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Fathoni -
Information Systems Universitas Sriwijaya Palembang, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Neuroscience Public Health Other

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Analisis Sentiment Pelanggan Terhadap Penilaian Produk Pada Toko Online Shop Amreta Menggunakan Metode Naïve Bayes Classification Alisia Silver Stone; Fathoni Fathoni
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4436

Abstract

Sentiment analysis or opinion mining is an analysis that aims to see the sentiment of people or groups regarding certain entities. The sentiments expressed by society can be in positive, negative and neutral form. One media that can be given an opinion by the public is in the e-commerce application,  namely the shopee application, shopee has a comment or assessment feature on the product that has been purchased. Toko which was used as a sample of researchersis an amreta online shop store  , based on the results of the identification of the problem, it was found that the fact was that many comments did not match the stars given so it can be said that the rating cannot represent that the store's performance is good or not. Therefore, to increase the profit of shop work, the amreta still needs to evaluate the store. In conducting an evaluation, the store needs to classify positive, negative or neutral comments. Analysis of customer sentiment towards product assessments in amreta online shop stores using the naive bayes classification method. The use of test data in this study was obtained from the sentiment of amreta online shop consumers as much as 2014 data,then the data was processed through  the data cleaning process  resulting in net data of 1899 data. Furthermore, the data preprocessing process is divided into 3 stages, namely Tokenize Data, Transform case and Stopword removal. After that, the analysis of data for the automatic labeling stage using Text Vectorize from the process obtained data division into 3 data groups of 71% or 1343 positive data, 3% or 52 negative data and 26% or 504 neutral data.  furthermore, it is processed using rapidminer tools while for operators in the form of algorithms using the Sentiment Naïve Bayes Classification model  through automatic calculations.  The results of the study can be concluded that the test data obtained have an accuracy level of 97.16% using the Naive Bayes Classification model.
Sentiment Analysis Pada Masyarakat Terhadap LRT Kota Palembang Menggunakan Metode Improved K-Nearest Neighbor Siti Nur Arafah; Fathoni Fathoni
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4434

Abstract

The LRT is a sustainable fast transportation system, which was built to overcome the congestion problem in the city of Palembang. In order to attract people's interest to switch to using public transportation compared to private transportation, one of them is by improving the quality of services provided. Sentiment analysis is used to classify positive and negative opinions on users of Palembang City LRT transportation services. In addition to retrieving data through crawling data on tweet data, the researchers also distributed questionnaires. In conducting the classification process of sentiment analysis, this study uses the Improved K-Nearest Neighbor method which is a modification of the K-Nearest Neighbor method. The results of this research are testing and training data on 1617 data records and the highest accuracy of 74.07% on 90% training data and 10% testing data, with 70% precision, 56% recall and 59% f-1 score, while the lowest accuracy with an accuracy of 63.04% on 50% training data and 50% testing data, with 44% precision, 42% recall and 42% f-1 score
Implementasi E-learning sebagai Komplemen dan Blanded Learning Untuk Meningkatkan Motivasi dan Hasil Belajar Pada Matakuliah Enterprise Resources Planning Fathoni -
Jurnal Sistem Informasi Vol 7, No 1 (2015)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (674.909 KB) | DOI: 10.36706/jsi.v7i1.1975

Abstract

Abstract   One of the main problems of education system in Indonesia is quality and output of learning process. This problem relates with the teaching and learning material availability which access is still constrained by time and distance. To overcome this problem, it needs a change in teaching and learning process paradigm as applying e-learning so that lifetime education for everyone can be implemented. The strategy of using e-learning for ERP subject as a part of learning can improve motivation and output of the teaching and learning process. To reach this goal, an e-learning model is developed. This model is supported by qualified multimedia teaching material which interest students to have blended learning, on-line interaction and discussions as well as complementing teaching and learning material through e-learning media. Keywords : e-learning, complement and blended learning, Motivation dan learning output. Abstrak   Salah satu masalah utama pada sistem pendidikan di Indonesia adalah masalah kualitas dan hasil dari proses pembelajaran. Masalah ini berhubungan dengan penyediaan materi dan bahan belajar yang belum dapat diakses secara luas tanpa dibatasi oleh kendala jarak dan waktu. Apabila kendala ini dapat diatasi maka misi untuk menerapkan pendidikan sepanjang hayat pada segenap lapisan masyarakat dapat diwujudkan. Dalam mewujudkan hal ini dibutuhkan perubahan pada paradigma proses belajar mengajar yang telah diterapkan selama ini seperti mengimplementasikan e-learning. Strategi penggunaan e-learning pada matakuliah ERP sebagai bagian dari proses pembelajaran dapat meningkatkan motivasi dan hasil pembelajaran yang telah dilakukan. Untuk mencapai tujuan tersebut dikembangkan model pembelajaran e-learning yang tepat  dan didukung bahan ajar multimedia yang berkualitas dan dapat menarik minat mahasiswa sehingga mahasiswa termotivasi untuk aktif belajar mandiri (blanded), berdiskusi dan berinteraksi secara on-line serta saling memperkaya materi ajar (komplemen) learning melalui media e-learning. Kata kunci : e-learning, komplemen dan blanded learning, Motivasi dan Hasil Belajar.
Multilabel sentiment analysis for classification of the spread of COVID-19 in Indonesia using machine learning Fathoni Fathoni; Erwin Erwin; Abdiansah Abdiansah
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp968-978

Abstract

This study aims to use datasets on Twitter to find out public opinion on the spread of coronavirus in Indonesia by conducting sentiment analysis. The resulting sentiment analysis will benefit the community by helping the Indonesian government take various strategic measures to prevent and counter the spread of the COVID-19. This research was conducted through the data collection stage, namely crawling data tweet words in Bahasa Indonesia containing the meaning of the spread of COVID-19, the next stage of the process of creating labels manually. Next, the pre-process stage by removing the character, symbols and special features from Twitter. The last stage, classification using learning machine with 3(three) methods namely K-nearest neighbor (K-NN), Naïve Bayes and decision tree. The study analyzed sentiment of 1,119 valid Tweets data and found that K-NN algorithm had the highest accuracy value compared to Naïve Bayes and decision tree algorithms, which was 95.10%. However, the Twitter data analyzed obtained 78.19% of Tweets that fall into the negative category and only 13.85% of public opinion that is positive. This indicates that most of the Tweets of Indonesians in twitter do not mean the spread of COVID-19 disease somewhere.
Co-Authors Abdiansah, Abdiansah Ahmad Rafid Rizqullah Ahmad Rifai Ali Ibrahim Alisia Silver Stone Allsela Meiriza, Allsela Alzaini, Akbar Apriansyah Putra Apriansyah Putra Ari Wedhasmara Dedy Kurniawan Endang Lestari Ruskan Erwin, Erwin Ken Ditha Tania M Ihsan Jambak Maharani Maharani Mira Afrina Pacu Putra Rahmat Fadli Isnanto Rahmat Izwan Heroza Rezqe, Beriadi Agung Nur Rio Abdi Sucipta Rizka Dhini Kurnia Sanjaya, M. Rudi Siti Nur Arafah Sultan Arba'i Syamsuryadi Syamsuryadi Tika Octri Dieni Yadi Utama