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All Journal IJEEIT : International Journal of Electrical Engineering and Information Technology Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Lontara Abdimas : Jurnal Pengabdian kepada Masyarakat Mangente: Jurnal Pengabdian kepada Masyarakat Jurnal Aplikasi Sains Data
Tresna Maulana Fahrudin
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

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Implementation of Deep Learning to Detect Indonesian Hoax News with Convolutional Neural Network Method Cheevin Yoviananda; Tresna Maulana Fahrudin
IJEEIT : International Journal of Electrical Engineering and Information Technology Vol 4 No 2 (2021): September 2021
Publisher : NAROTAMA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29138/ijeeit.v4i2.1525

Abstract

This study aims to establish and test a model that is used to determine valid news and hoax news. The method used is the Convolutional Neural Network (CNN) method and Word2Vec as embeddings. The research stages consist of data collection, pre-processing, word embeddings, model formation and testing the results obtained. The data used is 958 news. After testing with the distribution of data by 80% as training data and 20% as test data and 5 times epoch, the model that has been formed can determine valid news and hoax news well. In this study, a model with a vector dimension of 400 as input data and a multiple filter size of 3,4,5 became the best model. The resulting accuracy, precision and recall are 0.91. These results are influenced by the selection of the size of the vector dimensions on the output of Word2Vec, the selection of the filter size on the convolution layer and the addition of the Indonesian Wikipedia corpus into the corpus used.
SOSIALISASI PENANAMAN TANAMAN OBAT KELUARGA DAN MINUMAN HERBAL UNTUK MENINGKATKAN DAYA TAHAN TUBUH WARGA KELURAHAN KRAMATINGGIL GRESIK MELALUI PLATFORM YOUTUBE Erlyana Desy Rahmawati; Devita Inka Amalia; Lutvi Niswa Octaviana; Rika Nur Azizah; Tresna Maulana Fahrudin
Lontara Abdimas : Jurnal Pengabdian Kepada Masyarakat Vol 3 No 1 (2022): Juni
Publisher : Lembaga Penelitian Dan Pengabdian Masyarakat Politeknik Kesehatan Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53861/lomas.v3i1.275

Abstract

During the current COVID-19 pandemic, people are required to have a good immune system. To gain immunity, people can consume herbal drinks that can be made from the harvested plants of TOGA. Various types of TOGA plants that can be used such as lemongrass, turmeric, ginger, and java ginger. This plant can be planted in a narrow area or in a pot that can be placed in the yard of the house. This is in line with the condition of Kramatinggil Urban Village in Gresik District which does not have a large area as a planting medium so they can take advantage of the yard. The purpose of planting TOGA is to familiarize residents with planting original spices from Indonesian crops and processing herbal drinks in order to increase the immune system, especially for protection from COVID-19. The results of the program showed that TOGA planting video was watched 60 times and the video for making herbal drinks was watched 47 times by local residents. In addition, 30 bottles of herbal drinks have been distributed to residents of Kramatinggil Urban Village.
Penyuluhan Bahaya Pernikahan Dini dan Stunting di Desa Manduro Manggung Gajah Sebagai Upaya Pencegahan Stunting Arinil Ula Fil 'Izza; Nilna Zahrul Aini; Muhammad David Ade Pangestu; Cahyani Marisa Wulandari; Intan Septia Eka Fortuna; Fajar Zakly Herwanto; Nensi Agustina; Tresna Maulana Fahrudin
MANGENTE: JURNAL PENGABDIAN KEPADA MASYARAKAT Vol 2, No 1 (2022): PENDIDIKAN KARAKTER DAN PEMBERDAYAAN MASYARAKAT
Publisher : IAIN AMBON

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33477/mangente.v2i1.2812

Abstract

Stunting adalah sebuah masalah mengenai kurang gizi kronis yang disebabkan dari pola makan yang diberikan pada anak mulai dari 1000 hari pertama kehidupan anak sejak lahir sehingga anak menjadi lebih pendek dibanding anak seusianya. Ibu yang memiliki usia terlalu dini dalam menjalankan pernikahan lebih berisiko mengalami kejadian stunting terhadap calon anak. Kurangnya pemahaman akan apa itu stunting dan bahaya stunting itulah yang menyebabkan terjadinya pernikahan dini yang wajar. Penelitian ini memiliki tujuan untuk mengetahui apakah remaja di Desa Manduro Manggung Gajah sudah mengetahui tentang dampak pernikahan dini terhadap risiko stunting pada anak. Metode penelitian yang dilakukan menggunakan kuesioner dengan sistem pre-test dan post-test lalu data yang didapatkan kemudian dapat diolah dengan menghitung rata-rata jumlah responden yang memahami materi pernikahan dini dan stunting pada saat sebelum dan sesudah penyuluhan. Hasil penelitian yang diperoleh setelah dilakukan perhitungan rata-rata ialah adanya peningkatan pengetahuan Warga Manduro Manggung Gajah mengenai bahaya pernikahan dini dan stunting sebesar 39% dan menurunkan minat pernikahan dini pada laki-laki 47% dan perempuan 50%.
Analisis Sentimen Kepuasan Pengguna OYO DiPlaystore Dengan Multinoial Naive Bayes dan Chi-square Aziz, Rizky; Tresna Maulana Fahrudin; Wahyu Syaifullah Jauharis Saputra
JURNAL FASILKOM Vol. 14 No. 1 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i1.6943

Abstract

ABSTRACKOpinions play a crucial role in everyday life, significantly influencing human behavior and decisions. Especially in the context of business and organizations, consumer opinions about products and services are highly valuable. This study focuses on analyzing the sentiment of OYO application reviews on the Google Play Store, with the goal of classifying reviews as either positive or negative. OYO Hotels & Homes, a startup company in the accommodation sector originating from India, has achieved remarkable success with revenues reaching US$951 million in fiscal year 2019. The primary classification method used is Multinomial Naïve Bayes, which is an approach in supervised learning, along with Chi-Square feature selection to explore correlations between factors influencing user satisfaction. The research process includes data collection of reviews, preprocessing, labeling, and data splitting. Subsequently, TF-IDF weighting and Chi-Square feature selection are performed. The results of sentiment analysis indicate a dominance of positive reviews, reflecting user satisfaction with OYO services. The classification process uses the Multinomial Naïve Bayes algorithm, with an accuracy rate of 85.5% without feature selection, increasing to 87.00% with Chi-Square feature selection. These results demonstrate the effectiveness of the Multinomial Naïve Bayes algorithm and the importance of feature selection in sentiment analysis. Through a deeper understanding of user sentiment, companies can enhance service quality and respond to feedback more effectively, ensuring optimal customer satisfaction. This research has broad implications for sentiment analysis and the use of statistical methods to address complex issues in the technology industry. Keywords: Sentiment Analysis, OYO Application, Google Playstore, Multinomial Naïve Bayes, Chi-Square Feature Selection. Abstrak Opini memainkan peran krusial dalam kehidupan sehari-hari, memengaruhi perilaku dan keputusan manusia secara signifikan. Terutama dalam konteks bisnis dan organisasi, pendapat konsumen tentang produk dan layanan sangatlah berharga. Penelitian ini berfokus pada analisis sentimen ulasan aplikasi OYO di Google Playstore, dengan tujuan mengklasifikasikan ulasan menjadi positif atau negatif. OYO Hotels & Homes, sebuah perusahaan startup di sektor akomodasi yang berasal dari India, telah mencapai kesuksesan luar biasa dengan pendapatan mencapai US$951 juta pada tahun fiskal 2019. Metode klasifikasi utama yang digunakan adalah Multinomial Naïve Bayes, yang merupakan pendekatan dalam pembelajaran terawasi dan seleksi fitur Chi-Square untuk mengeksplorasi korelasi antara faktor-faktor yang memengaruhi kepuasan pengguna. Proses penelitian meliputi pengumpulan data ulasan, preprocessing, labeling, dan pembagian data. Selajutnya dilakukan pembobotan TF-IDF dan seleksi fitur Chi-Square. Hasil analisis sentimen memperlihatkan dominasi ulasan positif, menunjukkan kepuasan pengguna terhadap layanan OYO. Proses klasifikasi menggunakan algoritma Multinomial Naïve Bayes, dengan hasil akurasi model tanpa seleksi fitur sebesar 85.5%, meningkat menjadi 87.00% dengan seleksi fitur Chi-Square. Hasil ini menunjukkan efektivitas algoritma Multinomial Naïve Bayes dan pentingnya seleksi fitur dalam analisis sentimen. Melalui pemahaman yang lebih dalam terhadap sentimen pengguna, perusahaan dapat meningkatkan kualitas layanan dan merespons umpan balik dengan lebih baik, memastikan kepuasan pelanggan yang optimal. Penelitian ini memiliki implikasi luas dalam analisis sentimen dan penggunaan metode statistik untuk mengatasi masalah kompleks dalam industri teknologi.
Feature Importance-Guided Ensemble Classification for Predicting Recurrence in Differentiated Thyroid Cancer Muhammad Ghinan Navsih; Wahyu Putra Pratama; Hikmata Tartila; Dwi Arman Prasetya; Tresna Maulana Fahrudin
Jurnal Aplikasi Sains Data Vol. 1 No. 2 (2025): Journal of Data Science Applications.
Publisher : Program Studi Sains Data UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jasid.v1i2.22

Abstract

Accurate prediction of cancer recurrence is critical for improving patient monitoring and personalized treatment planning. In this study, we propose a machine learning framework to predict recurrence in patients with differentiated thyroid cancer using statistically selected clinical features. Feature relevance was assessed using ANOVA for ordinal/numerical variables and the Chi-square test for one-hot encoded categorical variables, allowing us to identify the most informative predictors. We then trained three distinct classifiers—Random Forest, Logistic Regression, and XGBoost—and combined them using a hard voting ensemble strategy. The proposed ensemble achieved an accuracy of 98.7% on the test set, with particularly strong precision and recall scores for the recurrent class, indicating its potential clinical utility. Interestingly, all three base classifiers produced identical predictions on the test data, suggesting the dataset’s strong internal structure and the effectiveness of our feature selection process. This work highlights the value of integrating statistical feature selection with ensemble modeling for robust and interpretable prediction in clinical oncology applications.
Co-Authors Andrew Arjunanda Yasin Arinil Ula Fil 'Izza Aziz, Rizky Cahyani Marisa Wulandari Cheevin Yoviananda Devita Inka Amalia Dwi Arman Prasetya Erlyana Desy Rahmawati Fajar Zakly Herwanto Hikmata Tartila Intan Septia Eka Fortuna Lutvi Niswa Octaviana Muhammad David Ade Pangestu Muhammad Ghinan Navsih Natalia Damastuti Nensi Agustina Nilna Zahrul Aini Pratama Ryan Harnanda Rika Nur Azizah Wahyu Putra Pratama Wahyu Syaifullah Jauharis Saputra