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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Teknologi dan Manajemen Informatika Prosiding Seminas Competitive Advantage Jurnal Teknologi Informasi dan Ilmu Komputer REKAYASA Register: Jurnal Ilmiah Teknologi Sistem Informasi Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization EnviroScienteae Syntax Literate: Jurnal Ilmiah Indonesia Indonesian Journal of Artificial Intelligence and Data Mining Applied Information System and Management Technomedia Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Mantik JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Mnemonic Jurnal Cahaya Mandalika Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Algoritme Jurnal Mahasiswa Teknik Informatika The Indonesian Journal of Computer Science Jurnal ilmiah teknologi informasi Asia International Journal of Information Systems and Technology (IJOINT)
Cahyo Crysdian
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Physics Social Sciences Other

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Rainfall Prediction Using Attention-Based LSTM Architecture Romadhani, Ahmad; Santoso, Irwan Budi; Crysdian, Cahyo
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8727

Abstract

This study addresses the challenge of accurately predicting rainfall in regions with complex climate dynamics, such as Malang Regency, East Java. It evaluates the performance of a Long Short-Term Memory (LSTM) model enhanced with the Bahdanau Attention Mechanism, comparing it with a Standard LSTM model in forecasting daily rainfall based on historical weather parameters including average temperature, relative humidity, sunshine duration, and wind speed. Using daily data from BMKG covering 2000 to 2023, both models underwent a structured machine learning process including data preprocessing, feature selection, model training, and evaluation. The Attention-Based LSTM consistently outperformed the Standard LSTM, particularly in handling rainfall anomalies, achieving an MSE of 0.00800 and RMSE of 0.08948, compared to 0.00817 and 0.09039 respectively for the Standard LSTM. These results demonstrate that integrating Bahdanau Attention improves the model’s focus on relevant temporal features, enhancing prediction accuracy and robustness. The architecture consisting of two LSTM cells combined with the attention mechanism effectively captures complex sequential patterns that the standard model tends to overlook. This research highlights the potential of attention mechanisms in time series weather prediction, contributing to more reliable early warning systems, adaptive agricultural strategies, and disaster risk reduction frameworks. Future work could explore hybrid models or incorporate additional weather features to further improve performance and generalization.
E-commerce Product Review Classification using Neural Network-Based Approach Ihtada, Fahrendra Khoirul; Abidin, Zainal; Crysdian, Cahyo
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.2845

Abstract

E-commerce has become an integral part of how people shop, with the rise of customer reviews on various platforms. These reviews provide important insights into product, customer service, and delivery. The growing volume of e-commerce reviews makes manual sorting time-consuming and error-prone for business owners. This study aims to classify e-commerce reviews into three categories: product, customer service, and delivery. The data was collected from e-commerce customer reviews on Tokopedia and labeled using crowdsourcing for ground truth. To classify the reviews, a Neural Network is performed with various numbers of node and learning rate. TF-IDF is also used for feature extraction to capture important features from the review data. From nine test scenarios, model B3 with 50 nodes in the first hidden layer and a learning rate of 0.1 provided the best performance with an accuracy of 65.85%, precision of 62.27%, recall of 58.61%, and f1-score of 59.71%. Validation using K-Fold Cross Validation shows an average accuracy of 64.17% at k=10. Word analysis with TF-IDF identified dominant words in each category. The B3 model is not yet able to classify reviews perfectly, due to the large and unbalanced dataset, less complex model architecture, and less effective TF-IDF preprocessing. However, this study shows potential for better classification in the future. With optimization, this model can be very useful for e-commerce business owners to gain insight from customer reviews and can help them to identify aspects that will lead to customer satisfaction and trust.
Prediksi Closing Price Saham Harian Berbasis Soft Computing Susilo, Ni`mah Firsta Cahya; Crysdian, Cahyo
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v9i6.15485

Abstract

Prediksi harga saham adalah tugas kompleks yang bergantung pada banyak faktor seperti kondisi politik, ekonomi global, laporan keuangan perusahaan, dan pendapatan. Oleh karena itu, untuk memaksimalkan keuntungan dan meminimalkan kerugian, diperlukan adanya teknik memprediksi nilai saham. Tujuan dari penelitian ini adalah untuk mengetahui pengaruh fitur terhadap perkiraan harga close price saham dan membandingkan kinerja multiple linear regression dan artificial neural network. Uji koefisien korelasi yang telah dilakukan menghasilkan nilai koefisien variabel terbesar dan terkecil dalam penelitian ini adalah 0,9. Algoritma multiple linear regression mengungguli artificial neural network dengan MAPE sebesar 0,900016423%.
Analysis of the Use of Random Forest Models to Measuring the Quality of Indonesian Higher Education Institutions Wiyono, Masdar; Crysdian, Cahyo; Hariyadi, Mokhamad Amin; Abidin, Zainal; Almais, Agung Teguh Wibowo
Rekayasa Vol 18, No 3: Desember, 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v18i3.32024

Abstract

This study investigates the performance of the Random Forest algorithm in measuring the quality of Higher Education Institutions (HEIs) in Indonesia. The current reliance on administrative evaluations and conventional accreditation processes often fails to capture the institutions’ actual performance comprehensively, indicating the need for a data-driven alternative. This research proposes the use of a Random Forest–based classification model to assess institutional quality based on relevant accreditation indicators. The RF-D model demonstrates optimal classification performance across three quality categories—Good, Very Good, and Excellent—with high precision, recall, and F1-scores for all classes. The Very Good category achieves a precision of 89% and a recall of 80%, while the Excellent category records the highest recall at 86%. Furthermore, the Area Under Curve (AUC) scores, which approach 1.0 for all categories, confirm the strong discriminative capability of the model. This study also highlights the influence of train–test data ratios on model stability. Extreme imbalances in data splitting can lead to overfitting or underfitting, emphasizing the importance of selecting an appropriate configuration during model development. Overall, the findings indicate that Random Forest, when optimized with suitable parameters, provides a more accurate, objective, and replicable approach for evaluating HEI quality in Indonesia. These results are expected to contribute to the development of a more transparent higher education assessment system and support data-driven decision-making among policymakers.
Smart Assessment menggunakan Backpropagation Neural Network Agung Teguh Wibowo Almais; Cahyo Crysdian; Khadijah Fahmi Hayati Holle; Akbar Roihan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 3 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i3.1469

Abstract

Penerapan scraping dan Backpropagation Neural Network dapat menjadikan penilaian Self- Assessment Questionnaire (SAQ) website Pemerintah Daerah Provinsi Jawa Timur lebih smart jika dibandingkan dengan model assessment yang sudah ada. Langkah awal yaitu melakukan scraping website Pemerintah Daerah Provinsi Jawa Timur untuk mendapatkan nilai SAQ. Hasil scraping tersebut akan digunakan sebagai data uji pada metode Backpropagation Neural Network, kemudian hasil data uji akan di proses menggunakan 4 jenis model data yang berbeda-beda dari segi jumlah iterasi dan hidden layer untuk mendapatkan akurasi terbaik. Pada model data A menggunakan iterasi 1000 dan 5 hidden layer menghasilkan nilai Mean Squared Error (MSE) 0,0117, Mean Absolute Percent Error (MAPE) 39,36% dan Akurasi 60.64%. Model data B menggunakan iterasi 1000 dan 7 hidden layer menghasilkan nilai MSE 0,0087, MAPE 29,49% dan Akurasi 70,50%. Model data C dengan menggunakan iterasi 2000 dan 9 hidden layer menghasilkan nilai MSE 0,0064, MAPE 24,46% dan Akurasi 75,53%. Model data D menggunakan iterasi 2000 dan 9 hidden layer menghasilkan nilai MSE 0,0036, MAPE 18,71% dan Akurasi 81,28%. Dari hasil ujicoba tersebut bahwa model data D yang menggunakan iterasi 2000 dan 9 hidden layer menghasilkan tingkat akurasi yang terbaik sehingga model data D dapat dijadikan acuan hasil penilaian website Pemerintah Daerah Provinsi Jawa Timur tahun 2021.
Utilizing Long Short-Term Memory (LSTM) Networks for Predicting Seismic-Induced Building Damage: A Bawean Region Case Study Zarkoni, Ahmad; Almais, Agung Teguh Wibowo; Crysdian, Cahyo; Hariyadi, Mokhamad Amin; Pagalay, Usman; Sugiharto , Tomy Ivan
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v20i1.1212

Abstract

This study examines the feasibility of employing Long Short-Term Memory (LSTM) networks to estimate earthquake-induced building damage using a focused dataset derived from the continuous 8-day mainshock–aftershock sequence that occurred in March 2024. A total of 483 events were analyzed, utilizing three readily available source parameters: magnitude, depth, and epicentral distance to predict the corresponding EMS-98 damage grade. The motivation for using an LSTM architecture stems from its capacity to model temporal dependencies within sequential seismic activity, despite the limited size of the dataset. The best-performing single-split model (B4) achieved a test R^2 of 0.5738 and an RMSE of 0.2997 on the held-out set. However, to obtain a more robust assessment of the model’s generalizability, a 5-fold TimeSeriesSplit cross-validation was conducted. The cross-validation procedure yielded a mean R^2 of 0.49 with a standard deviation of 0.27, and a mean RMSE of 0.33 with a standard deviation of 0.16. These results demonstrate that the LSTM model provides a credible baseline model for exploratory damage estimation, although a substantial portion of the variance remains unexplained due to the absence of geotechnical, soil amplification, and structural fragility information. The findings highlight the potential of sequence-based modeling for rapid damage estimation and underscore the need for integrating site-specific and structural variables in future work to enhance predictive accuracy.
Analisis Komparasi Kinerja Model SVM-TF-IDF dan LSTM dengan Embedding BERT untuk Klasifikasi Tingkat Literasi Digital christianto, yudhi; Crysdian, Cahyo; Abidin, Zainal
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i2.13597

Abstract

This study compares the performance of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) with BERT embedding for classifying users’ digital literacy levels from textual digital footprints, dataset of 1,500 Indonesian-language texts from platform X was annotated by three experts into low, medium, and high literacy categories. After text preprocessing, TF-IDF features were applied to SVM and BERT tokenization to LSTM. Models were evaluated using 5-Fold Cross-Validation to ensure reliability. Results show that LSTM-BERT achieved the highest performance (F1-Score = 73.8%) compared to SVM (70.50%), with confusion-matrix analysis indicating better accuracy in detecting high-literacy texts. These findings confirm that contextual linguistic patterns effectively represent digital literacy levels and highlight the potential of deep-learning approaches for scalable, objective, and automated literacy assessment based on text data.
Unsupervised Optimization of Boundary Information Based on the Coefficient of Variation to Improve Image Segmentation Cahyo Crysdian
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 12 No 1 (2026): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v12i1.4725

Abstract

The automatic retrieval of boundary information from image objects suffers from the problem of under and over-segmentation, where the former leads to missed object detection, while the latter delivers an improper object shape. A method to optimize the automatic retrieval of complete and proper boundary information is proposed in this research based on an unsupervised approach. The strategy is to utilize the trade-off between the coefficient of variation from shape distribution against the mean of entropy contribution from segmented regions. This mechanism relies on the assumption that the segmentation result of a natural image contains a prominent main object representation with its details which are presented as a normal distribution of segmented regions. The research also enhances the entropy-based segmentation evaluation by redefining the computation of image entropy and segmentation entropy. The experiment shows that the proposed approach is capable of reducing over-segmentation by 57.20% compared to the existing algorithm, while at the same time reducing the consumption time by 85.26%. The empirical evaluation shows that the proposed approach delivers the highest accuracy among other evaluated methods. Qualitative validation based on groups of human observers shows that the proposed approach is the most desired algorithm for producing boundary information and measuring segmentation quality. These findings suggest that the trade-off between the mean of entropy contribution from the segmented regions and the coefficient of variation from shape distribution becomes an effective feature for unsupervised retrieval of boundary information.
Analysis of the Use of Artificial Neural Network Models in Predicting Bitcoin Prices Muhammad Sahi; Muhammad Faisal; Yunifa Miftachul Arif; Cahyo Crysdian
Applied Information System and Management (AISM) Vol. 6 No. 2 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i2.29648

Abstract

Bitcoin is one of the fastest-growing digital currencies or cryptocurrencies in the world. However, the highly volatile Bitcoin price poses a very extreme risk for traders investing in cryptocurrencies, especially Bitcoin. To anticipate these risks, a prediction system is needed to predict the fluctuations in cryptocurrency prices. Artificial Neural Network (ANN) is a relatively new model discovered and can solve many complex problems because the way it works mimics human nerve cells. ANN has the advantage of being able to describe both linear and non-linear models with a fairly wide range. This research aims to determine the best performance and level of accuracy of the ANN model using the Back-Propagation Neural Network (BPNN) algorithm in predicting Bitcoin prices. This study uses Bitcoin price data for the period 2020 to 2023 taken from the CoinDesk market. The results of this study indicate that the ANN model produces the best performance in the form of four input nodes, 12 hidden nodes, and one output node (4-12-1) with an accuracy rate of around 3.0617175%.
Co-Authors Adhitya Prayoga Permana Agung Teguh Wibowo Almais Ahmad Latif Qosim Ajib Hanani Akbar Roihan Arafat, Ihsan Bagus Fahad Arief, Yunifa Miftachul Atmalia, Citra Fidya Aziz, Kevin Elmy Christianto, Yudhi Fachri, Ahnaf Febriyan Fatchurrohman Fatchurrohman, Fatchurrohman Fresy Nugroho Galib, Galan Ramadan Harya Ihtada, Fahrendra Khoirul Imammudin, M. Irwan Budi Santoso Johan Ericka Wahyu Prakasa Khadijah Fahmi Hayati Holle Khamidatullailiyah, Yayang Kurniawan, Andang Mahmudiah, Rikha Rizki Mauludiah, Siska Farizah Mochamad Imamudin Mokhamad Amin Hariyadi Muhammad Faisal Muhammad Faisal Muhammad Sahi Mustaqim, Eka Rifut Nur Okta Qomaruddin Aziz Pangestu, Denis Aji Pratama, Mohammad Yoga Putra, Hirga Ertama Putra, Kurniawan Tri Ririen Kusumawati Romadhani, Ahmad Roro Inda Melani Rounaqi, Muhammad Fairuz Zumar S, Puspa Miladin Setiawan, Dwi Risky Silfiyah, Chilmiatus Suarti, Isa Sugiharto , Tomy Ivan Susilo, Ni`mah Firsta Cahya Sya'roni Syawab, Moh Husnus Totok Chamidy Tri Gunantohadi Usman Pagalay Wiyono, Masdar Yunifa Miftachul Arif Yustina, Eva Zainal Abidin Zamzam, Muhammad Adib Zarkoni, Ahmad