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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 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) 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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Digital Transformation in Library Recommendation System Using k-NN Collaborative Filtering: Transformasi Digital dalam Sistem Rekomendasi Buku Perpustakaan Menggunakan k-NN Fachri, Ahnaf Febriyan; Faisal, Muhammad; Crysdian, Cahyo
Technomedia Journal Vol 10 No 1 (2025): June
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v10i1.2438

Abstract

Libraries, as essential information centers, play a crucial role in providing diverse resources to meet the information needs of visitors. In the digital age, libraries face challenges in efficiently managing their vast collections while offering personalized services that cater to the varying needs of users. The primary goal of this research is to improve the management of library resources by developing a personalized book recommendation system. This system aims to provide relevant book suggestions based on individual preferences, specifically tailored to the academic needs and interests of university students. To achieve this, the research applies a combination of User-Based Collaborative Filtering (UBCF) and k-Nearest Neighbors (k-NN) algorithms, which are powerful techniques in the field of data mining. These methods are used to analyze the academic performance (measured by the students' Indeks Prestasi Semester (IPS) scores) and book preferences to create a personalized recommendation system. The study demonstrates that the integration of UBCF and k-NN significantly enhances the accuracy and relevance of book recommendations, providing students with more tailored suggestions based on their academic achievements and preferences. The results indicate that such a recommendation system not only improves the user experience but also contributes to the enhancement of students' academic performance by offering them books that align with their learning needs, ultimately supporting the academic goals of higher education institutions.
Enhancing Repeat Buyer Classification with Multi Feature Engineering in Logistic Regression Mauludiah, Siska Farizah; Crysdian, Cahyo; Arif, Yunifa Miftachul
Applied Information System and Management (AISM) Vol 8, No 1 (2025): 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.v8i1.45025

Abstract

This study presents a novel approach to improving repeat buyer classification on e-commerce platforms by integrating Kullback-Leibler (KL) divergence with logistic regression and focused feature engineering techniques. Repeat buyers are a critical segment for driving long-term revenue and customer retention, yet identifying them accurately poses challenges due to class imbalance and the complexity of consumer behavior. This research uses KL divergence in a new way to help choose important features and evaluate the model, making it easier to understand and more effective at classifying repeat buyers, unlike traditional methods. Using a real-world dataset from Indonesian e-commerce with 1,000 records, divided into 80% for training and 20% for testing, the study uses logistic regression along with techniques like SMOTE for oversampling, class weighting, and regularization to fix issues with data imbalance and overfitting. Model performance is assessed using accuracy, precision, recall, F1-score, and KL divergence. Experimental results indicate that the KL-enhanced logistic regression model significantly outperforms the baseline, especially in balancing precision and recall for the minority class of repeat buyers. The unique contribution of this work lies in its synergistic use of KL divergence in both the feature engineering and evaluation phases, offering a robust, interpreted, and data-efficient solution. For e-commerce businesses, the findings translate into improved targeting of high-value customers, better personalization of marketing efforts, and more strategic allocation of resources. This research offers practical tips for enhancing predictive customer analytics and supports data-driven decision-making in digital commerce environments.
Klasifikasi User Review pada Aplikasi Online Travel Booking Menggunakan Multinomial Naïve Bayes Pratama, Mohammad Yoga; Cahyo Crysdian
The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i3.4199

Abstract

Perkembangan teknologi yang pesat telah membawa perubahan dalam berbagai aspek kehidupan, termasuk dalam sektor pariwisata. Aplikasi tiket travel online seperti Traveloka, pegipegi dan Tiket.com merupakan aplikasi travel yang sangat populer di Indonesia. penelitian ini bertujuan untuk mengukur performa dari metode Multinomial Naïve Bayes dalam mengklasifikasikan ulasan pengguna aplikasi tersebut menjadi kelas “satisfied” dan “unhappy”. Dataset berjumlah 1339 ulasan pengguna yang diambil dari Google Play Store. Uji coba dilakukan dengan tiga skenario rasio pembagian dataset (7:3, 8:2, 9:1) dan dievaluasi menggunakan confusion matrix dan K-Fold Cross Validation. Hasil uji coba menunjukkan skenario pembagian data 9:1 menghasilkan akurasi model tertinggi sebesar 81.34% dengan precision 81.47%, recall 81.44% dan F1-Score 81.34%. Analisa kata menggunakan TF-IDF menunjukkan bahwa kata-kata seperti “good”, “nice” dan “nice” mendominasi pada kelas “satisfied”, sedangkan kata seperti “price”, “cant”, dan “app” merupakan 3 kata yang paling mendominasi pada kelas “unhappy”. Dapat disimpulkan bahwa metode Multinomial Naïve Bayes memiliki performa yang baik untuk klasifikasi ulasan pengguna aplikasi travel online, dan semakin banyak dataset yang digunakan makan semakin bagus pula model yang dihasilkan.
Klasifikasi Penyakit pada Tanaman Berdasarkan Citra Daun Menggunakan Metode Convolutional Neural Network Pangestu, Denis Aji; Aziz, Okta Qomaruddin; Crysdian, Cahyo
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.235-248

Abstract

The agricultural sector is a vital part of the economy, providing food, raw materials, and employment opportunities. In Indonesia, this sector faces significant challenges, such as low interest from younger generations and plant disease issues. Plant disease identification typically requires the expertise of experienced professionals, but this process is time-consuming and costly. This research aims to develop a plant disease classification model using a Convolutional Neural Network (CNN) to assist farmers in identifying diseases in rice, corn, tomato, and potato plants based on leaf images. Testing was conducted with data splitting ratios of 70:30, 80:20, and 90:10, using both single-stage and multi-stage classification methods. The best results were achieved with an 80:20 data ratio using single-stage classification, with an average accuracy of 80%, precision of 80%, recall of 81%, and F1-score of 79%. This study demonstrates that the CNN method is effective in plant disease classification, achieving optimal performance at a 80:20 data ratio and in single-stage classification. It is hoped that this research can help farmers quickly and accurately identify and manage plant diseases, as well as encourage innovation in the agricultural sector. The implementation of CNN in plant disease classification shows great potential in enhancing the efficiency and accuracy of disease detection, ultimately supporting the sustainability and development of the agricultural sector.
PENILAIAN KINERJA PEGAWAI DENGAN METODE TOPSIS DAN BACKPROPAGATION NEURAL NETWORK Yuliawan, Audi Bayu; Hariyadi, M. Amin; Kusumawati, Ririen; Crysdian, Cahyo; Nugroho, Fresy
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.7826

Abstract

Transformasi digital melalui penerapan Industri 4.0 dan e-Government telah mengubah paradigma administrasi publik, sehingga menuntut sistem evaluasi kinerja pegawai yang lebih adaptif dan objektif. Penelitian ini bertujuan untuk mengklasifikasikan kinerja pegawai ke dalam lima kategori, yaitu "sangat baik", "baik", "cukup", "buruk", dan "sangat buruk", dengan menggunakan pendekatan Neural Network Backpropagation. Metodologi yang digunakan mencakup beberapa tahapan utama, dimulai dari proses preprocessing data yang menge-lompokkan kriteria penilaian ke dalam empat aspek: kualifikasi, kom-petensi, kinerja, dan disiplin. Selanjutnya, dilakukan seleksi fitur menggunakan metode Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), dan hasilnya digunakan sebagai data pelatihan pada model Neural Network Backpropagation. Hasil pelati-han menunjukkan performa model yang cukup baik, dengan nilai loss dan Mean Squared Error (MSE) sebesar 0,000465, Mean Absolute Per-centage Error (MAPE) sebesar 19,59%, dan akurasi mencapai 80,41%. Sementara itu, hasil eksperimen dengan metode TOPSIS secara terpisah mencatat akurasi sebesar 81% dan nilai loss sebesar 0,377. Kombinasi metode TOPSIS dan Neural Network Backpropagation ter-bukti efektif dalam mengklasifikasikan kinerja pegawai secara konsis-ten. Temuan ini memberikan kontribusi terhadap pengembangan sis-tem evaluasi kinerja berbasis kecerdasan buatan yang lebih akurat dan adaptif terhadap tantangan administrasi publik modern.
Rainfall Prediction Using Attention-Based LSTM Architecture Romadhani, Ahmad; Santoso, Irwan Budi; Crysdian, Cahyo
JURIKOM (Jurnal Riset Komputer) 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.
Classification of Vegetation Land Cover Area Using Convolutional Neural Network Galib, Galan Ramadan Harya; Santoso, Irwan Budi; Crysdian, Cahyo
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3050

Abstract

The decrease and reduction of vegetation land or forest area over time has become a serious and significant problem to be considered. Increasing the Earth’s temperature is a consequence of deforestation, which can contribute to climate change. The other issues that researchers face concern diversity and various objects in satellite imagery that may be difficult for computers to identify using traditional methods. This research aims to develop a model that can classify vegetation land cover areas on high-resolution images. The data used is sourced from the ISPRS (International Society for Photogrammetry and Remote Sensing) Vaihingen. The model used is a Convolutional Neural Network (CNN) with a VGG16-Net Encoder architecture. Tests were conducted on eight scenarios with training and test data ratios of 80:20% and 70:30%. The classifier method that we employed in this research is argmax and threshold. We also compared the performance of Neural Networks with two hidden layers and three hidden layers to investigate the impact of adding another layer on the Neural Network's performance in classifying vegetation land cover areas. The results show that using the threshold classifier method can save training time compared to the argmax method. By increasing the number of hidden layers in the neural network, model performance improves, as shown by increases in recall, accuracy, and F1-score metrics. However, there is a slight decrease in the precision metric. The model achieved its best performance with a precision (Pre) of 99.5%, accuracy (Acc) of 83.3%, and F1-score (Fs) of 70.3%, requiring a training time (T-time) of 16 minutes and 41 seconds and an inference time (I-time) of 0.1535 seconds.
Enhancing Repeat Buyer Classification with Multi Feature Engineering in Logistic Regression Mauludiah, Siska Farizah; Crysdian, Cahyo; Arif, Yunifa Miftachul
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): 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.v8i1.45025

Abstract

This study presents a novel approach to improving repeat buyer classification on e-commerce platforms by integrating Kullback-Leibler (KL) divergence with logistic regression and focused feature engineering techniques. Repeat buyers are a critical segment for driving long-term revenue and customer retention, yet identifying them accurately poses challenges due to class imbalance and the complexity of consumer behavior. This research uses KL divergence in a new way to help choose important features and evaluate the model, making it easier to understand and more effective at classifying repeat buyers, unlike traditional methods. Using a real-world dataset from Indonesian e-commerce with 1,000 records, divided into 80% for training and 20% for testing, the study uses logistic regression along with techniques like SMOTE for oversampling, class weighting, and regularization to fix issues with data imbalance and overfitting. Model performance is assessed using accuracy, precision, recall, F1-score, and KL divergence. Experimental results indicate that the KL-enhanced logistic regression model significantly outperforms the baseline, especially in balancing precision and recall for the minority class of repeat buyers. The unique contribution of this work lies in its synergistic use of KL divergence in both the feature engineering and evaluation phases, offering a robust, interpreted, and data-efficient solution. For e-commerce businesses, the findings translate into improved targeting of high-value customers, better personalization of marketing efforts, and more strategic allocation of resources. This research offers practical tips for enhancing predictive customer analytics and supports data-driven decision-making in digital commerce environments.
Clustering Gempabumi di Wilayah Regional VII Menggunakan Pendekatan DBSCAN Arafat, Ihsan Bagus Fahad; Hariyadi, Mokhamad Amin; Santoso, Irwan Budi; Crysdian, Cahyo
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 4: Agustus 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024106918

Abstract

Wilayah Regional VII meliputi Jawa Tengah, Yogyakarta, dan Jawa Timur merupakan wilayah tektonik yang aktif karena terletak di wilayah zona subduksi lempeng Indo-Australia dan Eurasia serta terdapat beberapa patahan aktif di daratan. Oleh karena itu, perlu dilakukan klasifikasi gempabumi untuk memetakan zona rawan gempabumi berdasarkan sumbernya di wilayah Regional VII berdasarkan kesamaan atribut salah satunya adalah berdasarkan karakteristik gempabumi dari sumber yang sama. Pada penelitian ini digunakan pendekatan algoritma Unsupervised Learning Clustering berbasis kepadatan yaitu, Density Based Spatial Clustering of Application with Noise atau DBSCAN, algoritma ini membutuhkan parameter input epsilon (ε) dan MinPts. Data yang digunakan pada penelitian ini adalah data gempabumi wilayah Regional VII tahun 2017 hingga 2021 yang diperoleh dari BMKG. Selanjutnya, proses clustering dilakukan dengan membagi data gempabumi berdasarkan periode yaitu periode tahunan dan periode lima tahun dengan tujuan untuk mengetahui pola cluster berdasarkan periode waktu. Hasil yang terbentuk selanjutnya dievaluasi menggunakan Silhouette Coefficient serta dibandingkan dengan peta Seismisitas Jawa yang telah ada dari katalog PuSGeN 2017. Hasil clustering menggunakan DBSCAN diperoleh jumlah cluster sebanyak 2 hingga 6 cluster dengan nilai Silhouette Coefficient terendah sebesar 0.270 untuk periode T5_2017-2021 dan tertinggi sebesar 0.499 untuk periode T1_2020. AbstractRegional VII area covering Central Java, Yogyakarta and East Java is an active tectonic region because it is located in the subduction zone of the Indo-Australian and Eurasian plates and there are several active faults on land. Therefore, it is necessary to classify earthquakes to map earthquake-prone zones based on their sources in Regional VII area based on the similarity of attibutes, based on the characteristics of earthquakes from the same source. In this study, a density-based Unsupervised Learning Clustering algorithm approach was used namely, Density Based Spatial Clustering of Application with Noise or DBSCAN, this algorithm requires the input parameters epsilon (ε) and MinPts. The data used in this study are earthquake data for Regional VII from 2017 to 2021 obtained from the BMKG. Then, the clustering process is carried out by dividing earthquake data based on the period, namely the annual period and the five-year period with the aim of knowing the pattern of cluster based on the time period. The results are then evaluated using the Sillhouette Coefficient and compared with the existing Java Seismicity map from the 2017 PuSGeN catalog. Clustering results using DBSCAN obtained a number of clusters of 2 to 6 clusters with the lowest Silhouette Coefficient value is 0.270 for the T5_2017-2021 period and the highest is 0.499 for the T1_2020 period.  
Chat Bot News Analysis to Support Education Services Using Neural Network Khamidatullailiyah, Yayang; Crysdian, Cahyo; Fatchurrohman, Fatchurrohman
International Journal of Information Systems and Technology Vol. 1 No. 02 (2025): International Journal of Information System and Technology (IJOINT)
Publisher : oneamrd (One a Mllions Dreams Corporation)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

During the Covid-19 pandemic, all learning is held online, so all lecture activities up to student administration must be done online. To make it easier for students to obtain information about academics and administration, a chatbot feature is needed which can provide information while communicating two-way to students as users. Chatbots can provide services practically, quickly, and responsively. In order for the chatbot to provide answers that match user expectations, the question sentences that enter the system must be classified properly and correctly. This study applies the Neural Network method to classify answers on chatbots. Neural Networks are used in research methods because they can build models easily and can be used to classify text with a high level of accuracy. To measure the performance of the chatbot system in providing appropriate answers, an evaluation is carried out by calculating the accuracy, precision, recall, and f-measuring values ​​using a confusion matrix. The results of the study show that the Neural Network method built on the chatbot system in classifying answers can run well with an accuracy value of 99.21%, precision of 88.09%, recall of 88.09%, and f-measure of 88.09%.
Co-Authors Adhitya Prayoga Permana Agung Teguh Wibowo Almais Ajib Hanani Akbar Roihan Arafat, Ihsan Bagus Fahad Arief, Yunifa Miftachul Atmalia, Citra Fidya Aziz, Kevin Elmy 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 Qosim, Ahmad Latif Ririen Kusumawati Romadhani, Ahmad Roro Inda Melani Rounaqi, Muhammad Fairuz Zumar S, Puspa Miladin Sahi, Muhammad 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 Yuliawan, Audi Bayu Yunifa Miftachul Arif Yustina, Eva Zainal Abidin Zamzam, Muhammad Adib Zarkoni, Ahmad