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All Journal Jurnal Transformatika EMITTER International Journal of Engineering Technology Jurnal Teknologi Informasi Cyberku
Dewi Nurdiyah
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Metode Otsu Untuk Segmentasi Citra Finir Menggunakan Komponen Hue Saturation Value Indra Abdam Muwakhid; Dewi Nurdiyah
Jurnal Transformatika Vol 15, No 2 (2018): January 2018
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v15i2.761

Abstract

Tahapan dalam pengolahan citra meliputi segmentasi citra. Dalam permasalahan menemukan sebuah citra objek dengan baground menjadi kendala dalam segmentasi. Penelitian ini menggunakan dataset citra finir berjumlahkan 10 citra sampel, agar segmentasi bisa maksimal dilakukan tahap preprocessing dengan merubah citra yang berdimensi besar menjadi kecil, agar proses segmentasi nantinya tidak terlalu lama. Selanjutnya citra hasil preprocessing di konversi warna red, green dan blue kedalam ruang warna hue saturation dan value, dari komponen saturation dan value dapat mempermudah dalam mensegmentasi citra finir. Sehingga dalam ruang warna tersebut diubah menjadi citra biner untuk dilakukan segmentasi menggunakan Otsu, yang mana menggunakan nilai Threshold (T) dengan nilai setengah dari perhitungan global threshold, dikarenakan dengan menggunakan nilai global threshold saja masih belum bisa mensegmentasi obek finir. Ujicoba sistem dilakukan dengan metode Jaccard Similarity dari 10 data citra finir menggunakan metode segmentasi Otsu dan data citra Ground Truth, sehingga sistem dapat mensegmentasi citra finir dengan prosentase keberhasilan dalam menentukan persamaan citra sebesar 97.05%.
KLASIFIKASI CITRA TELUR FERTIL DAN INFERTIL DENGAN ANALISIS TEKSTUR GRAY LEVEL CO-OCCURRENCE MATRIX DAN SUPPORT VECTOR MACHINE Dewi Nurdiyah; Stefanus Santosa; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 2 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (278.132 KB)

Abstract

Fertility eggs test are steps that must be performed in an attempt to hatch eggs. Fertility testusually use egg candling. The purpose of observation is to choose eggs fertile (eggs containedembryos) and infertile eggs (eggs that are no embryos). And then fertilized egg will be entered intothe incubator for hatching eggs and infertile can be egg consumption. However, there are obstaclesin the process of sorting the eggs are less time efficient and inaccuracies of human vision todistinguish between fertile and infertile eggs. To overcome this problem, it can be used ComputerVision technology is having such a principle of human vision. It used to identify an object basedon certain characteristics, so that the object can be classified. The aim of this study to classifyimage fertile and infertile eggs with SVM (Support Vector Machine) algorithm based on inputfrom bloodspot texture analysis and blood vessels with GLCM (Gray Level Co-occurrenceMatrix). Eggs image studied are 6 day old eggs. It is expected that the proposed method is anappropriate method for classification image fertile and infertile eggs.
IRAWNET: A Method for Transcribing Indonesian Classical Music Notes Directly from Multichannel Raw Audio Dewi Nurdiyah; Eko Mulyanto Yuniarno; Yoyon Kusnendar Suprapto; Mauridhi Hery Purnomo
EMITTER International Journal of Engineering Technology Vol 11 No 2 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i2.827

Abstract

A challenging task when developing real-time Automatic Music Transcription (AMT) methods is directly leveraging inputs from multichannel raw audio without any handcrafted signal transformation and feature extraction steps. The crucial problems are that raw audio only contains an amplitude in each timestamp, and the signals of the left and right channels have different amplitude intensities and onset times. Thus, this study addressed these issues by proposing the IRawNet method with fused feature layers to merge different amplitude from multichannel raw audio. IRawNet aims to transcribe Indonesian classical music notes. It was validated with the Gamelan music dataset. The Synthetic Minority Oversampling Technique (SMOTE) overcame the class imbalance of the Gamelan music dataset. Under various experimental scenarios, the performance effects of oversampled data, hyperparameters tuning, and fused feature layers are analyzed. Furthermore, the performance of the proposed method was compared with Temporal Convolutional Network (TCN), Deep WaveNet, and the monochannel IRawNet. The results proved that proposed method almost achieves superior results in entire metric performances with 0.871 of accuracy, 0.988 of AUC, 0.927 of precision, 0.896 of recall, and 0.896 of F1 score.
KLASIFIKASI SAMPAH ORGANIK  DAN NON ORGANIK MENGGUNAKAN TRANSFER LEARNING Huta Julu, Doly Ilham Saputra; Doly Ilham Saputra Huta Julu; Dewi Nurdiyah
Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v23i1.12201

Abstract

Pengelolaan sampah di Indonesia menghadapi tantangan serius dengan 7,2 juta ton sampah belum terkelola dengan baik dari 202 kabupaten/kota, mencemari lingkungan dan menghambat daur ulang berkelanjutan. Pemilahan sampah organik dan anorganik yang masih dilakukan secara manual rentan terhadap kesalahan manusia dan tidak efisien. Penelitian ini mengembangkan model klasifikasi sampah organik dan anorganik menggunakan metode transfer learning dengan tiga arsitektur CNN: VGG16, MobileNetV2, dan ResNet50V2. Dataset diambil dari kaggle Waste Classification Data yang telah melalui proses preprocessing. Hasil eksperimen menunjukkan bahwa MobileNetV2 unggul dengan akurasi 90,13%, presisi 96,25%, dan F1-Score 87,88%, waktu inferensi 127,76 ms. Arsitektur ini memberikan keseimbangan optimal antara performa tinggi dan efisiensi komputasi, sehingga ideal diterapkan pada perangkat pintar seperti ponsel dan sistem IoT dalam konteks manajemen sampah perkotaan. Penelitian ini menegaskan efektivitas transfer learning dalam membangun sistem klasifikasi sampah yang cerdas dan efisien untuk mendukung program pemilahan sampah di tingkat rumah tangga dan institusi.  
Co-Authors Doly Ilham Saputra Huta Julu Eko Mulyanto Yuniarno Huta Julu, Doly Ilham Saputra Indra Abdam Muwakhid Mauridhi Hery Purnomo Ricardus Anggi Pramunendar Stefanus Santosa Yoyon Kusnendar Suprapto