Article Per Year (5 Year)
p-Index From 2021 - 2026
2.064
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL PENGABDIAN KEPADA MASYARAKAT Edu Komputika Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JMM (Jurnal Masyarakat Mandiri) ILKOM Jurnal Ilmiah JETL (Journal Of Education, Teaching and Learning) Jurnal Teknologi Informasi dan Multimedia JINAV: Journal of Information and Visualization Jurnal Komunikasi Global
Nurhopipah, Ade
Department Of Informatics, Universitas Amikom Purwokerto
Aerospace Engineering Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
Indeks Literasi Digital Kader TP PKK Ade Nurhopipah; Primandani Arsi
Jurnal Komunikasi Global Vol 13, No 1 (2024)
Publisher : Program Studi Ilmu Komunikasi FISIP Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jkg.v13i1.36633

Abstract

Peningkatan kualitas literasi digital merupakan upaya penting terbentuknya Masyarakat digital sehat. Langkah awal yang perlu dilakukan adalah identifikasi sejauh mana kompetensi pengguna dunia digital. Penelitian ini melakukan pengukuran Indeks Literasi Digital dengan target kader Tim Penggerak Pemberdayaan Kesejahteraan Keluarga (TP PKK) di Desa Tambaksari Kidul, Kembaran, Banyumas. Target ini dipilih karena mereka merupakan salah satu pihak yang berperan signifikan dalam menentukan keberhasilan pendidikan non-formal. Tujuan penelitian ini dilakukan untuk memberikan gambaran tentang tingkat literasi kader PKK dibandingkan dengan Indeks Literasi Digital nasional di Indonesia. Instrumen yang digunakan adalah survei yang mengadopsi instrumen Kementerian Komunikasi dan Informatika berdasarkan Road Map Literasi Digital Indonesia 2020-2024. Sebanyak 93 responden berpartisipasi dalam penelitian ini yang menggunakan jenis teknik total sampling. Hasil analisis menunjukan bahwa indeks tertinggi adalah pada pilar budaya digital dengan skor 4,05 dan indeks terendah adalah pada pilar keamanan digital dengan skor 2,83. Kesimpulan dari analisis yang dilakukan Skor total Indeks Literasi Digital responden adalah 3,52, sedikit di bawah indeks nasional pada tahun 2022 yaitu sebesar 3,54.Improving digital literacy quality is essential to create a healthy digital society. The first step is to identify the extent of the user's competence in the digital world. This research measures the Digital Literacy Index targeting the Family Welfare Empowerment Team (TP PKK) cadres, in Tambaksari Kidul Village, Kembaran, Banyumas. This target was chosen because they are one of the parties who play a significant role in determining the success of non-formal education. This research aims to provide an overview of the literacy level of PKK cadres compared to the national Digital Literacy Index in Indonesia. The instrument used was a survey that adopted the Ministry of Communications and Information Technology's instrument based on the Indonesian Digital Literacy Road Map 2020-2024. 93 respondents participated in this research using a total sampling technique. The results show that the highest index is in the digital culture pillar, with a score of 4.05, and the lowest index is in the digital security pillar, with a score of 2.83. The respondents' total Digital Literacy Index score is 3.52, slightly below the national index in 2022, which is 3.54.
Penanganan Imbalanced Dataset untuk Klasifikasi Komentar Program Kampus Merdeka Pada Aplikasi Twitter Magnolia, Cindy; Nurhopipah, Ade; Kusuma, Bagus Adhi
Edu Komputika Journal Vol 9 No 2 (2022): Edu Komputika Journal
Publisher : Jurusan Teknik Elektro Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukomputika.v9i2.61854

Abstract

Imbalanced dataset merupakan hal yang sering ditemukan secara alami dalam proses penambangan data. Kondisi ini sangat mempengaruhi keakuratan klasifikasi data seperti yang terjadi dalam klasifikasi komentar program Kampus Merdeka yang peneliti lakukan. Penelitian ini akan fokus pada penanganan Imbalanced dataset untuk meningkatkan kinerja klasifikasi komentar yang berasal dari aplikasi Twitter. Data diklasifikasikan ke dalam empat kelas yaitu kelas 0 (untuk informasi), kelas 1 (untuk opini), kelas 2 (untuk pertanyaan), dan kelas 3 (untuk out of topic). Metode yang digunakan untuk balancing dataset adalah Undersampling, Oversampling menggunakan SMOTE dan ADASYN, serta Random Combination Sampling. Evaluasi performa dilakukan menggunakan algoritma Support Vector Machine (SVM) dengan perbandingan komposisi data training dan testing 80:20. Metode pembobotan data yang digunakan adalah Term Frequency-Inverse Document Frequency (TF-IDF) dengan nilai max_features 3000, 5000, dan 7000. Hasil pengujian awal menunjukan bahwa nilai akurasi dan F1-score pada Imbalanced dataset secara berurut-urut adalah 0,7 dan 0,7. Sedangkan metode penanganan Imbalanced dataset dapat meningkatkan nilai F1-score, kecuali pada penerapan metode Undersampling. Metode terbaik ditunjukan oleh penerapan ADASYN dengan nilai akurasi dan F1-score berurut-urut sebesar 0,9 dan 0,9. Penggunaan max_features pada TF-IDF juga mempengaruhi hasil performa klasifikasi, dengan max_features terbaik ditunjukan pada jumlah 5000.
Analysis of Detecting the Authenticity of Money Using the Edge Detection Method Widianto, Nabella Putri; Indartono, Kuat; Nurhopipah, Ade
JINAV: Journal of Information and Visualization Vol. 5 No. 1 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav2372

Abstract

One of the factors contributing to the increasing number of banknotes counterfeiting crimes is the technological advancements that have been made in the digital printing industry. Duplications that are very similar and even difficult to distinguish from the original banknote sheet are made easily due to high-quality duplication. Viewing, feeling, and looking carefully at a banknote is the traditional way to check the authenticity of a banknotes. And of course, due to the limitations of human capabilities, such methods are ineffective for many banknotes. Effectively and efficiently, edge detection can be completed by image processing or image processing methods. Using the camera, both real and fake banknotes are transferred. This results in two photos saved as JPG files representing both banknotes. After that, the conversion process to greyscale is carried out, edge observations are made on the image, and then a histogram of both images is created. The results of the histogram were compared and texture was analyzed as judged by the brightness and sharpness of the image. Keywords: Edge Detection, Fake Money, Histogram, Real Money, Texture Analysis.
A Soft Voting Ensemble Classifier to Improve Survival Rate Predictions of Cardiovascular Heart Failure Patients Munandar, Arif; Maulana Baihaqi, Wiga; Nurhopipah, Ade
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1632.344-352

Abstract

Cardiovascular disease is one of the deadliest diseases, claiming around 17 million lives worldwide each year. According to data from the World Health Organization (WHO), more than four out of five deaths from cardiovascular disease are caused by heart attacks and strokes, and one-third of these deaths occur prematurely in people under the age of 70. Machine learning approaches can be used to detect the disease. This research aims to improve the prediction model of cardiovascular heart failure patient survival using C4.5, KNN, Logistic Regression algorithms, and the ensemble learning method of Voting Classifier. Based on the testing results, each model showed a significant increase in accuracy in the 70:30 ratio. Logistic Regression and C4.5 achieved the same accuracy, 89.47%, KNN obtained 91.23%, and Voting Classifier experienced a considerable improvement, reaching 94.74%. In testing with ratios of 90:10, 80:20, and 70:30, KNN demonstrated high accuracy but had significant overfitting, with a difference of 7-9% between training and testing accuracy scores in the 90:10 and 80:20 ratios. On the other hand, Voting Classifier showed stable performance in the 70:30 ratio, with an accuracy difference between training and testing scores below 1%. The conclusion of this research is that the Voting Classifier can assist the performance improvement of algorithms for classifying the survival expectancy of cardiovascular heart failure patients into 'Survived' or 'Deceased', compared to Logistic Regression, KNN, and C4.5.
Co-Authors Agus Harjoko Anan Widianto Arif Munandar Dani Arifudin Ferdiyansyah, Achmad Indra Alan Nugroho Irfan Rifai Azziz Jali Suhaman Jali Suhaman Jali Suhaman Jali Suhaman Jali Suhaman Krisno, Krisno Kuat Indartono Kusuma, Bagus Adhi Larasati, Nurriza Amalia Magnolia, Cindy Maulana Baihaqi, Wiga Moza Tri Humanita Mutiara, Dwi Ayu Nuraida, Ida Primandani Arsi Uswatun Hasanah Uswatun Hasanah Uswatun Hasanah Widianto, Nabella Putri