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All Journal Instal : Jurnal Komputer Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence
Nainggolan, Irfan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics

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Comparison of Standard and Squeeze-and-Excitation Enhanced DenseNet Architectures for Tomato Leaf Disease Classification Using Data Augmentation Andriani, Tuti; Nainggolan, Irfan
Bahasa Indonesia Vol 17 No 08 (2025): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v17i08.429

Abstract

The advancement of deep learning has significantly improved the automation of plant disease detection through image classification. This study compares the performance of standard DenseNet121 and an enhanced version incorporating Squeeze-and-Excitation (SE) blocks for classifying tomato leaf diseases. A dataset derived from PlantVillage was used, covering multiple disease categories and healthy leaves. To improve generalization, extensive data augmentation techniques were applied. Both architectures were implemented and trained using PyTorch, with evaluation metrics including accuracy, precision, recall, F1-score, and inference time. The experimental results demonstrate that DenseNet121-SE significantly outperforms the standard DenseNet121, achieving a classification accuracy of 99.00%. The integration of SE blocks allows the model to recalibrate channel-wise features adaptively, enhancing sensitivity to important patterns while maintaining computational efficiency. This study highlights the effectiveness of attention mechanisms and data augmentation in improving classification performance and supports their practical application in intelligent agriculture systems.
Decision Support System for Selecting College Majors Based on Student Interests and Talents Using the SAW Method Nainggolan, Irfan; Simangunsong, Suhendra
Bahasa Indonesia Vol 17 No 08 (2025): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v17i08.430

Abstract

This article formulates and demonstrates a Decision Support System (DSS) model for selecting a college major by placing interest and talent/aptitude as the core criteria using the Simple Additive Weighting (SAW) method. The fully documented methodology includes criteria definition, normalization procedure (benefit/cost), weighting, score calculation, implementation pseudo-code, and weight sensitivity analysis. An illustrative study using a simulated dataset with five alternative study programs and six criteria shows consistent and transparent ranking for counselors and students. The results confirm the significance of interest-aptitude integration in recommendations, while demonstrating decision stability under moderate weight changes. Practical contributions include workflow design and functional specifications for web/desktop applications; further development is directed at AHP–SAW and fuzzy-SAW.
Analisis Sentimen Publik Berbasis KNN Terhadap Kinerja Purbaya dan Sri Mulyani Selama Sebulan anshari, ari; Diva, Krisna; Azwan, M; Nainggolan, Irfan; Wijaya, Rian Farta; Sitorus, Zulham
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 3 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i3.1461

Abstract

Masyarakat selalu khawatir tentang perubahan staf Menteri Keuangan karena mereka memainkan peran penting dalam menjaga stabilitas ekonomi negara. Purbaya Yudhi Sadewa menggantikan Sri Mulyani sebagai Menteri Keuangan dalam Kabinet Merah Putih. Karena mungkin mengungkapkan tingkat penerimaan publik dan kepercayaan terhadap kebijakan yang akan diterapkan, persepsi publik selama bulan pertama menjabat sangat penting. Studi ini menggunakan algoritma K-Nearest Neighbor (KNN) untuk menguji sentimen publik terhadap pemikiran di platform media sosial X selama bulan pertama masa jabatan kedua kedua tokoh tersebut. Dataset ini mencakup 2.071 cuitan dari Sri Mulyani (21 Oktober–20 November 2024) dan 2.960 cuitan dari Purbaya Yudhi Sadewa (8 September–7 Oktober 2025). Setelah praproses dan pelabelan data dengan IndoBERT, distribusi sentimen untuk Purbaya adalah 57,80% positif dan 42,20% negatif, sedangkan untuk Sri Mulyani adalah 46,74% positif dan 53,26% negatif. TF-IDF kemudian akan digunakan untuk ekstraksi fitur, sementara KNN dengan K=5 dan metrik jarak kesamaan kosinus akan digunakan untuk klasifikasi. Menurut hasil evaluasi, model KNN Sri Mulyani memiliki akurasi 77% dengan presisi 78%, recall 77%, dan skor F1 77%, sedangkan model KNN Purbaya memiliki akurasi 80% dengan presisi 80%, recall 78%, dan skor F1 79%.
Co-Authors Anshari, Ari azwan, m Diva, Krisna Simangunsong, Suhendra Sitorus, Zulham Tuti Andriani Wijaya, Rian Farta