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Comparison of Tropical Thunderstorm Estimation between Multiple Linear Regression, Dvorak, and ANFIS Wayan Suparta; Wahyu Sasongko Putro
Bulletin of Electrical Engineering and Informatics Vol 6, No 2: June 2017
Publisher : Institute of Advanced Engineering and Science

Thunderstorms are dangerous and it has increased due to highly precipitation and cloud cover density in the Mesoscale Convective System area. Climate change is one of the causes to increasing the thunderstorm activity. The present studies aimed to estimate the thunderstorm activity at the Tawau area of Sabah, Malaysia based on the Multiple Linear Regression (MLR), Dvorak technique, and Adaptive Neuro-Fuzzy Inference System (ANFIS). A combination of up to six inputs of meteorological data such as Pressure (P), Temperature (T), Relative Humidity (H), Cloud (C), Precipitable Water Vapor (PWV), and Precipitation (Pr) on a daily basis in 2012 were examined in the training process to find the best configuration system. By using Jacobi algorithm, H and PWV were identified to be correlated well with thunderstorms. Based on the two inputs that have been identified, the Sugeno method was applied to develop a Fuzzy Inference System. The model demonstrated that the thunderstorm activities during intermonsoon are detected higher than the other seasons. This model is comparable to the thunderstorm data that was collected manually with percent error below 50%.

Comparison of Tropical Thunderstorm Estimation between Multiple Linear Regression, Dvorak, and ANFIS Wayan Suparta; Wahyu Sasongko Putro
Bulletin of Electrical Engineering and Informatics Vol 6, No 2: June 2017
Publisher : Institute of Advanced Engineering and Science

Thunderstorms are dangerous and it has increased due to highly precipitation and cloud cover density in the Mesoscale Convective System area. Climate change is one of the causes to increasing the thunderstorm activity. The present studies aimed to estimate the thunderstorm activity at the Tawau area of Sabah, Malaysia based on the Multiple Linear Regression (MLR), Dvorak technique, and Adaptive Neuro-Fuzzy Inference System (ANFIS). A combination of up to six inputs of meteorological data such as Pressure (P), Temperature (T), Relative Humidity (H), Cloud (C), Precipitable Water Vapor (PWV), and Precipitation (Pr) on a daily basis in 2012 were examined in the training process to find the best configuration system. By using Jacobi algorithm, H and PWV were identified to be correlated well with thunderstorms. Based on the two inputs that have been identified, the Sugeno method was applied to develop a Fuzzy Inference System. The model demonstrated that the thunderstorm activities during intermonsoon are detected higher than the other seasons. This model is comparable to the thunderstorm data that was collected manually with percent error below 50%.

Comparison of Tropical Thunderstorm Estimation between Multiple Linear Regression, Dvorak, and ANFIS Wayan Suparta; Wahyu Sasongko Putro
Bulletin of Electrical Engineering and Informatics Vol 6, No 2: June 2017
Publisher : Institute of Advanced Engineering and Science

Thunderstorms are dangerous and it has increased due to highly precipitation and cloud cover density in the Mesoscale Convective System area. Climate change is one of the causes to increasing the thunderstorm activity. The present studies aimed to estimate the thunderstorm activity at the Tawau area of Sabah, Malaysia based on the Multiple Linear Regression (MLR), Dvorak technique, and Adaptive Neuro-Fuzzy Inference System (ANFIS). A combination of up to six inputs of meteorological data such as Pressure (P), Temperature (T), Relative Humidity (H), Cloud (C), Precipitable Water Vapor (PWV), and Precipitation (Pr) on a daily basis in 2012 were examined in the training process to find the best configuration system. By using Jacobi algorithm, H and PWV were identified to be correlated well with thunderstorms. Based on the two inputs that have been identified, the Sugeno method was applied to develop a Fuzzy Inference System. The model demonstrated that the thunderstorm activities during intermonsoon are detected higher than the other seasons. This model is comparable to the thunderstorm data that was collected manually with percent error below 50%.

SURVEI SITUS PEMBANGUNAN OBSERVATORIUM ASTRONOMI LAMPUNG DI TAHURA WAR, GUNUNG BETUNG Robiatul Muztaba; Annisa Novia Indra Putri; Nindhita Pratiwi; Wahyu Sasongko Putro; Wirid Birastri; Hakim L. Malasan
PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) Vol 7 (2018): PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) SNF2018
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Abstrak Pada tahun 2016, Institut Teknologi Sumatera bersama dengan Institut Teknologi Bandung, dan Pemerintah Provinsi Lampung menggagas pembangunan sebuah observatorium baru di Lampung. Kami memaparkan hasil survei astronomi pada akhir tahun 2017 di lokasi observatorium. Adapun tujuan dilakukannya survei astronomi yaitu untuk mengkaji kelayakan sebuah observatorium terhadap kondisi lingkungan dan kualitas langit, serta mendapatkan hasil pengukuran parameter atmosfer meliputi ekstingsi atmosfer, seeing, dan kecerlangan langit. Data hasil survei menjadi bahan pertimbangan dalam pemilihan instrumen dan jadwal rencana pengamatan. Dari hasil penelitian didapat bahwa rata-rata curah hujan minimum terjadi pada bulan Mei sampai Oktober, peta sebaran polusi cahaya dari pengukuran kecerlangan langit menunjukan adanya kontribusi polusi cahaya dari arah timur menuju pusat kota Bandar Lampung. Provinsi Lampung termasuk dalam kategori daerah transisi urban dengan nilai kecerlangan langit sebesar 20.85 mag arcsec-2. Sedangkan untuk pengukuran seeing didapat hasil 1.50â€ dan pengukuran ekstingsi menunjukkan grafik dengan kondisi atmosfer yang cukup baik untuk pengamatan. Kata-kata kunci: Survei Astronomi, Parameter Atmosfer, Kecerlangan Langit, Seeing, Ekstingsi. Abstract In 2016, Institut Teknologi Sumatera along with Institut Teknologi Bandung, and the Government of Lampung Province initiated the construction of a new observatory in Lampung. We present the results of astronomical surveys at the end of 2017 at the observatory site. The objective of the survey is to assess the feasibility an astronomical observatory to the environmental conditions and the quality of the sky, as well as to get the atmospheric parameters measurements to include atmospheric extinction, seeing, and sky brightness. The results are taken into account in the selection of instruments and schedules of observation plans. From our research, we get that the average minimum rainfall occurs in May through October, the light pollution distribution maps of the sky brightness measurements showed the presence of light pollution contribution from the east toward downtown Bandar Lampung. Lampung province included in the category of urban transition area with sky brightness value amounted to 20.85 mag arcsec-2. As for the seeing measurement, we get a value of about 1.50â€ and the measurement of extinction shows a graph with atmospheric conditions which is good enough for observation. Keywords: Astronomical Survey, Atmospheric Parameter, Sky Brightness, Seeing, Extinction.

From Line to Logic: STEM Learning Based on Line Follower Robot Program for Vocational Students' Logical Thinking Development Yandhika Surya Akbar Gumilang; Subairi Subairi; Abdur Rabi'; Saeed Abioye Bello; Wahyu Sasongko Putro; Binti Afifah
Smart Society Vol. 6 No. 1 (2026): Smart Society
Publisher : FOUNDAE (Foundation of Advanced Education)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/smartsociety.v6i1.1036

In the current digital era, logical thinking skills and technological understanding are increasingly essential for vocational high school (SMK) students preparing to enter a technology-driven workforce. This community service program aimed to strengthen students’ logical thinking skills through STEM-based learning activities focused on the configuration and programming of line follower robots. The program was conducted over one month and comprised three stages: focus group discussion with school partners, development of instructional materials, and training sessions. The training involved 17 students. Participants were introduced to fundamental concepts of line follower robots, including basic logic, sensors, and programming principles, and then applied this knowledge through practical tasks. Evaluation results showed that 76% of students expressed increased interest in further learning robotics, while all participants (100%) successfully completed the assigned task of programming the robot to navigate from the starting point to the finish line. These findings indicate that robotics-based learning effectively supports the program’s objective of enhancing logical thinking while simultaneously increasing students’ engagement with STEM concepts. By integrating theoretical explanations with direct practice, the line follower robot served as an accessible and meaningful medium for translating abstract logical reasoning into concrete technological applications. The main contribution of this community service activity lies in offering an applied STEM learning model for vocational high schools, particularly in contexts with limited prior exposure to robotics. This program provides a practical reference for integrating educational robotics into SMK learning environments to strengthen logical reasoning, technical competence, and students’ motivation to pursue STEM-related fields.

Mediator of Technology Competence and Solar Panel Module on Satisfaction, Motivation, and Self-Regulated Learning Farid Baskoro; Achmad Imam Agung; Fendi Achmad; Rifqi Firmansyah; Aristyawan Putra Nurdiansyah; Wahyu Sasongko Putro
Jurnal Pemberdayaan Masyarakat Vol 4, No 4 (2025)
Publisher : Yayasan Keluarga Guru Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46843/jpm.v4i4.579

The development of digital technology requires higher vocational education, particularly in engineering, to emphasize practical competency and independent learning. This study aims to analyze active engagement as a mediator in the relationship between technological competency and practical modules on learning satisfaction, motivation, and self-regulated learning. The study used a quantitative-correlation approach with the Structural Equation Modeling–Partial Least Squares (SEM-PLS) analysis technique on 80 electrical engineering students, Surabaya State University (Unesa) who were selected using purposive sampling. The results showed that technological competence had a significant effect on active involvement (? = 0.291; p 0.01) and motivation (? = 0.276; p 0.05). The practical module has a significant effect on learning satisfaction, active engagement is the most potent factor influencing learning satisfaction, motivation, and self-regulated learning. Active engagement as a crucial mediating mechanism within the technology-enhanced learning framework is related to engagement-driven learning through collaborative projects, problem-based learning, and digital simulations. These findings are relevant to contributing to the digital transformation of vocational education and the needs of the renewable energy industry.

Energy Density Prediction of Metal-Organic Frameworks (MOFs) From Synthesis Conditions Using Deep Neural Network (DNN): Hydrogen Storage Application Wahyu Sasongko Putro; Yandhika Surya Akbar Gumilang; Farid Baskoro
JURNAL HURRIAH: Jurnal Evaluasi Pendidikan dan Penelitian Vol. 7 No. 1 (2026): 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.v7i1.414

The global transition toward sustainable energy systems necessitates efficient and scalable hydrogen storage technologies. Metal–organic frameworks (MOFs) have emerged as promising candidates for hydrogen storage due to their high surface area, tunable pore structures, and favorable surface chemistry that enhance adsorption performance. However, real-time experimental measurement of hydrogen uptake using physical sensing systems is costly, computationally intensive, and operationally complex. To address these limitations, this study proposes a data-driven soft-sensor framework based on machine learning to predict energy density for hydrogen storage applications from synthesis parameters. High-fidelity secondary data sourced from an open-access Kaggle dataset were utilized, focusing on synthesis descriptors including metal type, oxidation state, temperature, and reaction time. Recognizing the intrinsic influence of transition metals on structural stability and adsorption behavior, a per-metal modeling strategy was implemented to capture material-specific relationships. A Deep Neural Network (DNN) employing a Multi-Layer Perceptron (MLP) architecture trained via backpropagation was developed to model nonlinear interactions between structural variables and energy density. To enhance interpretability, complementary linear regression models were also constructed, yielding explicit predictive equations. Model performance was rigorously evaluated using statistical error metrics, achieving a Mean Squared Error (MSE) of 0.0821 and a Root Mean Squared Error (RMSE) of 0.2852, demonstrating strong predictive capability and generalization across different metallic linkers. The low error values confirm that artificial neural network–based soft sensors provide a reliable, low-latency alternative to physical sensing systems for monitoring hydrogen storage performance. This approach significantly reduces experimental burden, accelerates materials screening, and supports intelligent optimization of hydrogen-based fuel cell technologies, contributing to the advancement of scalable clean energy infrastructure

SCIENTIFIC CREATIVITY PROJECT-BASED LEARNING FOR TECHNOLOGY-ORIENTED LEARNING: A VALIDITY AND PRACTICALITY ANALYSIS Dimas Arya Soeadyfa Fridyatama; Muhammad Roil Billad; Farid Baskoro; Wahyu Sasongko Putro; Delmio Ave Mara Gutteres de Sousa
EDURELIGIA: Jurnal Pendidikan Agama Islam Vol 10, No 2 (2026)
Publisher : Nurul Jadid University, Paiton Probolinggo, East Java

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/edureligia.v10i2.15551

This study aimed to develop and evaluate the validity and practicality of the Scientific Creativity Project-Based Learning (SCPjBL) model as an instructional approach that integrates scientific creativity, collaborative inquiry, reflective learning, and character-oriented project engagement. This study employed a research and development design involving 180 undergraduate students from two higher education institutions in Indonesia. Data were collected through expert validation sheets, observation instruments, student response questionnaires, and scientific creativity assessments. Three expert validators and four classroom observers participated in the evaluation process. The results showed that the SCPjBL model obtained a validity percentage of 98.00%, while the supporting learning tools and student response questionnaires achieved 97.00% and 99.00%, respectively. Reliability analysis indicated good internal consistency, with Cronbach’s Alpha of 0.85. Classroom observations also showed that the model could be implemented practically in project-based learning activities. These findings indicate that SCPjBL is a valid, reliable, and practical instructional model for promoting scientific creativity, collaborative inquiry, reflective learning, and character-oriented problem-solving in higher 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 Farid Baskoro; Widi Aribowo; Hisham Shehadeh; Hewa Majeed Zangana; Wahyu Sasongko Putro; Sri Dwiyanti; Aristyawan Putra Nurdiansyah
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

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.

A Longitudinal Evaluation of Student Knowledges and Skills Development Using Artificial Intelligence Wahyu Sasongko Putro; Rosita Rosita; Rina Dwijuliani
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

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

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