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All Journal STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Journal of Applied Data Sciences Prosiding University Research Colloquium Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Latief, Muhammad Abdul
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology

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Klasifikasi Bahan Biodegradable dan Non-Biodegradable Menggunakan Convolutional Neural Network (CNN) Latief, Muhammad Abdul; Azfa Riyyasy, Muhammad Rasikh; Ulya, Fadilla Zundina; Puspita, Popy Laras; Claudia, Gavrilla; Nabila, Luthfi Rakan
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 8, No 3 (2024)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/string.v8i3.19314

Abstract

Deep Learning is a new scientific field in the field of Machine Learning which has recently developed. Deep Learning has excellent capabilities in computer vision. One of its uses is in the case of classifying objects into biodegradable and non-biodegradable materials. By implementing the CNN method in this case, it is possible to classify biodegradable and non-biodegradable waste appropriately and efficiently. This study uses image data of biodegradable and non-biodegradable materials sourced from Kaggle. The stages in this study consist of six stages. The first stage is to retrieve the dataset. The second stage is the preprocessing stage by rescaling the image. The third stage is to create a CNN model. The fourth stage is model training to get higher accuracy. The fifth stage is model evaluation and the last is testing the model. From the classification test using the CNN method, an accuracy of 93% is obtained. So it can be concluded that the CNN method used in this paper is capable of performing a good classification.
Dimension-Expanding MLP in Transformer: Inappropriate Sentences and Paragraph Digital Content Filtering Wardhana, Ariq Cahya; Yunus, Andi Prademon; Adhitama, Rifki; Latief, Muhammad Abdul; Sofia, Martryatus
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.627

Abstract

The creation of digital content is now a pivotal element of today’s digital environment, driven by the need for both individuals and organizations to engage audiences effectively. As digital platforms grow in scope and impact, ensuring the security, professionalism, and appropriateness of user-generated content has become crucial. This study introduces a new approach for filtering inappropriate digital content by integrating dimension-expanding multi-layer perceptions (MLPs) into transformer architectures. The dimension-expanding MLP processed more high-dimensional features in the Transformers network, giving the ability to understand more specific contexts. Experimental findings reveal that the proposed model outperforms Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), Transformer (Baseline) in accuracy, computational efficiency, and scalability. The research highlights the model’s practical applications in areas like social media content moderation, legal document compliance monitoring, and filtering harmful content in e-learning and gaming platforms with 0.744 accuracy.
Serapan Karbon Ekosistem Pada Wilayah Perkotaan Surakarta, Jawa Tengah, Indonesia Cholil, Munawar; Danardono, D; Sunariya, M. Iqbal Taufiqurrahman; Fikriyah, Vidya Nahdiyatul; Latief, Muhammad Abdul; Wulandari, Kartika Cindi
Prosiding University Research Colloquium Proceeding of The 13th University Research Colloquium 2021: Kesehatan dan MIPA
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

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Abstract

Nilai serapan karbon ekosistem di kawasan perkotaan jarang diteliti karena kurangnya vegetasi sebagai media penjerap karbon. Padahal kawasan perkotaan memiliki tingkat emisi karbon tinggi yang harus diminimalkan jumlahnya. Di sisi lain, nilai serapan karbon di kawasan perkotaan sangatlah dinamis akibat adanya faktor alami dari lingkungan dan faktor non-alami akibat aktivitas antropogenik. Tujuan dari penelitian ini yaitu untuk mengidentifikasi nilai serapan karbondioksida di kawasan perkotaan dan untuk mengetahui variasi spasial nilai serapan karbon di kawasan perkotaan selama setahun. Hasil menunjukkan bahwa nilai serapan karbon di kawasan perkotaan tropis memiliki nilai yang cukup besar dibandingkan kawasan perkotaan di iklim sedang. Hal ini terjadi karena masih adanya ruang terbuka hijau berupa kebun di lahan pekarangan dan lahan pertanian.
Handling Imbalance Data using Hybrid Sampling SMOTE-ENN in Lung Cancer Classification Latief, Muhammad Abdul; Nabila, Luthfi Rakan; Miftakhurrahman, Wildan; Ma'rufatullah, Saihun; Tantyoko, Henri
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 1 (2024): March 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i1.3758

Abstract

The classification problem is one instance of a problem that is typically handled or resolved using machine learning. When there is an imbalance in the classes within the data, machine learning models have a tendency to overclassify a greater number of classes. The model will have low accuracy in a few classes and high accuracy in many classes as a result of the issue. The majority of the data has the same number of classes, but if the difference is too great, it will differ. The issue of data imbalance is also evident in the data on lung cancer, where there are 283 positive classes and negative classes 38. Therefore, this research aims to use a hybrid sampling technique, combining Synthetic Minority Over-sampling Technique (SMOTE) with Edited Nearest Neighbors (ENN) and Random Forest, to balance the data of lung cancer patients who experience class imbalance. This research method involves the SMOTE-ENN preprocessing method to balance the data and the Random Forest method is used as a classification method to predict lung cancer by dividing training data and testing 10-fold cross validation. The results of this study show that using SMOTE-ENN with Random Forest has the best performance compared to SMOTE and without oversampling on all metrics used. The conclusion is using the SMOTE-ENN hybrid sampling technique with the Random Forest model significantly improves the model's ability to identify and classify data.
Co-Authors Andi Prademon Yunus Ariq Cahya Wardhana Azfa Riyyasy, Muhammad Rasikh Claudia, Gavrilla Danardono, D Fikriyah, Vidya Nahdiyatul Henri Tantyoko Ma'rufatullah, Saihun Miftakhurrahman, Wildan Munawar Cholil Nabila, Luthfi Rakan Nabila, Luthfi Rakan Puspita, Popy Laras Rifki Adhitama, Rifki Sofia, Martryatus Sunariya, M. Iqbal Taufiqurrahman Ulya, Fadilla Zundina Wulandari, Kartika Cindi