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All Journal Telematika Sistemasi: Jurnal Sistem Informasi Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal on Software Engineering (IJSE) Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Paradigma Jurnal Komputasi JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika C.I.T. Medicom Akrab Juara : Jurnal Ilmu-ilmu Sosial Infotech: Jurnal Informatika & Teknologi Journal of Informatics Management and Information Technology Jurnal Abdimas Ekonomi dan Bisnis Reputasi: Jurnal Rekayasa Perangkat Lunak IMTechno: Journal of Industrial Management and Technology Sinergi: Jurnal Pengabdian Kepada Masyarakat Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat
Agustiani, Sarifah
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Social Sciences Other

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Journal : Jurnal Informatika

 1 
DETEKSI PENYAKIT ALZHEIMER MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN CORRELATION BASED FEATURE SELECTION Wildah, Siti Khotimatul; Agustiani, Sarifah; S, M. Rangga Ramadhan; Gata, Windu; Nawawi, Hendri Mahmud
Jurnal Informatika Vol 7, No 2 (2020): September 2020
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (297.966 KB) | DOI: 10.31294/ji.v7i2.8226

Abstract

Alzheimer merupakan kelainan berupa penimbunan plak atau protein tidak normal dalam otak sehingga menyebabkan hilangnya sel neuron dan menjadi salah satu pemicu penyakit demensia yang dapat mengakibatkan terhambatnya aktivitas sehari-hari karena penurunan daya ingat,kesulitan dalam berkomunikasi, tidak dapat berpikir jernih, terjadinya perubahan sikap dan perilaku hingga menimbulkan hilangnya kemampuan untuk mengurus diri sendiri. Di negara berpenghasilan tinggi penyakit ini diakui berada pada peringkat ke 7 sebagai penyakit fatal yang berujung pada kematian. Akan tetapi hingga saat ini belum ditemukan obat yang dapat menyembuhkan penyakit Alzheimer. Oleh sebab itu pentingnya deteksi dini agar dapat memulai untuk merencanakan perawatan dan kebutuhan medis yang memadai. Penelitian ini bertujuan untuk melakukan deteksi penyakit Alzheimer dengan menerapkan metode klasifikasi Naïve Bayes dan seleksi atribut menggunakan Correlation Based Feature Selection pada dataset OASIS Longitudinal. Tahapan analisa data menggunakan metode CRISP-DM. Hasil penelitian ini, menunjukan bahwa pada pengujian algoritma Naïve Bayes nilai akurasi yang didapatkan sebesar 93,83%, dan kurva ROC yang terbentuk memiliki nilai AUC sebesar 0,937% sedangkan pada pengujian algoritma Naïve Bayes dan Correlation Based Feature Selection menghasilkan nilai akurasi sebesar 94,64% dan kurva ROC yang terbentuk memiliki nilai AUC sebesar 0,945%. Sehingga dapat disimpulkan bahwa penerapan algoritma Naïve Bayes dan metode Correlation Based Feature Selection dapat meningkatkan nilai akurasi.
Comparative Optimization of EfficientNetB3, MobileNetV2, and ResNet50 for Waste Classification Agustiani, Sarifah; Haryani, Haryani; Junaidi, Agus; Putri, Rizky Rachma; Adam Z, Muhammad Ghaly
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.27533

Abstract

Waste management has become a critical challenge in efforts to maintain environmental sustainability and public health. Poorly managed waste can cause environmental pollution, reduce quality of life, and complicate recycling processes. To address this issue, this study aims to classify waste based on images while optimizing several deep learning architectures, namely EfficientNetB3, MobileNetV2, and ResNet50, to identify the best model for waste classification. The research methodology includes data collection, preprocessing, data augmentation, model development, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The dataset, obtained from the Kaggle platform, consists of 4,650 images divided into six categories: battery, glass, metal, organic, paper, and plastic. The results show that EfficientNetB3 with the Adam optimizer achieved the best performance, with accuracy, precision, recall, and F1-score all at 93%, followed by ResNet50 at approximately 91%, and MobileNetV2 ranging from 70–73% depending on the optimizer. The use of different optimizers was found to influence model performance, and data augmentation helped improve generalization, especially for classes with limited samples. Limitations of this study include the relatively limited dataset coverage. Future research is recommended to expand the dataset and explore alternative or hybrid architectures. These findings demonstrate the potential of deep learning–based systems in supporting sustainable waste management.
Co-Authors Abdul Latif Adam Z, Muhammad Ghaly Agung Sasongko Agus Junaidi Ali Mustopa, Ali Andi Saryoko Andreas Roy Prasetya Arifin, Yosep Tajul Atang Saepudin Atang Saepudin Cucu Ika Agustyaningrum Ganda Wijaya Gata, Windu Haliza Ramadhanti, Pristya Haryani Haryani Haryani Hendri Mahmud Nawawi Laksono Trisnantoro Maysaroh, Maysaroh Mustopa, Ali Nanik Wuryani Nawawi, Hendri Mahmud Prawikas, Amanda Pribadi, Denny Pristya Haliza Ramadhanti Putri, Rizky Rachma Rahmat Hidayat Rahmat Hidayat Rianto, Yan Riska Aryanti Rizky Ade Safitri Roy Prasetya, Andreas Rusda Wajhillah, Rusda S, M. Rangga Ramadhan Siti Khotimatul Wildah Siti Marlina, Siti Sopiyan Dalis Suharyanto Syamsul Bahri Titik Misriati Tri Wahyuni tri wahyuni Tya Septiani Nurfauzia Koeswara Wahyudin Wahyudin Wahyudin Wahyudin Windu Gata Windu Gata Yoseph Tajul Arifin Yudhistira Yudhistira yunita yunita