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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Edukasi dan Penelitian Informatika (JEPIN) EMITTER International Journal of Engineering Technology Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) eProceedings of Engineering
Kurniawan Nur Ramadhani
Universitas Telkom
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Nuclei Detection and Classification System Based On Speeded Up Robust Feature (SURF) Amalina, Neneng Nur; Ramadhani, Kurniawan Nur; Sthevanie, Febryanti
EMITTER International Journal of Engineering Technology Vol 7, No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (842.261 KB) | DOI: 10.24003/emitter.v7i1.288

Abstract

Tumors contain a high degree of cellular heterogeneity. Various type of cells infiltrate the organs rapidly due to uncontrollable cell division and the evolution of those cells. The heterogeneous cell type and its quantity in infiltrated organs determine the level maglinancy of the tumor. Therefore, the analysis of those cells through their nuclei is needed for better understanding of tumor and also specify its proper treatment. In this paper, Speeded Up Robust Feature (SURF) is implemented to build a system that can detect the centroid position of nuclei on histopathology image of colon cancer. Feature extraction of each nuclei is also generated by system to classify the nuclei into two types, inflammatory nuclei and non-inflammatory nuclei. There are three classifiers that are used to classify the nuclei as performance comparison, those are k-Nearest Neighbor (k-NN), Random Forest (RF), and State Vector Machine (SVM). Based on the experimental result, the highest F1 score for nuclei detection is 0.722 with Determinant of Hessian (DoH) thresholding = 50 as parameter. For classification of nuclei, Random Forest classifier produces F1 score of 0.527, it is the highest score as compared to the other classifier.
Kajian Ilmiah dan Deteksi Adiksi Internet dan Media Sosial di Indonesia Menggunakan XGBoost Rismala, Rita; Novamizanti, Ledya; Ramadhani, Kurniawan Nur; Rohmah, Yuyun Siti; Parjuangan, Sabam; Mahayana, Dimitri
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 7, No 1 (2021): Volume 7 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v7i1.43606

Abstract

Internet dan media sosial telah menjadi kebutuhan pokok manusia untuk mengakses informasi, terutama di masa pandemi COVID-19 saat ini. Hal ini penting untuk dikaji karena berdampak pada perilaku dan kesehatan psikologi seseorang. Berdasarkan sudut pandang filsafat sains, adiksi internet dan media sosial di Indonesia merupakan kenyataan saintifik karena telah memenuhi kriteria falsifikasi dan bisa diuji (testable) secara empiris. Hasil survei terhadap 1980 responden, diperoleh 25,56% responden teradiksi internet dan 20,2% teradiksi media sosial. Penelitian ini juga berhasil membangun model untuk mendeteksi adiksi internet dan media sosial menggunakan XGBoost, dengan F-Measure sebesar 69,23% untuk adiksi internet dan 67,66%  untuk adiksi media sosial. Oleh karena itu, fenomena adiksi internet dan media sosial ini perlu mendapatkan perhatian khusus agar dapat diantisipasi sejak dini.
Automatic whole-body bone scan image segmentation based on constrained local model Ema Rachmawati; Jondri Jondri; Kurniawan Nur Ramadhani; Achmad Hussein Sundawa Kartamihardja; Arifudin Achmad; Rini Shintawati
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2631

Abstract

In Indonesia, cancer is very burdensome financially for sufferers as well as for the country. Increasing the access to early detection of cancer can be a solution to prevent the situation from worsening. Regarding the problem of cancer lesion detection, a whole-body bone scan image is the primary modality of nuclear medicine for the detection of cancer lesions on a bone. Therefore, high segmentation accuracy of the whole-body bone scan image is a crucial step in building the shape model of some predefined regions in the bone scan image where metastasis was predicted to appear frequently. In this article, we proposed an automatic whole-body bone scan image segmentation based on constrained local model (CLM). We determine 111 landmark points on the bone scan image as the input for the model building step. The resulting shape and texture model are further used in the fitting step to estimate the landmark points of predefined regions. We use the CLM-based approach using regularized landmark mean-shift (RLMS) to lessen the effect of ambiguity, which was struggled by the CLM-based approach. From the experimental result, we successfully show that our proposed image segmentation system achieves higher performance than the general CLM-based approach.
Nuclei Detection and Classification System Based On Speeded Up Robust Feature (SURF) Neneng Nur Amalina; Kurniawan Nur Ramadhani; Febryanti Sthevanie
EMITTER International Journal of Engineering Technology Vol 7 No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (842.261 KB) | DOI: 10.24003/emitter.v7i1.288

Abstract

Tumors contain a high degree of cellular heterogeneity. Various type of cells infiltrate the organs rapidly due to uncontrollable cell division and the evolution of those cells. The heterogeneous cell type and its quantity in infiltrated organs determine the level maglinancy of the tumor. Therefore, the analysis of those cells through their nuclei is needed for better understanding of tumor and also specify its proper treatment. In this paper, Speeded Up Robust Feature (SURF) is implemented to build a system that can detect the centroid position of nuclei on histopathology image of colon cancer. Feature extraction of each nuclei is also generated by system to classify the nuclei into two types, inflammatory nuclei and non-inflammatory nuclei. There are three classifiers that are used to classify the nuclei as performance comparison, those are k-Nearest Neighbor (k-NN), Random Forest (RF), and State Vector Machine (SVM). Based on the experimental result, the highest F1 score for nuclei detection is 0.722 with Determinant of Hessian (DoH) thresholding = 50 as parameter. For classification of nuclei, Random Forest classifier produces F1 score of 0.527, it is the highest score as compared to the other classifier.
Deteksi dan Tracking Pemain Sepakbola menggunakan Histogram of Oriented Gradients (HOG) dan Kalman Filter Kurniawan Nur Ramadhani; Ade Saepul Mugni; Mohamad Syahrul Mubarok
Indonesia Journal on Computing (Indo-JC) Vol. 3 No. 1 (2018): Maret, 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2018.3.1.211

Abstract

Dalam penelitian ini, dibangun sebuah sistem untuk melakukan tracking pemain sepakbola pada data video. Penelitian ini menggunakan ekstraksi ciri Histogram of Oriented Gradient (HOG) yang cocok untuk digunakan pada kondisi intensitas pencahayaan tidak stabil. Selain melakukan deteksi pemain bola, dalam penelitian ini dilakukan pengklasifikasian tim menggunakan clustering pada vektor ciri color moment. Untuk menjaga performansi deteksi, dilakukan evaluasi tracking menggunakan Kalman Filter. Berdasarkan hasil penelitian, sistem tracking yang dibangun memberikan performansi F1-score tertinggi mencapai 0.87 (skala 0-1) dengan berbagai kondisi pencahayaan video.
Klasifikasi Ras Mongoloid Berbasis Citra Wajah menggunakan Algoritma k-Nearest Neighbors Febryanti Sthevanie; Kurniawan Nur Ramadhani; Hafidh Fikri Rasyid
Indonesia Journal on Computing (Indo-JC) Vol. 3 No. 1 (2018): Maret, 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2018.3.1.212

Abstract

Pada penelitian ini dibangun sistem untuk mengklasifikasi ras Mongoloid dan non-Mongoloid berdasarkan daerah periorbital wajah. Penelitian ini menggunakan metode ekstraksi ciri Local Binary Pattern (LBP) dan algoritma klasifikasi k-Nearest Neighbors (k-NN). Penelitian ini menggunakan citra wajah dari 996 individu berbeda. Dari penelitian ini, didapatkan konfigurasi parameter terbaik untuk algoritma LBP yaitu nilai P=8, R=4 dan ukuran grid 5x5. Sedangkan untuk k-NN didapatkan nilai optimal untuk parameter k=5. Nilai akurasi terbaik yang didapatkan pada sistem klasifikasi ras ini  menggunakan metode LBP dan k-NN adalah sebesar 91,88%.
Deteksi Serangan Spoofing Pada Citra Wajah menggunakan Ekstraksi Ciri Local Derivative Pattern Ni Gusti Ayu Mirah Eka Darmayanti; Kurniawan Nur Ramadhani; Anditya Arifianto
Indonesia Journal on Computing (Indo-JC) Vol. 3 No. 1 (2018): Maret, 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2018.3.1.213

Abstract

Pada penelitian ini, diusulkan sistem pendeteksi serangan spoofing pada citra wajah manusia menggunakan metode ekstraksi ciri Local Derivative Pattern (LDP). Metode klasifikasi yang digunakan adalah k-Nearest Neighbour (k-NN) dan Support Vector Machine (SVM). Penelitian ini menggunakan NUAA Imposter and Photograph Database sebagai datasetnya. Parameter optimal untuk ekstraksi ciri menggunakan LDP, adalah sebagai berikut: LDP orde ke-2 dengan radius bernilai 5 yang bersifat overlapping non-uniform menggunakan algoritma klasifikasi SVM dengan kernel Radial Basis Function. Performansi terbaik didapatkan menggunakan F1-Score sebesar 99.8%. Pola uniform pada LDP mempercepat waktu komputasi dengan rata-rata 2.09 detik, sedangkan waktu komputasi pola non-uniform yaitu 5.49 detik.
Pengenalan Angka Tulisan Tangan Menggunakan Diagonal Feature Extraction dan Artificial Neural Network Multilayer Perceptron M. Ardi Firmansyah; Kurniawan Nur Ramadhani; Anditya Arifianto
Indonesia Journal on Computing (Indo-JC) Vol. 3 No. 1 (2018): Maret, 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2018.3.1.214

Abstract

Pada  penelitian  ini  dibangun  sistem pengenalan angka tulisan tangan menggunakan metode ekstraksi ciri diagonal  dan  Artificial Neural Network Multilayer Perceptron. Pada ekstraksi ciri diagonal, citra dibagi menjadi beberapa area yang sama besar. Pada tiap area dihitung rata-rata nilai piksel pada setiap diagonalnya kemudian dirata-ratakan untuk mendapatkan nilai ciri pada area tersebut.  Ciri diagonal dikombinasikan dengan nilai rata-rata horizontal dan  vertikal  pada  matriks  area  tersebut  untuk  memperkuat  informasi  pada citra. Metode  ini  mencapai  akurasi  sebesar  92.80%  pada  tahap  pengujian menggunakan  1000  dataset  C1  dan  92.60%  pada  tahap  pengujian  menggunakan 1000 dataset MNIST. Kombinasi fitur diagonal dan rata-rata horizontal menghasilkan akurasi tertinggi dalam mengenali angka tulisan tangan.
Pengenalan Huruf Isyarat Tangan Menggunakan Ekstraksi Ciri Local Binary Pattern M. Adhi Satria; Kurniawan Nur Ramadhani; Anditya Arifianto
Indonesia Journal on Computing (Indo-JC) Vol. 3 No. 1 (2018): Maret, 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2018.3.1.215

Abstract

Pada penelitian ini dibangun sistem pengenalan huruf isyarat tangan menggunakan metode ekstraksi ciri Local Binary Patterns (LBP). Metode LBP memiliki kehandalan dalam melakukan analisis tekstur, mengatasi penskalaan dan citra yang kabur. Untuk algoritma klasifikasi, digunakan metode k-Nearest Neighbour (KNN) dan Support Vector Machine (SVM). Parameter LBP terbaik didapatkan untuk nilai R=10 dan P=16 menggunakan SVM dengan kernel Gaussian. Performansi terbaik dalam penelitian ini didapatkan untuk nilai F1-Score 99,84%.
Aerial Image Segmentation with Clustering Using Fireworks Algorithm Muhammad Hariz Arasy; Suyanto Suyanto; Kurniawan Nur Ramadhani
Indonesia Journal on Computing (Indo-JC) Vol. 4 No. 1 (2019): Maret, 2019
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2019.4.1.245

Abstract

Aerial images has different data characteristics when compared to other types of images. An aerial image usually contains small insignificant objects that can cause errors in the unsupervised segmentation method. K-means clustering, one of the widely used unsupervised image segmentation methods, is highly vulnerable to local optima. In this study, Adaptive Fireworks Algorithm (AFWA) is proposed as an alternative to the K-means algorithm in optimizing the clustering process in the cluster-based segmentation method. AFWA is then applied to perform aerial image segmentation and the results are compared with K-means. Based on the comparison using Probabilistic Rand Index (PRI) and Variation of Information (VI) evaluation metrics, AFWA produces an overall better segmentation quality.
Co-Authors Abdan Hafidh Ahnafi Achmad Hussein Sundawa Kartamihardja Ade Saepul Mugni Agnes Dirgahayu Palit Agnes Dirgahayu Palit Alda Putri Utami Amalina, Neneng Nur Anang Kurniawan Anditya Arifianto Arida Kartika Arie Ardiyanti Suryani Arif M Barmawi Arifudin Achmad Cherry Nasa Putra Diah Ajeng Dwi Yuniasih Dimitri Mahayana Ema Rachmawati Farhan Bary Maruanaya Febriyanti Sthevanie Febryanti Sthevanie Febryanty Sthevanie Fikri Firdaus Fitri Amalia Hadi Friendly Halomoan Sipayung Gamma Kosala Ghilman Muhammad Zaki Hafidh Fikri Rasyid I Putu Indra Aristya Ignasius Indra Kusuma Wijaya Indra Bayu Kusuma Jondri Jondri Ketut Sudyatmika Putra Ledya Novamizanti M. Adhi Satria M. Ardi Firmansyah M.Syahrul Mubarok Maulana Nur Hidayah Mochamad Rakha Luthfi Fahsya Mohamad Syahrul Mubarok Mohamad Syahrul Mubarok Muh Aswan Abidin1 Muhammad Afif Amanullah Fawwaz Muhammad Hariz Arasy Muhammad Rizki Atria Salim Muhammad Salman Farhan Naufal Ihsan Kusumayadhi Naufal Luthfi Saputra Neneng Nur Amalina Ni Gusti Ayu Mirah Eka Darmayanti Parjuangan, Sabam Pima Hani Safitri Prasti Eko Yunanto Rini Shintawati Riski Novanda Rita Rismala Sakinah Indriyani Suyanto Suyanto Tjokorda Agung Budi W Tjokorda Agung Budi Wirayuda YUYUN SITI ROHMAH