Penerapan Learning Vector Quantization (LVQ) untuk Klasifikasi Status Gizi Anak

AbstrakPenentuan klasifikasi status gizi anak yang sering dilakukan adalah berdasarkan indeks berat badan menurut tinggi badan (BB/TB). Pada Puskesmas Batupanjang, indeks antropometri tersebut dihitung secara manual untuk menilai status gizi anak sekolah dasar dengan menggunakan daftar tabel z-skor atau simpangan baku / standar deviasi (SD) WHO NCHS (National Centre for Health Statistic). Metode Learning Vektor Quantization (LVQ) dan salah satu algoritma pengembangannya yaitu LVQ3 digunakan dalam penelitian ini untuk menangani penilaian status gizi anak berdasarkan simpangan baku rujukan terhadap indeks berat badan dan tinggi badan tersebut. Variabel yang digunakan dalam penilaian status gizi anak adalah jenis kelamin, berat badan, tinggi badan, penyakit infeksi, nafsu makan, dan pekerjaan kepala keluarga (KK). Berdasarkan dari hasil penelitian dan pembahasan yang dilakukan, algoritma LVQ3 lebih baik diterapkan untuk klasifikasi status gizi anak dibandingkan dengan algoritma LVQ1. Penggunaan parameter window (ε) pada jaringan syaraf tiruan LVQ3 memberikan pengaruh positif yakni dapat meningkatkan performa dalam klasifikasi jika dibandingkan tanpa menggunakan window (LVQ1). Kata kunci— Antropometri,  Learning Vektor Quantization,  Z-skor.  AbstractThe shortest path determination of child nutrient that common uses is based on body weight index by body high level (BB/BT). In Batupanjang Puskesmas, that anthropometry index is calculated manually for assessing  the nutrition of children in elementary school by used z-score table list or deviation standard  (SD) WHO NCHS (National Centre for Health Statistic).Learning Vektor Quantization (LVQ) Method and one of its algorithm, LVQ3 is used for this research to handle appraisal of children nutrition status based on deviation standard reference for that weight and high index. The variable that used in this appraisal are genre, body weight, body high, infection disease, appetite, and father work.Based on result of this research and discuss that has been done, LVQ3 algorithm is better applied for children nutrient status classification than LVQ1 algorithm. Using of window parameter (ε) in neural network LVQ3 effect positive impact, that is can increase perform in classification than without used window (LVQ1). Keywords—Anthropometry,  Learning Vektor Quantization,  Z-score

Analisis sentimen larangan penggunaan obat sirup menggunakan algoritma naive bayes classifier

The Indonesian government made a policy to stop consuming syrup as a form of prevention against acute kidney failure, which affects many people in Indonesia. However, the policy has caused a lot of comments from the public. These public comments can be found on YouTube, because YouTube has a large data source opportunity to be used as a research material. These comments can be processed directly without using a machine, but it is less effective and efficient. Thus, the comments are processed using machine learning methods. Based on the earlier research, the naive bayes classifier algorithm tends to be simple and easy to use. In addition, this algorithm also has a high accuracy. The amount of data used in this study is 1000 YouTube comment data related to videos regarding the policy of prohibiting the use of syrup medicine, the comments are divided into 2 category, which are positive class and negative class. The results of labeling 1000 comments obtained 704 negative comments and 296 positive comments. Based on the experiments conducted using python programming language, the highest accuracy was obtained at 74% in 70:30 data split. Furthermore, in the balanced dataset (296 positive and 296 negative comments), the highest accuracy was obtained at 64.70% with in 80:20 data split.  These results represent that the naive bayes classifier algorithm is good enough at sentiment analysis about the policy of prohibiting the use of syrup drugs.Pemerintah Indonesia membuat kebijakan larangan penggunaan obat sirup sebagai bentuk pencegahan terhadap penyakit gagal ginjal akut yang banyak menyerang masyarakat di Indonesia. Namun kebijakan tersebut banyak menimbulkan komentar dari masyarakat. Komentar masyarakat tersebut dapat ditemukan pada media sosial YouTube, karena YouTube mempunyai sumber informasi yang dapat digunakan sebagai bahan penelitian. Komentar-komentar tersebut dapat diolah secara langsung tanpa bantuan mesin, tetapi kurang efektif dan efisien. Oleh karena itu, komentar tersebut diolah menggunakan metode pembelajaran mesin. Berdasarkan penelitian terdahulu, algoritma naive bayes classifier cenderung sederhana dan mudah digunakan. Selain itu, algoritma ini juga mempunyai hasil akurasi yang tinggi. Jumlah data yang digunakan pada penelitian ini yaitu 1000 data komentar YouTube terkait video mengenai kebijakan larangan penggunaan obat sirup, komentar tersebut dibagi menjadi 2 kategori yaitu kelas positif dan kelas negatif. Hasil pelabelan dari 1000 komentar tersebut mendapatkan 704 komentar negatif dan 296 komentar positif. Berdasarkan percobaan yang dilakukan menggunakan bahasa pemrograman python, akurasi tertinggi didapatkan sebesar 74% dengan perbandingan data 70:30 pada dataset awal. Kemudian pada dataset seimbang (296 komentar positif dan 296 komentar negatif), akurasi tertinggi didapatkan sebesar 64,70% pada perbandingan data 80:20.  Hasil tersebut menunjukkan bahwa algoritma naive bayes classifier cukup bagus saat melakukan analisa sentimen mengenai kebijakan larangan penggunaan obat sirup

AUTOMATIC CHORUS DETECTION FOR INDONESIAN MUSIC USING REFRAIN DETECTING METHOD (REFRAID)

Music has become an important part of human life, chorus is part of the musical structure that makes some impression on music, people are generally very familiar with the chorus in music because the chorus is often repeated on music. Automatic chorus detection is a part of Music Information Retrieval which is considered important for building music analysis system with human-like patterns. Refrain Detecting Method (RefraiD) select the chorus by grouping various repeating parts of the music, evaluating the intensity level of the melody from each group, then selecting the group with the highest melodic intensity as the chorus. This paper intends to implement RefraiD in Indonesian pop and dangdut music by downloading 20 pop music videos and 20 dangdut music videos from YouTube then process it with Information retrieval using Python. The results of this paper indicate that the RefraiD method can be used to detect the chorus on Indonesian music with F measure of 91.8% for dangdut music and 91.5% for pop music

Analisis sentimen komentar youtube terhadap Anies Baswedan sebagai bakal calon presiden 2024 menggunakan metode naive bayes classifier

One of the figures as a presidential candidate is Anies Baswedan, the former governor of DKI Jakarta who received many awards and has an effective work program policy for problems in the DKI Jakarta area. Many comments about Anies Baswedan as a 2024 presidential candidate are found on YouTube social media. Youtube facilitates users to provide comments in response to videos which can be used as sentiment analysis information to find out positive comments and negative comments. The algorithm used in this research is the naïve bayes classifier. There are five main processes in this research, namely data collection, text preprocessing, word weighting (TF-IDF), classification (Naïve Bayes Classifier) and testing. From 1009 comment data on Indonesian-language youtube related to the Anies Baswedan video as a 2024 presidential candidate. Based on the analysis results, there are 610 positive comments and 399 negative comments. The accuracy result using the naïve bayes classifier algorithm is 78% which is obtained by using a comparison of 90% training data and 10% test data.Suatu tokoh sebagai bakal calon presiden adalah Anies Baswedan mantan gubernur DKI Jakarta yang menerima banyak penghargaan dan memiliki kebijakan program kerja yang efektif dalam permasalahan di wilayah DKI Jakarta. Komentar mengenai anies baswedan sebagai bakal calon presiden 2024 banyak dijumpai pada media sosial youtube. Youtube  menfasilitasi pengguna untuk memberikan komentar dalam menanggapi video yang dapat dijadikan sebuah informasi analisis sentimen untuk mengetahui komentar positif serta komentar negatif. Algorima yang dipakai pada penelitian ini ialah naïve bayes classifier. Terdapat lima proses utama pada penelitian ini, yaitu penghimpunan data, pembobotan kata (TF-IDF), text preprocessing, klasifikasi (naïve bayes classifier) dan pengujian. Dari 1009 data komentar di youtube berbahasa Indonsia terkait video Anies Baswedan sebagai bakal calon presiden 2024. Berdasarkan hasil analaisis, terdapat 610 komentar positif serta 399 negatif. Hasil akurasi menggunakan algoritma naïve bayes classifier sebesar 78% yang di dapat dengan menggunakan perbandingan 10% data uji serta 90% data latih

The Classification of Children Gadget Addiction: The Employment of Learning Vector Quantization 3

The addiction of children to gadgets has a massive influence on their social growth. Thus, it is essential to note earlier on the addiction of children to such technologies. This study employed the learning vector quantization series 3 to classify the severity of gadget addiction due to the nature of this algorithm as one of the supervised artificial neural network methods. By analyzing the literature and interviewing child psychologists, this study highlighted 34 signs of schizophrenia with 2 level classifications. In order to obtain a sample of training and test data, 135 questionnaires were administered to parents as the target respondents. The learning rate parameter (α) used for classification is 0.1, 0.2, 0.3 with window (Ɛ) is 0.2, 0.3, 0.4, and the epsilon values (m) are 0.1, 0.2, 0.3. The confusion matrix revealed that the highest performance of this classification was found in the value of 0.2 learning rate, 0.01 learning rate reduction, window 0.3, and 80:20 of ratio data simulation. This outcome demonstrated the beneficial consequences of Learning Vector Quantization (LVQ) series 3 in the detection of children's gadget addiction