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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Pilar Nusa Mandiri Jurnal Bina Praja Bappenas Working Papers Jurnal Teknik Informatika (JUTIF) Buletin Poltanesa Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science Lokomotif Abdimas: Jurnal Pengabdian kepada Masyarakat BACA: Jurnal Dokumentasi dan Informasi
Rianto, Yan
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences

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Prediction of Netizen Tweets Using Random Forest, Decision Tree, Naïve Bayes, and Ensemble Algorithm Rianto, Yan; Kuntoro, Antonius Yadi
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 1 (2020): Article Research, October 2020
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v5i1.10565

Abstract

The current Governor of DKI Jakarta, even though he has been elected since 2017 is always interesting to talk about or even comment on. Comments that appear come from the media directly or through social media. Twitter has become one of the social media that is often used as a media to comment on elected governors and can even become a trending topic on Twitter social media. Netizens who comment are also varied, some are always Tweeting criticism, some are commenting Positively, and some are only re-Tweeting. In this research, a prediction of whether active Netizens will tend to always lead to Positive or Negative comments will be carried out in this study. Model algorithms used are Decision Tree, Naïve Bayes, Random Forest, and also Ensemble. Twitter data that is processed must go through preprocessing first before proceeding using Rapidminer. In trials using Rapidminer conducted in four trials by dividing into two parts, namely testing data and training data. Comparisons made are 10% testing data: 90% Training data, then 20% testing data: 80% training data, then 30% testing data: 70% training data, and the last is 35% testing data: 65% training data. The average Accuracy for the Decision Tree algorithm is 93.15%, while for the Naïve Bayes algorithm the Accuracy is 91.55%, then for the Random Forest algorithm is 93.41, and the last is the Ensemble algorithm with an Accuracy of 93, 42%. here.
Twitter Comment Predictions on Dues Changes BPJS Health In 2020 Fahlapi, Riza; Rianto, Yan
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 1 (2020): Article Research, October 2020
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v5i1.10588

Abstract

The Social Security Administering Body (BPJS) is a facility established by The government in providing services to citizens in The field of health welfare. The Spirit of cooperation in the utilization of health services which is very much currently a constraint in the budget is still insufficient in covering health services as a whole. For this reason, government policy is following with PERPRES No. 75 in 2019, the Government officially raised the BPJS Health contributions for 2020. The increase in BPJS Health contributions certainly caused a lot of comments. Namely Twitter, one of the social media that is used by the public to express disapproval or support for this government policy. This study, testing was carried out related to the prediction of comments from social media on community responses to the increase in BPJS Health contributions taken by the government. In the test carried out 3 (three) input algorithms. For every single algorithm including getting results through the K-NN method with an accuracy of 71.83% and AUC value of 0812, for the Naïve Bayes method produces an accuracy of 81.63% and AUC value of 0586. As for the C 4.5 method, the accuracy is 65.37% and the AUC value is 0628. While testing conducted through the Ensembles Vote method which combines the 3 algorithms above gives the best results with an accuracy of 80.10% and AUC value is 0871 for Twitter comment predictions.
The Bookkeeping System Policy and Literacy Movement in Indonesia Putera, Prakoso Bhairawa; Ningrum, Sinta; Widianingsih, Ida; Rianto, Yan; Suryanto, Suryanto
Bappenas Working Papers Vol 5 No 3 (2022): November 2022
Publisher : Kementerian Perencanaan Pembangunan Nasional (Bappenas)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47266/bwp.v5i3.182

Abstract

The paper aims to reveal the book system policy in Indonesia and the efforts made to increase interest in reading, including the movement undertaken to improve literacy in Indonesia. Identification and mapping have been executed through the regulatory survey method on the peraturan.go.id database, while slip and analysis have been exercised through content analysis. The paper provides empirical insights into the bookkeeping system and movement undertaken to improve literacy in Indonesia. National policies have supported the bookkeeping system in Indonesia. Meanwhile, to increase interest in reading, a literacy movement was initiated by President Susilo Bambang Yudhoyono on November 1, 2007, through the Law of the Republic of Indonesia Number 43 of 2007. Indonesia's literacy movement is "the national movement for reading fondness." This paper reveals that policy support at the national (state) level also needs to get help at the regional (local) level so that the literacy movement can be implemented up to the regional level. In addition, the central government's commitment is not only in the form of policy support but needs to be followed by several joint movements and incentives.
Pelatihan Transformasi Digital Organisasi Melalui Implementasi AI Tanpa Coding Pada Remaja Islam Al Hikmah (RISMAH) Pratama Putra, Zico; Parningotan Manik , Lindung; Gata , Windu; Rianto, Yan; Kurniawati, Laela
LOKOMOTIF ABDIMAS: Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 2 (2024)
Publisher : LPPM UIN Sulthan Thaha Saifuddin Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30631/lokomotifabdimas.v3i2.2744

Abstract

Pada era modern saat ini teknologi berkembang dengan sangat pesat khususnya dalam bidang komputer yang telah didukung dengan teknologi artificial intelligence. Artificial intelligence sebuah studi tentang bagaimana membuat komputer dapat melakukan hal-hal yang pada saat ini dapat dilakukan lebih baik dari apa yang telah dilakukan oleh manusia. Dalam pembuatan aplikasi AI, saat ini pengguna atau developer tidak perlu lagi menuliskan kode. Dalam rangka melaksanakan kegiatan Tri Dharma Perguruan Tinggi yaitu Pengabdian kepada Masyarakat, Fakultas Teknologi Informasi Universitas Nusa Mandiri akan menyelenggarakan pelatihan dengan tema “Pelatihan Transformasi Digital Organisasi melalui Implementasi AI Tanpa Coding Pada Remaja Islam Al Hikmah (RISMAH)”. Kegiatan ini bertujuan untuk meningkatkan wawasan dan kemahiran peserta untuk membuat aplikasi artificial intelligence dengan mudah tanpa menggunakan coding, pelaksanaan kegiatan ini dilakukan dengan 3 tahapan yaitu persiapan, pelaksanaan yang terakhir evaluasi dan monitorin dengan hasil yang dicapai dari kegiatan ini adalah adalah peserta memiliki keterampilan untuk membuat aplikasi artificial intelligence dengan mudah tanpa menggunakan coding.
Revolutionizing Corporate Event Planning with AI: A Cost-Efficiency Strategy for BuatEvent.id Supriyadi, Muhammad; Rianto, Yan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1929

Abstract

BuatEvent.id leverages an AI-driven platform for event planning, powered by Gemini.ai—a sophisticated NLP model with an accuracy rate of 92.5%. The system integrates multiple technologies, including PHP, Python, Golang, Flutter, and MySQL, to automate essential processes, achieving a 25% improvement in planning precision. This study aims to evaluate the role of AI in enhancing budget management and corporate event customization. By addressing the inefficiencies of conventional event planning, this platform optimizes workflows, enhances overall productivity, and offers a seamless user experience customized to cater to a wide range of client requirements. The results demonstrate a 92.5% accuracy in processing user queries and a 25% increase in event planning efficiency, highlighting the platform’s ability to deliver cost-effective and personalized solutions. These figures were obtained through internal testing using a dataset of 200 annotated user queries. The platform primarily targets corporate events, including workshops, product launches, and business meetings.For example, the system was successfully deployed during a corporate training event in Jakarta, where it reduced planning time by 30%.
AI-Powered: Leveraging Teachable Machine for Real-time Scanner Marcelly, Frizca Fellicita; Rianto, Yan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1931

Abstract

Effective inventory control is essential in optimizing profitability through cost control and efficiency expectations. Conventional inventory techniques frequently find it difficult to adjust to the fast-changing restaurant setting, resulting in surplus stock, inventory deficits, and unnecessary food waste. Nonetheless, a notable shift is approaching, as the incorporation of artificial intelligence (AI) may help address this issue. AI-powered inventory management systems help restaurants optimize stock levels, reduce waste, and predict demand more accurately, leading to improved efficiency and increased profitability. This study explores how AI-driven inventory management enhances efficiency, reduces waste, and automates restocking in the restaurant sector, with a particular focus on TastyGo's integration of Teachable Machine and TensorFlow Lite. The suggested solution uses picture recognition for real-time inventory tracking, and machine learning models to predict demand and replenishment automation. TastyGo can expedite supply chain management, save waste through predictive analytics, and improve its inventory by employing these AI techniques. This study shows how AI-driven solutions may boost decision-making, reduce food waste, and greatly increase operational efficiency, all of which can result in higher profitability. The findings highlight how AI technologies have the potential to revolutionize conventional inventory management systems in the restaurant industry.
Real-Time Road Damage Detection on Mobile Devices using TensorFlow Lite and Teachable Machine Nova, Lusindah; Rianto, Yan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1933

Abstract

This study presents a mobile-based road damage detection system using Teachable Machine and TensorFlow Lite to support real-time monitoring and efficient infrastructure maintenance. The system identifies road damage types such as cracks, potholes, and uneven surfaces. The RDD2020 dataset is used for model training, with preprocessing steps including augmentation, normalization, and resizing. A Convolutional Neural Network (CNN) model is trained through Teachable Machine for ease of customization. TensorFlow Lite is employed for on-device inference, with optimization techniques like quantization and pruning applied to improve speed and reduce model size. The system is evaluated using precision, recall, F1-score, and accuracy metrics under varying lighting and weather conditions. The final model is deployed in a mobile app using TensorFlow Lite Interpreter for efficient performance. Experimental results show high detection accuracy, with a precision of X% and F1-score of Y% (insert actual values). This approach offers a lightweight, cost-effective solution for road maintenance authorities and urban planners. Future enhancements include dataset expansion, integration with mapping tools, and improved robustness in diverse environments. Overall, the proposed system enables real-time, accurate road damage detection and supports smarter, eco-friendly infrastructure management.
PEMETAAN TOPIK NILAI PUBLIK DALAM PENELITIAN Wulandari, Rika; Rochima, Emma; Rianto, Yan; Endyana, Cipta
BACA: Jurnal Dokumentasi dan Informasi Vol. 41 No. 2 (2020): BACA: Jurnal Dokumentasi dan Informasi (Desember)
Publisher : Direktorat Repositori, Multimedia, dan Penerbitan Ilmiah - Badan Riset dan Inovasi Nasional (BRIN Publishing)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.baca.v41i2.683

Abstract

This study aims to provide an overview of how public value research topics developed from 2001 to 2020. The mapping is expected to provide an overview of the development of research and provide input on the topic of public value research. This study uses the Scopus database from 2001 to 2020 to calculate citation analysis using the Publish or Perish (PoP) application and mapping publications using the VOSViewer application. From the analysis of literature studies based on the Scopus database from 2001 to 2020 272 journal articles are discussing public value research, it was found that the most publications were in 2019 (47 publications) and the highest research was dominated by the United Kingdom (65). For the highest number of citations, the publication with the title "From new public management to public value: Paradigmatic change and managerial implications" was 368 citations. Social science subjects are the most discussed in research on public values. The results of the visualization with the VOSViewer application for a public value research network visualization map are divided into 4 clusters. Density visualization maps can be seen that research on the topic of public value with the subject of performance management and cultural policy is still small, so there is still a chance for research. Based on the analysis of the research topic of public value, there is still a large enough opportunity for research to be carried out, especially in Indonesia based on the Scopus database, researchers have not researched the topic of public value research.
COMPARISON OF ARIMA, LSTM, AND GRU MODELS FOR FORECASTING SALES OF HIT AEROSOL PRODUCTS Sunendar, Nendi; Rianto, Yan
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6412

Abstract

A more accurate forecasting model, such as LSTM, can significantly enhance business efficiency by providing more reliable predictions of future sales, allowing for better inventory management, optimized production schedules, and more precise distribution planning. This leads to reduced costs, minimized stockouts, and improved customer satisfaction. This study evaluates the forecasting performance of ARIMA, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models using sales data from 2021 to 2023. The models are assessed based on Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Results show that LSTM outperforms the other models with a MAPE of 10.76%, followed by ARIMA at 11.23% and GRU at 11.47%. These findings highlight the advantages of deep learning methods, particularly LSTM, in capturing complex patterns and trends in time series data. The study demonstrates the potential of these models to optimize sales forecasting, aiding decision-making processes in production and distribution planning.
APPLICATION OF ARTIFICIAL NEURAL NETWORK METHODS TO DETECT HEART ATTACKS Hamzah, Nasir; Rianto, Yan
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6413

Abstract

A heart attack is a medical emergency caused by restricted blood flow to the heart, commonly leading to myocardial infarction due to blood clots or fat accumulation. Early detection of heart disease is crucial to support prevention efforts and assist healthcare professionals in timely diagnosis and treatment. This study applies the Backpropagation Neural Network (BPNN) algorithm as an intelligent computing method for heart attack detection. Experimental results demonstrate a prediction accuracy of 96.47%, confirming the effectiveness of artificial neural networks in identifying heart attacks in patients. These findings highlight the potential of BPNN as a reliable and precise early detection system, which can support more accurate clinical decision-making and improve the effectiveness of heart attack prevention and treatment.
Co-Authors Abdullah, Ifnu Abdul Aziz Abdurrahman, Muhammad Farhan Aldi, Kenny Antonius Yadi Kuntoro Cipta Endyana Dedi Priansyah Emma Rochima Fabiyanto, Dedik Fahlapi, Riza Gata , Windu Hanggoro, Dimas Banu Dwi Ida Widianingsih Laela Kurniawati, Laela Lindung Parningotan Manik Marcelly, Frizca Fellicita Muhammad Firmansyah, Muhammad Nasir Hamzah Nova, Lusindah Nugraha, Thomas Fajar Parningotan Manik , Lindung Prakoso Bhairawa Putera Pratama Putra, Zico Prawira, Nurul Adi Rika Wulandari Sadikin, Rifki Septiani, Riska Kurnia Sinta Ningrum Sunendar, Nendi Supriyadi, Muhammad Suryanto Suryanto