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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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PREDIKSI STUNTING PADA BALITA DI RUMAH SAKIT KOTA SEMARANG MENGGUNAKAN NAIVE BAYES Widya Cholid Wahyudin; Fida Maisa Hana; Agung Prihandono
JURNAL ILMU KOMPUTER DAN MATEMATIKA Vol 4, No 1 (2023): JURNAL ILMU KOMPUTER DAN MATEMATIKA
Publisher : Universitas Muhammadiyah Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26751/jikoma.v4i1.1792

Abstract

Stunting is chronic malnutrition caused by insufficient nutritional intake over a long period of time due to the provision of food that is not in accordance with needs. This study focuses on malnutrition in toddlers. Stunting in toddlers is more common in toddlers aged 12-59 months than toddlers aged 0-24 months. Stunting can have short and long-term impacts. This study used toddler data for 2018 which was obtained from the Semarang City Health Center with toddlers aged 0-59 months. This research aims to value the classification results of stunting nutritional status in toddlers using the Naive Bayes Classifier algorithm. The Naive Bayes Classifier algorithm is one of the algorithms used for the classification process that can solve problems with large amounts of data so that it can produce a probability value for a hypothesis that is sought. It is proved by the results of testing with the Naive Bayes Classifier algorithm, which was carried out on all data in a dataset of 300 records, the accuracy achieved is 85.33%.
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.
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 Kalimah, Sinta Nur Khoirul Umam Haqiqi Luqman Nur Hakim Mahdarul Huda Ahmad Manggalastawa, Manggalastawa Moh Akbar Munajad Muadzah, Muadzah Nuril Lutvi Azizah Nurul Iman Rahmawan Bagus Trianto Rangga Primadasa Sabiq Ismail Saharani, Safira Saiful Ulya Sayyidati Maulia Ashfa 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