Dhiyaussalam

@poliban.ac.id

Department of Electrical Engineering
Politeknik Negeri Banjarmasin

Dhiyaussalam


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https://researchid.co/dhiyaussalam

EDUCATION

Master of Informatics - UIN Sunan Kalijaga Yogyakarta
8

Scopus Publications

34

Scholar Citations

2

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • XR-powered ESP for IoT: Evaluating student perceptions across vocational learner backgrounds
    Ahmad Yusuf, Kun Nursyaiful Priyo Pamungkas, Siti Kustini, Dhiyaussalam
    Computers and Education X Reality, 2026
    Extended reality (XR)technologies have redefined how education is designed and experienced. This has sparked considerable interest in integrating such advanced tools into language education. However, most applications remain limited to English-only contexts and general English instruction. This study evaluates a bilingual XR-powered ESP platform that integrates English and Indonesian for Internet of Things (IoT) education in vocational higher education. The platform situates XR within a vocational ESP framework, extending its use beyond general English and applying it to a discipline-specific domain . A quantitative survey design was employed to examine student perceptions across various demographic factors, including gender, age, year of study, prior experience with XR, educational background, and English proficiency. The results indicated that the platform was broadly inclusive across most demographic factors, while differences in educational background and language proficiency significantly shaped affective and technological perceptions. Positive responses were particularly evident in Technology, Learning Outcomes, and Affective Elements, whereas lower ratings in Sense of Community suggest the need for stronger collaborative features. These findings demonstrate that XR-powered ESP functions as both a cognitive-technical tool and an affective-linguistic intervention, highlighting its potential to support bilingual and domain-specific learning. The study advances current understanding of XR in ESP. It aligns with the broader vision of revolutionizing language education through advanced XR technologies by contributing an inclusive yet differentiated model for vocational higher education .
  • Improving predictive accuracy: A comparative study of SMOTE and machine learning on heart disease data
    Dhiyaussalam, Kun Nursyaiful Priyo Pamungkas, Shofwatul ’Uyun
    Aip Conference Proceedings, 2026
  • Field-Ready IoT Design for Leachate Water Quality Monitoring with Fuzzy Risk Levels
    Dhiyaussalam Dhiyaussalam, Reza Fauzan, Ahmad Yusuf, Joni Riadi, Jazuli Fadil, Nurmahaludin
    Journal of Physics Conference Series, 2026
    Landfill leachate poses a persistent threat to water quality due to its complex and fluctuating composition. However, conventional monitoring methods often fail to capture rapid changes that can lead to environmental and regulatory risks. This study introduces a field-ready Internet of Things (IoT) system that integrates industrial-grade probes, a Raspberry Pi edge gateway, and a cloud-based backend to provide continuous and traceable leachate monitoring. A fuzzy inference engine anchored on regulatory standards and site-specific statistics translates multi-parameter measurements into Normal, Warning, and Critical risk levels. To enhance reliability, a dual-stage hysteresis mechanism stabilizes alarm states by combining asymmetric value thresholds with a short persistence window, thereby reducing false toggling under noisy conditions. Evaluation during a 21-day landfill pilot demonstrated 100% data delivery, a median latency of 1.00 s, availability of 99.24%, and rapid recovery with queued data replayed within seconds after induced outages. Risk labels generated at the edge and recomputed at the server matched for all samples, ensuring both timely alerts and auditability. The results confirm that combining resilient IoT telemetry with interpretable fuzzy risk reasoning can provide practical decision support for landfill operators while sustaining compliance reporting under intermittent connectivity.
  • Portable Hypertension Detection System Using MPX5010DP Sensor and Certainty Factor Method on Android Platform
    Ahmad Yusuf, Reza Fauzan, Chindy Wulandari, Indri Yani, Muhammad Iqbal Firdaus, Dhiyaussalam
    Journal of Physics Conference Series, 2026
    This study proposes designing and developing a prototype portable hypertension detection system that combines an MPX5010DP pressure sensor, an ESP8266 microcontroller, and an Android application with Certainty Factor (CF)-based diagnosis, connected to Firebase. The system measures blood pressure through a cuff connected to the MPX5010DP sensor, transmits the readings to a mobile application, and uses the CF method to determine real-time hypertension status. The evaluation of the prototype demonstrated its feasibility as a portable hypertension detection system that integrates sensing, wireless transmission, and intelligent classification. The prototype achieved a maximum absolute error of 2.0 mmHg for systolic and 4.9 mmHg for diastolic measurements relative to a reference digital sphygmomanometer over seven paired trials (corresponding maximum relative errors: 1.7% and 5.3%). All systolic and diastolic estimates fell within ±5 mmHg of the reference. Furthermore, the Android application supports real-time signal visualization, on-device inference, and cloud-based data reporting. Future research will focus on integrating additional physiological sensors, improving the Certainty Factor inference algorithm, and conducting large-scale validation under clinical supervision to enhance the system’s diagnostic reliability and clinical applicability.
  • Short-Horizon Forecasting of Daily PM2.5 with Tree-Based Models and Seasonal Features
    Dhiyaussalam Dhiyaussalam, Ahmad Yusuf
    Proceedings Icsintesa 2025 2025 5th International Conference of Science and Information Technology in Smart Administration, 2025
  • Measuring Student Perceptions in the Educational Metaverse using NPS-Based Group Analysis
    Abdul Rozaq, Rahimi Fitri, Ahmad Yusuf, Dhiyaussalam
    Proceeding 2025 IEEE 11th Information Technology International Seminar Itis 2025, 2025
    The rise of metaverse technologies has created new opportunities for transforming education. While there is growing interest in the metaverse, systematic evaluations of student perceptions remain limited and crucial. This study evaluates student perceptions of an educational metaversebased campus tour, using the Net Promoter Score (NPS) and thematic feedback analysis. Students rated their likelihood to recommend the platform on a $0-10$ scale. The NPS was 41.2 (Excellent), showing general satisfaction and recommendation willingness. Subgroup analysis revealed minor age differences but more substantial variation by gender and prior experience: female respondents (57.1) and first-time users (54.6) were highly positive, while male respondents (37.0) and frequent users (0.0) were more critical. Thematic insights explained that younger students emphasized usability and engagement, older students utility and performance, males technical concerns, females satisfaction, and experienced users highlighted expectation gaps and performance critiques. The findings suggest the educational metaverse effectively engages novices but requires refinements to satisfy advanced users. This approach demonstrates how NPS-based group analysis, enriched with qualitative insights, can serve as a practical and scalable framework for evaluating user experience in educational metaverse platforms.
  • Optimization of Random Forest Hyperparameters with Genetic Algorithm in Classification of Lung Cancer
    Dhiyaussalam, Shofwatul Uyun
    6th International Seminar on Research of Information Technology and Intelligent Systems Isriti 2023 Proceeding, 2023
    Lung cancer is a type of cancer with the highest death rate compared to other cancers. Cancer can be classified using histopathological methods which are obtained using biopsies. Manual classification of cancer on histopathological images is work intensive and highly susceptible to human error. Cancer classification from histopathological images can be done using computer assistance using computer vision and machine learning. This research proposes the following stages: data collection, feature extraction from images, feature selection, building a Random Forest model, optimizing hyperparameters using a Genetic Algorithm, and evaluating the performance of the model. The histopathological images that have been collected will have their color and texture features extracted. The extraction process produces 9 RGB features and 9 HSV features for color features. Meanwhile, texture features produce 6 types of features, namely dissimilarity, correlation, homogeneity, contrast, ASM, and energy, which are then searched for values from four different angles to produce 24 texture features. A total of 42 features were produced. All these features are then selected using the correlation coefficient and the remaining 24 features will be used to build a classification model using Random Forest. The classification model that has been built is then optimized by setting hyperparameters automatically so that the resulting model is reliable and better than general models. The hyperparameters that are optimized are $n$ estimators, max depth, max features, and criterion. By using a Genetic Algorithm, all hyperparameters are adjusted automatically to get hyperparameters with the best model performance. The Random Forest model with hyperparameters with default values succeeded in getting an accuracy of 98.82 % and a 10-fold cross-validation value of 99.39%. Meanwhile, the model that has been optimized using the Genetic Algorithm with the best hyperparameters $n$ estimators = 300, max depth = 100, max features = log2, and criterion = entropy produces an accuracy of 98.83% and a 10-fold cross-validation value of 99.50%. The Random Forest model with hyperparameters optimized using the Genetic Algorithm succeeded in outperforming the Random Forest model with default hyperparameters. It is proven that optimizing hyperparameters using a Genetic Algorithm can improve the performance of the Random Forest model.
  • Classification of Headache Disorder Using Random Forest Algorithm
    Dhiyaussalam, Adi Wibowo, Fajar Agung Nugroho, Eko Adi Sarwoko, I Made Agus Setiawan
    Icicos 2020 Proceeding 4th International Conference on Informatics and Computational Sciences, 2020
    Headache disorder is one of the most often illness. At least 50% of the world’s population has experienced a headache. Primary headaches have several types; migraine, tension, cluster, and medication overuse. Computer aid for diagnosis could help people locate the headache type without the need to meet the doctor. The Random Forest algorithm was used in this study to produce a reliable model for classifying the headaches types and generate feature importance. In this study, the Migbase dataset was used, and several parameters of the algorithm were tuned to produce the best model. Based on the experiment results, the best accuracy reaching 99,56% with the Random Forest parameters are 100 for n_estimators, 33 for max_features, and 5 for max_depth.

RECENT SCHOLAR PUBLICATIONS

  • XR-powered ESP for IoT: Evaluating student perceptions across vocational learner backgrounds
    A Yusuf, KNP Pamungkas, S Kustini, D Dhiyaussalam
    Computers & Education: X Reality 8, 100136 , 2026
    2026
    Citations: 2
  • A Lightweight Classical Machine Learning Pipeline for Rice NPK Deficiency Classification Using Hand-Crafted Feature Fusion
    Dhiyaussalam, KNP Pamungkas, WA Saputra, A Yusuf
    Journal of Information Systems and Informatics 8 (2), 1780-1811 , 2026
    2026
  • Improving predictive accuracy: A comparative study of SMOTE and machine learning on heart disease data
    Dhiyaussalam, KNP Pamungkas, S ’Uyun
    AIP Conference Proceedings 3326 (1), 060005 , 2026
    2026
  • Portable Hypertension Detection System Using MPX5010DP Sensor and Certainty Factor Method on Android Platform
    A Yusuf, R Fauzan, C Wulandari, I Yani, MI Firdaus, Dhiyaussalam
    Journal of Physics: Conference Series 3188 (1), 012021 , 2026
    2026
  • Field-Ready IoT Design for Leachate Water Quality Monitoring with Fuzzy Risk Levels
    D Dhiyaussalam, R Fauzan, A Yusuf, J Riadi, J Fadil, Nurmahaludin
    Journal of Physics: Conference Series 3188 (1), 012020 , 2026
    2026
  • Short-Horizon Forecasting of Daily PM2. 5 with Tree-Based Models and Seasonal Features
    D Dhiyaussalam, A Yusuf
    2025 5th International Conference of Science and Information Technology in … , 2025
    2025
  • Measuring Student Perceptions in the Educational Metaverse using NPS-Based Group Analysis
    A Rozaq, R Fitri, A Yusuf, Dhiyaussalam
    2025 IEEE 11th Information Technology International Seminar (ITIS), 1-6 , 2025
    2025
  • Predicting Respiratory Conditions Using Random Forest and XGBoost
    D Dhiyaussalam, A Yusuf, I Wardiah, NL Putri
    Journal of Information Systems and Informatics 7 (2), 1642-1657 , 2025
    2025
  • Impact of Feature Selection on the Performance of KNN and SVM in Heart Disease Prediction
    Dhiyaussalam, MH Noor, I Wardiah
    Tech: Journal of Engineering Science 1 (1), 14-25 , 2025
    2025
  • Operational Assessment of Shell and Tube High Pressure Heater in PT Sumber Segara Primadaya's 300 MW Unit 2 Power Plant
    M Mulyono, ET Efendi, RMFF Setiyawan, DK Sandi, D Dhiyaussalam
    Eksergi: Jurnal Teknik Energi 21 (01), 20-24 , 2025
    2025
  • Simulation of Automatic Solar Tracker Control System Using Proteus Application
    ET Efendi, BS Wibowo, D Dhiyaussalam, AK Wardhany, A Wibisono
    Eksergi: Jurnal Teknik Energi 20 (03), 77-79 , 2024
    2024
  • Optimization of Random Forest Hyperparameters with Genetic Algorithm in Classification of Lung Cancer
    Dhiyaussalam, S Uyun
    2023 6th International Seminar on Research of Information Technology and … , 2023
    2023
    Citations: 1
  • OPTIMALISASI HYPERPARAMETER RANDOM FOREST MENGGUNAKAN ALGORITMA GENETIKA PADA KLASIFIKASI KANKER PARU-PARU
    Dhiyaussalam
    UIN SUNAN KALIJAGA YOGYAKARTA , 2023
    2023
  • Merancang Strategi Pemasaran di Era Digital pada UMKM Rumah Makan Padang Pergaulan Yogyakarta
    DGA Candra, HA Ariesta, Dhiyaussalam, A Fatwanto
    Jurnal Bakti Saintek: Jurnal Pengabdian Masyarakat Bidang Sains dan … , 2022
    2022
    Citations: 6
  • Classification of headache disorder using random Forest algorithm
    Dhiyaussalam, A Wibowo, FA Nugroho, EA Sarwoko, IMA Setiawan
    2020 4th International Conference on Informatics and Computational Sciences … , 2020
    2020
    Citations: 25

MOST CITED SCHOLAR PUBLICATIONS

  • Classification of headache disorder using random Forest algorithm
    Dhiyaussalam, A Wibowo, FA Nugroho, EA Sarwoko, IMA Setiawan
    2020 4th International Conference on Informatics and Computational Sciences … , 2020
    2020
    Citations: 25
  • Merancang Strategi Pemasaran di Era Digital pada UMKM Rumah Makan Padang Pergaulan Yogyakarta
    DGA Candra, HA Ariesta, Dhiyaussalam, A Fatwanto
    Jurnal Bakti Saintek: Jurnal Pengabdian Masyarakat Bidang Sains dan … , 2022
    2022
    Citations: 6
  • XR-powered ESP for IoT: Evaluating student perceptions across vocational learner backgrounds
    A Yusuf, KNP Pamungkas, S Kustini, D Dhiyaussalam
    Computers & Education: X Reality 8, 100136 , 2026
    2026
    Citations: 2
  • Optimization of Random Forest Hyperparameters with Genetic Algorithm in Classification of Lung Cancer
    Dhiyaussalam, S Uyun
    2023 6th International Seminar on Research of Information Technology and … , 2023
    2023
    Citations: 1
  • A Lightweight Classical Machine Learning Pipeline for Rice NPK Deficiency Classification Using Hand-Crafted Feature Fusion
    Dhiyaussalam, KNP Pamungkas, WA Saputra, A Yusuf
    Journal of Information Systems and Informatics 8 (2), 1780-1811 , 2026
    2026
  • Improving predictive accuracy: A comparative study of SMOTE and machine learning on heart disease data
    Dhiyaussalam, KNP Pamungkas, S ’Uyun
    AIP Conference Proceedings 3326 (1), 060005 , 2026
    2026
  • Portable Hypertension Detection System Using MPX5010DP Sensor and Certainty Factor Method on Android Platform
    A Yusuf, R Fauzan, C Wulandari, I Yani, MI Firdaus, Dhiyaussalam
    Journal of Physics: Conference Series 3188 (1), 012021 , 2026
    2026
  • Field-Ready IoT Design for Leachate Water Quality Monitoring with Fuzzy Risk Levels
    D Dhiyaussalam, R Fauzan, A Yusuf, J Riadi, J Fadil, Nurmahaludin
    Journal of Physics: Conference Series 3188 (1), 012020 , 2026
    2026
  • Short-Horizon Forecasting of Daily PM2. 5 with Tree-Based Models and Seasonal Features
    D Dhiyaussalam, A Yusuf
    2025 5th International Conference of Science and Information Technology in … , 2025
    2025
  • Measuring Student Perceptions in the Educational Metaverse using NPS-Based Group Analysis
    A Rozaq, R Fitri, A Yusuf, Dhiyaussalam
    2025 IEEE 11th Information Technology International Seminar (ITIS), 1-6 , 2025
    2025
  • Predicting Respiratory Conditions Using Random Forest and XGBoost
    D Dhiyaussalam, A Yusuf, I Wardiah, NL Putri
    Journal of Information Systems and Informatics 7 (2), 1642-1657 , 2025
    2025
  • Impact of Feature Selection on the Performance of KNN and SVM in Heart Disease Prediction
    Dhiyaussalam, MH Noor, I Wardiah
    Tech: Journal of Engineering Science 1 (1), 14-25 , 2025
    2025
  • Operational Assessment of Shell and Tube High Pressure Heater in PT Sumber Segara Primadaya's 300 MW Unit 2 Power Plant
    M Mulyono, ET Efendi, RMFF Setiyawan, DK Sandi, D Dhiyaussalam
    Eksergi: Jurnal Teknik Energi 21 (01), 20-24 , 2025
    2025
  • Simulation of Automatic Solar Tracker Control System Using Proteus Application
    ET Efendi, BS Wibowo, D Dhiyaussalam, AK Wardhany, A Wibisono
    Eksergi: Jurnal Teknik Energi 20 (03), 77-79 , 2024
    2024
  • OPTIMALISASI HYPERPARAMETER RANDOM FOREST MENGGUNAKAN ALGORITMA GENETIKA PADA KLASIFIKASI KANKER PARU-PARU
    Dhiyaussalam
    UIN SUNAN KALIJAGA YOGYAKARTA , 2023
    2023