Article Per Year (5 Year)
p-Index From 2020 - 2025
3.654
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Teknik ITS IPTEK The Journal for Technology and Science Proceedings of KNASTIK JUTI: Jurnal Ilmiah Teknologi Informasi JTSL (Jurnal Tanah dan Sumberdaya Lahan) PROCEEDING IC-ITECHS 2014 JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Conference SENATIK STT Adisutjipto Yogyakarta SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal ULTIMA Computing JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Sewagati NERO (Networking Engineering Research Operation)
Ahmad Saikhu
Departemen Teknik Informatika Institut Teknologi Sepuluh Nopember Surabaya
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 33 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 33 Documents
Search

 1   2   3   4 
Multivariate Time Series Forecasting Using Recurrent Neural Networks for Meteorological Data Hariadi, Victor; Saikhu, Ahmad; Zakiya, Nurotuz; Wijaya, Arya Yudhi; Baskoro, Fajar
SENATIK STT Adisutjipto Vol 5 (2019): Peran Teknologi untuk Revitalisasi Bandara dan Transportasi Udara [ISBN 978-602-52742-
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/senatik.v5i0.365

Abstract

Rainfall is related to a number of factors that are interdependent and influenced by dynamic global time, region and climate factors. Determination of relevant predictors is important for the efficiency of the rainfall estimator model. Although some climate modeling studies in one region/country have high accuracy, this model is not necessarily suitable for other regions. Determination of predictor variables by considering spatio-temporal factors and local / global features results in a very large number of inputs. Feature selection produces minimal input so that it gets relevant predictor variables and minimizes variable redundancy. Recurrent Neural Networks is one of the artificial neural networks that can be used to predict time series data. This study aims to predict rainfall by combining the SVM classification method and the RNN method. Tests on the Perak 1 daily and monthly weather data (WMO ID: 96933) and Perak 2 Station daily and monthly data(WMO ID: 96937), showed high accuracy results with an R2 are 92.1%; 94.1%; 90.9% and 89.6%.
Accelerating real-time deterministic discovery through single instruction multiple data graphical processor unit for executing distributed event logs Fauzan, Hermawan; Sarno, Riyanarto; Saikhu, Ahmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4214-4227

Abstract

With the rapid expansion of process mining implementation in global enterprises distributed across numerous branches, there is a critical requirement to develop an application qualified for real-time operation with fast and precise data integration. To address this challenge, computational parallelism emerges as a feasible solution to accelerate data analytics, with graphical processor unit (GPU) computing currently trending for achieving parallelism acceleration. In this study, we developed a process mining application to optimize parallel and distributed process discovery through a combination of central processing unit (CPU) and GPU computing. The use of this computing combination is leveraged for executing multi-windowing threads within multi-instruction, multiple data (MIMD) in the CPU for streaming distributed event logs, using multi-instruction, single data (MISD) within the CPU to deploy a large footprint pipeline to the GPU, and then utilizing single instruction, multiple data (SIMD) to execute global thread discovery within the GPU. This method significantly accelerates performance in real-time distributed discovery. By reducing branch divergence in SIMD on the global thread GPU parallelism, it outperformed local-thread CPU execution in deterministic discovery, speeding up from 10 to 40 times under specific conditions using a novel min-max flag algorithm implemented within the main steps of the process discovery.
ANALISIS EFEKTIVITAS APLIKASI MYITS THESIS MENGGUNAKAN CONFIRMATORY FACTOR ANALYSIS UNTUK PENINGKATAN LAYANAN PENYELENGGARAAN UJIAN PADA PROGRAM DOKTOR ILMU KOMPUTER Ambarwati, Lina -; D'layla, Adifa Widyadhani Chanda; Saikhu, Ahmad
Networking Engineering Research Operation Vol 9, No 2 (2024): Nero - November 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i2.28201

Abstract

In the global era, Information Systems in higher education institutions are a must. Institut Teknologi Sepuluh Nopember (ITS) continuously makes efforts to develop information systems for various academic and non-academic services for the lecturer and students. One of the academic services developed at ITS is myITS Thesis application. myITS Thesis is one of the applications prepared for the management of scholars Final Project. The Computer Science Doctoral Program (PDIK) has implemented the application for examination services including qualification hearings progress, seminars, and closed dissertation hearings. This study aims to measure the effectiveness of using the myITS Thesis application in managing PDIK dissertation research services to stakeholders, especially PDIK scholars. Measurements were carried out by surveying through questionnaires with PDIK participants who used the application in the 2023/2024 academic year. The effectiveness of the application is measured through five factors, namely System Quality (KS), Information Quality(KI), System Use(PS), User Satisfaction(KP), and Individual Impact(DI). The five factors are measured through 30 Question Indicators to 55 respondents. The results of the questionnaire survey were processed using descriptive analysis and CFA modeling. CFA is used to measure validity and reliability through Standardized Loading Factor (SLF), Cronbach Alpha (CA), and Composite Reliability (CR) values. It is concluded from the modeling results based on validity and reliability measurements that the KS factor is valid with CA value=0.931 and reliable with CR value=0.73, the KI factor is valid with CA value=0.923 and reliable with CR value=0.706, the PS factor is valid with CA value=0.95 and reliable with CR value=0.734. While the KP factor is valid with CA value=0.972 and reliable with CR value= 0.814. Therefore, the myITS Thesis application has been quite effective in improving exam administration services.Keywords: questionnaire, descriptive analysis, PDIK, myITS Thesis, CFA modeling
FACE RECOGNITION USING DEEP NEURAL NETWORKS WITH THE COMBINATION OF DISCRETE WAVELET TRANSFORM, STATIONARY WAVELET TRANSFORM, AND DISCRETE COSINE TRANSFORM METHODS Akbar, Afrizal Laksita; Fatichah, Chastine; Saikhu, Ahmad
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 18, No. 2, July 2020
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v18i2.a1000

Abstract

Personal identification can be done by using face, fingerprint, palm prints, eye’s retina, or voice recognition which commonly called as biometric methods. Face recognition is the most popular and widely used among those biometric methods. However, there are some issues in the implementation of this method: lighting factor, facial expression, and attributes (chin, mustache, or wearing some accessories). In this study, we propose a combination method of Discrete Wavelet Transform and Stationary Wavelet Transform that able to improve the image quality, especially in the small-sized image. Moreover, we also use Histogram Equalization in order to correct noises such as over or under exposure, Discrete Cosine Transform in order to transform the image into frequency domain, and Deep Neural Networks in order to perform the feature extraction and classify the image. A 10-fold cross-validation method was used in this study. As the result, the proposed method showed the highest accuracy up to 92.73% compared to Histogram Equalization up to 80.73%, Discrete Wavelet Transform up to 85.85%, Stationary Wavelet Transform up to 64.27%, Discrete Cosine Transform up to 89.50%, the combination of Histogram Equalization, Discrete Wavelet Transform, and Stationary Wavelet Transform up to 69.77%, and the combination of Stationary Wavelet Transform, Discrete Wavelet Transform, and Histogram Equalization up to 77.39%.
DETECTION OF FRAUDULENT ATM TRANSACTIONS USING RULE-BASED CLASSIFICATION TECHNIQUES Deni Ekel Ramanda Sembiring Pelawi; Ahmad Saikhu
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6401

Abstract

The significant rise in ATM fraud—reflected in 130,472 suspicious transactions reported in Indonesia in 2022—highlights the urgent need for accurate and efficient real-time fraud detection systems. This study evaluates two complementary detection approaches using a dataset of 20,000 anonymized ATM transactions collected from XYZ Bank between January and December 2022, each labeled by internal fraud analysts as fraud or non-fraud. The models compared are a Rule-Based Classifier and a Decision Tree classifier. The Decision Tree demonstrates strong overall performance, achieving 98% accuracy, 75% precision, 79% recall, and a 77% F1-score, indicating a reliable ability to detect diverse fraud patterns. In contrast, the Rule-Based Classifier yields 60% accuracy, 97% precision, 60% recall, and a 74% F1-score, showing high precision with fewer false alarms but a limited ability to detect varied fraud cases. These results emphasize the trade-off between specificity and sensitivity in static versus adaptive models. To address this, a hybrid detection framework is proposed—combining rule-based screening to filter obvious non-fraud cases, followed by Decision Tree analysis to handle more complex patterns. This approach aims to reduce unnecessary transaction holds and improve detection reliability. This study contributes to the limited comparative research on fraud detection methods using real ATM transaction data within the Indonesian banking context. Future research will focus on adaptive learning models to maintain performance against evolving fraud behaviors in dynamic financial systems.
An Adaptive Stacking An Adaptive Stacking Approach for Monthly Rainfall Prediction with Hybrid Feature Selection: Hybrid Feature Selection Zulfa, Ahmad; Saikhu, Ahmad; Pradana, Hilmil; Budiawan, Irvan
ULTIMA Computing Vol 17 No 1 (2025): Ultima Computing: Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v17i1.4157

Abstract

Rainfall is a critical climatic element for water resource management, agriculture, and hydrometeorological disaster mitigation. However, its nonlinear and fluctuating characteristics require a careful and adaptive predictive approach. This study aims to develop a monthly rainfall prediction model using an Adaptive Stacking Ensemble method combined with a hybrid feature selection framework. The feature selection integrates three techniques”correlation analysis, feature importance from Random Forest, and Recursive Feature Elimination (RFE)”through a voting mechanism. Three machine learning algorithms, namely Random Forest, K-Nearest Neighbors (KNN), and XGBoost, are used as base learners. The meta-learner is adaptively selected based on the best-performing base model. Model performance is evaluated using R², RMSE, and MAE metrics. The proposed method is expected to produce a more accurate, stable, and reliable predictive model to support climate-based decision-making. By leveraging the hybrid feature selection framework, the model effectively identifies the most relevant weather variables related to monthly rainfall patterns, thereby reducing model complexity without sacrificing accuracy. The adaptive stacking approach also offers flexibility in capturing nonlinear relationships between features and targets, while enhancing model generalization across seasonally varying data. Experiments were conducted on long-term weather datasets, and the results demonstrate that the proposed model outperforms single models and conventional ensemble methods. This research contributes to the development of more robust, data-driven climate prediction systems that can be applied across sectors affected by rainfall variability.
PREDICTION OF MULTIVARIATE TIME SERIES DATA USING ECHO STATE NETWORK AND HARMONY SEARCH Al Haromainy, Muhammad Muharrom; Fatichah, Chastine; Saikhu, Ahmad
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 2, Juli 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i2.a1051

Abstract

Multivariate time series data prediction is widely applied in various fields such as industry, health, and economics. Several methods can form prediction models, such as Artificial Neural Network (ANN) and Recurrent Neural Network (RNN). However, this method has an error value more significant than the development method of RNN, namely the Echo State Network (ESN). The ESN method has several global parameters, such as the number of reservoirs and the leaking rate. The determination of parameter values dramatically affects the performance of the resulting prediction model. The Harmony Search (HS) optimization method is proposed to provide a solution for determining the parameters of the ESN method. The HS method was chosen because it is easier to implement, and based on other research, the HS method gets the optimum value better than other meta-heuristic methods. The methods compared in this study are RNN, ESN, and ESN-HS. Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) are used to measure the error rate of forecasting results. ESN got a smaller error value than RNN, and ESN-HS produced a minor error value among the other trials, namely 0.782e-5 for RMSE and 0.28% for MAPE. The HS optimization method has successfully obtained the appropriate global parameters for the ESN prediction model.
PEMODELAN PERUBAHAN LAHAN DAN TUTUPAN LAHAN BERBASIS MARKOV-CHAIN DI KABUPATEN GUNUNG KIDUL Rahmawati, Septi Sri; Setyowati, Ratih; Ramlah; Azizah, Salsabila Nur; Ardiansyah, Ramadhani Muhammad Yusuf; Saikhu, Ahmad
Jurnal Tanah dan Sumberdaya Lahan Vol. 12 No. 2 (2025)
Publisher : Departemen Tanah, Fakultas Pertanian, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2025.012.2.19

Abstract

The standard values of quality and quantity of living needs continue to increase over time, leading to competition in the utilization of agricultural and non-agricultural land. This results in uncontrolled land use conversion. Land use changes in Gunungkidul between the 1940s and 1970s caused widespread deforestation, making the area barren. Conservation efforts up to the early 2000s yielded positive results. However, population growth and the expansion of activity centers in Gunungkidul during the 2000s may trigger further deforestation. This study analyzes spatiotemporal land use changes in Gunungkidul Regency over the period 2015–2023. Land use data were obtained from Landsat and Sentinel-2 satellite imagery and analyzed using Geographic Information Systems (GIS) and accuracy assessment through ground checks. The results show significant land use changes, particularly the conversion of vegetation into built-up areas, with an increase/decrease in area from 2015 to 2023 of 72.65%. The most significant changes occurred in Wates District, the administrative center of Gunungkidul Regency, forming a pattern concentrated around service centers and spreading linearly along access routes to these centers. Land changes were also observed in the northern and southern regions with a scattered pattern. These changes are driven by population growth and regional development in Gunungkidul Regency. Land use changes may lead to land degradation, highlighting the importance of this study in providing crucial information for better spatial planning and land management in the future.  
THE EFFECT OF FACIAL ACCESSORY AUGMENTATION ON THE ACCURACY OF DEEP LEARNING-BASED FACIAL RECOGNITION SYSTEMS Hidayat, Ahmad Nur; Suciati, Nanik; Saikhu, Ahmad
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 3 (2025): Juni 2025
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3846

Abstract

Abstract: Face recognition based on deep learning has become an important technology in many areas. However, these systems often face challenges in real-world conditions, such as when the face is partially covered by accessories such as masks or glasses. This study aims to evaluate the effect of data augmentation by adding facial accessories (masks, glasses, and a combination of both) and geometric augmentation on the accuracy of face recognition systems. There are three types of datasets used in this method: the original dataset (category 1), the dataset with facial accessories augmentation (category 2), and the dataset with geometric augmentation (category 3). Data augmentation was performed on the training dataset to increase diversity, followed by the face detection process using SCRFD and feature extraction with ArcFace. The model was then trained using Multi-Layer Perceptron (MLP). Based on the results, adding face accessories (category 2) made the model a lot more accurate, hitting 99% accuracy. In category 3, adding geometric features improved accuracy to 91%. Other evaluation metrics, such as precision, recall, and F1-score, also showed improvement after augmentation. This study concludes that facial accessories augmentation is more effective in improving the accuracy and robustness of face recognition models compared to geometric augmentation.Keywords: augmentation; deep learning; face recognition; glasses. Abstrak: Pengenalan wajah berbasis deep learning telah menjadi salah satu teknologi penting dalam berbagai aplikasi. Namun, sistem ini sering kali menghadapi tantangan dalam kondisi dunia nyata, seperti saat wajah tertutup sebagian oleh aksesori seperti masker atau kacamata. Penelitian ini bertujuan untuk mengevaluasi pengaruh augmentasi data dengan menambahkan aksesori wajah (masker, kacamata, dan kombinasi keduanya) serta augmentasi geometris terhadap akurasi sistem pengenalan wajah. Metode yang digunakan melibatkan tiga kategori dataset: dataset asli tanpa augmentasi (kategori 1), dataset dengan augmentasi aksesoris wajah (kategori 2), dan dataset dengan augmentasi geometris (kategori 3). Augmentasi data dilakukan pada dataset pelatihan untuk meningkatkan keberagaman, diikuti dengan proses deteksi wajah menggunakan SCRFD dan ekstraksi fitur dengan ArcFace. Model kemudian dilatih menggunakan Multi-Layer Perceptron (MLP). Hasil penelitian menunjukkan bahwa augmentasi aksesoris wajah (kategori 2) memberikan peningkatan signifikan pada akurasi model, mencapai 99%, sedangkan kategori 3 dengan augmentasi geometris mencapai akurasi 91%. Metrik evaluasi lainnya, seperti precision, recall, dan F1-score, juga menunjukkan peningkatan setelah augmentasi. Penelitian ini menyimpulkan bahwa augmentasi aksesoris wajah lebih efektif dalam meningkatkan akurasi dan ketahanan model pengenalan wajah dibandingkan dengan augmentasi geometris.Kata kunci: augmentasi; deep learning; kacamata; pengenalan wajah.
Garch Model Hybridization With Feed Forward Neural Network Algorithm Approach For Predicting The Volatility Of The Composite Stock Price Index Putra Wiratama, Rangga Kurnia; Saikhu, Ahmad; Suciati, Nanik
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1278

Abstract

Stock market volatility is a crucial indicator in measuring investment risk and influencing investor decision-making, where proper understanding of volatility movements can help investors optimize their investment portfolios. Time series data from the stock exchange shows complex heteroscedasticity characteristics, where volatility levels can change dynamically over time, creating distinct challenges in modeling and prediction. The implementation of the hybrid model is carried out by integrating the advantages of both models, where GARCH is used to capture volatility clustering characteristics, while FFNN is utilized to capture complex non-linear patterns in the data. By using evaluation of several comprehensive error measurement metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), to ensure model reliability in various aspects of prediction. The use of the GARCH-FFNN hybrid model is expected to provide more accurate volatility predictions compared to using GARCH or FFNN models separately, with potential improvements in prediction accuracy and adaptability to changing market conditions. These findings provide important contributions to stock market volatility modeling and can serve as a reference for investors, portfolio managers, and financial practitioners in making better investment decisions, as well as paving the way for the development of more sophisticated volatility prediction models in the future
Co-Authors Adi Setyo Nugroho Aditya Pradana , Muhamad Hilmil Muchtar afrizal laksita akbar, afrizal laksita Alif Hidayat, Dedi Arman Ambarwati, Lina - Anak Agung Istri Sri Wiadnyani Anggraini, Ratih Nur Esti Ardiansyah, Ramadhani Muhammad Yusuf Arya Yudhi Wijaya Arya Yudi Wijaya Arya Yudi Wijaya Azizah, Salsabila Nur Baskoro, Fajar Bilqis Amaliah Bryan Alfadhori Budiawan, Irvan Buliali, Joko Lianto Chastine Fatichah D'layla, Adifa Widyadhani Chanda Daniel Oranova Siahaan Dedy D. Prastyo Deneng Eka Putra Deni Ekel Ramanda Sembiring Pelawi Djoko Kuswanto F.X. Arunanto Fajar Baskoro Fajar Baskoro Fauzan, Hermawan FX Arunanto Ginardi, R.V. Hari Gracius Cagar Gunawan Hari Ginardi Hidayat, Ahmad Nur Imam Hambali Ivanda Zevi Amalia Joko Lianto Joko Lianto Laili Rochmah Muhammad Hermansyah Muhammad Muharrom Al Haromainy Muhammaf Sjahid Akbar Nanik Suciati Neni Alya Firdausanti Noor Fitria Azzahra Noor Fitria Azzahra NUR LAILA Pradana, Hilmil Pradana, Muhamad Hilmil Muchtar Aditya Pratiwi, Adinda Putri Putra Wiratama, Rangga Kurnia Qolby, Lailly Syifa'ul Rakhmat Arianto Ramlah Riyanarto Sarno Rully Soelaiman Rully Soelaiman Santi Puteri Rahayu Sarwosri Sarwosri Septi Sri Rahmawati Setiawan Setiawan Setyowati, Ratih Suwadi Suwadi Victor Hariadi Wijaya, Arya Yudi Yudhi Purwananto Zakiya, Nurotuz Zulfa, Ahmad