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All Journal Indonesian Journal of Tropical and Infectious Disease Jurnal Pengabdian Magister Pendidikan IPA Indonesian Physical Review Indonesian Journal of Electrical Engineering and Computer Science Indonesian Applied Physics Letters ABSYARA: Jurnal Pengabdian Pada Masyarakat Jurnal Info Sains : Informatika dan Sains
Alfian Pramudita Putra
Bachelor Of Biomedical Engineering Study Program, Faculty Of Science & Technology, Universitas Airlangga, Surabaya
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health Other

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Journal : Indonesian Applied Physics Letters

 1 
Application of ANFIS-based Non-Linear Regression Modelling to Predict Concentration Level in Concentration Grid Test as Early Detection of ADHD in Children Sayyidul Istighfar Ittaqillah; Delfina Amarissa Sumanang; Quinolina Thifal; Akila Firdausi Harahap; Akif Rahmatillah; Alfian Pramudita Putra; Riries Rulaningtyas; Osmalina Nur Rahma, S.T., M.Si.
Indonesian Applied Physics Letters Vol. 4 No. 1 (2023): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v4i1.48153

Abstract

Concentration is the main asset for students and serves as an indicator of successful learning implementation. One of the abnormal disturbances that can occur in a child's concentration development is attention deficit hyperactivity disorder (ADHD). The prevalence of ADHD in Indonesia in 2014 reached 12.81 million people due to delayed management in addressing ADHD. Therefore, early detection of ADHD is necessary for prevention. ADHD detection can be done by testing the level of concentration using a concentration grid. However, a method is needed that can be applied to uncooperative young children who are not familiar with numbers. Therefore, research was conducted with an innovative approach using a combination of EEG-ECG to classify concentration levels. The data used in this study were primary data from 4 participants with 5 repetitions. The data were processed in the preprocessing stage, which involved noise filtering and Butterworth filtering. The features used in this study were BPM (beats per minute), alpha, theta, and beta EEG signals, which would later become inputs for the Adaptive Neuro-Fuzzy Inference System (ANFIS). The output shows that the combination of EEG-ECG has the potential to predict concentration test results. Using BPM, alpha, theta, and beta signals can serve as parameters for predicting the concentration grid test values using ANFIS effectively. In the ANFIS model with 4 features, an accuracy of 99.997% was obtained for the training data and 80.2142% for the testing data. This result could be developed for early detection of ADHD based on concentration levels so the learning implementation could be more effective.
Automatic Detection of Escherichia coli Bacteria from Tryptic Soy Agar Image Using Deep Learning Method Yonanda, Yusril Putra; Putra, Alfian Pramudita; Purwanti, Endah
Indonesian Applied Physics Letters Vol. 4 No. 2 (2023): December
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v4i2.46793

Abstract

Escherichia coli is a normal bacterial flora that lives in the human intestine, is harmless and is part of a healthy digestive tract. However, there are several strains of pathogenic Escherichia coli that can cause infections in the digestive tract, namely diarrhea. Diarrheal disease in Indonesia needs treatment and study because most of the diagnoses are still based on clinical diagnosis. Conventional methods used for the detection of Escherichia coli bacteria include culture methods, biochemical tests, and serological tests. This method has the disadvantage of requiring a long time, a large number of samples, and a relatively high error in reading the results. Therefore, the detection process needs to be done automatically using the Faster R-CNN deep learning method. In this research, we used Faster R-CNN with Inception v2 and ResNet-50 architecture and added augmentation and Image Enhancement to the Tryptic Soy Agar image dataset. The test results show that the addition of Image Enhancement greatly affects model performance and the model that has the best performance and is most appropriate to use is the Faster R-CNN ResNet-50 architecture with the addition of Contrast Stretching and Gaussian Filters to the image dataset. This model has 91% accuracy, 90% precision, 95% recall, and 92% F-1 score.
The Innovation of 3D Printing Application in The Making of Bone Scaffold as Spinal Tuberculosis Drug Delivery System Wardhani, Inten Firdhausi; Hikmawati, Dyah; Putra, Alfian Pramudita; Aminatun; Parastuti, Frazna
Indonesian Applied Physics Letters Vol. 6 No. 1 (2025): Volume 6 No. 1 – December 2025
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v6i1.84057

Abstract

The third highest number of tuberculosis (TB) cases was found in Indonesia. In severe cases, there is a chance for this disease to happen in the spine, which is known as spinal tuberculosis. This study examined an innovation that combined 3D-printed bone scaffolds with an injectable bone substitute (IBS) in paste form. Five pore-size variations of the bone scaffolds (600, 800, 1000, 1200, and 1400) µm were printed using an FDM 3D printer and based on Polylactide Acid (PLA) filaments. Moreover, the IBS paste was produced based on nano-hydroxyapatite (nano-HA), gelatin, hydroxypropyl methylcellulose (HPMC), and streptomycin (TB drug). The FTIR test indicates some functional groups were recorded and identified as typical bonds owned by each constituent material: stretching C-H for PLA, PO43- which represented nano-HA, amine for gelatin, stretching C-OH for HPMC, and ether for streptomycin. Furthermore, various pore-size 3D-printed bone scaffolds were characterized by their porosity, resulting in a range of 55.860% to 68.017%. The result of SEM revealed that the IBS-associated scaffold still had micropores on the surface of the scaffold. These pores let the drug load in the IBS paste release, which could be proven by drug release and the anti-TB test. Moreover, this combined biomaterial was confirmed to be a non-toxic, biodegradable material. The innovation of IBS-associated 3D-printed bone scaffold for future treatment of spinal TB represents a potential breakthrough in the medical field. This technology enables localized and regulated drug delivery, reduces systemic adverse effects, and accelerates recovery. Islam considers health as part of hifdz an-nafs (protection of life), one of the primary objectives of maqasid al-shari’ah (Islamic teachings). This development underlined that such innovations are not only scientifically significant but also carry substantial shari (Islamic legal) legitimacy.
Co-Authors Agustiyanto, Frans R Akila Firdausi Harahap Alvie Aditya, Shabrina Ama, Fadli Aminatun Delfina Amarissa Sumanang Dita Ayu Mayasari, Dita Ayu Eko Budi Kuntjoro, Eko Budi Fatkhunisa Rahmawati, Fatkhunisa Fitriyatul Qulub Franky Chandra Satria Arisgraha Hatta, Agus Muhamad I Made Mas Dwiyana Prasetya Wibawa Katherin Indriawati Katherine Katherine Kezia Sarahsophia Immanuel Ryadi Khouliya Zalda Khusnul Ain Nafisa Rahmatul Laili Alami Nita Lutfiyah Osmalina Nur Rahma Parastuti, Frazna Perkasa, Sigit Dani Prihartini Widiyanti Quinolina Thifal Rahmatillah, Akif Rifai Chai Riries Rulaningtyas Satrio Adhitioso Sayyidul Istighfar Ittaqillah Solly Aryza Usamah, Shofi Afghania Wardhani, Inten Firdhausi Yonanda, Yusril Putra