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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Scientax: Jurnal Kajian Ilmiah Perpajakan Indonesia
Aris Budi Santoso
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Computer Science & IT Economics, Econometrics & Finance Education Engineering

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Application of data mining to taxpayers issuing fictitious tax invoice using classification techniques Yusrifaizal Gumilar Winata; Fauziah Noor; Muhammad Futhra Bahar; Aris Budi Santoso; Eddy Sukarno
Scientax: Jurnal Kajian Ilmiah Perpajakan Indonesia Vol. 5 No. 1 (2023): October: Navigating Changes, Embracing the Tax Reform
Publisher : Directorate General of Taxes

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52869/st.v5i1.573

Abstract

Pajak adalah tulang punggung penerimaan negara, namun penerimaan pajak tidak berjalan optimal karena adanya tindak pidana perpajakan. 44 % dari keseluruhan tindak pidana pajak berasal dari penerbitan Faktur Pajak Tidak berdasarkan transaksi sebenarnya. Hal tersebut mengurangi pendapatan dari sektor PPN bahkan dapat mengambil uang negara menggunakan restitusi PPN. Untuk menghadapi hal tersebut DJP dapat menggunakan data mining untuk melakukan audit yang lebih efektif dan efisien. Penelitian menggunakan metodologi CRISP-DM dan Python diterapkan kepada 1071 data wajib pajak penerbit dan 2142 wajib pajak bukan penerbit. Hasil dari penelitian memberikan model yang memiliki Prediction Efficiency sebesar 83,56%, reduction in Examination Effort sebesar 69,31%, dan Strike Rate sebesar 90,77%. Model tersebut kemudian dapat digunakan dengan Streamlit dan memprediksi 8 WP penerbit dengan probability 75% dari data deployment yang terdiri dari 1000 baris data.
Incorporating Stock Prices and Social Media Sentiment for Stock Market Prediction: A Case of Indonesian Banking Company Dhenda Rizky Pradiptyo; Irfanda Husni Sahid; Indra Budi; Aris Budi Santoso; Prabu Kresna Putra
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.74486

Abstract

Forecasting the stock market is one of the most popular topics to be discussed in many fields. Many studies, especially in information technology have been conducted machine learning algorithms to achieve a more accurate prediction of the stock market. This research aims to find the effectiveness in predicting stock market performance by utilizing social media sentiment in combination with historical data. In addition, this research uses a machine learning algorithm to train a model to predict the stock price of each bank and training the model on a dataset that included the historical stock prices of the bank, as well as the sentiment scores of the social media posts about the bank and evaluate the performance of the model by comparing the predicted stock prices to the actual stock prices. The research shows that the R2 and RMSE score model that has been built with its historical data has slightly better performance than the model that has been built with the combination of historical data and social media sentiment. The finding indicates that the research method is closely correlated and affected to the performance of the stock market prediction.
Twitter Sentiment Analysis Towards Candidates of the 2024 Indonesian Presidential Election Cahyanti, Rhoma; Desiana Nurul Maftuhah; Aris Budi Santoso; Indra Budi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5839

Abstract

Long before the elections were held, the topic related to elections was widely discussed on news portals and social media, including Twitter. A few studies related to the Indonesian election have tried to predict candidates who will run for the presidential election, but there has been no research that examines public sentiment on social media towards each of the potential candidates. The main objective of this study is to analyze the public sentiment in Twitter towards potential candidates for the 2024 Indonesian presidential election. This research seeks to fill the gaps in previous research and become a reference for further research regarding sentiment analysis for election prediction using Twitter. The presidential candidates used in the research are the top 3 candidates based on the Poltracking survey, namely Ganjar Pranowo, Prabowo Subianto, and Anies Baswedan. The data were taken from January until October 2022, more than a year before the general election began. To predict the sentiment, four different machine-learning methods were used and compared to each other. There are Naïve Bayes, Support Vector Machines, Random Forests, and Neural Networks. Based on the sentiment results of each candidate, the highest sentiment towards Prabowo is neutral (55.49%), the highest sentiment towards Ganjar is positive (61.34%), and the highest sentiment towards Anies is neutral (44.84%). Results from the study also show that Anies was the presidential candidate who received more negative sentiment than the other two (56.63%). Meanwhile, Ganjar Pranowo got the most positive sentiment of all (42,69%). For the neutral sentiment, Anies Baswedan also got the most results (39,87%), followed by Prabowo (38.99%) and Ganjar Pranowo (21.14%). The result of the study also discovered that random forest and neural networks have the best performance for sentiment analysis.
Customer Satisfaction Evaluation in Online Food Delivery Services: A Systematic Literature Review Adimas Fiqri Ramdhansya; Shella Maria Vernanda; Indra Budi; Prabu Kresna Putra; Aris Budi Santoso
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6205

Abstract

The rapid growth of online food delivery services has heightened the need for effective customer satisfaction measurement. This systematic literature review examines 476 papers, selecting 15 key studies to identify prevailing evaluation approaches. Findings reveal that sentiment analysis and PLS-SEM are the most frequently used analytical methods, each appearing in six studies. Satisfaction measurement relies on sentiment polarity scores in five studies and SERVQUAL frameworks in three studies. Data collection primarily involves surveys in seven studies and user-generated content in six studies, but limited demographic diversity reduces generalizability. Three key future research directions emerge. Advanced analytical techniques appear in 5 of 11 future works in the analysis methods domain. Expanding evaluation metrics is mentioned in 6 of 12 proposals in the evaluation domain. Exploring demographic context is highlighted in 10 of 25 recommendations in the dataset’s domain, with dataset development receiving twice the attention of methodological advancements. These results provide researchers with a structured framework for customer satisfaction evaluation while guiding food delivery platforms in refining service quality. By systematically mapping current methodologies and future priorities, this study bridges gaps between academia and industry, ensuring more effective customer satisfaction assessments.
Co-Authors Adimas Fiqri Ramdhansya Cahyanti, Rhoma Desiana Nurul Maftuhah Dhenda Rizky Pradiptyo Eddy Sukarno Fauziah Noor Indra Budi Irfanda Husni Sahid Muhammad Futhra Bahar Prabu Kresna Putra Shella Maria Vernanda Yusrifaizal Gumilar Winata