Classification of Alcohol Consumption among Secondary School Students

Palaniappan, Shamala; Hameed, Norhamreeza A; Samsudin, Noor Azah

doi:10.30630/joiv.1.4-2.64

Cited by 9 publications

(4 citation statements)

References 7 publications

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“…Data pre-processing is important since the dataset contains noisy, inconsistent, missing, and outdated values [46]. Data pre-processing is vital prior to dataset classification to improve data quality by identifying and removing noisy data.…”

Section: 2data Pre-processingmentioning

confidence: 99%

Multi-classifier models to improve the accuracy of fish landing application

2024

IJATEE

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Despite the numerous fish classification systems developed over the years, they often suffer from poor prediction accuracy, necessitating further improvement. This study addresses this issue by comparing the performance of different classifiers on fish landing datasets (2005-2019) obtained from the Department of Fisheries Malaysia (DOFM). The focus is on the East Coast of Peninsular Malaysia. The classifiers evaluated include Sequential minimal optimization (SMO), naïve Bayes (NB), multi-layer perception (MLP), instance-based for k-nearest neighbor (IBK), and random forest (RF). The performance of each classifier is assessed using classification accuracy and confusion matrix metrics, employing a 10-fold cross-validation method. Additionally, a multi-classification technique is applied to enhance the accuracy of individual classifiers and determine the most effective approach for generating an accurate dataset. The study reveals that the combinations RF+SMO+NB+MLP and SMO+RF+NB+MLP outperform single classifiers and other fusion methods, achieving the highest accuracy at 80.95%. This indicates that a multi-classifier approach can significantly enhance the performance of individual classifiers. The findings highlight the effectiveness of the multi-classifier approach in improving prediction accuracy for fish classification. The identified combinations, RF+SMO+NB+MLP and SMO+RF+NB+MLP, demonstrate superior performance and can serve as a robust methodology for fish landing classification in the context of the East Coast of Peninsular Malaysia. Further research and implementation of such multi-classifier approaches could contribute to more accurate and reliable fish classification systems.

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Section: 2data Pre-processingmentioning

confidence: 99%

Multi-classifier models to improve the accuracy of fish landing application

2024

IJATEE

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“…Penelitian tentang konsumsi alkohol pada siswa telah dilakukan dengan beberapa algoritma data mining di antaranya penelitian yang dilakukan oleh Palaniappan [6], model klasifikasi yang dikembangkan dengan menggunakan algoritma AutoMLP mencapai akurasi lebih tinggi yakni 64,54% jika dibandingkan dengan Artificial Neural Networks (ANN) hanya 61,78%. Fabio et al, menerapkan teknik klasifikasi dan clustering untuk prediksi konsumsi alkohol dengan membuat segmentasi data menggunakan K-Means dan teknik data mining seperti Decision Tree, SVM, Bayesian Network, dan KNN.…”

Section: Pendahuluanunclassified

“…Setelah tahapan data-cleaning selesai dan diasumsikan bahwa atribut sudah dapat diakses sebagai kolom, tranformasi data dilakukan, di antaranya atribut konsumsi alkohol di hari kerja (Dalc), konsumsi alkohol di akhir pekan (Walc), jumlah ketidakhadiran (Absence), dan nilai periode pertama sampai periode ketiga(G1-G3). Kemudian, target atribut (Alc) dirumuskan menggunakan persamaan (6) [6]. Selanjutnya, atribut "Absence" dimodifikasikan ke dalam nilai biner, di mana absence dirumuskan dengan persamaan (7) dan dengan mengasumsikan bahwa semakin tinggi jumlah ketidakhadiran maka semakin tinggi obsesi untuk meminum alkohol.…”

Section: Gambar 1 Metodologi Yang Diusulkanunclassified

Komparasi Kinerja Algoritma Data Mining pada Dataset Konsumsi Alkohol Siswa

Sagala

Tampubolon

2018

khif

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Data mining melakukan proses ekstraksi pengetahuan yang diperoleh dari sekumpulan data dalam jumlah besar. Penelitian ini bertujuan untuk menerapkan dan melakukan analisis kinerja algoritma data mining untuk memprediksi konsumsi alkohol dan menganalisis faktor-faktor yang terkait pada siswa tingkat menengah. Adapun tahapan yang dilakukan ialah pra-proses data, seleksi fitur, klasifikasi, dan evaluasi model. Pada tahap praproses, beberapa fitur diubah menjadi bentuk yang sesuai untuk memudahkan proses klasifikasi. Selanjutnya, algoritma Gain Ratio dan Feature Correlation-Based Filter (FCBF) digunakan untuk memilih fitur-fitur yang relevan dan penting untuk digunakan dalam tahapan klasifikasi. Decision Tree C5.0, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), dan Naive Bayes (NB) dieksekusi pada kelompok fitur yang terpilih. Akurasi model yang dibangun dievaluasi menggunakan 10-fold Cross-Validation (CV). Hasil penelitian menunjukkan bahwa model klasifikasi yang dibangun menggunakan Naïve Bayes memiliki nilai akurasi tertinggi dengan menggunakan 5 fitur terbaik dari Gain Ratio. Selain itu, penggunaan metode pemilihan fitur mampu meningkatkan performa dari seluruh klasifier secara umum. Pengujian lebih lanjut pada data yang sama maupun berbeda perlu dilakukan untuk mendapatkan gambaran lebih mendalam mengenai kinerja algoritma-algoritma yang digunakan.

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“…After processing the identification pattern of E-nose scent sensors using the backpropagation ANN method [9], [10], the types of coffee identified are Robusta and Arabica [11]. The backpropagation ANN method classifies the color levels of roasted coffee beans with an accuracy value of up to 97.5% [12].…”

Section: Introductionmentioning

confidence: 99%

Identification of Coffee Types Using an Electronic Nose with the Backpropagation Artificial Neural Network

Susanti,

Zaini,

Hidayat

et al. 2023

JOIV : Int. J. Inform. Visualization

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Coffee is one of the famous plants’ commodities in the world. There are some coffee powders such as Arabica dan Robusta. This study aimed to identify two various coffee powders, Arabica and Robusta based on the blended aroma profiles, employing the backpropagation Artificial Neural Network (ANN). Four taste sensors were employed, namely TGS 2602, 2610, 2611, and 2620, to capture the diverse coffee aroma. These detectors were combined with the aroma sensors having transducers integrated with signal amplifiers or processors, which featured a load of 10 KΩ resistance. Three aroma types were investigated, namely Arabica coffee, Robusta coffee, and without coffee beans. The neural network architecture consisted of four inputs from all sensors, with one hidden layer housing eight neurons. Two neuron outputs were employed for classification, with 70 samples used for training ANN for each type. During the training phase, the developed neural network showed an impressive accuracy rate of 91.90%. TGS 2602 and 2611 sensors showed the most significant differences among the three aroma types. When analyzing ground Robusta coffee, TGS 2602 and 2611 sensors recorded 2.967 volts and 1.263 volts, with a gas concentration of 17.92 ppm and 2441.8 ppm. Similarly, the sensors for ground Arabica coffee displayed 3.384 volts and 1.582 volts with a gas concentration of 20.445 ppm and 3058.5 ppm in both TGS 2602 and 2611, respectively. The implemented ANN with aroma sensor as input successfully identify the coffee powders.

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Classification of Alcohol Consumption among Secondary School Students

Cited by 9 publications

References 7 publications

Multi-classifier models to improve the accuracy of fish landing application

Multi-classifier models to improve the accuracy of fish landing application

Komparasi Kinerja Algoritma Data Mining pada Dataset Konsumsi Alkohol Siswa

Identification of Coffee Types Using an Electronic Nose with the Backpropagation Artificial Neural Network

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