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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications JOIV : International Journal on Informatics Visualization Jurnal CoreIT CogITo Smart Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Manajemen Informatika Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Ilmiah Realtech Jurnal Filsafat Indonesia Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Multimedia Trend and Technology Jurnal Rekayasa elektrika Jurnal Informatika Polinema (JIP) PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Rinaldi Munir
Bandung Institute of Technology, Jl. Ganesha 10, Bandung 40132, Indonesia
Arts Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Watermarking Study on The Vector Map Hartanto Tantriawan; Rinaldi Munir
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 1 (2023): Maret 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i1.22211

Abstract

In addition to being employed in a variety of military and security applications, GIS vector maps are frequently used in social, environmental, and economic applications like navigation, business planning, infrastructure & utility allocation, and disaster management. Given the high value of this map, copyright protection is implemented in the watermarking as a required safeguard against unauthorized modification and exchange of GIS vector maps. Watermarking is inserting information (watermark) stating ownership of multimedia data. This paper discusses several approaches that can be used to watermark vector maps, including using the space-domain algorithm and transform-domain algorithm. Second The watermarking algorithm was developed with the following quality metrics: fidelity, robustness, capacity, complexity, and security. The challenge in this study is that the higher the capacity, the lower the fidelity value. Low fidelity causes map properties to be lost, making the map unusable. These two things need to be balanced.
Algoritma Enkripsi Selektif Citra Digital dalam Ranah Frekuensi Berbasis Permutasi Chaos Rinaldi Munir
Jurnal Rekayasa Elektrika Vol 10, No 2 (2012)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v10i2.82

Abstract

This paper presents a selective image encryption in frequency domain. At first, the image is transformed into frequency domain with Discrete Cosine Transform (DCT), and then DCT coefficients are scanned in zigzag, and elements of the low-frequency sub-band are extracted. Encryption is performed only on selected elements by scrambling them using 2D chaos map, namely Arnold Cat Map. Next, IDCT is applied to obtain the encrypted image. The encryption algorithm is included in lossy encryption. Experiments on both grayscale images and color images show that the images can be encrypted succesfully. Histograms of the encrypted images differ significantly from histogram of the original images, and the pixels in the encrypted images are not longer correlated.
Curating Multimodal Satellite Imagery for Precision Agriculture Datasets with Google Earth Engine Bagus Setyawan Wijaya; Rinaldi Munir; Nugraha Priya Utama
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.399

Abstract

In the era of modern agriculture, satellite imagery has been widely used to monitor crops, one of which is paddy. This paper tries to describe the vegetation indices, climate, and soil index features related to paddy plants and curates a collection of satellite imagery on the Google Earth Engine (GEE). This paper reveals how GEE can be used to collect and process multimodal satellite imagery to form a precision agriculture dataset. The objective of this study is to establish a comprehensive precision agriculture dataset by leveraging multimodal satellite imagery to monitor paddy crops. The data collected as a dataset originates from 306 locations in Karawang Regency, Indonesia, during the 2019-2020 period. In the first step, we identify the relevant features essential for paddy crop analysis. Subsequently, we carefully select image collections within GEE based on these features. Afterward, we perform data acquisition and necessary preprocessing through the Google Colab environment. The results showed that satellite imagery from Sentinel-2 outperforms Landsat 8 in terms of spatial and temporal resolution. Apart from that, the generated dataset successfully captures the growth patterns of paddy plants.
PERBANDINGAN BERBAGAI METODE STEGANOGRAFI PADA CITRA DIGITAL Ratih Kartika Dewi; Rinaldi Munir
Jurnal Informatika Polinema Vol. 9 No. 3 (2023): Vol 9 No 3 (2023)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v9i3.1336

Abstract

Topik keamanan informasi selalu menarik untuk dibahas. Steganografi merupakan salah satu teknik yang digunakan untuk meningkatkan keamanan informasi yang banyak dikembangkan saat ini serta memiliki tujuan menyembunyikan keberadaan pesan untuk menghindari kecurigaan dari pihak lain. Penyisipan pesan ke dalam media pembawa tidak membuat kualitas media pembawa itu berubah dan media yang telah disisipi pesan tidak dapat dibedakan secara kasat mata dengan media aslinya. Media digital yang banyak digunakan dalam steganografi saat ini adalah citra. Oleh karena itu, penelitian ini menyediakan perbandingan metode steganografi pada citra digital, sehingga diharapkan dapat memberikan gambaran mengenai karakteristik citra setelah disisipi pesan menggunakan berbagai algoritma steganografi. Pada bagian akhir paper ini juga akan dibahas mengenai tantangan dan peluang penelitian lanjutan.
Combining Two Chaos Maps and Determining Selective Methods for MSB Bits in a Digital Image Encryption Algorithm Munir, Rinaldi
Journal of Multimedia Trend and Technology Vol. 3 No. 3 (2024): Journal of Multimedia Trend and Technology
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/jmtt.v3i3.63

Abstract

In order to reduce the computational volume, we employ selective encryption approaches in conjunction with a chaos-based picture encryption algorithm in this research. Two chaos maps are used, namely Arnold Cat Map and Logistic Map. Before encryption, the image is scrambled with Arnold Cat Map, then selective encryption techniques are applied by selecting only four MSB bits from each pixel to be XORed with the keystream generated from the Logistic Map. Experimental results show that the use of both chaos functions can produce confusion and diffusion effects, and the use of selective encryption techniques only processes 50% of the entire image data. The encrypted image shows a relatively uniformly distributed histogram, making it difficult for attackers to perform statistical analysis to find the key or plain image. Chaos sensitivity shows that this algorithm is safe from exhaustive-key search attacks. This method is sensitive to modest changes in the key, making it safe from exhaustive-key search assaults, according to experiments conducted by gently altering the initial value of chaos. This technique is immune to brute-force attacks because of its sufficiently huge key space.
Optimalisasi Rekomendasi Rute Pada Perencanaan Perjalanan Wisata: Studi Pustaka: Optimization Route Recommendation-Based Tourist Trip Design Problem: A Literature Study Ramdani, Ahmad Luky; Widyantoro, Dwi Hendratmo; Munir, Rinaldi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 2 (2024): MALCOM April 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i2.1213

Abstract

Tourist trip design problems (TTDP) merupakan permasalahan yang berkaitan dengan bidang pariwisata. TTDP berkaitan dengan perencanaan pengguna dalam melakukan perjalanan wisata berdasarkan pada tempat wisata yang menarik. Dalam sistem rekomendasi, TTDP merupakan permasalahan yang menarik. Hal ini karena tidak hanya digunakan untuk menemukan tempat wisata yang sesuai dengan pengguna, tetapi juga untuk menggabungkan tempat wisata ke dalam rute perjalanan yang praktis dengan mempertimbangkan batasan. Pada artikel ini bertujuan menyajikan penelitian sebelumnya yang berkaitan dengan proses optimasi rekomendasi perjalanan dan bagaimana permasalahan tersebut dimodelkan menggunakan pendekatan yang berbeda untuk mencari solusi yang optimal. Selain itu peluang penelitian yang dapat dilakukan untuk meningkatkan performa rekomendasi. Berdasarkan synthetic literatur review (SLR) dalam penelitian ini, didapatkan peluang penelitian yang dapat dilakukan untuk mendapatkan rekomendasi rute perjalanan yang optimal seperti kombinasi algoritma metaheuristic atau algoritma bio-inspired. Selain itu pada personalisasi pengguna terkait tempat wisata, terdapat peluang mengimplementasikan algorime deep learning seperti LTSM, Transformer, Bert sebagai nilai tempat wisata dari sisi pengguna
Storychart: A Character Interaction Chart for Visualizing the Activities Flow Abidin, Zainal; Munir, Rinaldi; Akbar, Saiful; Mandala, Rila; Widyantoro, Dwi H.
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1608

Abstract

Event-predicate-based storyline extraction results in a chronologically ordered activity journal. The extraction results contain complex human activities, so the activity journal requires a visualization model to describe actor interactions. This paper proposes a chart to visualize the activities' flow to describe the characters' interactions in an activity journal. This chart is called a storychart. Storycharts have an actor channel that can accept single entities or teams. The actor channel allows changing the type from single to a team or vice versa and moving members to other teams. The activity channel serves as a connector to accommodate interactions between actors. The activity channel provides a visual space for the elements of what, where, and when. Event predicates are the core of what. Therefore, the storychart visualizes the event predicate using glyphs to attract the reader’s attention. The main contribution of this paper is to introduce a team channel that can visualize the identity of team members and an activity channel that can visualize the details of events. We invited participants to discover the reader’s perception of the ease of team recognition and the integrity of the meaning of the narrative visualized by the storychart. Participants involved in the evaluation were filtered by literacy score. Evaluation of storychart reading showed that readers could easily distinguish teams from single actors, and storycharts could convey the story in the activity journal with little reduction in meaning.
MENGAMANKAN FILE RAHASIA MENGGUNAKAN HYBRID KRIPTOGRAFI Permana , John Swatrahadi; Hendrana, Hendrana; Munir, Rinaldi
Jurnal Review Pendidikan dan Pengajaran Vol. 7 No. 1 (2024): Volume 7 No 1 Tahun 2024
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jrpp.v7i1.26156

Abstract

Penyelenggara sistem elektronik mengelola sitem aplikasi mengelola file dari pengguna yang sifatnya rahasia. File yang bersifat rahasia yang dikelola menggunakan suatu sistem informasi perlu dilindungi dari ancaman kebocoran data. Salah satu metode yang dapat digunakan untuk melindungi file tersebut adalah menggunakan metode hybrid cryptography yaitu algoritma Adcanced Enryption Standart atau AES dikombinasikan dengan Rivest Shamir Adleman atau RSA. File rahasia yang diupload ke sistem aplikasi sebelum disimpan file tersebut dienkripsi menggunakan kriptografi kunci simetri AES, dengan kunci yang degenerate oleh sistem. Kunci yang degenerate oleh sistem tersebut sifatnya rahasia yang perlu diamankan. Untuk mengamankan kunci dari file rahasia yang disimpan pada database diamankan Kembali menggunakan kriptografi kunci asimetris Rivest Shamir Adlemen. Hasil penelitian menunjukkan waktu yang diperlukan untuk mengamankan file rahasia cukup singkat sehingga tidak mempengaruhi kinerja sistem secara keseluruhan. Proses enkripsi maupun dekripsi hybrid kriptografi AES dan RSA memerlukan waktu rata-rata sebesar 295,8774 bytes / micro second. Proses enkripsi lebih cepat 0,633537 kali dibandingkan dengan proses dekripsi. Metode hybrid kriptografi AES dan RSA cukup kuat untuk melindungi file berklasifikasi rahasia.
Segmentasi Awan pada Citra Satelit Multispektral Menggunakan Convolutional Neural Networks Wijaya, Bagus Setyawan; Munir, Rinaldi; Utama, Nugraha Priya
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 6: Desember 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025125

Abstract

Citra satelit multispektral adalah jenis citra yang diambil oleh satelit penginderaan jauh yang menangkap informasi dari berbagai rentang spektrum elektromagnetik. Citra satelit multispektral memiliki peran yang sangat penting karena kemampuannya untuk memberikan informasi untuk area yang luas secara berkala. Akan tetapi, salah satu permasalahan utama dari citra satelit multispektral adalah kontaminasi awan. Tutupan awan pada area yang luas menyebabkan informasi yang ada pada citra satelit menjadi bias. Oleh karena itu, segmentasi awan yang akurat pada citra satelit multispektral menjadi sangat penting. Penelitian ini berfokus untuk mengembangkan model segmentasi awan berbasis Convolutional Neural Networks (CNN) dengan kinerja yang baik. Penelitian diawali dengan proses pembuatan dataset citra satelit multispektral Sentinel-2 Level-2A. Algoritma s2cloudless digunakan untuk membentuk label dengan 4 kelas, yaitu: shadow, clear, cirrus, dan cloud. Selanjutnya, model segmentasi awan berbasis CNN dikembangkan berdasarkan beberapa model segmentasi semantik yang ada. Model tersebut dilatih dan dievaluasi pada 11.240 citra yang telah dibuat sebelumnya. Melalui ablation study, diperoleh model segmentasi awan terbaik yaitu Deeplabv3+ dengan backbone ResNet18. Arsitektur tersebut memberikan kinerja yang sangat menjanjikan dengan nilai F1-score, pixel accuracy, dan mIoU sebesar 0.922, 0.923, dan 0.733 secara berurutan. Dengan demikian penelitian terkait citra satelit diharapkan menjadi lebih akurat dalam melakukan klasifikasi atau prediksi objek yang ada di permukaan bumi.   Abstract Multispectral satellite imagery is a type of imagery captured by remote sensing satellites that record data from various ranges of the electromagnetic spectrum. Its importance lies in its ability to provide information over large areas periodically. However, one of the main challenges with multispectral satellite imagery is cloud contamination. Cloud cover over large regions can bias the information captured in the imagery. Therefore, accurate cloud segmentation in multispectral satellite imagery is crucial. This study focuses on developing a high-performance cloud segmentation model based on Convolutional Neural Networks (CNN). The research began with the creation of a multispectral satellite imagery dataset from Sentinel-2 Level-2A. Labels with four classes—shadow, clear, cirrus, and cloud—were generated using the s2cloudless algorithm. Subsequently, a CNN-based cloud segmentation model was developed using several existing semantic segmentation models. The model was trained and evaluated on 11,240 images from the dataset. Through an ablation study, the best cloud segmentation model was identified as Deeplabv3+ with a ResNet18 backbone. This architecture demonstrated a highly promising performance, achieving F1-score, pixel accuracy, and mIoU values of 0.922, 0.923, and 0.733, respectively. As a result, this research is expected to improve the accuracy of satellite imagery classification and object prediction on the Earth's surface.
Continuous Sign Language Recognition for Quranic Recitation by Deaf People Using Deep Learning Brianorman, Yulrio; Munir, Rinaldi; Maulidevi, Nur Ulfa
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v19i1.1600

Abstract

This study proposes a deep learning-based system for recognizing Quranic recitation in the sign language, aimed at enhancing accessibility for the Deaf Muslim community. A central contribution is the construction of a novel dataset comprising videos from three Deaf signers performing Surah Al-Fatihah and Surah Al-Ikhlas, guided by the 2022 official Quranic sign language standard introduced by Indonesia’s Ministry of Religious Affairs. The recognition task is framed as a continuous sign language recognition (CSLR) problem to handle unsegmented input sequences. Five pre-trained convolutional neural networks—EfficientNet, GoogleNet, MobileNetV2, ResNet18, and ShuffleNet—were evaluated as visual feature extractors. These were followed by a temporal encoder composed of 1D CNN and BiLSTM, with sequence alignment performed using the Connectionist Temporal Classification (CTC). The experimental results show that ResNet18 and MobileNetV2 achieved the best performance with Word Error Rates (WER) of 5.00% and 7.92% on the test set, respectively. A cross-participant evaluation was also conducted to assess model generalization, although the results revealed performance gaps likely due to signer variation and limited data. The study highlights the suitability of lightweight and residual architectures for CSLR tasks in religious contexts and provides a benchmark for future research on inclusive sign language technologies. In cross-participant evaluation, the model achieved a validation WER of 8.44% on seen signers and 50.46% on an unseen signer, reflecting generalization challenges commonly observed in low-resource CSLR settings. The proposed system lays the groundwork for AI-assisted Quranic education tools tailored to the Deaf Muslim population.
Co-Authors Agus Nursikuwagus Akbar, Saiful Andreas Prasetia Antonius Sigit Bagus Setyawan Wijaya Bambang Riyanto Daniel Silli Bataona Dimitri Mahayana Dwi H. Widyantoro Dwi Hendratmo Widyantoro Erry Koryanti Gerung, Altiano R Gloria Christiana Manulangga Hafni Syaeful Sulun Hafni Syaeful Sulun Harlili Harlili Hartanto Tantriawan Hendrana, Hendrana Hendri Hermawan Adinugraha Ignatius Wiseto Prasetyo Agung Imam Habibi Iping Supriana Iping Supriana Suwardi Iping Supriana Suwardi Jemsrado Sine Junaidy Budi Sanger Made Windu Antara Kesiman Masayu Leylia Khodra Menur Wahyu Pangestika, Menur Wahyu Miguel Angelo Lamongi Mohamad Idris Mokhamad Syaroni Nugraha Priya Utama Nur Ulfa Maulidevi Pantouw, Maria L. Permana , John Swatrahadi Pribadi, Fithratulhay Purwoko, Agus Ramdani, Ahmad Luky Ratih Kartika Dewi Rila Mandala Riza Ibnu Adam Sahambangung, Johanis R. Sarwono Sutikno Sarwono Sutikno SATRIYAS ILYAS Sulun, Hafni Syaeful Tedjo Darmanto Tedjo Darmanto Tutun Juhana Utama, Nugraha Priya Wijaya, Bagus Setyawan Zainal Abidin