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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer) Jurnal Mantik TIN: TERAPAN INFORMATIKA NUSANTARA Infotech: Journal of Technology Information
Abdul Fadlil
Universitas Ahmad Dahlan, Yogyakarta
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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PERANCANGAN SINGLE AXIS SOLAR TRACKING TERINTEGRASI IoT GUNA MENINGKATKAN KAPASITAS PANEL SURYA Rosalina Rosalina; Tole Sutikno; Abdul Fadlil
Infotech: Journal of Technology Information Vol 12, No 1 (2026): JUNI
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v12i1.589

Abstract

The global energy crisis is a complex phenomenon involving an imbalance between increasingly limited energy supplies and ever-rising demand, driven by geopolitical dynamics, shifts in the energy market, and the challenges of transitioning to renewable energy sources. In a country as vast as Indonesia, the potential for developing renewable energy such as solar, hydro, wind, and geothermal energy is significant. This potential presents an opportunity for Indonesia’s resilience in the national energy sector. Utilizing solar energy through solar panels requires an optimized system to maximize the generated electrical power. This research aims to design an IoT-integrated single-axis solar tracking system using an ESP32 microcontroller to enhance the capacity of solar panels. The research methodology includes designing an algorithm to control the rotation angle of the solar tracker using an LDR sensor to determine the intensity of the sun’s electromagnetic waves and to drive the servo motor so that the panel is perpendicular to the sunlight. Solar panel temperature monitoring is performed using a MAX6675 sensor. For real-time current and voltage measurements, an INA219 sensor is used. The results of the solar panel data, processed adaptively by a control algorithm based on sensor data, are displayed on the LCD screen and transmitted to the Blynk IoT platform. The research findings indicate that the maximum power capacity recorded on the Blynk monitoring app was 1.75 watts at 12:00 PM, when solar intensity was at its peak. After 12:00 PM, power generation began to decline in tandem with the decrease in sunlight intensity in the afternoon. The data on power generation from this single-axis solar tracking system indicates that the use of this algorithm is capable of increasing the power absorbed by the solar panels and optimizing the use of solar energy.
Comparison of Logistic Regression and Random Forest Performance in Student Dropout Prediction based on Multi Source Data Sartika Lina Mulani Sitio; Sunardi; Abdul Fadlil
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

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Abstract

The high rate of student dropouts is one of the important challenges in higher education because it can affect academic quality, learning effectiveness, and the performance of educational institutions. This condition encourages the need for a prediction system that is able to identify students at risk of dropouts early so that preventive measures can be taken appropriately. This study aims to compare the performance of Logistic Regression and Random Forest algorithms in predicting student dropout based on multi-source. The dataset consists of 4,424 student data with 34 attributes covering academic, demographic, socioeconomic, and academic administration aspects. The research stages include data preprocessing, target transformation into binary classification, feature scaling, data sharing using an 80:20 scheme, and handling class imbalances using the Synthetic Minority Oversampling Technique (SMOTE). Furthermore, a modeling process was carried out using Logistic Regression and Random Forest algorithms to predict the risk of student dropout. Model evaluation was carried out using accuracy, precision, recall, F1-score, and Area Under Curve Receiver Operating Characteristic (AUC-ROC). The results showed that Random Forest performed better than Logistic Regression with an accuracy of 0.884, precision of 0.842, recall of 0.785, F1-score of 0.812, and AUC-ROC of 0.930. Meanwhile, Logistic Regression obtained an accuracy of 0.871, precision of 0.780, recall of 0.835, F1-score of 0.806, and AUC-ROC of 0.928. These results show that Random Forest is more effective in handling complex relationships in multi-source data for student dropout predictions
Analisis Sinyal Fisiologis Frekuensi Tinggi untuk Ekstraksi Fitur Kebugaran Berbasis HRV dan Signal Processing Iwan Giri Waluyo; Sunardi; Abdul Fadlil
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

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Abstract

Heart Rate Variability (HRV) is a non-invasive biomarker that reflects the activity of the autonomic nervous system and is closely related to physical fitness and recovery. This study aims to analyze physiological signals based on frequency domain components to extract fitness-related features using signal processing techniques. However, previous studies have shown limitations in utilizing comprehensive HRV frequency analysis for fitness evaluation, thus motivating this study to focus on frequency-based physiological interpretation. Electrocardiogram (ECG) signals were obtained from the PhysioNet database and processed through filtering, R peak detection, and RR interval extraction. Frequency domain analysis was performed using Power Spectral Density (PSD) to obtain spectral features, including Low Frequency (LF) (0.04–0.15 Hz), High Frequency (HF) (0.15–0.40 Hz), and LF/HF ratio. The results showed that the LF component exhibited a dominant peak around 0.1 Hz with values ranging from 0.008–0.009 s²/Hz, while the HF component ranged from 0.002–0.003 s²/Hz and had a broader distribution. An LF/HF ratio greater than 2 indicated a predominance of sympathetic activity. These findings suggest that HRV energy distribution is concentrated in the low-frequency band, reflecting a stable physiological state that has not yet reached optimal recovery. This study demonstrates that frequency-based HRV analysis using signal processing provides meaningful physiological insights for fitness evaluation without relying on machine learning models
Pengembangan dan Validasi Sistem Pemantauan Mikroklimat Real-Time Berbasis IoT sebagai Pendukung Evaluasi Kondisi Pengeringan Pascapanen pada Solar Dome Dryer Joko Riyanto; Anton Yudhana; Abdul Fadlil
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10165

Abstract

Continuous monitoring of temperature and humidity is necessary to evaluate the microclimate conditions that affect the drying performance of agricultural products in the Solar Dome Dryer. However, manual monitoring has not been able to provide real-time data, while previous research has largely focused on the implementation of devices without validating the measurements against standard measuring instruments under operational drying conditions. This research aims to develop a real-time microclimate monitoring system based on the Internet of Things to support the evaluation of post-harvest drying performance on the Solar Dome Dryer. The system was developed using the DHT22 sensor, ESP32 microcontroller, Wi-Fi connection, and cloud platform to display temperature and humidity data through a web-based and smartphone dashboard. Testing was conducted on the functions of acquisition, transmission, data visualisation, and sensor accuracy by comparing the measurement results with the standard measuring instrument UNI-T UT333. During the testing, the system was able to record and transmit microclimate data stably within the temperature range of 29.1–65.6 °C and relative humidity of 13.7–79.5% RH. In a limited test of 100 transmission cycles with a 10-minute interval, all data were successfully received by the server under network conditions during the testing. Validation using 50 pairs of data resulted in an MAE of 0.150 °C and an RMSE of 0.159 °C for temperature, as well as an MAE of 0.286% RH and an RMSE of 0.293% RH for humidity. The MAPE values were 0.34% and 0.92%, respectively. The contribution of this research is the development of a low-cost microclimate monitoring framework that integrates acquisition, transmission, storage, real-time visualisation, and statistical sensor validation under the operational conditions of the Solar Dome Dryer. The system can be used as a data source to support drying condition evaluations, with the limitation that accuracy validation above 60 °C has not yet been conducted using appropriate reference instruments.
Co-Authors Anton Yudhana Bambang Subana Dikky Praseptian M Fitrah Juliansyah Herman Herman Ibnu Muakhori Imam Riadi Iwan Giri Waluyo Joko Riyanto Muhammad Amirul Mu'min Murni Musri Iskandar Nasution Nur Makkie Perdana Kusuma Rezki Ramdhani Rosalina Rosalina Rusydi Umar Sartika Lina Mulani Sitio Sunardi Sunardi, Sunardi Tole Sutikno Yuniarti Lestari