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All Journal International Journal of Applied Sciences and Smart Technologies Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Malcom: Indonesian Journal of Machine Learning and Computer Science International Journal of Applied Information Systems and Informatics
Al-Husaini, Muhammad
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Computer Science & IT Education Energy Engineering Industrial & Manufacturing Engineering

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Rancang Bangun Sistem Informasi Deteksi Dini Stunting dengan Metode Artificial Neural Network Lukmana, Hen Hen; Al-Husaini, Muhammad; Puspareni, Luh Desi; Hoeronis, Irani
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 3 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i3.80119

Abstract

Stunting pada anak merupakan masalah kesehatan malnutrisi kronis yang menjadi perhatian serius di Indonesia.  Stunting dapat terjadi pada anak yang mengalami kekurangan gizi kronis, terutama pada usia 0-23 bulan. Faktor-faktor yang menyebabkan stunting pada anak sangat kompleks dan melibatkan berbagai faktor seperti gizi, kesehatan, sosial ekonomi, lingkungan, genetik dan peilaku. Penelitian ini bertujuan untuk merancang dan mengembangkan sistem informasi deteksi dini stunting menggunakan teknologi artificial neural network yang dilengkapi dengan stacking classifiers dengan dikombinasikan ensemble machine learning gradient boosting, random forest dan output estimator regresi logistik, selain itu pengembangan sistem ini dilakukan dengan menggunakan metode pengembangan waterfall. Sistem ini diharapkan dapat memprediksi risiko stunting secara akurat berdasarkan data pertumbuhan anak, serta memberikan rekomendasi intervensi yang tepat. Penggunaan neural network memungkinkan analisis data yang kompleks dan pembaruan model secara berkala dengan hasil rataan akurasi prediksi kombinasi beberapa algoritma menggunakan model stacking classifiers dan cross validation tersebut menghasilkan akurasi yang stabil di 86,22% berdasarkan dataset 10 ribu label target prediksi. Hasil dari penelitian berdasarkan model pengembangan dan pelatihan model ini mencakup analisis kebutuhan sistem, perancangan dan desain sistem dengan UML, implementasi sistem dengan fitur pengecekan stunting, artikel edukasi, konsultasi, login dan registrasi, dan hasil pengujian dengan System Usability Scale (SUS) dengan nilai rata-rata 81 yang termasuk pada grade A dan blackbox testing dengan hasil sesuai harapan.
Perancangan Sistem Monitoring Cerdas Berbasis Internet of Things (IoT) dengan Algoritma Random Forest Regression untuk Deteksi Ketinggian pada Tanaman Tomat Cherry: Design of an Intelligent Monitoring System Based on Internet of Things (IoT) with Random Forest Regression Algorithm for Height Detection in Cherry Tomato Plants Putra, Vito Hafizh Cahaya; Al-Husaini, Muhammad; Wahyu, Ari Purno; Raharja, Agung Rachmat
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 1 (2025): MALCOM January 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i1.1612

Abstract

Tomat cherry merupakan komoditas bernilai di Indonesia dengan permintaan yang meningkat setiap tahunnya. Penelitian ini mengembangkan sistem pemantauan cerdas berbasis Internet of Things (IoT) untuk tanaman tomat cherry menggunakan algoritma Random Forest Regression (RFR). Sistem ini memanfaatkan mikrokontroler ESP32 dan lima sensor untuk memantau parameter lingkungan, serta aktuator untuk pengaturan kondisi optimal. Data sensor diproses dan disimpan di platform Thingspeak dan diintegrasikan dengan Google Colab untuk prediksi ketinggian tanaman. Hasil prediksi ditampilkan di layar LCD dan dikirimkan sebagai notifikasi melalui aplikasi Telegram. Penelitian ini mengisi kesenjangan dari studi sebelumnya dengan mengintegrasikan berbagai sensor, aktuator, dan platform cloud dalam satu sistem yang komprehensif. Evaluasi sistem menunjukkan nilai Mean Squared Error (MSE) sebesar 0.8294 dan R^2 Score sebesar 0.8939, serta hasil pengujian Black Box Testing memastikan fungsionalitas optimal dalam berbagai skenario. Hasil penelitian ini dapat memberikan manfaat dalam penerapan teknologi IoT dan machine learning untuk monitoring dan pengelolaan tanaman tomat cherry, harapannya meningkatkan efisiensi dan produktivitas pertanian.   
Comparison of Support Vector Machine, Random Forest and XGBoost for Sentiment Analysis on Indodax Naufalino, Moch. Alfarros Difa; Al-husaini, Muhammad; Rianto, Rianto
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5894

Abstract

The rapid growth of digital assets like Bitcoin and cryptocurrencies has increased the need for secure trading platforms such as Indodax. With the growing number of users, reviews on platforms like Google Play Store provide valuable insights into user experience and satisfaction. This research applies Machine Learning methods to classify user review sentiments by comparing three main algorithms Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). One of the main challenge in sentiment analysis is the presence of irrelevant or redundant features, which can reduce model accuracy and increase computational costs. The Feature Selection Chi-Square technique is used to filter the most influential features, enhancing model efficiency without losing critical information. Experimental results show that SVM delivers the best performance compared to Random Forest and XGBoost. Before applying Chi-Square, SVM achieved 91% accuracy, which increased to 94% after applying the feature selection technique. The number of features used was reduced from 52,312 to 2,000 without significant information loss. This combination of SVM and Feature Selection Chi-Square proves to be an efficient and accurate solution for analyzing user sentiment on crypto trading platforms like Indodax. This method is expected to improve the responsiveness of trading applications to user needs and serve as a foundation for further research in Machine Learning-based sentiment analysis.
ENSEMBLE MACHINE LEARNING WITH NEURAL NETWORK STUNTING PREDICTION AT PURBARATU TASIKMALAYA Al-Husaini, Muhammad; Lukmana, Hen Hen; Rizal, Randi; Puspareni, Luh Desi; Hoeronis, Irani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2421

Abstract

This research uses an ensemble model and neural network method that combines several machine learning algorithms used in the prediction of stunting and nutritional status children in Purbaratu Tasikmalaya. This ensemble method is complemented by a combination of the prediction results of several algorithms used to improve accuracy. The data used is anthropometry-based calculations of 195 toddlers with 39% of related stunting from 501 total data in Purbaratu Tasikmalaya City; high rates of stunting this research urgent to make a stable model for prediction. The results of this study are significant as they provide a more accurate and efficient method for predicting stunting and nutritional status in children, which can be crucial for early intervention and prevention strategies in public health and nutrition. The best accuracy value for some of these categories is 98, 21% for the Weight/Age category with the xGBoost algorithm, 97.7% of the best accuracy results with the Random Forest and Decision Tree algorithms for the Height/Age category, the Weight/Height category with the best accuracy of 97.4% for the Random Forest and xGBoost algorithms, and the use of neural network models resulted in an accuracy of 99.19% for Weight/Age and Height/Age while for Weight/Height resulted in an accuracy of 91.94%..
Implementation of Neural Collaborative Filtering for Social Aid Recipient Recommendation Febriyanto, Erick; Tarempa, Genta Nazwar; Dewi, Euis Nur Fitriani; Al-Husaini, Muhammad; Faishal, Rifda Tri
Journal of Applied Information System and Informatic (JAISI) Vol 3, No 2 (2025): November 2025
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v3i2.16944

Abstract

Social assistance needs system accurate recommendations for ensure distribution appropriate target. Research This aims to implement Neural Collaborative Filtering (NCF) to recommend recipient help social based on integration of dynamic parameters of poverty data. The NCF method was chosen Because his ability combines Generalized Matrix Factorization (GMF) and Multi-Layer Perceptron (MLP) to catch non-linear relationship between data. The dataset is taken from 845 recipients assistance in Cijulang Village, District Ciamis, with criteria covering employment, income, health, and family history assistance. The preprocessing stage includes data cleaning, label encoding, one-hot encoding, and data splitting (training-validation 80:20). The NCF architecture is built with embedding layer (dimension 32), hidden layer MLP (128-64-32 neurons), and output layer that combines GMF and MLP. Evaluation using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results show that the model achieves RMSE 0.63 and MAE 0.47 on the training data, but overfitting occurred with a validation RMSE of 1.40 and MAE of 1.24. Analysis indicates the need for hyperparameter optimization (e.g., regulation, dropout rate) for an increase in generalization. Findings This prove NCF potential in increase accuracy recommendation help social, at the same time highlight importance data handling no balance and sparsity in context poverty. Implications study covers improvement transparency distribution assistance and reduction jealousy social through recommendation data -based. This study gives contribution methodological in NCF adaptation for sector public.
Braille Pattern Detection Modeling Using Inception V3 Architecture Using Median Filter Implementation and Segmentation Latif, Abdul; Yuliyanti, Siti; Al-Husaini, Muhammad
International Journal of Applied Sciences and Smart Technologies Volume 07, Issue 2, December 2025
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v7i2.12781

Abstract

This study aims to detect Braille letter patterns using the InceptionV3 architecture combined with the application of median filter and image segmentation. The dataset consists of 4,160 Braille images, with an average of 160 images for each letter from A to Z. The data is divided into 3,900 images for training, which are then split into 3,120 images for training and 780 images for validation, and 260 images are used for testing. Each image is resized to 299x299 pixels before being fed into the model. This study uses 100 epochs and applies early stopping to avoid overfitting. Two learning rate values are tested, namely 0.001 and 0.0001. The results show that the application of a median filter and segmentation significantly improves model performance, producing better accuracy, precision, recall, and F1 values compared to models without these techniques. At a learning rate of 0.001, the model achieves 99.65% accuracy, 99.62% precision, and 99.61% recall. On the other hand, without a median filter and segmentation at a learning rate of 0.0001, although accuracy and precision decreased, the values still reached 99.65% and 99.62%.
Co-Authors Abdul Latif Agung Rachmat Raharja Ari Purno Wahyu Wibowo Dewi, Euis Nur Fitriani Erick Febriyanto Faishal, Rifda Tri Genta Nazwar Tarempa Hafizh Cahaya Putra, Vito Irani Hoeronis Lukmana, Hen Hen Naufalino, Moch. Alfarros Difa Puspareni, Luh Desi Rianto, Rianto Rizal, Randi Siti Yuliyanti, Siti