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All Journal Narra J Jurnal Teknologi Informasi Heca Journal of Applied Sciences Leuser Journal of Environmental Studies Ekonomikalia Journal of Economics Journal of Educational Management and Learning Infolitika Journal of Data Science Grimsa Journal of Science Engineering and Technology
Suhendra, Rivansyah
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Public Health Other

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Evaluation of Gradient Boosted Classifier in Atopic Dermatitis Severity Score Classification Suhendra, Rivansyah; Suryadi, Suryadi; Husdayanti, Noviana; Maulana, Aga; Noviandy, Teuku Rizky; Sasmita, Novi Reandy; Subianto, Muhammad; Earlia, Nanda; Niode, Nurdjannah Jane; Idroes, Rinaldi
Heca Journal of Applied Sciences Vol. 1 No. 2 (2023): October 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v1i2.85

Abstract

This study investigates the application of the Gradient Boosting machine learning technique to enhance the classification of Atopic Dermatitis (AD) skin disease images, reducing the potential for manual classification errors. AD, also known as eczema, is a common and chronic inflammatory skin condition characterized by pruritus (itching), erythema (redness), and often lichenification (thickening of the skin). AD affects individuals of all ages and significantly impacts their quality of life. Accurate and efficient diagnostic tools are crucial for the timely management of AD. To address this need, our research encompasses a multi-step approach involving data preprocessing, feature extraction using various color spaces and evaluating classification outcomes through Gradient Boosting. The results demonstrate an accuracy of 93.14%. This study contributes to the field of dermatology by providing a robust and reliable tool to support dermatologists in identifying AD skin disease, facilitating timely intervention and improved patient care.
Penerapan Media Pembelajaran Berbasis Animasi Flash Pada Mata Pelajaran Rangakain Listrik di SMK Negeri 1 Padang Juliwardi, Ilham; Ardiansyah, Muhammad; Suhendra, Rivansyah; Walid, Muhammad; Astrianda, Nica
Jurnal Teknologi Informasi Vol 1, No 2 (2022): Oktober
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (263.321 KB) | DOI: 10.35308/jti.v1i2.6454

Abstract

This research is oriented to the learning outcomes of students who have not yet reached the KKM in the subject of electrical circuits for class X TITL at SMKN 1 Padang. Many factors affect the low student learning outcomes such as the learning process using conventional methods and the media used by the teacher in learning is a module and only uses the blackboard. Based on these factors, research is conducted in the form of experiments to improve student learning outcomes by using media-based flash animation learning media. The method used in this research is quasi-experimental with Pretest-Posttest One Group Design. The subjects of this study were students of class X SMKN 1 Padang semester January-June 2016, which amounted to 31 students. Collecting data in this study using a test (Pretest-Posttest), namely objective questions. The test questions used were tested first to determine the validity, reliability, level of difficulty, and discriminating power of the questions. Based on the results of the study obtained 25 items. The results showed that there was an increase in student learning outcomes of class X TITL SMKN 1 Padang in the subject of Electrical Engineering. The increase occurred at a high level (0.71) where 18 students were at a high level and 13 other students were at an increasing level. Based on the results of the study, it can be concluded that the application of flash animation media on electrical circuit subjects for class X TITL SMKN 5 Padang can improve student learning outcomes at a high level (0.71).
Evaluation of atopic dermatitis severity using artificial intelligence Maulana, Aga; Noviandy, Teuku R.; Suhendra, Rivansyah; Earlia, Nanda; Bulqiah, Mikyal; Idroes, Ghazi M.; Niode, Nurdjannah J.; Sofyan, Hizir; Subianto, Muhammad; Idroes, Rinaldi
Narra J Vol. 3 No. 3 (2023): December 2023
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v3i3.511

Abstract

Atopic dermatitis is a prevalent and persistent chronic inflammatory skin disorder that poses significant challenges when it comes to accurately assessing its severity. The aim of this study was to evaluate deep learning models for automated atopic dermatitis severity scoring using a dataset of Aceh ethnicity individuals in Indonesia. The dataset of clinical images was collected from 250 patients at Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia and labeled by dermatologists as mild, moderate, severe, or none. Five pre-trained convolutional neural networks (CNN) architectures were evaluated: ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The evaluation metrics, including accuracy, precision, sensitivity, specificity, and F1-score, were employed to assess the models. Among the models, ResNet50 emerged as the most proficient, demonstrating an accuracy of 89.8%, precision of 90.00%, sensitivity of 89.80%, specificity of 96.60%, and an F1-score of 89.85%. These results highlight the potential of incorporating advanced, data-driven models into the field of dermatology. These models can serve as invaluable tools to assist dermatologists in making early and precise assessments of atopic dermatitis severity and therefore improve patient care and outcomes.
Utilization of Drone with Thermal Camera in Mapping Digital Elevation Model for Ie Seu'um Geothermal Manifestation Exploration Security Bahri, Ridzky Aulia; Noviandy, Teuku Rizky; Suhendra, Rivansyah; Idroes, Ghazi Mauer; Yanis, Muhammad; Yandri, Erkata; Nizamuddin, Nizamuddin; Irvanizam, Irvanizam
Leuser Journal of Environmental Studies Vol. 1 No. 1 (2023): July 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v1i1.40

Abstract

Geothermal energy is a viable alternative energy source, particularly in Indonesia. This study was conducted at Ie Seu’um, Mount Seulawah Agam, which is a potential site for a geothermal power plant with an estimated electrical output of 150 megawatts. The objective of this study was to analyze and construct a digital elevation model (DEM) map of the geothermal manifestations. We analyzed water temperature, FLIR (Forward Looking Infrared) temperature, and temperature data from Landsat 8 satellite imagery. To map the heat signature of geothermal features, we utilized the DJI Phantom 4 Standard equipped with the FLIR One Gen 2 sensor. Additionally, we used the Milwaukee Mi306 to calculate the water temperature. Each test was conducted three times to obtain an optimal average level of accuracy. The DEM map was created to assess the level of safety in geothermal manifestation exploration. Elevation and slope values were analyzed to generate a 3D map display, providing a clearer image of the research site. In conclusion, drones prove to be an excellent method for ensuring the safety of exploration in geothermal manifestation areas.
Characterization of Geochemical and Isotopic Profiles in the Southern Zone Geothermal Systems of Mount Seulawah Agam, Aceh Province, Indonesia Lala, Andi; Yusuf, Muhammad; Suhendra, Rivansyah; Maulydia, Nur Balqis; Dharma, Dian Budi; Saiful, Saiful; Idroes, Rinaldi
Leuser Journal of Environmental Studies Vol. 2 No. 1 (2024): April 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v2i1.172

Abstract

The Seulawah Agam geothermal area exhibits significant potential as a source of energy for power generation, with an estimated capacity of 130 MW. Geological and geochemical investigations indicate that the Seulawah Agam geothermal system is part of the extensive Sumatra Fault. Analysis of the geochemical composition of geothermal water at the South Zone manifestation location of Mount Seulawah Agam, Aceh Province-Indonesia, involves examining cation (K+, Na+, Ca2+, and Mg2+), anion (Cl-, HCO3-, and SO42-), and isotope (δD and δ18O) contents. This data aids in estimating reservoir temperatures using geothermometer equations. Surface characteristics of the South Zone manifestation reveal neutral to alkaline pH values (6.02 to 8.68), relative temperatures (29.97 to 42.57 ºC), conductivity (49.8 to 100.7 mV), and TDS (Total Dissolved Solids) ranging from 352.6 to 497.0 mg/L. The dominant water composition is sodium–calcium–bicarbonate (Ca–Na–HCO3), indicating a bicarbonate water type. Average temperature depths in the South Zone manifestation of Mount Seulawah Agam are estimated as follows: Alue Ie Seu’um around 288.84 ± 2.19 ºC, Alue Ie Masam around 304.17 ± 20.9 ºC, Alue PU around 290.02 ± 6.85ºC, and Alue Teungku around 265±11.39 ºC. Isotope data (δD and δ18O) suggest meteoric water as the source for this manifestation. Fluid geochemical analysis indicates the potential for utilizing the geothermal manifestations of the South Zone of Mount Seulawah Agam for geothermal development or the construction of a geothermal power plant, given its high enthalpy system with an average temperature exceeding 225 ºC. Further research, including data drilling, is essential to gather precise subsurface data. Additionally, the Aceh Provincial Government should formulate policies to identify strategic areas for geothermal development, leveraging the existing exploitable potential.
Deep Learning-Based Bitcoin Price Forecasting Using Neural Prophet Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Suhendra, Rivansyah; Adam, Muhammad; Rusyana, Asep; Sofyan, Hizir
Ekonomikalia Journal of Economics Vol. 1 No. 1 (2023): July 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/eje.v1i1.51

Abstract

This study focuses on using the Neural Prophet framework to forecast Bitcoin prices accurately. By analyzing historical Bitcoin price data, the study aims to capture patterns and dependencies to provide valuable insights and predictive models for investors, traders, and analysts in the volatile cryptocurrency market. The Neural Prophet framework, based on neural network principles, incorporates features such as automatic differencing, trend, seasonality considerations, and external variables to enhance forecasting accuracy. The model was trained and evaluated using performance metrics such as RMSE, MAE, and MAPE. The results demonstrate the model's effectiveness in capturing trends and predicting Bitcoin prices while acknowledging the challenges posed by the inherent volatility of the cryptocurrency market.
Enhancing Water Quality Assessment in Indonesia Through Digital Image Processing and Machine Learning Iffaty, Athiya; Salsabila, Adinda; Rafiqhi, Adis Aufa; Suhendra, Rivansyah; Yusuf, Muhammad; Sasmita, Novi Reandy
Grimsa Journal of Science Engineering and Technology Vol. 1 No. 1 (2023): October 2023
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjset.v1i1.3

Abstract

Indonesia's diverse climate types, influenced by its unique geographical features, pose significant environmental challenges, including water quality issues related to turbidity and Total Dissolved Solids (TDS). Many Indonesians lack awareness of water quality, particularly turbidity, which can harbor harmful microorganisms. To address these challenges, this study employs digital image processing and machine learning, specifically Support Vector Machine (SVM) algorithms, for water quality assessment. A dataset of 80 water images, categorized into seven turbidity classes, is used to train and test the model. Results show a clear correlation between turbidity levels and TDS concentrations and pH values. The system accurately assesses water suitability for different sources, offering a user-friendly and cost-effective solution for water quality monitoring in dynamic environmental conditions. However, limitations include the dataset size and the narrow focus on turbidity. Future research could expand to encompass a broader range of water quality factors. This approach holds promise for enhancing water quality management in Indonesia and similar regions.
Optimizing University Admissions: A Machine Learning Perspective Maulana, Aga; Noviandy, Teuku Rizky; Sasmita, Novi Reandy; Paristiowati, Maria; Suhendra, Rivansyah; Yandri, Erkata; Satrio, Justinus; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 1 No. 1 (2023): August 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i1.46

Abstract

The university admission process plays a pivotal role in shaping the future of aspiring students. However, traditional methods of admission decisions often fall short in capturing the holistic capabilities of individuals and may introduce bias. This study aims to improve the admission process by developing and evaluating machine learning approach to predict the likelihood of university admission. Using a dataset of previous applicants' information, advanced algorithms such as K-Nearest Neighbors, Random Forest, Support Vector Regression, and XGBoost are employed. These algorithms are applied, and their performance is compared to determine the best model to predict university admission. Among the models evaluated, the Random Forest algorithm emerged as the most reliable and effective in predicting admission outcomes. Through comprehensive analysis and evaluation, the Random Forest model demonstrated its superior performance, consistency, and dependability. The results show the importance of variables such as academic performance and provide insights into the accuracy and reliability of the model. This research has the potential to empower aspiring applicants and bring positive changes to the university admission process.
ANFIS-Based QSRR Modelling for Kovats Retention Index Prediction in Gas Chromatography Idroes, Rinaldi; Noviandy, Teuku Rizky; Maulana, Aga; Suhendra, Rivansyah; Sasmita, Novi Reandy; Muslem, Muslem; Idroes, Ghazi Mauer; Jannah, Raudhatul; Afidh, Razief Perucha Fauzie; Irvanizam, Irvanizam
Infolitika Journal of Data Science Vol. 1 No. 1 (2023): September 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i1.73

Abstract

This study aims to evaluate the implementation and effectiveness of the Adaptive Neuro-Fuzzy Inference System (ANFIS) based Quantitative Structure Retention Relationship (QSRR) to predict the Kovats retention index of compounds in gas chromatography. The model was trained using 340 essential oil compounds and their molecular descriptors. The evaluation of the ANFIS models revealed promising results, achieving an R2 of 0.974, an RMSE of 48.12, and an MAPE of 3.3% on the testing set. These findings highlight the ANFIS approach as remarkably accurate in its predictive capacity for determining the Kovats retention index in the context of gas chromatography. This study provides valuable perspectives on the efficiency of retention index prediction through ANFIS-based QSRR methods and the potential practicality in compound analysis and chromatographic optimization.
Cardiovascular Disease Prediction Using Gradient Boosting Classifier Suhendra, Rivansyah; Husdayanti, Noviana; Suryadi, Suryadi; Juliwardi, Ilham; Sanusi, Sanusi; Ridho, Abdurrahman; Ardiansyah, Muhammad; Murhaban, Murhaban; Ikhsan, Ikhsan
Infolitika Journal of Data Science Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i2.131

Abstract

Cardiovascular Disease (CVD), a prevalent global health concern involving heart and blood vessel disorders, prompts this research's focus on accurate prediction. This study explores the predictive capabilities of the Gradient Boosting Classifier (GBC) in cardiovascular disease across two datasets. Through meticulous data collection, preprocessing, and GBC classification, the study achieves a noteworthy accuracy of 97.63%, underscoring the GBC's effectiveness in accurate CVD detection. The robust performance of the GBC, evidenced by high accuracy, highlights its adaptability to diverse datasets and signifies its potential as a valuable tool for early identification of cardiovascular diseases. These findings provide valuable insights into the application of machine learning methodologies, particularly the GBC, in advancing the accuracy of CVD prediction, with implications for proactive healthcare interventions and improved patient outcomes.
Co-Authors Afidh, Razief Perucha Fauzie Asep Rusyana Bahri, Ridzky Aulia Dharma, Dian Budi Earlia, Nanda Hizir Sofyan Husdayanti, Noviana Idroes, Ghazi M. Idroes, Ghazi Mauer Iffaty, Athiya Ikhsan Ikhsan Irvanizam, Irvanizam Juliwardi, Ilham Karma, Taufiq Khairul, Mhd Lala, Andi Maulana, Aga Maulydia, Nur Balqis Mikyal Bulqiah, Mikyal Muhammad Adam, Muhammad Muhammad Ardiansyah Muhammad Subianto Muhammad Walid Muhammad Yanis Muhammad Yusuf Murhaban Murhaban Muslem Muslem, Muslem Nica Astrianda Niode, Nurdjannah Jane Nizamuddin Nizamuddin Noviandy, Teuku R. Noviandy, Teuku Rizky Nurdjannah J. Niode Paristiowati, Maria Rafiqhi, Adis Aufa Ramadeska, Siti Raudhatul Jannah Ridho, Abdurrahman Rinaldi Idroes Safhadi, Aulia Al-Jihad Saiful Saiful Salsabila, Adinda Sanusi Sanusi Sasmita, Novi Reandy Satrio, Justinus Suryadi Suryadi Yandri, Erkata Zurnila Marli Kesuma