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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informatika Polinema (JIP) Jurnal Penelitian Pendidikan Indonesia
Frangky Tupamahu
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Library & Information Science Other

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Analisis Koefisien Cepstral Emosi Berdasarkan Suara Ismail Mohidin; Frangky Tupamahu
Journal of Applied Informatics and Computing Vol 1 No 2 (2017): Desember 2017
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1024.129 KB) | DOI: 10.30871/jaic.v1i2.523

Abstract

Abstract - The speech signal carries some sort of information, which consists of the intent to be conveyed, who speaks the information, and the emotional information that shows the emotional state of the utterance. One of the characteristics of human voice is the fundamental frequency. In this study the selection of features and methods of classification and recognition is important to recognize the emotional level (anger, sadness, fear, pleasure and neutral) contained in the dataset, this research proposes design through two main processes of training and introduction recognition). Experiments conducted using the Indonesian emotion voice dataset and the Mel-Frequency Cepstrum Coefficients (MFCC) algorithm were used to extract features from sound emotion. MFCC produces 13 cepstral coefficients of each of the sound emotion signals. This coefficient is used as an input of classification of emotional data from 250 data sampling.
SEGMENTASI BERBASIS K-MEANS PADA DETEKSI CITRA PENYAKIT DAUN TANAMAN JAGUNG Ulla Delfana Rosiani; Cahya Rahmad; Marcelina Alifia Rahmawati; Frangky Tupamahu
Jurnal Informatika Polinema Vol. 6 No. 3 (2020): Vol 6 No 3 (2020)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v6i3.331

Abstract

Penyakit tanaman adalah kondisi dimana sel dan jaringan tanaman tidak berfungsi secara normal yang ditimbulkan karena gangguan secara terus menerus oleh agen patogen atau faktor lingkungan yang menghasilkanperkembangan gejala penyakit. Perkembangan gejala penyakit tersebut menyebabkan rendahnya produktifitas dan gagal panen bagi petani jagung. Maka dalam pengolahan citra digital dapat digunakan untuk membantumengidentifikasi penyakit daun tanaman jagung. Pada penelitian ini, fitur yang digunakan adalah warna pada proses segmentasi dan pendekatan warna. Fitur tersebut didapatkan dari penelitian transformasi warna RGB keCIE L*a*b*, histogram a* digunakan untuk proses segmentasi berbasis K-Means sebagai input dengan menentukan jumlah cluster awal adalah k=3, merandom centroid, menghitung jarak nilai pixel ke centroid,mengelompokkan nilai pixel berdasarkan jarak minimum, menghitung rata-rata cluster untuk centroid baru dan jika masih terdapat nilai pixel yang berpindah maka proses random centroid masih dilakukan hingga tidakadanya nilai pixel yang berpindah. Hasil dari citra yang telah tersegmentasi dan menggunakan pendekatan warna Euclidean Distance dapat mengidentifikasi antara penyakit hawar daun dan bercak daun. Penelitian tersebutmenggunakan dataset sejumlah 30 jenis A dan B untuk training dan 10 data untuk testing telah memperoleh akurasi sebesar 90%.
Deep learning and digital image transformation for identifying early childhood creativity Indarwati, Anik; Frangky Tupamahu; Firsta Hanni Enggaring Galih
JPPI (Jurnal Penelitian Pendidikan Indonesia) Vol. 11 No. 4 (2025): JPPI (Jurnal Penelitian Pendidikan Indonesia)
Publisher : Indonesian Institute for Counseling, Education and Theraphy (IICET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29210/020256600

Abstract

The assessment of creativity in early childhood is important for identifying developmental potential, yet existing methods such as manual scoring of the Test for Creative Thinking–Drawing Production (TCT-DP) are time-consuming and susceptible to subjectivity. This study proposes an automated framework combining Discrete Wavelet Transform (DWT) and Convolutional Neural Networks (CNNs) to support creativity assessment in children aged 5–8 years based on TCT-DP drawings. A dataset of 100 drawings, scored by expert raters, was preprocessed and decomposed using a two-level Daubechies db4 wavelet to extract spatial-frequency features. These features were used as inputs to a CNN model trained to classify creativity levels. Model performance was evaluated using accuracy, F1-score, ROC-AUC, and Pearson’s correlation with expert scores. The proposed model achieved 87% accuracy and a correlation of r = 0.74, indicating moderate agreement with expert ratings. While results suggest potential for improving efficiency and consistency, findings remain exploratory due to limited sample size.
Co-Authors Firsta Hanni Enggaring Galih Indarwati, Anik Ismail Mohidin Marcelina Alifia Rahmawati Rahmad, Cahya Ulla Delfana Rosiani