Article Per Year (5 Year)
p-Index From 2021 - 2026
5.5
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah Kursor Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi JURNAL EKONOMI DAN BISNIS AIRLANGGA EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi dan Bisnis Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Teknik Komputer AMIK BSI INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi KACANEGARA Jurnal Pengabdian pada Masyarakat Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Telematika Indonesian Journal of Applied Informatics Jurnal Sistem Cerdas JISKa (Jurnal Informatika Sunan Kalijaga) Syntax Idea JIKA (Jurnal Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Aiti: Jurnal Teknologi Informasi Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Indonesian Community Journal Journal of Social Science J-Icon : Jurnal Komputer dan Informatika Journal of Computing Theories and Applications Asian Journal of Management, Entrepreneurship and Social Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Komtika (Komputasi dan Informatika) Jurnal Abdi Rakyat
Rianto Rianto
Universitas Teknologi Yogyakarta
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Public Health Social Sciences Other

Published : 40 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 40 Documents
Search

 1   2   3   4 
Enhancing SVM-Based Classification Performance on Indonesian Sentences through TF-IDF and Directional Augmentation Rianto, Rianto; Humanika, Eko Setyo; Untoro, Iwan Hartadi Tri
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 10 No 1 (2026)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v10i1.25179

Abstract

Background: The distinction between standard and non-standard Indonesian sentences is traditionally well-defined, yet the ubiquity of digital communication has increasingly blurred these boundaries. This convergence introduces significant lexical ambiguity in formal contexts, complicating the performance of automated text classification systems. Objective: This study aims to enhance the robustness of Support Vector Machine (SVM) classification by addressing these linguistic irregularities through TF-IDF vectorization and a targeted directional augmentation strategy. Methods: A corpus comprising 5,394 labeled sentences was processed under a strict anti-leak grouping strategy to rigorously prevent semantic leakage between training, validation, and testing sets. To resolve decision boundary overlaps often missed by the baseline model, manual directional augmentation was applied, specifically targeting ambiguous sentence structures to enrich the training distribution and linguistic diversity. Results: The experiments demonstrated that directional augmentation significantly refined the model's decision margins. While the baseline model achieved a test accuracy of 94.39%, the augmented approach substantially improved generalization capabilities across unseen groups, elevating validation accuracy from 96.11% to 97.39% and test accuracy to 96.16%. Conclusion: These findings substantiate that structurally enriching the dataset effectively mitigates overfitting and improves sensitivity. However, given the scalability constraints of manual intervention, future research should prioritize automated augmentation techniques and contextual embeddings to handle deep linguistic nuances further.
Kombinasi Algoritma Kriptografi Vigenere Cipher dan SHA256 untuk Keamanan Basis Data Rian Oktafiani; Erik Iman Heri Ujianto; Rianto Rianto
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5583

Abstract

An organization must consider and manage the security of data storage in databases or databases, and special procedures are needed to protect data from various security risks. The problem in this study is that the population data contained in the Girisuko village administrative service information system has not been encrypted or secured. This can pose a risk that the data stored in the database can be intercepted and misused. In this study, the cryptographic technique used was a combination of the Vigenere Cipher and SHA 256 algorithms to secure or encrypt databases, especially population data in the Girisuko village administrative service information system. The text in the database is encrypted using the Vigenere Cipher, and SHA-256 is used to generate a hash value or a random value that is different from the text in the database. Messages will be encrypted using the Vigenere Cipher and then hashed with SHA-256 simultaneously. As a result, it will be difficult for an attacker to decrypt the text stored in the database because they have to break the Vigenere Cipher encryption, and also have to solve the hash value generated using SHA-256. This combination aims to increase security and maintain the confidentiality of messages from attackers. The application of the Vigenere Cipher and SHA to the village administration service information system application with a real-time database works well, as evidenced by the fast running-time of 0.39 seconds the data encryption process uses the Vigenere Cipher with 894,968 keys/second and an analyzed key length of 7 characters then text on population database successfully secured. By conducting this research, it is hoped that it can contribute to improving database system security.
H-ASICS: Desain Intrusion Detection System Adaptif Berbasis Hybrid Deep Learning untuk Infrastruktur Kritis Andri Yudha Pratama; Ujianto, Erik IH; Rianto, Rianto
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11006

Abstract

The digital transformation of critical infrastructure, particularly Smart Grid and SCADA systems, has exposed new vulnerabilities to complex cyber-attacks such as False Data Injection (FDI), necessitating proactive defense mechanisms that transcend conventional approaches. Through a Systematic Literature Review (SLR) of 51 state-of-the-art studies (2022–2026), this research confirms a paradigm shift from static Deep Learning models toward adaptive, transparent, and decentralized detection ecosystems. Addressing the critical trade-off between high accuracy and operational latency, this study proposes the conceptual framework of H-ASICS (Hybrid Adaptive System for Infrastructure Critical Security). Based on a closed-loop MAPE-K architecture, H-ASICS dynamically selects the most optimal detection algorithms switching between Hybrid CNN-LSTM for complex spatial-temporal patterns and LightGBM for edge computing efficiency. Addressing the critical trade-off between high accuracy and operational latency, this study proposes the conceptual framework of H-ASICS (Hybrid Adaptive System for Infrastructure Critical Security). Based on a closed-loop MAPE-K architecture, H-ASICS dynamically selects the most optimal detection algorithms switching between Hybrid CNN-LSTM for complex spatial-temporal patterns (yielding up to 99.81% detection accuracy) and LightGBM for edge computing efficiency (reducing operational latency to under 10 ms). The superiority of H-ASICS is further reinforced by the integration of Explainable AI (XAI) and blockchain technology to guarantee the transparency of mitigation decisions and the immutability of cyber forensic data. This proposed architecture provides a strategic roadmap for next-generation security systems that are not only accurate and resilient but also highly accountable.
Resolving Visual Ambiguity in Wood Grain Classification Using InceptionV3 and Label Smoothing Rianto Rianto; Sulistyo Dwi Sancoko; Eko Setyo Humanika
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7599

Abstract

Wood type classification in the timber industry is frequently hindered by the high visual similarity of grain patterns, a structural challenge that is particularly pronounced with small-scale datasets. This study systematically resolves this fine-grained visual ambiguity by developing a rigorously optimized InceptionV3 framework. Initial baseline evaluations conducted on a dataset of 800 images encompassing four wood species, namely Pterospermum javanicum (Bayur), Magnolia champaca (Cempaka), Tectona grandis (Jati), and Melia azedarach (Mindi), revealed that a standard ResNet50 architecture experienced a severe performance degradation, generating a random guess accuracy of merely 25 percent. Addressing this severe architectural inadequacy, the proposed InceptionV3 model integrates highly targeted spatial augmentations, a Dropout rate of 0.6, and L2 regularization. Furthermore, the optimization strategy deploys Stochastic Gradient Descent with Nesterov momentum and Label Smoothing to explicitly mitigate intra-class visual similarities. Consequently, the proposed framework achieved a robust overall accuracy of 89 percent, surpassing the baseline by a substantial 64 percent, alongside a macro-averaged F1 Score of 0.89. These empirical findings substantiate that specific architectural fine-tuning and advanced stochastic regularization are highly essential for capturing subtle wood-grain patterns, thereby offering a highly reliable, automated quality-control solution for the timber industry.
Mapping Biometric Security Paradox: A Behavioral Study of Perception and Awareness Among Indonesian Digital Natives Erik Iman Heri Ujianto; Rianto Rianto
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15935

Abstract

 The rapid adoption of smartphones among Indonesian digital natives has increased reliance on biometric authentication systems. However, empirical evidence regarding the relationship between user satisfaction and security risk awareness remains limited, particularly in developing-country contexts. This study investigates the behavioral dynamics of biometric security perception among 266 respondents, consisting of 221 high school students and 45 university students in Indonesia. A Python-based computational pipeline incorporating Akaike Information Criterion (AIC) validation and 1,000-iteration stochastic bootstrapping was employed to evaluate nonlinear behavioral patterns using Polynomial Regression and Ordinary Least Squares (OLS) multivariate analysis. The results confirm the existence of a nonlinear Security Paradox. While the overall population demonstrates a positive quadratic trajectory, the university student group exhibits a concave-down parabolic relationship (a=−0.0460), indicating a decline in perceived utility beyond a specific security threshold. The identified behavioral breaking point occurs at X≈5.45 (95% CI: 2.99–20.77), suggesting that excessive security hardening may reduce perceived usability and increase cognitive friction. Furthermore, the ablation analysis reveals that security risk awareness (p<0.001) is the strongest predictor of user satisfaction, exceeding the influence of daily usage intensity. Segment-level analysis further demonstrates behavioral divergence between respondent groups. High school students exhibit relatively uniform satisfaction toward biometric systems, whereas university students display greater variability and more critical perceptions regarding authentication friction. These findings indicate that highly rigid security configurations may become less effective for users with higher digital literacy and risk awareness. This study contributes a computationally validated behavioral framework for understanding security–utility trade-offs and provides a conceptual foundation for developing adaptive, user-centric, and friction-aware biometric authentication systems.
Analisis Perbandingan Algoritma Decision Tree dengan Random Forest dalam Deteksi Bot DDOS Putra, Kristianto Pratama Dessan; Rianto, Rianto; Ujianto, EIH
IJAI (Indonesian Journal of Applied Informatics) Vol 10, No 1 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v10i1.103161

Abstract

Abstrak : Tingkat penetrasi internet yang semakin meningkat setiap tahunnya juga berpengaruh pada banyaknya peralihan layanan dari konvensional ke platform internet. Peralihan layanan tersebut terbukti membawa dampak baik, seperti meningkatnya volume penjualan produk. Namun, di sisi lain dengan semakin banyaknya peralihan layanan ke platform internet maka semakin banyak pula celah-celah keamanan yang dapat dieksploitasi, salah satunya serangan bot DDos. Oleh karena itu, diperlukan adanya sistem yang mampu mendeteksi serangan bot DDos dan algoritma yang akan dianalisis dalam penelitian ini adalah Decision Tree dan Random Forest. Penelitian ini akan membandingkan kedua algoritma tersebut untuk menentukan algoritma yang paling optimal dalam mendeteksi serangan bot DDos. Penelitian ini menggunakan dua dataset dalam proses implementasi algoritma, yaitu KDD CUP 1999 dan CICIDS 2017. Ruang lingkup dari perbandingan kedua algoritma meliputi tingkat akurasi dan durasi waktu pemrosesan data. Hasil dari penelitian menunjukkan bahwa algoritma Random Forest unggul tipis dalam hal tingkat akurasi dibandingkan dengan Decision Tree, yaitu 0.9998 untuk Random Forest berbanding 0.9997 untuk Decision Tree. Namun, algoritma Decision Tree unggul jauh dalam hal durasi waktu dibandingkan dengan Random Forest, yaitu 20-30 detik untuk Decision Tree berbanding 210-300 detik untuk Random Forest. Hal tersebut dapat terjadi dikarenakan Random Forest memproses lebih banyak pohon kemungkinan dibandingkan Decision Tree.=============================================Abstract : The increasing internet penetration each year also affects the shift of services from conventional methods to internet platforms. This shift has proven to bring positive impacts, such as an increase in product sales volume. However, there are increasingly more security vulnerabilities that can be exploited, such as DDoS bot attacks. Therefore, a system that capable to detect bot DDoS attacks is needed. This study compares these two algorithms (Decision Tree and Random Forest) to determine which is the most optimal for detecting bot DDoS attacks. The scope of the comparison includes accuracy levels and data processing time. The results show that Random Forest slightly outperforms Decision Tree in terms of accuracy, with a score of 0.9998 for Random Forest compared to 0.9997 for Decision Tree. However, Decision Tree is significantly superior in processing time compared to Random Forest (20–30 seconds for Decision Tree versus 210–300 seconds for Random Forest). This occurs because Random Forest processes more trees than Decision Tree. 
Peningkatan Sensitivitas Support Vector Machine Pada Klasifikasi Kalimat Baku Dan Tidak Baku Bahasa Indonesia Rianto, Rianto; Humanika, Eko Setyo; Tri Untoro, Iwan Hartadi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 3: Juni 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026133

Abstract

Kalimat dalam bahasa Indonesia dapat diklasifikasikan menjadi dua jenis, yaitu kalimat baku dan tidak baku. Kalimat baku digunakan sebagai bahasa resmi dalam acara formal, sementara kalimat tidak baku umum ditemukan dalam komunikasi sehari-hari. Perkembangan teknologi digital turut mendorong pergeseran penggunaan bahasa, sehingga banyak kalimat tidak baku muncul dalam konteks formal. Penelitian ini bertujuan untuk meningkatkan akurasi model klasifikasi kalimat baku dan tidak baku berbahasa Indonesia. Metode yang digunakan adalah supervised learning dengan Support Vector Machine (SVM) menggunakan dataset berjumlah 2.000 kalimat. Peningkatan akurasi dilakukan melalui TF-IDF Vectorizer dan augmentasi data. Hasil penelitian menunjukkan bahwa sebelum improvisasi, akurasi model mencapai 98.6% dengan total empat kesalahan klasifikasi pada kalimat baku pendek dan ambigu. Setelah improvisasi, akurasi meningkat menjadi 99.3%, dengan jumlah kesalahan total menurun menjadi tiga. Kebaruan penelitian ini terletak pada fokus klasifikasi kalimat baku dan tidak baku dalam konteks bahasa Indonesia, yang masih jarang dieksplorasi. Kontribusinya adalah menyediakan model yang dapat menjadi dasar aplikasi pemeriksa tata bahasa atau sistem penyaringan teks formal. Namun, penelitian ini memiliki keterbatasan pada augmentasi data yang masih dilakukan secara manual. Penelitian selanjutnya diharapkan dapat mengembangkan sistem augmentasi otomatis serta menambah jumlah dataset untuk meningkatkan generalisasi model dan penerapan lebih luas dalam NLP Indonesia.   Abstract Sentences in Indonesian can be classified into two types: standard and non-standard. Standard sentences are used as the official language in formal events, while non-standard sentences are used in daily conversation. The development of digital technology has contributed to the language shift, which causes many non-standard sentences to be used in standard contexts. This study aims to improve the accuracy of the classification model for standard and non-standard sentences in Indonesian. The method used is supervised learning with a Support Vector Machine (SVM) using a dataset of 2,000 sentences consisting of standard and non-standard forms. Accuracy improvement is carried out through TF-IDF Vectorizer and data augmentation. The results showed that the model accuracy reached 98.6% before improvisation, with four misclassifications in short and ambiguous standard sentences. After improvisation, the accuracy increased to 99.3%, with the number of errors decreasing to three. The novelty of this study lies in its focus on addressing sentence classification into standard and non-standard forms in the Indonesian language, which has rarely been explored compared to other NLP tasks. Its practical contribution is to provide a baseline model that can support grammar-checking tools or text filtering systems in digital platforms. However, this study has limitations in data augmentation, which still needs to be done manually. Future research is expected to develop an automatic augmentation system and increase the number of datasets to improve model generalization and broaden its application in Indonesian NLP.
Teknologi Kecerdasan Buatan Untuk Mengembangkan Desain Motif Batik Kontemporer Rianto Rianto; Enny Itje Sela; Nur Wening
Jurnal ABDI RAKYAT Vol. 1 No. 2 (2024): JURNAL ABDI RAKYAT
Publisher : Universitas Teknologi Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46923/jar.v1i2.448

Abstract

Artificial intelligence technology in producing contemporary batik motif designs is an innovative phase in the creative industry. The development of technology, Natural Language Processing, allows text to be translated into images, providing an excellent opportunity to accelerate the design process while enriching creative ideas. This community service program aims to train batik artisans in adopting information technology, especially artificial intelligence, to create new, attractive motif designs. The training includes using an AI-based platform and design transfer techniques to fabric media. The result of this activity is a contemporary batik motif that targets millennials with their distinctive style. This technology provides two main advantages: 1) time efficiency in design creation and 2) broad creative inspiration through automatic exploration of motif data. Both of these advantages show that the application of artificial intelligence in batik design supports innovation and competitiveness in the modern market.
KEAMANAN INFORMASI (INFORMATION SECURITY) PADA APLIKASI SISTEM INFORMASI MANAJEMEN SUMBER DAYA MANUSIA Enggar Novianto; Erik Herman Heri Ujianto; Rianto Rianto
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 8 No 1 (2023): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v8i1.2966

Abstract

The development of information technology in the current era is growing rapidly, which is indicated by the emergence of many innovative programs in personal information services. One of the contenders for such information is SIMPEG, an application from Sebelas Maret University. The purpose of this study is to determine the application of information security to address the aspects of confidentiality, integrity and availability of information. This application ensures the security of user's personal information and employee data. The results of this study are that employees are required to register an account using an email that has been registered with SSO. Users of this application are lecturers and education staff including general users and SIMPEG operators. The SIMPEG application also guarantees the confidentiality of each employee's personal data and can only be seen by Sebelas Maret University internal employees. The purpose of registering an account through NIP/NIK and employee staff email is with the aim of preventing actions that can harm the institution. One of the rules for using this application is that it is forbidden to update, copy and delete data unless the SIMPEG operator is responsible for updating the data.
Uncovering Security Vulnerabilities in Electronic Medical Record Systems: A Comprehensive Review of Threats and Recommendations for Enhancement Dian Wijayanti; Erik Iman Heri Ujianto; Rianto Rianto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28192

Abstract

Cybersecurity is a critical concern for healthcare organizations in the digital era, as patient data privacy faces significant risks from numerous vulnerabilities. Given the escalating cyberattacks in healthcare, understanding EMR system vulnerabilities has become imperative. This study aimed to find the main weaknesses in Electronic Health Record (EHR) systems and suggest proven methods to improve security and keep patient information private. Utilizing a cross-sectional analysis, we assessed the effectiveness of current security protocols against identified threats. We systematically reviewed 25 recent, high-quality articles (from 2020 to 2023) on EMR vulnerabilities, selected based on their relevance and the efficacy of their proposed solutions. Our analysis revealed that system architecture flaws and credential misuse represented the most significant threats, with hacking incidents most frequently targeting these weaknesses. The analysis identified six key threat categories to EMR security: compromised access, system architecture flaws, data sharing challenges, hacking, credential misuse, and non-compliance with regulations. This framework introduced a multi-layered defense strategy, unique in incorporating both technical and behavioral security measures. The study provided a novel framework combining technological and management safeguards, offering a fresh perspective on modern EMR vulnerabilities. The detailed threat categorization gave healthcare organizations a strategic basis for improved security planning and resource allocation. The actionable insights from this study could greatly enhance EMR security protocols in healthcare settings, potentially reducing data breaches and improving patient trust. Further research was warranted to test the effectiveness of the proposed framework across various healthcare environments.
Co-Authors Amelia Citra Dewi Andri Yudha Pratama Arief Hermawan Aziz Perdana Daegal, Andre Demas Risdho Listianto Dewi, Septi Riana Dian Wijayanti Donny Avianto Edi Priyanto Eko Setyo Humanika Endang Setyawati Enggar Novianto Enggar Novianto Enny Itje Sela Enny Itje Sela Erik Herman Heri Ujianto Erik IH Ujianto Erik Iman Heri Ujianto Erik Iman Heri Ujianto Erik Iman HU Evada Rustina Fatih Ahmad Zakaria Hening Nakuloadi Muhammad Rizki Muhammad Rizki Munawaroh, Emi Nadia Pramesti Nur Wening Oktafiani, Rian Paulus Insap Santosa Pramesti, Nadia Putra, Kristianto Pratama Dessan Ratika Tulus Wahyuhana Retno Prihatini Rian Oktafiani Rian Oktafiani Rubangi Rubangi Santika, Yollandaru Yoga Saputra, Candra Heru Setiafindari, Widya Siska Howay Sulistyo Dwi Sancoko Syafrudin, Teguh Syahab, Alfin Syarifuddin Teguh Widodo Teguh Widodo Tri Untoro, Iwan Hartadi Ujianto, EIH Ujianto, Erik IH Zuzzaifa, Nur