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All Journal Bulletin of Electrical Engineering and Informatics JURNAL PENDIDIKAN TAMBUSAI Indonesian Journal of Electrical Engineering and Computer Science
Ummi Hanifah
Program Magister Pembangunan Wilayah dan Kota, Universitas Diponegoro
Electrical & Electronics Engineering Social Sciences

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Classification of endometrial adenocarcinoma using histopathology images with extreme learning machine method Rulaningtyas, Riries; Rahaju, Anny Setijo; Dewi, Rosa Amalia; Hanifah, Ummi; Purwanti, Endah; Rahma, Osmalina Nur; Katherine, Katherine
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp961-971

Abstract

As many as 70-80% of endometrial cancer cases are endometrial adenocarcinoma. Histopathological assessment is based on the degree of differentiation, into well-differentiated, moderate-differentiated, and poorly-differentiated. Management and prognosis differ between grades, so differential diagnosis in determining the degree of tumor differentiation is crucial for appropriate treatment decisions. Histopathological image analysis offers detailed diagnostic results, but manual analysis by a pathologist is very complicated, error-prone, quite tedious, and time-consuming. Therefore, an automatic diagnostic system is needed to assist pathologists in grading the tumor. This research aims to determine the degree of differentiation of endometrial adenocarcinoma based on histopathological images. The extreme learning machine (ELM) method performs image classification with gray level run long matrix (GLRLM) features and a combination of local binary pattern (LBP)-GLRLM features as input. Experimental results show that the ELM model can achieve satisfactory performance. Training accuracy, testing accuracy, and model precision with GLRLM features were 97.13%, 91.33%, and 80% and combined LBPGLRLM features were 91.03%, 71.33%, and 100%. Overall, the model created can determine the degree of tumor differentiation and is useful in providing a second opinion for pathologists.
Online PID-neural network for tracking lower limb rehabilitation exoskeleton angular position Hanifah, Ummi; Adinda, Aura; Rahmatillah, Akif; Sapuan, Imam; Ain, Khusnul; Septanto, Harry; Chai, Rifai
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9395

Abstract

Gait trajectory tracking control is an essential component of a lower limb rehabilitation exoskeleton (LLRE). Meanwhile, the proportional-integral-derivative (PID) controller remains popular for a variety of applications, including LLRE. Nonetheless, employing PID presents a significant issue, namely determining how to choose or tune the parameters. This paper addresses the LLRE’s hipknee angular position tracking system based on an online PID-NN controller, i.e., a PID controller, whose parameters are online modified by a trained neural network (NN). A proposed framework for designing the PID-NN controller is elaborated. Numerical verifications are carried out by comparing the performance of the PID-based control system, whose parameters have been tuned using Ziegler-Nichols (ZN), without and using NN. Performance comparisons involving the presence of external disturbance are also carried out. The simulation results show that the proposed PID-NN-based control system provides better performance with lower mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) values.
Pengaruh Vidio Pembelajaran Capcut Terhadap Kemampuan Matematika Anak Usia 4-5 Tahun di Taman Kanak-Kanak ABA NUSTIM Hanifah, Ummi; Zulminiati, Zulminiati
Jurnal Pendidikan Tambusai Vol. 8 No. 2 (2024)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v8i2.14891

Abstract

Penelitian ini dilaksanakan dengan latar belakang kemampuan anak belum mengalami perkembangan menyangkut tentang kemampuan matematika yang mencakup atas mengklasifikasikan, mengurutkan, mencocokkan dan membandingkan. Penelitan ini diadakan dengan tujuan untuk mengetahui pengaruh dari penerapan video ajar menggunakan capcut pada upaya pemberian stimulus terhadap kemampuan matematika pada Taman kanak-kanak (TK) ABA NUSTIM. Penelitian ini termasuk dalam jenis pendekatan kuantitatif melalui desain quasi eksperimen. Populasi untuk penelitian yakni semua anak pada TK ABA NUSTIM Tahun Ajaran 2023/2024, cara menetapkan sampelnya dilaksanakan melalui penggunaan teknik cluster sampling, dimana anak kelas A1 ditetapkan menjadi kelas eksperimen, dan kelas A2 menjadi kelas kontrol. Teknik analisis data memakai pengujian normalitas, homogenitas, dan pengujian hipotesis. Berdasarkan dari hasil penelitian, diraih hasil dimana varians data N-gain yakni setara pada kelas kelas eksperimen dan kontrol. Selanjutnya ditinjau melalui pengujian hipotesis diraih nilai sig (2-tailed) yakni pada angka 0,000<0,05. Maka bisa didapatkan kesimpulan yakni video ajar menggunakan capcut memberikan pengaruh terhadap kemampuan matematika dari anak yang berumur 4-5 tahun pada TK ABA NUSTIM.
Co-Authors Adinda, Aura Anny Setijo Rahaju, Anny Setijo Chai, Rifai Dewi, Rosa Amalia Imam Sapuan Katherine Kho Khusnul Ain Osmalina Nur Rahma Rahmatillah, Akif Riries Rulaningtyas Septanto, Harry Solly Aryza Zulminiati Zulminiati