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All Journal Sainteks AKSIOMA: Jurnal Program Studi Pendidikan Matematika QALAMUNA: Jurnal Pendidikan, Sosial, dan Agama Metode : Jurnal Teknik Industri JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) Psychology, Evaluation, and Technology in Educational Research Jurnal Kridatama Sains dan Teknologi Journal of Innovation and Future Technology (IFTECH) Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Sistem Informasi dan Sistem Komputer Mejuajua Jurnal Dinamika Informatika (JDI) BERBAKTI: Jurnal Pengabdian Kepada Masyarakat Jurnal Abdimas Indonesia Jurnal Bisnis Digital dan Sistem Informasi Jurnal Ilmu Komputer dan Matematika
Fida Maisa Hana
Universitas Muhammadiyah Kudus
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Transportation Other

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Implementasi Algoritma CNN dengan Arsitektur MobileNet untuk Klasifikasi Citra Daun Herbal Fida Maisa Hana; Agung Prihandono; Agung Bakti; Nuril Lutvi Azizah; Imam Prayogo Pujiono
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9444

Abstract

Indonesia's diversity is very rich, one of which is that Indonesia has herbal plants which people believe are natural medicines for curing diseases. Herbal leaves show different variations in size and shape for each type, indicating that each leaf has special characteristics, shape, texture and size. Researchers used one of the Deep Learning methods, namely Convolutional Neural Network (CNN) for classifying herbal leaves. The field of image classification has found CNNs to be quite effective for image classification. CNN is a type of neural network with convolutional layers that has the ability to automatically extract important features from images. MobileNet is a CNN structure created by Google. MobileNet has advantages in efficient use of computing resources. Specifically, in the MobileNet network model, an attention module was added to improve the model's ability to extract more detailed image features, and dropout technology was added to prevent overfitting. This research method includes image preprocessing, training a convolutional neural network-based model, and evaluating its performance using accuracy, precision, recall, and F1 score metrics. Training was conducted for 20 epochs, and testing was conducted using data separated from the training data. The evaluation results show that the MobileNet model has the ability to extract visual features and produce herbal leaf image classification with an accuracy rate of 97.50% and precision, recall, and F1 scores of 98% each. The proposed model can be used in mobile-based herbal leaf identification applications due to its high performance and lightweight architecture. The stable accuracy curve at the final epoch indicates that the model does not experience significant overfitting and is able to generalize well to the test data
Klasifikasi Komentar Toksik Berbahasa Indonesia di Media Sosial Berbasis Fine-Tuning IndoBERT Luqman Nur Hakim; Fida Maisa Hana; Widya Cholid Wahyudin
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9449

Abstract

Social media has become a primary platform for Indonesian society to interact and exchange information online. However, freedom of expression in digital spaces is often misused through the use of harsh, offensive, and hateful language. This study aims to develop a toxic comment classification model for the Indonesian language using the IndoBERT architecture through a fine-tuning process. IndoBERT was selected for its capability to understand bidirectional semantic context and its pretraining on a Bahasa Indonesia corpus, making it suitable for handling informal language styles, abbreviations, and common code-mixing phenomena in social media texts. The dataset used in this study is the Indonesian Abusive and Hate Speech Twitter Text, consisting of 12,942 entries 11,647 for training and 1,295 for validation. The research was conducted online using Google Colaboratory with GPU acceleration. The research stages included data preprocessing, tokenization, model training, and evaluation using precision, recall, F1-score, and confusion matrix as metrics. Evaluation results show that the fine-tuned IndoBERT model achieved high performance, with an average precision of 0.8842, recall of 0.884, F1-score of 0.883, and accuracy of 0.8834. These results indicate balanced performance across classes and strong model stability in detecting both toxic and non-toxic comments. This study contributes to the development of an automated Indonesian-language content moderation system, which can be deployed as a comment detection module via API. Although limited to Twitter data and binary classification, this model has the potential to be extended toward multi-class and cross-platform classification in supporting safer and healthier digital spaces in Indonesia.
Development of 3D Animated Story Media Through DRTA Strategy to Improve Reading Comprehension Skills of Elementary School Students Manggalastawa Manggalastawa; Dhina Cahya Rohim; Fida Maisa Hana; Sinta Nur Kalimah
QALAMUNA: Jurnal Pendidikan, Sosial, dan Agama Vol. 18 No. 1 (2026)
Publisher : Lembaga Penerbitan dan Publikasi Ilmiah Program Pascasarjana IAI Sunan Giri Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37680/qalamuna.v18i1.8243

Abstract

Reading comprehension is a fundamental literacy skill that supports students’ learning across various subjects. However, observations in elementary schools show that many students still struggle to understand narrative texts due to limited comprehension skills and the lack of engaging and innovative learning materials. At the same time, technological advancements provide opportunities to develop digital learning media that can enhance motivation and comprehension. One promising innovation is the use of 3D animated stories, which can present narrative content in a more vivid, interactive, and meaningful way. This study aims to develop 3D animated story learning media integrated with the Directed Reading Thinking Activity (DRTA) strategy to improve the reading comprehension skills of fourth-grade elementary school students. The research employed the ADDIE development model and was conducted at an elementary school in Kudus Regency, Central Java, Indonesia. The developed product consists of 3D animated learning media and a learning guidebook. Validation results from media experts, subject matter experts, and elementary school learning device experts indicated that the product was highly valid. Field testing showed an improvement in students’ reading comprehension, as reflected by higher posttest scores compared to pretest scores. The effectiveness test revealed a high N-Gain value of 0.73. In addition, practicality assessments through student and teacher questionnaires produced very positive results, with scores of 94% and 87.5%, respectively. These findings indicate that 3D animated stories integrated with the DRTA strategy are effective and practical for improving elementary students’ reading comprehension, particularly in narrative text learning.
Klasifikasi Kinerja Penjualan Produk Nike Menggunakan Algoritma Random Forest dengan Pendekatan Hold-Out dan K-Fold Cross Validation Nisa, Divta Khoirun; Hana, Fida Maisa; Ulya, Saiful
Sainteks Vol. 23 No. 1 (2026): April
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/sainteks.v23i1.29930

Abstract

Dinamika industri ritel menuntut pemanfaatan data transaksi besar untuk pengambilan keputusan strategis. Penelitian ini bertujuan mengklasifikasi kinerja penjualan produk Nike ke dalam kategori rendah, sedang, dan tinggi menggunakan algoritma Random Forest. Penelitian ini memberikan kontribusi melalui pengujian model menggunakan dua pendekatan, yaitu Hold-Out dan K-Fold Cross Validation, untuk menjamin stabilitas performa. Dataset yang digunakan merupakan data sekunder dari Kaggle sebanyak 9.360 baris data transaksi Nike di Amerika Serikat periode 2020-2021. Tahapan penelitian meliputi preprocessing data melalui label encoding, pembagian data, pemodelan Random Forest, serta evaluasi menggunakan confusion matrix, hasil pengujian menunjukkan bahwa model memiliki performa yang sangat tinggi, dengan tingkat akurasi pada metode Hold-Out mencapai 98,13%. Sementara itu, pengujian menggunakan 10-Fold Cross Validation menghasilkan  akurasi tertinggi mencapai 94,39% pada fold ke-4. Secara keseluruhan, nilai weighted average precision, recall, dan F1-score mencapai 0,98 yang membuktikan efektivitas algoritma Random Forest dalam memberikan klasifikasi yang akurat. Temuan ini diharapkan dapat mendukung manajemen dalam pengambilan keputusan berbasis data di sektor ritel.
Klasifikasi Gangguan Tidur Menggunakan Algoritma XGBoost dengan SMOTE dan Grid Search Moh. Indra Kholid Khoirusshofi; Fida Maisa Hana; Taftazani Ghazi Pratama
Sainteks Vol. 23 No. 1 (2026): April
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/sainteks.v23i1.30171

Abstract

Gangguan tidur merupakan permasalahan kesehatan yang memiliki prevalensi tinggi dan berdampak signifikan terhadap kualitas hidup, sehingga diperlukan sistem deteksi dini yang akurat dan efisien. Perkembangan machine learning membuka peluang untuk membangun model klasifikasi gangguan tidur berbasis data, namun ketidakseimbangan kelas pada dataset medis sering menjadi tantangan yang menurunkan performa model, khususnya pada kelas minoritas. Penelitian ini bertujuan membangun model klasifikasi gangguan tidur menggunakan algoritma Extreme Gradient Boosting (XGBoost) dengan penerapan Synthetic Minority Oversampling Technique (SMOTE) dan optimasi hiperparameter menggunakan Grid Search. Dataset yang digunakan adalah Sleep Health and Lifestyle Dataset yang terdiri dari 374 data dengan tiga kelas target, yaitu Insomnia, No Disorder, dan Sleep Apnea. Penelitian ini menguji empat skenario model, yaitu XGBoost tanpa SMOTE dan Grid Search, XGBoost dengan SMOTE, XGBoost dengan Grid Search, serta kombinasi SMOTE dan Grid Search. Evaluasi kinerja model dilakukan menggunakan metrik accuracy, precision, recall, F1-score, Confusion Matrix, serta ROC Curve. Hasil penelitian menunjukkan bahwa penerapan SMOTE meningkatkan sensitivitas model terhadap kelas minoritas, sedangkan optimasi hiperparameter menggunakan Grid Search meningkatkan stabilitas dan akurasi model secara keseluruhan. Kombinasi SMOTE dan Grid Search menghasilkan performa terbaik dengan akurasi mencapai 97% serta nilai precision, recall, dan F1-score yang seimbang pada seluruh kelas. Selain itu, evaluasi menggunakan ROC Curve menunjukkan nilai AUC pada rentang 0,99 hingga 1,00, yang mengindikasikan kemampuan model yang sangat baik dalam membedakan setiap kelas. Hasil ini menunjukkan bahwa pendekatan yang diusulkan mampu meningkatkan performa klasifikasi gangguan tidur dan berpotensi menjadi alternatif dalam pengembangan sistem deteksi dini gangguan tidur.
Co-Authors Achmad Ridwan Achmad Ridwan, Achmad Ade Ima Afifa Himayati Ade Kurnia Bayu Firdaus Afrisia, Silviana Putri Agung Bakti Agung Prihandono Ahadin, Akbar Ihsanul Ahmad, Saiful Andi Kurniawan Arlinwibowo, Janu Avira Budianita Cikita Berlian Hakim Deka Setia Negara Dhina Cahya Rohim Diva Anastasya Dwi Elysia Dinata Eko Hari Rachmawanto Elisya Pujasari Fadhila, Vera Farikha Tuzzahro Firmansyah, Nunung Agus Hafni Aulida Hafni Aulida Harjanti, Agustina Eka Hawa Suci Hermawan Abdillah Hamka Ihsanul Ahadin, Akbar Imam Abdul Rozaq Imam Prayogo Pujiono Indanah Janu Arlinwibowo Khoirul Umam Haqiqi Luqman Nur Hakim Mahdarul Huda Ahmad Manggalastawa Manggalastawa Manggalastawa, Manggalastawa Moh Akbar Munajad Moh. Indra Kholid Khoirusshofi Muadzah, Muadzah Nisa, Divta Khoirun Nuril Lutvi Azizah Nurul Iman Rahmawan Bagus Trianto Rangga Primadasa Sabiq Ismail Saharani, Safira Saiful Ulya Sayyidati Maulia Ashfa Sinta Nur Kalimah Siti Mahgfiroh Soma Setiawan Soma Setiawan P.N Syahrul Azmi Nawaf Taftazani Ghazi Pratama Tri Febriani Dewi Widya Cholid Widya Cholid Wahyudin Widya Cholid Wahyudin Yunus Mustaqim Yunus Mustaqim, Yunus