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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Informatika dan Teknik Elektro Terapan InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JIKO (Jurnal Informatika dan Komputer) Information System for Educators and Professionals : Journal of Information System KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal Teknik Informatika UNIKA Santo Thomas JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY Jurnal ICT : Information Communication & Technology Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Jurnal Informatika dan Rekayasa Perangkat Lunak JATI (Jurnal Mahasiswa Teknik Informatika) MEANS (Media Informasi Analisa dan Sistem) Instal : Jurnal Komputer Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Informasi interaktif : jurnal informatika dan teknologi informasi
Ade Rizki Rinaldi
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Other

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Analisis Data Hasil Laporan Skripsi Berbasis Aspect Based Sentiment Analysis Menggunakan Algoritma K-Means Clustering Nana Suarna; Dadang Sudrajat; Umi Hayati; Ade Rizki Rinaldi; Agus Bahtiar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This study discusses the application of Aspect-Based Sentiment Analysis (ABSA) combined with the K-Means Clustering algorithm to analyze student thesis report data. The research scope includes text data processing from VAK (Visual, Auditory, Kinesthetic) learning style questionnaires to identify research aspects and automatically group thesis themes. The objective is to obtain a structured and representative mapping of students’ research themes based on their fields of study. The methodology involves several stages, including text preprocessing, TF-IDF weighting, aspect extraction using ABSA, and clustering with K-Means, validated through the Davies-Bouldin Index (DBI). The dataset consists of 976 textual entries derived from student questionnaire responses. The results indicate that the optimal cluster is achieved at k = 3 with a DBI value of 3.276, forming three main groups: (1) data mining, (2) statistical analysis, and (3) learning technology. The study concludes that the combination of ABSA and K-Means is effective in accurately classifying research themes and provides an analytical foundation for academic decision-making regarding student research trends.
Klasifikasi Telur Fertil dan Infertil Berbasis Hybrid MobileNetV3 dengan Mekanisme Attention dan Texture Fusion Bani Nurhakim; Dadang Sudrajat; Tati Suprapti; Ade Rizki Rinaldi; Agus Bahtiar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Accurate fertile-infertile egg classification is crucial to improve hatching productivity and sorting efficiency. This study proposes MobileFusionV3, a MobileNetV3 architecture enriched with CBAM (Convolutional Block Attention Module) and Hybrid Texture Fusion (LBP and GLCM) to combine deep and texture features to be more robust to candling illumination variations. A dataset of 1,275 candling images (675 fertile, 600 infertile) was subjected to preprocessing (resizing, normalization, background enhancement) and realistic data augmentation (rotation, brightness/contrast changes, Gaussian noise, illumination variations). The model was trained using transfer learning, early stopping, and an evaluation scheme based on accuracy, precision, recall, F1-score, and AUC. The test results showed an accuracy of 97.2%, precision of 96.8%, recall of 97.5%, F1 of 97.1%, and AUC of 0.99, surpassing previous designs that did not use attention mechanisms and texture fusion. Grad-CAM++ analysis confirms the model's focus on physiologically relevant regions (embryonic shadow and air-cell), thus improving the reliability of interpretation. These findings indicate that lightweight, efficient designs based on attention and texture fusion have the potential to be implemented in smart hatchery systems and edge/mobile devices while maintaining high accuracy.
Mengoptimalkan Kinerja Naïve Bayes Pada Ancaman Modern Dengan Menggunakan PCA Pada Data Intrusion Detection System (IDS) Kevin Salsabil Arlandy; Ahmad Faqih; Ade Rizki Rinaldi
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 8, No 1 (2025): Januari
Publisher : Akademi Ilmu Komputer Ternate

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v8i1.303

Abstract

Abstrak: Intrusion Detection System (IDS) digunakan untuk mendeteksi serangan atau aktivitas mencurigakan dalam jaringan. Dengan meningkatnya ancaman siber modern, penelitian ini mengusulkan kombinasi metode Naïve Bayes dan Principal Component Analysis (PCA) untuk meningkatkan akurasi dan efisiensi deteksi. Metode tambahan PCA dapet mereduksi dimensi dataset menjadi 30 komponen utama tanpa kehilangan informasi penting, menggunakan dataset UNSW-NB15. Proses melibatkan standarisasi data dengan StandardScaler, reduksi dimensi menggunakan PCA, serta evaluasi model Naïve Bayes pada dataset dengan dan tanpa PCA. Analisis ini menggunakan program Python yang di eksekusi dengan Google Collab, dengan hasil menunjukkan bahwa model dengan PCA mencapai akurasi sebesar 96.65% dengan recall 1.00 untuk kelas ancaman, meskipun presisi masih rendah (0.49). Sebaliknya, tanpa PCA, akurasi hanya mencapai 92.72% dengan presisi 0.31 untuk kelas yang sama. Selain itu, penggunaan PCA berhasil mengurangi waktu komputasi dari 1 menit menjadi 30 detik. Kombinasi dengan teknik reduksi dimensi Principal Component Analysis (PCA) menunjukkan kinerja yang lebih baik dalam mengklasifikasikan data pada sistem Intrusion Detection System (IDS). PCA dan Naïve Bayes terbukti menjanjikan dalam mendeteksi ancaman modern, meskipun masih diperlukan perbaikan untuk mencapai kinerja yang lebih optimal.Kata kunci: Intrusion Detection System, Naïve Bayes, PCA, Keamanan JaringanAbstract:An Intrusion Detection System (IDS) is used to detect attacks or suspicious activities in the network. With the increase of modern cyber threats, this research proposes a combination of Naïve Bayes and Principal Component Analysis (PCA) methods to improve detection accuracy and efficiency. The additional PCA method can reduce the dataset dimension to 30 principal components without losing important information, using the UNSW-NB15 dataset. The process involves data standardization with Standard-Scaler, dimensionality reduction using PCA, and Naïve Bayes model evaluation on the dataset with and without PCA. This analysis used a Python program executed with Google Collab, with the results showing that the model with PCA achieved an accuracy of 96.65% with a recall of 1.00 for the threat class. However, the precision was still low (0.49). In contrast, without PCA, the accuracy only reached 92.72% with a precision of 0.31 for the same class. In addition, the use of PCA successfully reduced the computation time from 1 minute to 30 seconds combination with the Principal Component Analysis (PCA) dimension reduction technique shows better performance in classifying data in the Intrusion Detection System (IDS). PCA and Naïve Bayes proved promising in detecting modern threats, although improvements are still needed to achieve more optimal performance.Keywords: Intrusion Detection System, Naïve Bayes, PCA, Network Security
Co-Authors Ade Irma Purnama Sari Ade Irma Purnamasari agus bahtiar Ahmad Faqih Ahmad Faqih Ahmad Syaeful Ma’arief Alfi Rizqi Falih Amelia, Mita Amida, Anggi Fitria Aria Pratama Arif Rinaldi Dikananda Asep Budiyanto Aziz Chairul Imam Bani Nurhakim Basysyar, Fadhil Muhammad Bima Anurgraha Djajnegara Cep Lukman Rohmat Cep Lukman Rohmat Dadang Sudrajat Dea Miftahul Huda Dian Ade Kurnia Dikananda, Arif Rinaldi Dodi Solihudin Edi Tohidi Fathrurrahman Fathurrohman Fathurrohman Fidya Arie Pratama Gifthera Dwilestari Hayati, Umi Hery Widijanto Iin Iin Solihin Iin, Iin Irfan Ali Irma Agustina Jayawarsa, A.A. Ketut Kevin Salsabil Arlandy Kurnia, Dian Ade Lana Surlanto Muhamad Abdurohman Muhamad Aditya Sunaryo Mulyawan Mulyawan Mulyawan, Mulyawan Nana Suarna Narasati, Riri Narasati Ningsih, Indah Ratna Nining Rahaningsih Noviati Odi Nurdiawan Odi Nurdiawan Permana, Sandy Eka Pratama, Denni Pratama, Fidya Arie Raditya Danar Dana Rahma, Aliya Anisa Ryan Hamonangan Saeful, Agung Sandy Eka Permana Septianto, Muhamad Arif Syarif Maulana Yaasin Tati Suprapti Tineka Handayani Tohidi, Edi Tuti Hartati Umi Hayati Wahyu Saputra