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All Journal Jurnal Informatika Jurnal Pilar Nusa Mandiri Jurnal AbdiMas Nusa Mandiri
Nawawi, Hendri Mahmud
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Computer Science & IT Decision Sciences, Operations Research & Management Education Energy Environmental Science Mathematics Physics Social Sciences Other

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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.
PELATIHAN PEMANFAATAN DIGITAL PARENTING CONTROL MENGGUNAKAN GOOGLE FAMILY LINK PADA IBU TAMAN KAMPUNG TANGGUH Kahfi, Ahmad Hafidzul; Nugraha, Fitra Septia; Ridwansyah, Ridwansyah; Nawawi, Hendri Mahmud
Jurnal AbdiMas Nusa Mandiri Vol 6 No 1 (2024): Periode April 2024
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/abdimas.v6i1.4788

Abstract

The use of digital technology is increasingly widespread in the modern era, which is changing the way parents educate their children. "Google Parenting" emerged as a term that reflects parents' efforts to face the challenges of parenting in the digital era. This concept emphasizes the importance of parents understanding the impact of technology on children's development as well as appropriate development strategies to use it wisely while maintaining balance in family life. This service activity aims to provide understanding to parents about using the Google Family Link application. The first stage includes theoretical explanations, direct practice using the application, and interactive discussions. The material is delivered through multimedia techniques using a laptop and the internet. This activity is not only informative but also involves active participants through modules, pretests, posttests and discussions. The results of the training for PKK RW 012 Taman Kampung Tangguh mothers showed an increase in their awareness and understanding of digital risks for children as well as their ability to control children's online activities. This helps reduce the risk of exposure to age-inappropriate content and improves communication between mother and child regarding digital safety.
KOMPARASI ALGORITMA NEURAL NETWORK DAN NAÏVE BAYES UNTUK MEMPREDIKSI PENYAKIT JANTUNG Nawawi, Hendri Mahmud; Purnama, Jajang Jaya; Hikmah, Agung Baitul
Jurnal Pilar Nusa Mandiri Vol 15 No 2 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (934.982 KB) | DOI: 10.33480/pilar.v15i2.669

Abstract

Heart disease is one of the types of deadly diseases whose treatment must be dealt with as soon as possible because it can occur suddenly to the sufferer. Factors of heart disease that are recognized based on the condition of the body of a sufferer need to be known from an early age so that the risk of possible instant attacks can be minimized or can be overcome in various ways such as a healthy lifestyle and regular exercise that can regulate heart health in the body. By looking at the condition of a person's body based on sex, blood pressure, age, whether or not a smoker and some indicators that become a person's characteristics of heart disease are described in a study using the Neural Network and Naïve Bayes algorithm with the aim of comparing the level of accuracy to attributes influential to predict heart disease, so the results of this study can be used as a reference to predict whether a person has heart disease or not.
IMAGE BACKGROUND PROCESSING FOR COMPARING ACCURACY VALUES OF OCR PERFORMANCE Kholifah, Desiana Nur; Nawawi, Hendri Mahmud; Thira, Indra Jiwana
Jurnal Pilar Nusa Mandiri Vol 16 No 1 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1274.786 KB) | DOI: 10.33480/pilar.v16i1.1076

Abstract

Optical Character Recognition (OCR) is an application used to process digital text images into text. Many documents that have a background in the form of images in the visual context of the background image increase the security of documents that state authenticity, but the background image causes difficulties with OCR performance because it makes it difficult for OCR to recognize characters overwritten by background images. By removing background images can maximize OCR performance compared to document images that are still background. Using the thresholding method to eliminate background images and look for recall values, precision, and character recognition rates to determine the performance value of OCR that is used as the object of research. From eliminating the background image with thresholding, an increase in performance on the three types of OCR is used as the object of research.
Co-Authors Agung Baitul Hikmah, Agung Baitul Agustiani, Sarifah Gata, Windu Kahfi, Ahmad Hafidzul Kholifah, Desiana Nur Nugraha, Fitra Septia Purnama, Jajang Jaya Ridwansyah Ridwansyah S, M. Rangga Ramadhan Siti Khotimatul Wildah Thira, Indra Jiwana