Computer Science, Information Systems, Artificial Intelligence
6
Scopus Publications
818
Scholar Citations
16
Scholar h-index
20
Scholar i10-index
Scopus Publications
EVALUATING DEEP LEARNING ARCHITECTURES FOR CO2 EMISSIONS FORECASTING: TCN, LSTM, AND HYBRID APPROACHES WITH HYPERPARAMETER OPTIMIZATION Roni Yunis, T. Henny Febriana Harumy, Syahril Efendi Eastern European Journal of Enterprise Technologies, 2025 The object of the study is CO2 emission prediction using deep learning models. The problem lies in developing accurate models capable of handling temporal dependencies and periodic patterns in CO2 data. To address this, three deep learning models – temporal convolutional network (TCN), long short-term memory (LSTM), and a hybrid TCN-LSTM are evaluated. These models are optimized using random search and Bayesian optimization. Results indicate that the Hybrid TCN-LSTM model, optimized via random search, performs best, achieving MAE: 1.0269, R2: 0.9305, and MAPE: 4.47%. TCN excels at capturing periodic patterns through dilated convolutions, while LSTM handles long-term dependencies. Their integration combines these strengths, improving accuracy. Optimal hyperparameters (learning rate: 0.000539, dropout rate: 0.5) enhance robustness. Random search outperforms Bayesian optimization in navigating complex search spaces and avoiding local optima. Key findings include the hybrid model's ability to address short-term periodicity and long-term trends, and Random Search’s reliability over Bayesian methods in this context. These insights advance time series forecasting methodologies and support robust predictive frameworks. Practically, they aid environmental policy, energy planning, and carbon trading by enabling data-driven decisions for emission reduction. However, implementation requires high-quality historical data and sufficient computational resources
BIG DATA ANALYTICS FOR SEASONAL CROP PATTERNS: INTEGRATING MACHINE LEARNING TECHNIQUES Roni Yunis, Arwin Halim, Irpan Adiputra Pardosi Eastern European Journal of Enterprise Technologies, 2024 This study addresses the challenge of predicting rice growing season lengths, crucial for agricultural planning in tropical regions. Climate variability and season timing create uncertainties in decision-making, and while machine learning is widely used in agriculture, a gap persists in integrating spatial-temporal data for accurate season length prediction and region-specific pattern analysis influenced by rainfall. Using a combination of Random Forest algorithms with hyperparameter optimization (grid search), and clustering techniques such as PCA, K-Means, and Hierarchical Clustering, this study analyzes key features such as the start of the season (SOS), end of the season (EOS), and their significance indicators (sig_sos and sig_eos). The findings reveal a strong correlation (0.98) between SOS and EOS, with an optimal growing season ranging from day 93 to day 207 (113.82 days). The Random Forest model, optimized with Grid Search, achieved a MSE of 28.9474 and an R2 of 0.8636, showing an outstanding predictive result. SHAP and LIME analyses identified sos and eos as the most influential predictors, while cluster analysis highlighted three distinct growing season groups characterized by variations in rainfall and seasonal stability. These results underscore the importance of understanding localized agricultural conditions and provide actionable insights for optimizing planting schedules, resource allocation, and climate adaptation strategies. By integrating advanced machine learning techniques with spatial-temporal data, this study establishes a foundation for improving agricultural resilience and sustainability in the face of climate variability
Enhancing Student Dropout Prediction Using Chi-Square, SMOTE-ENN, and Hyperparameter Tuning of Random Forest Andri, Roni Yunis, Djoni, Ng Poi Wong, Robin, Darwin 2024 9th International Conference on Informatics and Computing Icic 2024, 2024 Reducing dropout rates is crucial for enhancing human capital and education standards. Existing methods, such as Random Forest with Chi-Square and SMOTE-ENN, effectively addressed class imbalance and improved prediction accuracy for dropout data. However, there is still a research gap in achieving optimal model performance. This study addresses the gap by incorporating hyperparameter tuning alongside ChiSquare for feature selection and SMOTE-ENN for handling class imbalance. The dataset was segmented into training and evaluation subsets through the implementation of 10 -fold cross-validation. The testing was conducted with seven variations, namely building and implementing a Random Forest model using the default parameters from the Weka tool and applying six different hyperparameter tuning techniques. The results showed that Hyperband, along with other techniques like TPE, RandomSearch, and BO-TPE, led to substantial improvements in model accuracy, precision, and F-measure, and achieved perfect AUC scores. However, BO-GP and Nevergrad did not improve model performance. These findings suggest that the combination of SMOTE-ENN, Chi-Square, and hyperparameter tuning can enhance the effectiveness of dropout prediction models, with potentially positive implications for early intervention strategies in educational institutions.
Optimizing Random Forest Classification Using Chi-Square and SMOTE-ENN on Student Drop-Out Data Andri, Roni Yunis, Tanti 2023 8th International Conference on Informatics and Computing Icic 2023, 2023 Dropout is a particular concern for countries striving to increase human capital. Various attempts have been made by universities to minimize the number of dropouts. Machine learning has also developed various predictive models to determine the likelihood of students dropping out. However, there is a challenge in dropout data, specifically the problem of class imbalance, where the number of students who drop out (minority class) is significantly less than those who do not drop out (majority class). This imbalance can reduce the model’s ability to classify students at risk of dropping out. This study proposes classification optimization using the Random Forest algorithm to handle class imbalances in student dropout data. To overcome class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) techniques are used. Additionally, the attribute selection method is also applied to enhance the predictive results. The test results demonstrate that the combination of implementing feature selection with Chi-Square, followed by class imbalance handling with SMOTE-ENN, provided the most optimal predictive performance for identifying the status of both dropouts and graduates.
Application of Blockchain Technology to Prevent The Potential Of Plagiarism in Scientific Publication Andi, Ronsen Purba, Roni Yunis Proceedings of 2019 4th International Conference on Informatics and Computing Icic 2019, 2019 Blockchain is an emerging technology that has many potential applications. The blockchain contains a certain and verifiable record of every single transaction ever made. In this paper, we introduce the application for the prevention of potential plagiarism based on decentralized architecture and public-key cryptosystem, such that no need for trusted third party. We use SHA-256 as hash function and Elliptic Curve as digital signature algorithm. The results show that any attempt to plagiarize a submitted paper will violate the rules. The transmission of a paper is also encrypted through the use of complex cryptographic principles and security algorithms such that nobody can see or alter the paper. Even the reviewer is unable to change the paper because by doing such action the blockchain will report the violation.
A Proposed of IT Governance Model for Manage Suppliers and Operations Using COBIT 5 Framework Roni Yunis, Djoni, Angela Proceedings of 2019 4th International Conference on Informatics and Computing Icic 2019, 2019 Many enterprises that have used information technology as one of the supporting factors in achieving the enterprises business goals, but many do not have a good governance model for alignment between business objectives and IT goals. In the research, two problems need to be appropriately managed by the enterprise. The operational activities and procedures required to provide internally outsourced IT services and relationship with suppliers related to the system used. COBIT 5 is a framework for IT governance. The domain in COBIT 5 that can handle these problems is the Manage Suppliers (APO10) domain that deals with Manage Operations (DSS01) that are related to the organization's operational management. The objectivity of this research is to assess the level of current IT governance capabilities and targets to within reach in the future. The gap results are expected to build upon for making recommendations for improvement, and the proposed IT governance model for the enterprise.
RECENT SCHOLAR PUBLICATIONS
Comparison of Machine Learning Methods with Optimization for Paddy Production Prediction R Yunis, IA Pardosi Jurnal Sifo Mikroskil 27 (1) , 2026 2026
Fine-Tuning Hybrid Deep Learning for Sentiment Analysis of Indonesian Product Reviews A Halim, R Yunis, E Halim CommIT (Communication and Information Technology) Journal 20 (1), 127-137 , 2026 2026
Analisis Time Series dan Perancangan Dashboard untuk Memprediksi Penjualan dengan Metode Prophet dan SARIMAX B Khaw, R Irwanto, R Yunis, E Elly Jurnal Sifo Mikroskil 26 (2) , 2025 2025 Citations: 2
EVALUATING DEEP LEARNING ARCHITECTURES FOR CO2 EMISSIONS FORECASTING: TCN, LSTM, AND HYBRID APPROACHES WITH HYPERPARAMETER OPTIMIZATION R Yunis, T Harumy, FH, S Efendi Eastern-European Journal of Enterprise Technologies 5 (10), 20-32 , 2025 2025 Citations: 1
Skin Lesion Diagnosis through Deep Learning and Hybrid Texture Feature Augmentation IA Pardosi, R Yunis, A Halim Teknika 14 (2), 264-269 , 2025 2025 Citations: 1
Strengthening Digital Literacy and Organizational Management System Based on ISO 21001 to Support Kurikulum Merdeka: Penguatan Literasi Digital dan Sistem Manajemen Organisasi … R Yunis, S Sudarto, SO Ginting, M Jeni, R Riri, VG Wijaya Dinamisia: Jurnal Pengabdian Kepada Masyarakat 9 (2), 404-414 , 2025 2025 Citations: 1
PENGUATAN LITERASI DIGITAL DAN SISTEM MANAJEMEN ORGANISASI BERBASISKAN ISO 21001 UNTUK MENDUKUNG KURIKULUM MERDEKA R YUNIS, SO GINTING, M JENI, VG WIJAYA DINAMISIA: JURNAL PENGABDIAN KEPADA MASYARAKAT 9 (2), 404-414 , 2025 2025
Enhancing Student Dropout Prediction Using Chi-Square, SMOTE-ENN, and Hyperparameter Tuning of Random Forest R Yunis, NP Wong 2024 Ninth International Conference on Informatics and Computing (ICIC), 1-6 , 2024 2024
BIG DATA ANALYTICS FOR SEASONAL CROP PATTERNS: INTEGRATING MACHINE LEARNING TECHNIQUES. R Yunis, A Halim, IA Pardosi Eastern-European Journal of Enterprise Technologies 132 (4) , 2024 2024 Citations: 2
Hybridization Model for Air Pollution Prediction Using Time Series Data R Yunis, A Andri, D Djoni Cogito smart journal 10 (1), 1-14 , 2024 2024 Citations: 6
Enhancing Rice Production Prediction: A Comparative Machine Learning Analysis of Climate Variables R Yunis, Sudarto, IA Pardosi Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI 13 (1), 91-104 , 2024 2024 Citations: 3
PENGUATAN LITERASI DIGITAL DALAM MENGEDUKASI DAN MENUMBUHKEMBANGKAN JIWA KEWIRAUSAHAAN SISWA SEKOLAH MENENGAH KEJURUAN SO GINTING, R YUNIS JMM (JURNAL MASYARAKAT MANDIRI) 8 (6), 6440-6451 , 2024 2024
Pemanfaatan Figma Dalam Perancangan User Interface E-Commerce H HITA, D DJONI, C CULITA, Y RONI NUSANTARA: JURNAL PENGABDIAN KEPADA MASYARAKAT Учредители: Politeknik … , 2024 2024 Citations: 5
PENERAPAN TEKNOLOGI INFORMASI DALAM MENINGKATKAN KETERLIBATAN DAN KERTERHUBUNGAN ALUMNI SMA R YUNIS, SO GINTING JURNAL MASYARAKAT MANDIRI (JMM) 8 (3), 3007-3019 , 2024 2024
Optimizing Random Forest Classification using Chi-Square and SMOTE-ENN on student drop-out data R Yunis 2023 Eighth International Conference on Informatics and Computing (ICIC), 1-5 , 2023 2023 Citations: 5
Evaluasi Tata Kelola Teknologi Informasi Pada PT Indako Trading Coy Dengan Menggunakan Framework COBIT 2019 Domain APO12 S Howard, T Wijaya, R Yunis Jurnal SIFO Mikroskil 24 (2), 157-172 , 2023 2023 Citations: 1
Audit tata kelola ti menggunakan cobit 2019 domain apo-12 pada universitas mikroskil C Wijaya, M Sukamto, R Yunis, M Megawati Jurnal SIFO Mikroskil 24 (2), 197-210 , 2023 2023 Citations: 13
Peran interaktivitas dalam penggunaan e-learning: perluasan model utaut YM Saragih, ES Panjaitan, R Yunis Jurnal Teknologi Informasi Dan Ilmu Komputer 10 (1), 123-130 , 2023 2023 Citations: 9
Hospital Enterprise Architecture Design Using EA3 Cube Framework CS Daeli, ES Panjaitan, R Yunis INFOKUM 10 (5), 440-446 , 2022 2022 Citations: 1
Evaluation of information technology governance at Mikroskil University using COBIT 2019 framework with BAI11 domain AB Sipayung, R Yunis International Journal of Research and Applied Technology (INJURATECH) 2 (2 … , 2022 2022 Citations: 24
MOST CITED SCHOLAR PUBLICATIONS
Perancangan model enterprise architecture dengan TOGAF architecture development method R Yunis, K Surendro Seminar Nasional Aplikasi Teknologi Informasi (SNATI) , 2009 2009 Citations: 155
Pengembangan E-Learning Berbasiskan LMS untuk Sekolah, Studi Kasus SMA/SMK di Sumatera Utara R Yunis, K Telaumbanua Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI) 6 (1), 32-36 , 2017 2017 Citations: 51
Jurnal Mantik J Sihotang, ES Panjaitan, R Yunis Jurnal Mantik 4 (3), 2194-2203 , 2020 2020 Citations: 43
Pengembangan Model Arsitektur Enterprise Untuk Perguruan Tinggi R Yunis, K Surendro, ES Panjaitan JUTI: Jurnal Ilmiah Teknologi Informasi, 9-18 , 2010 2010 Citations: 40
Arsitektur Bisnis: Pemodelan Proses Bisnis dengan Object Oriented R Yunis, K Surendro, K Telaumbanua Seminar Nasional Informatika (SEMNASIF) 1 (5) , 2010 2010 Citations: 40
Model enterprise architecture untuk perguruan tinggi di Indonesia R Yunis, K Surendro Seminar Nasional Informatika (SEMNASIF) 1 (5) , 2009 2009 Citations: 40
Reduksi Atribut Pada Dataset Penyakit Jantung dan Klasifikasi Menggunakan Algoritma C5. 0 DP Utomo Universitas Mikroskil , 2020 2020 Citations: 39
Analisis Runtun Waktu Untuk Memprediksi Jumlah Mahasiswa Baru Dengan Model Random Forest M Rianto, R Yunis Paradigma 23 (1), v23i1 , 2021 2021 Citations: 36
Pemilihan Metodologi Pengembangan Enterprise Architecture untuk Indonesia R Yunis, K Surendro, ES Panjaitan Prosiding SNIKA 3 (1), A53-A59 , 2008 2008 Citations: 33
Implementasi Enterprise Architecture Perguruan Tinggi R Yunis, K Surendro Seminar Nasional Aplikasi Teknologi Informasi (SNATI) , 2010 2010 Citations: 30
Analisis Runtun Waktu Untuk Memprediksi Jumlah Mahasiswa Baru Dengan Model Arima AU Jamila, BM Siregar, R Yunis Paradigma 23 (1), 85 , 2021 2021 Citations: 28
Analisis Kesuksesan Penerapan Sistem Informasi Data Pokok Pendidikan (DAPODIK) pada SD Kabupaten Batu Bara R Yunis, FL Ibsah, D Arisandy Jurnal SIFO Mikroskil 18 (1), 71-82 , 2017 2017 Citations: 28
Application of Blockchain technology to prevent the potential of plagiarism in scientific publication R Purba, R Yunis 2019 Fourth International Conference on Informatics and Computing (ICIC), 1-5 , 2019 2019 Citations: 25
Evaluation of information technology governance at Mikroskil University using COBIT 2019 framework with BAI11 domain AB Sipayung, R Yunis International Journal of Research and Applied Technology (INJURATECH) 2 (2 … , 2022 2022 Citations: 24
Penerapan Enterprise Architecture Framework untuk Pemodelan Sistem Informasi R Yunis, T Theodora Jurnal SIFO Mikroskil 13 (2), 159-168 , 2012 2012 Citations: 24
Analisis Runtun Waktu Untuk Memprediksi Jumlah Mahasiswa Baru Dengan Model Prophet Facebook FTB Sitepu, VA Sirait, R Yunis Paradigma 23 (1), 99-105 , 2021 2021 Citations: 19
Penguatan Promosi Melalui Media Website pada Hotel Alvina Pematangsiantar R Yunis, S Ariwibowo Dinamisia: Jurnal Pengabdian Kepada Masyarakat 5 (3), 772-782 , 2021 2021 Citations: 15
Audit tata kelola ti menggunakan cobit 2019 domain apo-12 pada universitas mikroskil C Wijaya, M Sukamto, R Yunis, M Megawati Jurnal SIFO Mikroskil 24 (2), 197-210 , 2023 2023 Citations: 13
Effect of attitude on mobile banking acceptance using extended UTAUT model A Angelia, ES Panjaitan, R Yunis Jurnal Mantik 5 (2), 1006-1013 , 2021 2021 Citations: 12
Pemanfaatan TOGAF ADM untuk Perancangan Model Enterprise Architecture R Yunis, K Surendro, E Panjaitan Jurnal Informatika Komputer 14 (2), 131-141 , 2009 2009 Citations: 12
