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All Journal bit-Tech Jurnal Informatika Teknologi dan Sains (Jinteks)
Wahyu Syaifullah Jauharis Saputra
Universitas Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT Control & Systems Engineering

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DIAGNOSIS AWAL AUTISM SPECTRUM DISORDER MENGGUNAKAN ALGORITMA FUZZY K-NEAREST NEIGHBOR Maurisa Arimbi Putri; Chrystia Aji Putra; Wahyu Syaifullah Jauharis Saputra
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 6 No 2 (2024): EDISI 20
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v6i2.3463

Abstract

Autism Spectrum Disorder (ASD) merupakan salah satu gangguan kondisi neurodevelopmental yang ditandai oleh adanya gangguan kognitif, perilaku dan interaksi sosial. ASD dapat terjadi pada siapapun dan laju terjadinya kondisi ini meningkat dari tahun ke tahun. Para ahli mengatakan jika identifikasi dilakukan pada anak sejak usia dini, maka pertolongan dapat dilakukan dengan lebih efektif. Hal ini dikarenakan otak akan lebih mudah menyerap perubahan dalam masa perkembangan. Deteksi dini ASD dapat dilakukan melalui konsultasi dengan psikolog. Namun, ada biaya dengan jumlah tak sedikit yang perlu dikeluarkan. Adanya teknologi komputer dapat membantu dalam melakukan prediksi awal berdasarkan gejala yang dialami. Pada penelitian ini digunakan algoritma data mining yaitu Fuzzy K-Nearest Neighbor (FKNN) untuk melakukan prediksi awal ASD. Hasil penelitian menunjukkan bahwa prediksi ASD menggunakan algoritma FKNN mendapatkan nilai akurasi tertinggi sebesar 96,1% dengan menggunakan jumlah data latih sebanyak 70% dari keseluruhan data dalam dataset.
EfficientNetB4–Vision Transformer Fusion for Chili Leaf Disease Classification Using Multi-Source Datasets Reza Putri Angga; Wahyu Syaifullah Jauharis Saputra; Alfan Rizaldy Pratama
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3753

Abstract

Chili plants are a commodity susceptible to plant pest organism attacks that can significantly reduce productivity. Visual identification of chili diseases by farmers is often inaccurate due to symptom similarity across disease categories, necessitating a technology-based approach capable of performing classification automatically and accurately. This study proposes a hybrid model combining EfficientNetB4 and Vision Transformer for chili leaf disease classification into four categories healthy, yellowish, curl leaf, and spot leaf. EfficientNetB4 extracts local features through compound scaling and MBConv blocks, while ViT models global relationships among image regions through self-attention, enabling a semantically meaningful integration of local and global feature representations that addresses the individual limitations of CNN and transformer-based architectures. The dataset integrates 4,000 secondary images from GitHub and 800 primary images collected directly from chili cultivation fields in Central Java, with splitting performed separately per source to ensure proportional distribution across subsets. To evaluate generalization capability, the model was assessed across three scenarios: training and testing on secondary data only 98.25%, testing on primary field data without prior field exposure 87.50%, and training and testing on integrated data 99.17%, with a perfect accuracy of 100% on the primary-only test set. These results demonstrate that incorporating field-collected data into training directly bridges the generalization gap caused by domain shift between laboratory and real-world conditions, outperforming both single-architecture and previous hybrid approaches reported in prior studies. The findings provide a methodological foundation for developing robust automated disease detection systems applicable across diverse agricultural crops and real-world farming environments.
Co-Authors Alfan Rizaldy Pratama Maurisa Arimbi Putri Putra, Chrystia Aji Reza Putri Angga