Article Per Year (5 Year)
p-Index From 2021 - 2026
2.127
P-Index
This Author published in this journals
All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL PENDIDIKAN TAMBUSAI Building of Informatics, Technology and Science Journal of Electrical Engineering and Computer (JEECOM) International Journal of Business and Information Technology Jurnal Res Justitia : Jurnal Ilmu Hukum Papua Journal Of Diplomacy And International Relations Jurnal Ilmu Ekonomi Mulawarman (JIEM) Jurnal Pengabdian Masyarakat Bangsa Bulletin of Early Childhood Jurnal Intelek Insan Cendikia Idealita: Jurnal Pendidikan dan Sosial Keagamaan Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Musfiroh Musfiroh, Musfiroh
FKIP Universitas Lampung, Jl. Prof. Dr. Soemantri Brojonegoro No. 1
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Hubungan Penggunaan Tiktok Yang Berlebihan Terhadap Kesehatan Mental Pada Mahasiswa Isak Iskandar; musfiroh, musfiroh
Jurnal Intelek Insan Cendikia Vol. 2 No. 5 (2025): MEI 2025
Publisher : PT. Intelek Cendikiawan Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study examines how excessive TikTok usage correlates with the mental health status of college students. Using a quantitative correlational design, data collection involved online questionnaires that assessed how often and how long students used TikTok, alongside mental health assessments utilizing the DASS-21 instrument. The findings indicate that many students tend to open TikTok automatically, feel anxious when they are not engaged with the platform, and experience sleep disruptions because of extended use. The data suggests a moderate to high engagement level, which can potentially lead to psychological problems such as stress, anxiety, and depression. The research highlights the necessity of enhancing digital literacy, managing screen time effectively, and encouraging healthier digital habits to reduce adverse effects. Recommendations include awareness campaigns, educational programs, and support systems aimed at helping students control their media activity and sustain their mental well-being.
Klasifikasi Penyakit Daun Tanaman Berbasis Citra Menggunakan Convolutional Neural Network Data Augmentation Suci, Bintang Dyas; Musfiroh, Musfiroh; Sefriani, Shintia Putriayu; Sumanto, Sumanto; Pakpahan, Roida; Budiawan, Imam
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 10, No 1 (2026): SEMNAS RISTEK 2026
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v10i1.8894

Abstract

Penelitian ini membahas penerapan Convolutional Neural Network (CNN) yang dikombinasikan dengan teknik augmentasi data untuk klasifikasi penyakit daun tanaman berbasis citra. Permasalahan utama penelitian ini adalah keterbatasan jumlah data latih yang dapat memengaruhi kinerja model klasifikasi. Tujuan penelitian adalah mengevaluasi efektivitas augmentasi data dalam meningkatkan performa model CNN pada dataset berskala terbatas. Dataset yang digunakan adalah Plant Disease Recognition Dataset yang terdiri dari 1.523 citra dengan tiga kelas, yaitu Healthy, Powdery Mildew, dan Rust. Penelitian ini menggunakan metode eksperimen dengan tahapan praproses data, augmentasi data, pelatihan model, serta evaluasi performa yang seluruhnya dilakukan menggunakan Google Colab. Teknik augmentasi yang diterapkan meliputi rotasi, zoom, dan horizontal flip. Hasil penelitian menunjukkan bahwa model CNN mampu mencapai akurasi validasi yang baik, meskipun performa klasifikasi antar kelas masih bervariasi, khususnya pada kelas Rust yang memiliki karakteristik visual kompleks, sebagaimana ditunjukkan melalui confusion matrix dan classification report. Selain itu, penelitian ini mengimplementasikan skema prediksi real-time sebagai proof-of-concept. Secara keseluruhan, hasil penelitian menunjukkan bahwa kombinasi CNN dan augmentasi data efektif untuk klasifikasi penyakit tanaman pada kondisi keterbatasan data dan sumber daya komputasi.
Detection of Diabetic Retinopathy Using Hybrid InceptionResNetV2-KELM Method Musfiroh, Musfiroh; Novitasari, Dian C Rini; Hakim, Lutfi; Damayanti, Adelia; Haq, Dina Zatusiva; Aisah, Siti Nur
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11967

Abstract

Diabetic Retinopathy (DR) is a complication of Diabetes Mellitus (DM), both type 1 and type 2 DM. Based on its severity, DR is divided into mild DR, moderate DR, severe DR, and proliferative DR stages. Manual detection is difficult because there is a fairly small difference between normal and DR. The Computer-Aided Diagnosis (CAD) system is a solution for detecting the severity of DR quickly and accurately so that DR sufferers do not get worse, which can cause blindness. This study uses fundus images from the Mesindor dataset consisting of four classes, namely normal, mild DR, moderate DR, and severe DR, with the InceptionResNetV2-KELM hybrid method. InceptionResNetV2 is used as a feature extraction and Kernel Extreme Learning Machine (KELM) as its classification. Several types of kernels are applied as model trials. The results show the highest sensitivity lies in the polynomial kernel experiment with a sensitivity value of 99.88%, an accuracy of 99.88%, and a specificity of 99.96%. The method used is able to detect very well and is quite time-effective compared to conventional CNN.
Co-Authors Abu Tholib Al abdillah, Pipuk Zainita Choirun Nisa Apriliani, Elina Intan Belannisa, Kartika Damayanti, Adelia Erlangga, Erwin Fauziyah, Nur Lina Haq, Dina Zatusiva Imam Budiawan Isak Iskandar Lutfi Hakim Miftahul Jannah Muhammad Saleh Novitasari, Dian C Rini Pakpahan, Roida Putroue Keumala Intan Rukmana, Bella Shintia Safiulloh, Safiulloh Sari, Dian Candra Rini Novita Sefriani, Shintia Putriayu Siti Nur Aisah Suci, Bintang Dyas Sugiarti, Rini Sumanto, Sumanto Wisnawa, Gede Gangga Zainal Arifin