Abstract:A fuzzy logic-based control system in household split-type air conditioners (AC) offers an alternative approach to reducing excess energy consumption without compromising thermal comfort. This study aims to test the effectiveness…
ctiveness of three types of membership functions (MF), namely triangular, trapezoidal, and Gaussian, in improving energy efficiency and the stability of room temperature and humidity control. Simulations were performed using MATLAB software with the Mamdani fuzzy inference method and centroid defuzzification technique. The three MF were tested using 30 sets of temperature and humidity data to analyze their effect on fan speed and power consumption. The simulation results show that the trapezoidal MF provides the highest energy efficiency of 57.24%, followed by the Gaussian MF at 56.80% and the triangular MF at 53.71%. These findings indicate that fuzzy systems can significantly reduce energy consumption compared to conventional air conditioner controllers. This research is expected to serve as a reference in the development of more energy-efficient intelligent control systems.
Abstract:Abstract: Obesity is an escalating global health concern, with unhealthy lifestyle patterns contributing significantly to its development. This study aims to evaluate and compare three clustering techniques for categorizing…
ing lifestyle patterns and obesity-related factors: K-Means, Agglomerative Clustering, and Gaussian Mixture Model (GMM). The data used in this study is sourced from the Food Nutrition dataset, which includes variables such as dietary habits, physical activity, and socio-economic status. The three clustering methods were assessed using evaluation metrics such as Silhouette Score, Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The findings revealed that K-Means exhibited the best performance in terms of cluster separation with a Silhouette Score of 0.5559, while GMM showed better flexibility in handling more complex data. Although Agglomerative Clustering produced acceptable results, it had a higher overlap between clusters compared to the other methods. This study offers valuable insights into selecting the most appropriate clustering technique based on the data characteristics.
Keywords: agglomerative; clustering; GMM; k-means; lifestyle patterns; obesity
Abstrak: Obesitas menjadi masalah kesehatan yang semakin meningkat di seluruh dunia, dengan pola hidup yang tidak sehat berperan besar dalam perkembangannya. Penelitian ini bertujuan untuk membandingkan tiga metode clustering dalam mengelompokkan pola gaya hidup dan faktor yang memengaruhi obesitas, yaitu K-Means, Agglomerative Clustering, dan Gaussian Mixture Model (GMM). Data yang digunakan diperoleh dari dataset Food Nutrition yang mencakup informasi terkait pola makan, aktivitas fisik, serta faktor sosial-ekonomi. Ketiga metode tersebut diuji dengan menggunakan beberapa metrik evaluasi, seperti Silhouette Score, Davies-Bouldin Index (DBI), dan Calinski-Harabasz Index (CHI). Hasil penelitian menunjukkan bahwa K-Means memiliki kinerja terbaik dalam hal pemisahan klaster, dengan nilai Silhouette Score sebesar 0.5559, sementara GMM lebih fleksibel dalam menangani data yang lebih kompleks. Meskipun Agglomerative Clustering memberikan hasil yang dapat diterima, tumpang tindih antar klaster lebih besar dibandingkan dengan kedua metode lainnya. Penelitian ini memberikan pemahaman yang lebih baik mengenai pemilihan metode clustering yang tepat berdasarkan karakteristik data yang digunakan.
Kata kunci: agglomerative; clustering; GMM; k-means; obesitas; pola gaya hidup
Abstract:Abstract: SMP Muhammadiyah 5 Samarinda still relies on manual evaluation with limited data analysis tools in predicting student academic achievement. This study aims develop a system for predicting the learning achievement…
nt of students at SMP Muhammadiyah 5 Samarinda using the Naive Bayes classification method. The dataset used consists of 192 student exam scores covering academic scores, attendance, parents’ education and income, and living conditions as independent variables, while the dependent variable is the achievement label (achieved or not achieved). The preprocessing stage includes label normalization, feature selection, and median imputation to handle missing data. The dataset was divided into 75% training data and 25%. The model was implemented as a pipeline consisting of a median imputer and a Gaussian Naive Bayes classifier. The evaluation results showed that the model achieved an accuracy of 79.2%, with a perfect recall value (1.00) in the high-achieving class and (0.64) in the low-achieving class. This shows that the model is quite effective in identifying high-achieving students. The trained model was then integrated into a Flask-based web application, which enables online predictions through a simple form interface, facilitating contextual interpretation. This system is expected to assist in educational decision-making by helping teachers identify students’ achievement levels early on and design more targeted learning interventions.
Keywords: academic performance; educational data mining; naive bayes; prediction system; student achievement
Abstrak: SMP Muhammadiyah 5 Samarinda masih bergantung pada evaluasi manual dengan alat analisis data terbatas dalam melakukan prediksi prestasi akademik siswa. Penelitian ini bertujuan mengembangkan sistem prediksi prestasi belajar siswa SMP Muhammadiyah 5 Samarinda menggunakan metode klasifikasi Naive Bayes. Dataset yang digunakan terdiri atas 192 data nilai ujian siswa yang mencakup skor akademik, kehadiran, pendidikan dan pendapatan orang tua, serta kondisi tempat tinggal sebagai variabel independen, sedangkan variabel dependen berupa label prestasi (berprestasi atau tidak berprestasi). Tahap preprocessing meliputi normalisasi label, seleksi fitur, serta imputasi median untuk menangani data yang hilang. Dataset dibagi menjadi 75% data latih dan 25%. Model diimplementasikan dalam bentuk pipeline yang terdiri atas median imputer dan Gaussian Naive Bayes classifier. Hasil evaluasi menunjukkan bahwa model mencapai akurasi sebesar 79,2%, dengan nilai recall sempurna (1,00) pada kelas berprestasi dan lebih rendah (0,64) pada kelas tidak berprestasi. Hal ini menunjukkan bahwa model cukup efektif dalam mengidentifikasi siswa berprestasi. Model yang telah dilatih kemudian diintegrasikan ke dalam aplikasi web berbasis Flask, yang memungkinkan prediksi secara daring melalui antarmuka formulir sederhana untuk mendukung interpretasi kontekstual. Sistem ini diharapkan dapat membantu untuk pengambilan keputusan dalam pendidikan dengan membantu guru mengidentifikasi tingkat prestasi siswa sejak dini dan merancang intervensi pembelajaran yang lebih terarah.
Kata kunci: prestasi akademik; penambangan data Pendidikan; naive bayes; sistem prediksi; prestasi siswa
Abstract:Abstract: In the human body there are parts that act as tools for tearing and tearing food which are commonly called teeth, teeth are sharp and hard parts of the body. Dental caries is the medical term for cavities, dental…
al caries is an infectious disease that damages the hard lining of the teeth. The presence of bacteria or germs found in the oral cavity causes cavities in the teeth. Medical images are useful for seeing the extent of the damage to cavities. Not all images have good quality detection results or have noise. Therefore, Image Processing is used by applying the Canny and Sobel method of edge detection to an image in order to process the results of segmentation and object identification in dental caries images. There are many operators in edge detection, including the Roberts Operator, Prewitt Operator, Laplacian Operator and Laplacian of Gaussian (LOG). The purpose and benefits of establishing an image processing application using Matlab software with the Canny and Sobel method to produce the appearance of an object's boundary line in an image. The results of this study can be concluded from the two operators regarding the advantages and disadvantages of the two operators.
Keywords: dental caries; edge detection; image; matlab; canny method; sobel method
Abstrak: Pada tubuh manusia ada bagian yang berperan sebagai alat untuk merobek dan mengoyak makanan yang biasa disebut dengan gigi, gigi merupakan bagian tubuh yang tajam dan keras. Karies gigi adalah istilah medis dari gigi berlubang, karies gigi merupakan penyakit infeksi yang merusak jaringan lapisan keras gigi. Adanya bakteri atau kuman yang terdapat pada rongga mulut menjadi penyebab gigi menjadi berlubang. Citra medis berguna untuk melihat sejauh apa kerusakan pada gigi yang berlubang. Tidak semua citra memiliki hasil pendeteksian yang bagus kualitasnya atau memiliki derau (noise). Oleh sebab itu digunakanlah Pengolahan Citra dengan menerapkan deteksi tepi metode Canny dan Sobel pada suatu citra guna untuk memproses hasil segmentasi dan identifikasi objek pada citra karies gigi. Ada banyak operator dalam deteki tepi, diantaranya yaitu, Operator Roberts, Operator Prewitt, Operator Laplacian dan Laplacian of Gaussian (LOG). Tujuan dan manfaat dari dibentuknya sebuah aplikasi pengolahan citra menggunakan software Matlab dengan metode Canny dan Sobel untuk menghasilkan penampakan garis batas suatu objek pada citra. Hasil dari penelitian ini dapat disimpulkan dari kedua operator mengenai kelebihan dan kekurangan dari kedua operator tersebut.
Kata kunci: citra; deteksi tepi; karies gigi, matlab, metode canny, metode sobel
Abstract:Automatic detection of hate speech and abusive language is crucial for combating online toxicity. This study explores Gaussian Naive Bayes for multi-label classification of hate speech on Indonesian Twitter, including target,…
rget, category, and level. We combined TF-IDF features with contextual BERT embeddings. The model achieved balanced performance for general hate speech and good non-abusive language detection. However, it exhibited limitations with imbalanced data and specific hate speech types. The classifier consistently favored the majority class (non-hateful/non-abusive) across labels, particularly struggling with HS_Gender, HS_Physical, etc. This suggests difficulty detecting less frequent but potentially severe hate speech, likely due to limited training data. Overall accuracy and F1-scores confirm that while Gaussian Naive Bayes is efficient, it lacks robustness for nuanced multi-label classification with imbalanced datasets. This necessitates exploring alternative approaches for effectively detecting specific and less frequent hate speech.