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All Journal Jurnal Teknologi dan Manajemen Informatika CommIT (Communication & Information Technology) Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika JIKO (Jurnal Informatika dan Komputer) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jiko (Jurnal Informatika dan komputer) JURIKOM (Jurnal Riset Komputer) JURTEKSI Building of Informatics, Technology and Science Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Ilmiah Smart Journal of Applied Computer Science and Technology (JACOST) Bulletin of Computer Science Research Journal of Artificial Intelligence and Engineering Applications (JAIEA) Majalah Ilmiah METHODA TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Journal of Computing Theories and Applications QOSIM: Jurnal Pendidikan, Sosial & Humaniora Jurnal Ilmiah Smart Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Robet Robet
STMIK TIME
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences

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Image Encryption using Half-Inverted Cascading Chaos Cipheration De Rosal Ignatius Moses Setiadi; Robet Robet; Octara Pribadi; Suyud Widiono; Md Kamruzzaman Sarker
Journal of Computing Theories and Applications Vol. 1 No. 2 (2023): JCTA 1(2) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9388

Abstract

This research introduces an image encryption scheme combining several permutations and substitution-based chaotic techniques, such as Arnold Chaotic Map, 2D-SLMM, 2D-LICM, and 1D-MLM. The proposed method is called Half-Inverted Cascading Chaos Cipheration (HIC3), designed to increase digital image security and confidentiality. The main problem solved is the image's degree of confusion and diffusion. Extensive testing included chi-square analysis, information entropy, NCPCR, UACI, adjacent pixel correlation, key sensitivity and space analysis, NIST randomness testing, robustness testing, and visual analysis. The results show that HIC3 effectively protects digital images from various attacks and maintains their integrity. Thus, this method successfully achieves its goal of increasing security in digital image encryption
Integrating Hybrid Statistical and Unsupervised LSTM-Guided Feature Extraction for Breast Cancer Detection De Rosal Ignatius Moses Setiadi; Arnold Adimabua Ojugo; Octara Pribadi; Etika Kartikadarma; Bimo Haryo Setyoko; Suyud Widiono; Robet Robet; Tabitha Chukwudi Aghaunor; Eferhire Valentine Ugbotu
Journal of Computing Theories and Applications Vol. 2 No. 4 (2025): JCTA 2(4) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12698

Abstract

Breast cancer is the most prevalent cancer among women worldwide, requiring early and accurate diagnosis to reduce mortality. This study proposes a hybrid classification pipeline that integrates Hybrid Statistical Feature Selection (HSFS) with unsupervised LSTM-guided feature extraction for breast cancer detection using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Initially, 20 features were selected using HSFS based on Mutual Information, Chi-square, and Pearson Correlation. To address class imbalance, the training set was balanced using the Synthetic Minority Over-sampling Technique (SMOTE). Subsequently, an LSTM encoder extracted non-linear latent features from the selected features. A fusion strategy was applied by concatenating the statistical and latent features, followed by re-selection of the top 30 features. The final classification was performed using a Support Vector Machine (SVM) with RBF kernel and evaluated using 5-fold cross-validation and a held-out test set. Experimental results showed that the proposed method achieved an average training accuracy of 98.13%, F1-score of 98.13%, and AUC-ROC of 99.55%. On the held-out test set, the model reached an accuracy of 99.30%, precision of 100%, and F1-score of 99.05%, with an AUC-ROC of 0.9973. The proposed pipeline demonstrates improved generalization and interpretability compared to existing methods such as LightGBM-PSO, DHH-GRU, and ensemble deep networks. These results highlight the effectiveness of combining statistical selection and LSTM-based latent feature encoding in a balanced classification framework.
SECURE DOCUMENT NOTARIZATION: A BLOCKCHAIN-BASED DIGITAL SIGNATURE VERIFICATION SYSTEM Nicholas Tio; Octara Pribadi; Robet Robet
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 3 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10811

Abstract

The increasing need for trustworthy digital document verification presents challenges in ensuring authenticity, transparency, and tamper resistance without relying on centralized authorities. This study aims to develop and evaluate a decentralized document notarization system using Ethereum and IPFS that offers secure, transparent, and cost-efficient verification. The system employs modular smart contracts deployed through a factory pattern to create user-specific verifier instances, enabling document submission, revocation, and verification using keccak-256 hashes, ECDSA signatures, and IPFS content identifiers. Methods include contract development, deployment on a local Hardhat network, performance benchmarking, and front-end integration for user interaction. Results show that verifier deployment consumes approximately 1.19 million gas (≈$85 at 20 gwei), document submission around 85 thousand gas (≈$6), and revocation about 50 thousand gas (≈$3.50). Client-side operations such as hashing and IPFS pinning occur in under 50 milliseconds, while real-world blockchain confirmations take 10–30 seconds. The findings demonstrate that decentralized notarization using Ethereum and IPFS is both technically feasible and economically viable. Future enhancements, including Layer 2 rollups, batch notarization, and privacy-preserving features such as encrypted IPFS pinning or zero-knowledge proofs, are proposed to further improve scalability, cost-efficiency, and data confidentiality
PERFORMANCE EVALUATION OF HYBRID CLUSTERING K-MEANS AND DBSCAN WITH FEATURE WEIGHT OPTIMIZATION Vic Devlin; Robet Robet; Octara Pribadi
JIKO (Jurnal Informatika dan Komputer) Vol 9 No 1 (2026)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v9i1.10859

Abstract

This research evaluates the performance of a hybrid clustering model that integrates K-Means and DBSCAN, enhanced through Feature Weight Optimization (FWO) using a Genetic Algorithm (GA), to achieve more precise consumer data segmentation. Two benchmark datasets, Customer Personality Analysis (CPA) and Online Retail (OR), were utilized to examine how different clustering techniques respond to variations in data structure. The feature weighting process was optimized using GA to improve the representational contribution of each variable toward the final cluster configuration. The Silhouette Score was adopted as the primary evaluation metric to measure intra-cluster cohesion and inter-cluster separation. Experimental findings reveal that for the CPA dataset, the Hybrid + FWO method achieved the best performance with a Silhouette Score of 0.9600, while the K-Means + FWO method recorded the highest score of 0.9804 on the OR dataset. Across all scenarios, the inclusion of FWO consistently enhanced clustering stability and interpretability. These results highlight that algorithm selection must consider dataset characteristics, and that feature weight optimization is pivotal in strengthening segmentation quality and ensuring more meaningful insights in consumer behavior analytics.
Performance Analysis of Machine Learning Model Combination for Spaceship Titanic Classification using Voting Classifier Haria Wirawan; Robet Robet; Jackri Hendrik
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 3 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10866

Abstract

The Spaceship Titanic dataset is fictional yet complex and challenging, featuring a mix of numerical and categorical features and missing values. This study aims to evaluate the performance of three machine learning model scenarios for classifying passenger status as “Transported” or “not”. The three scenarios implemented include linear-like models, a combination of the Top 5 Diverse models, and tree-based/ensemble models, each using a voting classifier approach. The voting model is employed because it can combine the strengths of multiple algorithms to reduce bias and variance, thus improving overall prediction accuracy and stability. The voting mechanism aggregates predictions from several base classifiers using two strategies: hard voting, which selects the majority class, and soft voting, which averages the predicted probabilities across models. The dataset was obtained from Kaggle and processed through several stages: data preprocessing, data splitting, model training, and evaluation. The evaluation results show that the tree-based/ensemble scenario achieved the highest accuracy of 90.38%, followed by the Top 5 Diverse model combination at 87.31% and the Linear-like model at 76.51%. Visualization using the confusion matrix, ROC Curve, and Feature importance analysis further supports the claim that ensemble models are superior at detecting complex classification patterns. These findings suggest that tree-based ensemble models provide the most optimal approach for classification tasks on a dataset like Spaceship Titanic.
Pengaruh Penggunaan Aplikasi Canva dalam Pembelajaran Matematika terhadap Motivasi Belajar Siswa Kelas VIII Poppy Amalia; Jihan Hidayah Putri; Robet
Jurnal QOSIM : Jurnal Pendidikan, Sosial & Humaniora Vol 4 No 3 (2026): 2026
Publisher : Yayasan pendidikan dzurriyatul Quran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61104/jq.v4i3.6461

Abstract

Penelitian ini dilatarbelakangi oleh rendahnya motivasi belajar siswa dalam pembelajaran matematika serta kurangnya penggunaan media pembelajaran yang menarik dan interaktif di kelas. Penggunaan aplikasi Canva sebagai media pembelajaran digital diharapkan dapat meningkatkan motivasi belajar siswa. Penelitian ini bertujuan untuk mengetahui pengaruh penggunaan aplikasi Canva dalam pembelajaran matematika terhadap motivasi belajar siswa kelas VIII SMP Bina Satria Mulia. Penelitian ini menggunakan metode kuantitatif dengan desain control group design. Sampel penelitian terdiri dari dua kelas, yaitu kelas VIII-1 sebagai kelas kontrol dan kelas VIII-2 sebagai kelas eksperimen dengan jumlah keseluruhan 28 siswa. Teknik pengumpulan data dilakukan menggunakan angket motivasi belajar siswa yang disusun berdasarkan skala Likert. Data dianalisis melalui uji normalitas, uji homogenitas, dan uji-t menggunakan bantuan IBM SPSS Statistics 26. Hasil penelitian menunjukkan bahwa penggunaan aplikasi Canva memberikan pengaruh positif terhadap motivasi belajar siswa. Hal ini ditunjukkan dari meningkatnya persentase motivasi belajar siswa pada kelas eksperimen dibandingkan kelas kontrol setelah diberikan perlakuan. Selain itu, hasil uji-t memperoleh nilai signifikansi (2-tailed) sebesar 0,000 < 0,05 sehingga terdapat pengaruh signifikan. Dengan demikian, dapat disimpulkan bahwa penggunaan aplikasi Canva dalam pembelajaran matematika berpengaruh signifikan terhadap motivasi belajar siswa kelas VIII SMP Bina Satria Mulia.
Comparative Analysis of LSTM-Based Models for Daily Gold Price Forecasting Using Time Series and Sentiment Features Yessica Yamin; Robet; Hendri
Jurnal Teknologi dan Manajemen Informatika Vol. 12 No. 1 (2026): Juni 2026
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v12i1.16309

Abstract

This study aims to improve the accuracy of gold price forecasting by combining statistical and deep learning methods with sentiment analysis. Three models were developed and compared: (1) a pure Long Short-Term Memory (LSTM) model, (2) a hybrid LSTM + Prophet model, and (3) a hybrid LSTM + Prophet + Sentiment model. The datasets consisted of daily gold prices and financial news sentiment from 2013 to 2023. Each model was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared (R2), and Mean Absolute Percentage Error (MAPE). The pure LSTM model achieved an R2 of 0.9375, while the hybrid LSTM + Prophet model improved performance to 0.9394 with lower error rates. The integration of sentiment data resulted in stable but not significantly higher accuracy. Overall, the hybrid LSTM + Prophet model produced the best results, confirming that combining statistical trend decomposition with deep learning effectively enhances forecasting stability and interpretability for financial time series data such as gold prices.
Perancangan Aplikasi Web Chatbot Multi-Bahasa Berbasis NPL Translator API Dengan Multibahasa Terjemahan Robet Robet; Johanes Terang Kita Perangin Angin; Randy Wilson
Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Vol. 5 No. 1 (2025): Maret
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/juritek.v5i1.4150

Abstract

Perangkat lunak komputer yang dapat meniru dan memproses interaksi manusia yang diucapkan atau ditulis disebut chatbot multibahasa, atau chatterbot. Dengan chatbot, orang dapat berkomunikasi dengan perangkat digital seolah-olah mereka berbicara dengan orang sungguhan. Tujuan dari penelitian pengembangan ini adalah untuk menggunakan teknik pembelajaran mesin untuk membangun dan membuat aplikasi chatbot multibahasa dengan kemampuan penerjemahan. Proses pengembangan sistem memerlukan sejumlah fase. Salah satu pendekatan untuk pengembangan perangkat lunak adalah Siklus Hidup Pengembangan Perangkat Lunak. Metode Transformer dipilih sebagai pendekatan pengembangan sistem untuk penelitian ini. Tujuan yang diantisipasi dari penelitian ini adalah: untuk membuat aplikasi chatbot multibahasa berbasis situs web menggunakan NPL Translator API. Pengguna yang mengalami kendala bahasa mungkin merasa lebih mudah untuk menggunakan penerjemah otomatis bawaan aplikasi obrolan. Karena sifatnya yang berbasis web, program obrolan ini memfasilitasi komunikasi pengguna dari lokasi mana pun.
Penerapan Algoritma Transformer dalam Aplikasi Parafrase Teks Otomatis Robet Robet; Kelvin Leonardi Kohsasih; Jenime Darwin
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 1 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No1.pp103-109

Abstract

The development of Natural Language Processing (NLP) technology has enabled the creation of automated text manipulation applications, one of which is text paraphrasing. This study aims to implement a Transformer architecture with a focus on Indonesian text for automatic text paraphrasing applications. The model used is a pre-trained Text-to-Text Transfer Transformer (T5), which is fine-tuned using an Indonesian text corpus called the Indo-T5 model. During the training process, the model is trained to understand language structure and context in order to generate paraphrases that are not only grammatically correct but also semantically preserved. Evaluation was conducted using BLEU and ROUGE metrics to measure the similarity between the generated paraphrased texts and manual references. The evaluation results show that the model is capable of producing coherent, relevant paraphrased texts with a good level of lexical variation with a BLEU score of 50.1, and ROUGE-L of 61.7. Thus, this study demonstrates that Transformer-based models can be effectively applied to the task of text paraphrasing in Indonesian.
Aplikasi Deteksi Usia Berbasis Citra Menggunakan Model Deep Learning dengan Arsitektur CNN Robet Robet; Chandra Chandra; Jerico Setiawan
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 1 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No1.pp97-102

Abstract

This research aims to design and implement an age detection application based on facial images using a deep learning approach with a Convolutional Neural Network (CNN) architecture. The model is built to recognize and extract facial features in order to estimate an individual’s age automatically. Facial image datasets were obtained from public sources and enhanced through augmentation techniques such as rotation, flipping, and lighting adjustment to increase data variability. The training process involved splitting the data into training, validation, and testing sets. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The gender detection system achieved an accuracy of 82.99% with a precision of 80.95% for males and 84.47% for females. Recall scores were 85.15% for males and 80.12% for females. For age detection, precision, recall, and F1-score varied across different age groups. Overall, the model demonstrates exemplary performance in age prediction, though it still faces challenges in distinguishing closely spaced age categories.
Co-Authors Agus Maringan Siahaan Agus Maringan Siahaan Alvin Alvin Alvin Alvin, Alvin Andi Andi Andy Andy Andy Andy Ari Rizkita Ari Rizkita Arnold Adimabua Ojugo Barus, Hanisa putri Bayu Teta Bimo Haryo Setyoko Br Tarigan, Widia CANDRA GUNAWAN Carles Juliandy Chandra Chandra De Rosal Ignatius Moses Setiadi Didik Aryanto Didik Aryanto Donna Natalia edi edi Edi Wijaya Edi Wijaya Eferhire Valentine Ugbotu Etika Kartikadarma Feriani Astuti Tarigan Feriani Astuti Tarigan Feriani, Feriani Astuti Tarigan Haria Wirawan Hendri Hendri Hendri Hendri Hoki, Leony Hose, Fernando Iqbal Giffari Ritonga Jackri Hendrik Jenime Darwin Jerico Setiawan Johanes Terang Kita Perangin Angin Jonathan Kevin Fernando Juan Charles Kelvin Leonardi Kohsasih Lase, Wisriani Liecero, Marcus Md Kamruzzaman Sarker Michael Angelo Nicholas Tio Nurhayati Harahap Octara Pribadi Panjaitan, Tabitha Martha Agustine Perangin Angin, Johanes Poppy Amalia Putri, Jihan Hidayah Randy Wilson Rizkita, Ari Robin Robin Robin Robin, Robin Robin Zheng Satriya Miharja Satriya Miharja Siahaan, Agus Maringan Simanjuntak, Martina Sances Sinaga, Asra Gretya Siregar, Tarq Hilmar Suyud Widiono Suyud Widiono Tabitha Chukwudi Aghaunor Vic Devlin Yehezkiel E.A Parapak Yessica Yamin Yuliana Yuliana