2019
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (239.28 KB) | DOI: 10.24176/simet.v10i1.2967

Abstract

Universitas AMIKOM Yogyakarta merupakan Universitas yang telah berdiri sejak tahun 1994 dan telah banyak menghasilkan mahasiswa yang berbakat dalam bidang komputer. Setiap tahunnya Universitas AMIKOM Yogyakarta memiliki mahasiswa baru yang jumlahnya terus bertambah hingga saat ini, namun sering kali sejumlah peserta didik pada Universitas AMIKOM Yogyakarta mengundurkan diri. Dengan menggunakan teknik klasfikasi probabilistik sederhana yaitu Algoritma Naive Bayes dapat dilakukan prediksi terhadap pengunduran diri mahasiswa dengan menghitung sekumpulan probabilitas dari jumlah frekuensi dan kombinasi nilai dari dataset yang didapatkan. Implementasi Naive Bayes diharapkan mampu untuk memprediksi pengunduran diri agar pihak lembaga dapat melakukan pencegahan terhadap pengunduran diri mahasiswa. Hasil uji coba dari 120 dataset yang dibagi menjadi 70% data training dan 30% data testing diperoleh nilai error sebesar 22,22%. Sedangkan tingkat akurasi yang diperoleh sebesar 77,78% dengan hasil prediksi 22 mahasiswa diprediksi bertahan dan 14 mahasiswa diprediksi mengundurkan diri.
Implementasi Aplikasi Sentimen Pada Data Twitter Jelang Pemilu 2024 Humam, Choirul; Laksito, Arif Dwi
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5051

Abstract

Elections are one of the most important democratic processes, giving citizens the right to choose their leaders. In today's digital era, social media is an increasingly important information source influencing public perception. Twitter has been a social media from the past until now that still exists in finding information. Tweets are one of the most frequently used services to express opinions or opinions to the public. Sentiment analysis as an application of Natural Language Processing (NLP) is helpful in understanding public opinion towards prospective leaders and issues discussed during election campaigns. The motivation for this study is to conduct text classification using a deep learning model called LSTM and to compare the use of oversampling and non-oversampling methods. This research started by collecting datasets from Twitter, labelling, pre-processing, creating and evaluating the model, and implementing it into the web application. The experiment showed that the random oversampling technique gets more significant accuracy than non-oversampling. Random oversampling produces an accuracy of 0.82 at epoch 25, while non-oversampling reaches an accuracy of 0.61 at epoch 50
Sentiment Analysis of Pedulilindungi Application Reviews Using Machine Learning and Deep Learning Dwijaya, Ahmad Rais; Laksito, Arif Dwi
Jurnal Riset Informatika Vol. 5 No. 2 (2023): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.207

Abstract

The COVID-19 pandemic that hit the world at the end of early 2020 caused many losses. The Indonesian government has established various ways to reduce the path of the COVID-19 pandemic by launching the PeduliLindungi application to reduce the spread of COVID-19. Various layers of society responded to the launch of the application with various opinions. This research mainly analyzes public opinion sentiment toward the PeduliLindungi application, as determined by 10,000 reviews on the Google Play Store. This study aims to compare the performance of deep learning and machine learning models in sentiment analysis. The stages of the research method begin with data collection methods, data pre-processing, and sentiment analysis using a machine learning model with the embedding of the word TF-IDF, which includes the Nave Bayes algorithm, Decision Tree, Random Forest, K-Nearest Neighbour, and SVM. As for the deep learning model with the fastText word embedding word representation technique using the LSTM algorithm, an evaluation is carried out using the confusion matrix. The results of this study state that deep learning models perform better than machine learning models.
Co-Authors Acihmah Sidauruk Adninda, Gardyas Bidari Anas Fikri Hanif Burhanudin, Nazil Ilham Darmawan, Briga Dwijaya, Ahmad Rais Eli Pujastuti, Eli Ervan Triyanto Hafizh Fauzi Fathurahman Hayaty, Mardhiya Heri Sismoro Humam, Choirul Kartikakirana, Renindya Azizza Mahanggara, Andika Mahanggara, Andika Muhammad Darwis Arifin Muhammad Resa Arif Yudianto Muhammad Royyan Saputra Nur ‘Azah Nurcholis, Muhammad Tofa Rahmi, Alfie Nur Sasongko, Theopilus Bayu Sumarni Adi Syauqani Juliansyah