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All Journal International Journal of Electrical and Computer Engineering Buletin Ilmiah Sarjana Teknik Elektro Jurnal Hurriah: Jurnal Evaluasi Pendidikan dan Penelitian
Putro, Wahyu Sasongko
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Humanities Computer Science & IT Education Electrical & Electronics Engineering Mathematics

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The preliminary study of carbon x-change rakyat using blockchain application Putro, Wahyu Sasongko; Rahmi, Nitia; Asditama, Raditya Yoga; Akbar, Nur Arifin
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp672-680

Abstract

Today’s air pollution is detrimental to the environment, particularly in Indonesia. Carbon dioxide (CO2) and nitrogen oxide (NOx) are present in the atmosphere due to air pollution. Many individuals employ reforestation to lessen the influence of CO2 and NOx gases on the atmosphere. However, in the digitalized era, lowering carbon emissions may also be accomplished through a carbon credit exchange. Thus, in this study we investigate the performance of the carbon x-change rakyat (CXR) based on blockchain platform utilizing the stress test approach. We provided four scenarios with 10,000 to 100,000 transactions evaluated on the CXR blockchain system i.e., transfer, insert, remove, and update. The outcome demonstrates CXR’s effectiveness with 100% success and 0% failure rate based on testing and statistical computations calculation. The mean absolute error (MAE), variance accounted for (VAF), and percent error (PE) are obtained with values ranging from 0.38% to 4.67%. In this study, the transaction per-second (TPS) is used to calculate include error request (IER) and exclude error request (EER) values around 312 to 746 milliseconds (ms). In addition, the TPS of CXR based on blockchain platform is a capability to create and trace database carbon certificate ownership (nonfinancial activity). It means CXR based on the blockchain platform has a fast response to process carbon certificate ownership for transactions across local and international countries in the world.
AI-Based Business Model Analysis of Education-Focused Beauty Salon Entrepreneurship Putro, Wahyu Sasongko; Dwijuliani, Rina; Rosita, Rosita
JURNAL HURRIAH: Jurnal Evaluasi Pendidikan dan Penelitian Vol. 6 No. 2 (2025): Jurnal Hurriah: Journal of Educational Evaluation and Research
Publisher : Yayasan Pendidikan dan Kemanusiaan Hurriah Aceh

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Abstract

This study aimed to analyse business models using AI to enhance education in beauty salon entrepreneurship over Surabaya, East Java, Indonesia. Here, the data observation is taken from a famous beauty salon group at Surabaya with three service products such as Make Up, Hair Treatment, and Facial. The daily basis data from customers who use three products was analysed to obtain an estimation value of each service product. In this study, the Artificial Neural Network (ANN) method is performed to find an estimation value with Multi-Layer Perceptron (MLP) architecture with two to three variation hidden layers. The Levenberg-Marquardt backpropagation algorithm is also used to obtain RMSE over training value. The result shows the three products Make Up, Hair Treatment, and Facial were compared by customer basis over famous beauty salon group, Surabaya. Here, The ANN model with four hidden layers MLP architecture with 1000 iterations in the training process. The statistical calculation such as MSE of 172, RMSE of 0.812, MAE of 1.234, and MAPE of 3.123% indicate that the model performs exceptionally well, with minimal errors in predictions, respectively. ANN model is proposed to develop a business intelligence system in the near future in beauty salon entrepreneurship
A Longitudinal Evaluation of Student Knowledges and Skills Development Using Artificial Intelligence Putro, Wahyu Sasongko; Rosita, Rosita; Dwijuliani, Rina
JURNAL HURRIAH: Jurnal Evaluasi Pendidikan dan Penelitian Vol. 6 No. 4 (2025): Jurnal Hurriah: Journal of Educational Evaluation and Research
Publisher : Yayasan Pendidikan dan Kemanusiaan Hurriah Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56806/jh.v6i4.388

Abstract

This study seeks to examine the progression of students' knowledge (P) and skills (K) scores in an Indonesian Vocational High School (SMK), focused on Multimedia, through an artificial intelligence (AI)–driven longitudinal analysis methodology. The research data comprises academic scores from 32 students gathered over five semesters and examined via data preprocessing, descriptive statistical analysis, and machine learning modelling employing the Random Forest algorithm. The results show that both knowledge and skills scores have been going up steadily over the semesters. The predictive model based on Random Forest works very well, with a high level of accuracy and a low level of prediction error. Additionally, Pearson correlation analysis and simple linear regression demonstrate that knowledge significantly and positively influences students' skills (p < 0.05), suggesting that proficiency in cognitive dimensions directly facilitates the enhancement of practical skills in vocational education. These results validate that the amalgamation of longitudinal analysis and artificial intelligence can enhance data-driven learning assessment and promote more precise academic decision-making in vocational education
K-Nearest Neighbors for Smart Solution Transportation: Prediction Distance Travel and Optimization of Fuel Usage and Charging Recommendations for ICE Vehicles Based in Surabaya Baskoro, Farid; Aribowo, Widi; Shehadeh, Hisham; Zangana, Hewa Majeed; Putro, Wahyu Sasongko; Dwiyanti, Sri; Nurdiansyah, Aristyawan Putra
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.15068

Abstract

Surabaya ranks 9th in Southeast Asia and 44th globally in the TomTom Traffic Index, with an average travel time of ±22 minutes for a 10 km distance, longer than Jakarta’s ±20 minutes. Given these traffic conditions, this study examines the application of the K-Nearest Neighbors (KNN) algorithm to predict vehicle travel distance based on remaining fuel consumption and provides recommendations for the nearest Gas Station (SPBU) based on the predicted distance. The study seeks to provide accurate distance predictions and recommend the nearest Gas Station (SPBU) for users based on fuel consumption and the predicted route, helping to navigate Surabaya’s congested traffic efficiently. The data used includes various levels of fuel consumption: 0.02, 0.06, 0.10, 0.14, 0.16, 0.20, and 0.24 liters for engines of 110, 125, and 150 cc. The model evaluation results, using three metrics: MAE, MAPE, and RMSE show that KNN performs excellently at low fuel consumption levels. At a consumption rate of 0.02 liters, the model produces a low MAE of 0.347, MAPE of 31.21%, and RMSE of 0.40, indicating minimal prediction error. The model's performance remains consistent at a consumption of 0.06 liters with MAE of 0.330, MAPE of 9.90%, and RMSE of 0.41, demonstrating a high level of accuracy. Technically, the implementation of this model can help reduce traffic congestion by directing vehicles to the nearest gas stations, thereby minimizing sudden stops on the road, improving traffic flow, and reduce wasted time spent searching for distant gas stations.
Co-Authors Asditama, Raditya Yoga Dwijuliani, Rina Farid Baskoro Nur Arifin Akbar Nurdiansyah, Aristyawan Putra Rahmi, Nitia Rosita Rosita Shehadeh, Hisham Sri Dwiyanti Widi Aribowo Zangana, Hewa Majeed