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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 InComTech: Jurnal Telekomunikasi dan Komputer JURNAL REKAYASA TEKNOLOGI 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) Jurnal Teknologi Informasi dan Terapan (J-TIT) Syntax Idea JIKA (Jurnal Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Aiti: Jurnal Teknologi Informasi Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Journal Automation Computer Information System (JACIS) INTERNATIONAL JOURNAL OF EDUCATION, INFORMATION TECHNOLOGY, AND OTHERS Indonesian Community Journal Jurnal Algoritma 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 Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Enhancing SVM-Based Classification Performance on Indonesian Sentences through TF-IDF and Directional Augmentation Rianto Rianto; Eko Setyo Humanika; Iwan Hartadi Tri Untoro
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.
Analisis Perbandingan Algoritma Decision Tree dengan Random Forest dalam Deteksi Bot DDOS Kristianto Pratama Dessan Putra; Rianto Rianto; EIH Ujianto
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. 
Kerangka Multi-Tahap WSM dan PROMETHEE II untuk Seleksi Vendor Cloud Bank Digital Andri Yudha Pratama; Rianto Rianto
Journal Automation Computer Information System Vol. 6 No. 2 (2026): November (In Progress Issue)
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v6i2.205

Abstract

Transformasi digital perbankan mendorong adopsi cloud computing sebagai infrastruktur utama layanan keuangan modern. Pemilihan vendor cloud pada bank digital merupakan tantangan multidimensi yang mencakup aspek keamanan, ketersediaan layanan, latensi, kapasitas, dan biaya operasional, serta kepatuhan terhadap regulasi Otoritas Jasa Keuangan (OJK) dan Bank Indonesia. Penelitian ini mengusulkan kerangka sistem rekomendasi multi-tahap berbasis Multi-Criteria Decision Making (MCDM) yang mengintegrasikan Weighted Sum Model (WSM) dan PROMETHEE II. Dataset sintetis 100.000 kandidat vendor direduksi menjadi 50.000 sampel melalui stratified sampling proporsional dengan 256 strata. Pada Fase 1, WSM menyaring 10.000 vendor terbaik menggunakan ambang batas skor 0,633624. Pada Fase 2, PROMETHEE II menghasilkan peringkat berbasis Net Flow (Φ). Vendor CLOUD_071856 meraih peringkat pertama dengan Φ = +0,6295, Security Index 10,00/10, dan SLA Uptime 99,854%. Kerangka yang diusulkan terbukti efektif, skalabel secara komputasional, dan relevan dengan kebutuhan regulasi nasional dalam mendukung pengambilan keputusan pemilihan vendor cloud pada sektor perbankan digital Indonesia
Improving Online Exam Verification with Class-Weighted and Augmented CNN Models Ilham Fanani; Rianto Rianto
Jurnal Teknologi Informasi dan Terapan Vol 11 No 2 (2024): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i2.435

Abstract

The COVID-19 pandemic has shifted interactions to virtual platforms, significantly impacting education, particularly online exams. However, these online exams have vulnerabilities, including exam jockeys. This study proposes a face classification model using a Convolutional Neural Network (CNN) to verify online exam takers. The model uses preprocessing techniques, i.e. normalization, data augmentation, and class weighting, to balance data and enhance generalization utilizing TensorFlow. The results show an overall accuracy of 85%, with a precision of 86.34%, a recall of 84.24%, an F1-score of 85.28% for legal takers, and a precision of 83.65%, recall of 85.81%, and an F1-score of 84.71% for illegal takers. These results indicate the model's balanced performance between legal and illegal classes. By integrating CNN with tailored preprocessing and training strategies, this study addresses gaps in existing authentication methods, offering a robust approach to online exam verification. The proposed model shows a chance for practical applications. However, further optimization through larger datasets and advanced augmentation techniques is recommended to improve its accuracy and adaptability to diverse real-world contexts
Decision Support System For Manager Placement In The Plantation Industry Using Topsis Method Teguh Widodo; Nur Wening; Rianto Rianto
Journal Of Social Science (JoSS) Vol 3 No 7 (2024): JOSS : Journal of Social Science
Publisher : Al-Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/joss.v3i7.344

Abstract

Accuracy in placing employees determines the performance of a company. Likewise done by PT XYZ in determining the placement of managers in the plantation industry by using a decision support system. This is done in order to minimize the level of subjectivity of the manager placement determination system at PT XYZ. This research aims to provide alternative preference values to prospective employees who will occupy manager positions in the plantation industry. The method used in the placement of managers with a decision support system is the technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method. The criteria used in this method are 7 criteria taken based on the criteria for BUMN talent management according to the Regulation of the Minister of BUMN Number PER-3 / MBU / 03/2023, these criteria are Professional Work Period, Variety of Work Experience, Managerial Competence, Technical Competence, Educational Strata, Performance Assessment Results, Level of Punishment that has been received. The results of this study are from the results of the calculation analysis through the TOPSIS method on 7 alternatives, then there is name number 5 managed to get the best score of 0.80 and was determined as a preference to be placed in class A garden.
Optimasi Deteksi DDoS Berbasis Hybrid CNN-LSTM dengan Recursive Feature Elimination pada Software-Defined Networking Wayan Praka; Andri Yudha Pratama; Erik IH Ujianto; Rianto Rianto
InComTech : Jurnal Telekomunikasi dan Komputer Vol. 16 No. 2 (2026)
Publisher : Department of Electrical Engineering

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

Abstract

Keamanan pada arsitektur Software-Defined Networking (SDN) menghadapi ancaman kritis dari evolusi serangan Distributed Denial of Service (DDoS) yang mengeksploitasi celah Single Point of Failure pada controller. Penelitian ini bertujuan mengoptimalkan model deep learning melalui pengembangan arsitektur Ultra-Fast Hybrid CNN-LSTM yang terintegrasi dengan teknik seleksi fitur Recursive Feature Elimination (RFE). Menggunakan dataset modern CIC-DDoS2019 yang memiliki dimensi tinggi, penelitian ini berhasil mereduksi 77 fitur asli menjadi 25 fitur paling berpengaruh guna mengatasi masalah curse of dimensionality dan meminimalkan beban komputasi. Hasil eksperimen menunjukkan bahwa model optimasi mencapai tingkat akurasi sebesar 91,80% dengan keunggulan signifikan pada aspek efisiensi teknis. Dibandingkan dengan model baseline, arsitektur ini memberikan peningkatan waktu pelatihan sebesar 18,91% lebih cepat 64,42 detik dan responsivitas deteksi 12,63% lebih tinggi dengan latensi hanya 0,00003826 detik per paket. Temuan ini menegaskan bahwa sinergi antara reduksi fitur RFE dan arsitektur hibrida yang ramping sangat efektif dalam menjamin ketersediaan layanan pada infrastruktur SDN secara real-time dengan penggunaan sumber daya yang minimal.
Pipeline MCDM Hibrida Skalabel untuk Seleksi Vendor Cloud pada Perbankan Digital: Integrasi AHP, WSM, dan PROMETHEE II Berbasis Threshold Domain Aware Meidhika Nawa Sapta; Rianto Rianto
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 10, No 3 (2026): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v10i3.28045

Abstract

Seleksi vendor cloud pada lingkungan enterprise skala besar merupakan permasalahan pengambilan keputusan multikriteria (Multi-Criteria Decision Making/MCDM) yang kompleks, terutama ketika jumlah kandidat mencapai puluhan hingga ratusan ribu dan setiap alternatif harus dievaluasi secara konsisten berdasarkan dimensi teknis, operasional, dan finansial. Pendekatan MCDM tunggal umumnya kurang memadai karena tidak mampu mengoptimalkan efisiensi komputasi sekaligus mempertahankan presisi perangkingan. Penelitian ini mengusulkan pipeline MCDM hibrida multi-stage yang mengintegrasikan tiga metode secara sekuensial, yaitu Analytic Hierarchy Process (AHP) untuk pembobotan kriteria berbasis konsistensi, Weighted Sum Model (WSM) sebagai mekanisme penyaringan awal yang efisien secara komputasi, dan PROMETHEE II sebagai metode perangkingan akhir berbasis perbandingan berpasangan yang presisi. Studi kasus difokuskan pada domain perbankan digital dengan kriteria evaluasi meliputi Security Index, SLA Uptime, Latency, Throughput, dan Operational Cost. Eksperimen dilakukan menggunakan dataset sintetis yang merepresentasikan 100.000 kandidat vendor cloud. Pipeline ini berhasil mereduksi ruang pencarian menjadi 50.000 kandidat terbaik, kemudian menjadi 10.000 kandidat terpilih melalui fase WSM, sebelum diproses oleh PROMETHEE II dengan algoritma blockwise vectorized untuk efisiensi memori. Hasil AHP menunjukkan tingkat konsistensi tinggi dengan Consistency Ratio (CR) sebesar 0,0146, jauh di bawah ambang batas 0,1. Security Index memperoleh bobot tertinggi (0,418884), diikuti SLA Uptime (0,264204), Latency (0,148925), Throughput (0,100129), dan Operational Cost (0,067858), mencerminkan prioritas domain perbankan digital. Hasil evaluasi menunjukkan bahwa pipeline hibrida ini mampu menghasilkan perangkingan yang stabil, konsisten, dan sesuai konteks domain, sekaligus menjaga trade-off antara skalabilitas komputasi dan akurasi keputusan pada seleksi vendor cloud skala enterprise.
Co-Authors Andri Yudha Pratama Andri Yudha Pratama Anggara, Afwan Apriansyah, Ferryma Arba Arief Hermawan Aziz Perdana Daegal, Andre Demas Risdho Listianto Dewi, Septi Riana Dian Wijayanti Donny Avianto Edi Priyanto EIH Ujianto 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 Ilham Fanani Kristianto Pratama Dessan Putra Meidhika Nawa Sapta Muhammad Rizki Muhammad Rizki Munawaroh, Emi Nadia Pramesti Nur Wening Oktafiani, Rian Paulus Insap Santosa Pramesti, Nadia 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 Utami, Wahyu Sri Wayan Praka Zuzzaifa, Nur