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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Konstruksia Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Jurnal Komtika (Komputasi dan Informatika) Jurnal Teknologi Terpadu JISKa (Jurnal Informatika Sunan Kalijaga) Indonesian Journal of Business Intelligence (IJUBI) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Jumat Informatika: Jurnal Pengabdian Masyarakat Jurnal Puan Indonesia Journal of Nexural Intelligence
Rudy Ansari
Universitas Muhammadiyah Banjarmasin
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Transportation Other

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TINJAUAN SISTEMATIS TREN, METODE, DAN DATA PADA PREDIKSI KELULUSAN MAHASISWA Rudy Ansari; Rudy Ansari; Sunardi Sunardi; Imam Riadi
Indonesian Journal of Business Intelligence (IJUBI) Vol 8 No 2 (2025): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v8i2.6551

Abstract

Penelitian tentang prediksi kelulusan mahasiswa banyak dipublikasikan akan tetapi biasanya metode beserta data yang dihasilkan dikemas secara terpisah dan kompleks sehingga gambaran tentang topik prediksi kelulusan mahasiswa saat ini kurang komprehensif. Tinjauan literatur ini bertujuan untuk mengidentifikasi dan menganalisis tren penelitian, dataset, dan metode tentang prediksi kelulusan mahasiswa yang dipublikasikan antara tahun 2020-2025. Berdasarkan kriteria inklusi dan ekslusi, tercatat sebanyak 75 artikel dari 199 artikel yang bersumber pada jurnal kuartil 1-4. Tinjauan literatur sistematis dapat didefinisikan sebagai proses mengidentifikasi, menilai, dan menginterpretasikan semua bukti penelitian yang tersedia untuk memberikan jawaban atas pertanyaan penelitian yang spesifik. Hasil analisis dalam lima tahun terakhir mengungkapkan bahwa penelitian prediksi kelulusan mahasiswa terdapat empat topik yaitu prediksi/klasifikasi, analisis dataset, pengelompokan (clustering), dan estimasi. Selain itu,  terdapat juga dua tren yang dibahas yaitu pemilihan fitur (feature selection) dan data tidak seimbang (imbalance data). Kategori data yang digunakan pada lima tahun terakhir lebih banyak menggunakan data private atau data real sebanyak 91% daripada data public. Metode yang paling sering digunakan pada topik-topik tersebut adalah Random Forest (RF), dan paling jarang yaitu metode Artificial Neural Network (ANN). Terdapat juga penggabungan metode untuk optimasi parameter di beberapa klasifikasi.
PENGENALAN APLIKASI JIRA DALAM MANAJEMEN PROYEK DI SMK YPKK 1 SLEMAN, YOGYAKARTA Rudy Ansari; Rudy Ansari; Irwansyah Irwansyah; Irwansyah Irwansyah; Nia Ekawati; Nia Ekawati; Herman Herman; Sunardi Sunardi
PUAN INDONESIA Vol. 8 No. 1 (2026): Jurnal PUAN Indonesia Vol. 8 No. 1 Juli 2026
Publisher : ASOSIASI IDEBAHASA KEPRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37296/jpi.v8i1.517

Abstract

The community service activity at SMK YPKK 1 Gamping aims to align the vocational education curriculum with industry standards through the introduction of Agile and Scrum methods using the JIRA application. The main challenge faced was the students' lack of understanding of professional project management tools, so this training focused on mastering JIRA features such as Scrum/Kanban boards and issue tracking. Through training and mentoring of 30 students, there was a significant increase in competence. Data shows that the highest score on the pre-test was 80 (9 participants), which then increased on the post-test to a perfect score of 100 achieved by 15 participants. The evaluation results concluded that 50% of participants had achieved the maximum level of understanding in operating JIRA. Thus, this implementation successfully bridged the gap between academic theory and the practical needs of the workplace, while equipping students with globally relevant project management skills.
Klasifikasi Buah Kelapa Sawit dengan Convolutional Neural Network Arsitektur Inception-v4 Theresia Kurniati Seran; Septyan Eka Prastya; Muhammad Zulfadhilah; Rudy Ansari
Jurnal Teknologi Terpadu Vol 12 No 1 (2026): Juli, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v12i1.2597

Abstract

The palm oil industry plays a crucial role in Indonesia’s economy, making fruit classification by ripeness levels essential to ensuring the quality of palm oil production. This study aims to develop a classification system for oil palm fruits into two categories: ripe and unripe, using a Convolutional Neural Network with the Inception-v4 architecture. The dataset consists of 2,900 images, divided into training (2,000), validation (500), and testing (400) sets. The research stages include data collection, pre-processing (duplicate detection, augmentation, and normalization), model training with Inception-v4, evaluation, and result interpretation. Model performance was evaluated using accuracy, precision, recall, f1-score, and confusion matrix. Results indicate that Inception-v4 achieved the highest validation accuracy of 95% in classifying oil palm fruit. Further experiments were conducted using various optimizers (SGD, Adam, RMSprop, Adagrad, Adadelta) to enhance performance. This study confirms that Inception-v4 is highly effective for oil palm fruit classification and can be applied in plantation industries to improve harvest efficiency and production quality.
Deep Learning Approaches For Distributed Denial Of Service (DDOS) Attack Detection In Software-Defined Networking: A Systematic Literature Review Ade Davy Wiranata; Intan Murniasih; Rudy Ansari
Journal of Nexural Intelligence Vol. 1 No. 1 (2026): Journal of Nexural Intelligence
Publisher : Citra Air Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71200/nexural.v1.i1.263

Abstract

Software-Defined Networking (SDN) has emerged as a foundational paradigm for programmable, centrally-managed networks, but its logically centralised control plane is highly attractive to Distributed Denial of Service (DDoS) adversaries. Traditional signature- and threshold-based defences struggle against polymorphic and low-rate attack patterns, motivating a rapid migration toward Deep Learning (DL) based detection. This Systematic Literature Review (SLR), conducted in accordance with the PRISMA 2020 guideline and a PICOC framework, identifies, classifies, and analyses 62 primary studies published between January 2020 and February 2026 on DL-based DDoS detection in SDN. Three research questions are answered, covering publication venues, the most active researchers, and the architectures, datasets, and evaluation metrics employed. The findings reveal that Convolutional Neural Networks (38.7%), hybrid CNN-LSTM models (24.2%), and Transformer/Graph Neural Networks (14.5%) dominate recent designs, while the InSDN and CIC-DDoS2019 datasets are the de-facto benchmarks. Macro-averaged accuracy across high-quality studies exceeds 99%, yet real-time deployment, explainability, and cross-dataset generalisability remain open challenges. The review provides a consolidated knowledge map and an empirically grounded research agenda for the next generation of intelligent SDN defences
Co-Authors Ade Davy Wiranata Ahmad Faisal Hamidi Ayu Ahadi Ningrum Bambang Lareno, Bambang ELIA ANGGARINI Finki Dona Marleny Gazali, Mukhaimy Herman Herman Ihdalhubbi Maulida Imam Riadi Intan Murniasih Irwandy Muzaidi Irwansyah Irwansyah Irwansyah Irwansyah Kamarudin Maman Fatahulrahman Mambang Maulida, Ihdalhubbi Meila Izzana, Meila Muhammad Alfiyani Muhammad Ziki Elfirman Muhammad Ziki Elfirman Muhammad Zulfadhilah Mukhaimy Gazali Nahdi Saubari Nahdi Subairi Nia Ekawati Nia Ekawati Ricardus A P, Ricardus A Rudy Ansari Septyan Eka Prastya Sunardi, Sunardi Theresia Kurniati Seran Windarsyah Windarsyah Yuslena Sari, Yuslena