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All Journal Natural Science: Journal of Science and Technology Jurnal INKOM Jurnal Buana Informatika Jurnal Simantec Jurnal Ilmiah Kursor Research Journal of Life Science Jurnal Teknologi Informasi dan Ilmu Komputer The Journal of Pure and Applied Chemistry Research SMATIKA Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Farmasi Sains dan Komunitas JURNAL MANAJEMEN DAN PELAYANAN FARMASI (Journal of Management and Pharmacy Practice) Jurnal SOLMA JAST : Jurnal Aplikasi Sains dan Teknologi Jurnal Teknik Informatika (JUTIF) JAPI: Jurnal Akses Pengabdian Indonesia Sainsbertek Jurnal Ilmiah Sains & Teknologi Prosiding Seminar Nasional Pengabdian Masyarakat Journal of Embedded Systems, Security and Intelligent Systems JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Smatika Jurnal : STIKI Informatika Jurnal
Kestrilia Rega Prilianti
Program Studi Teknik Informatika, Universitas Ma Chung
Agriculture, Biological Sciences & Forestry Arts Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Other

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Penerapan PECS-Bahan Ajar Autisme Romy Budhi Widodo; Windra Swastika; Kestrilia Rega Prilianti
Prosiding Seminar Nasional Pengabdian Masyarakat Universitas Ma Chung Vol. 1 (2021): Prosiding Seminar Nasional Pengabdian Masyarakat Ma Chung 2021
Publisher : Ma Chung Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (466.607 KB) | DOI: 10.33479/senampengmas.2021.1.1.333-340

Abstract

Kegiatan yang dilakukan bertujuan untuk memberikan nilai lebih kepada mitra. Pada kesempatan ini, kelompok kami berusaha ingin memberikan nilai lebih kepada salah satu Wisma Epilepsi yang ada di kota Malang. Di wisma tersebut terdapat anak autis, ASD (Autism Spectrum Disorder) dan sekaligus hiperaktif yang berusia 14 tahun. Dari hasil pengamatan oleh pembina, anak asuh dengan ASD ini memiliki tipe belajar visual. Alat-alat bermain dan belajar visual yang telah dimiliki perlu dianalisis dan jika perlu ditingkatkan keberagamannya sesuai kerjasama dengan penanggung jawab wisma. Keragaman bahan ajar juga disesuaikan dengan kemajuan pemahaman konsep dari anak tersebut. Beberapa alat belajar juga sering mengalami kerusakan sehingga diperlukan cara melindungi alat-alat belajar tersebut dari koyak dan kerusakan. Luaran sosial yang diharapkan adalah adanya peningkatan kemampuan dan kreatifitas anak ASD di Wisma Epilepsi tersebut. Pada kegiatan ini usaha menerapkan PECS-picture exchange communication system-dilakukan meski kendala pertemuan fisik yang jumlahnya tidak dapat terlalu banyak disebabkan ketidaknyamanan interaksi fisik di masa pandemi. Hasil pengamatan dari beberapa kali pertemuan dengan menerapkan PECS diperoleh bahwa penggabungan PECS dan model 3 dimensi, lebih sesuai digunakan, daripada hanya metode PECS saja, pada subjek
Implementasi Perbaikan Kualitas Citra Tanaman terhadap Perbedaan Kamera untuk Prediksi Pigmen Fotosintesis berbasis Machine Learning Felix Adrian Tjokro Atmodjo; Kestrilia Rega Prilianti; Hendry Setiawan
Jurnal Buana Informatika Vol. 14 No. 01 (2023): Jurnal Buana Informatika, Volume 14, Nomor 1, April 2023
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v14i01.6997

Abstract

Implementation of Plant Image Quality Improvement based on Machine Learning on Camera Variation to Predict Photosynthetic Pigments. Pigments are natural dyes found in plants and animals. In photosynthesis, there are 3 essential pigments: chlorophyll, cartenoid, and anthocyanin. Pigment analysis can be performed with High Performance Liquid Chromatography (HPLC) and a spectrophotometer. However, HPLC and spectrophotometers require high resources and time. Thus, the Fuzzy Piction Android application built using the FP3Net model is the best choice in pigment prediction since it is low on cost and accessible. However, the Fuzzy Piction produces different performance, which is affected by light conditions and camera specifications. The experiment used ten sample images for Jasminum sp., P. betle, Syzygium oleina of green and red variations, and Graptophyllum pictum leaves with three smartphone cameras and three lighting levels. Improvements using 3D-TPS produced the best SSIM values in the range of 0.9191 – 0.9797 for images Syzygium oleina of green and red variations leaves, and the predicted MAE value of pigment was 0.0296 – 0.0492.Keywords: 3D-TPS, plant leaves, pigment, image quality improvement Implementasi Perbaikan Kualitas Citra Tanaman terhadap Perbedaan Kamera untuk Prediksi Pigmen Fotosintesis berbasis Machine Learning. Pigmen merupakan pewarna alami yang ditemukan pada tumbuhan dan hewan. Dalam proses fotosintesis terdapat tiga pigmen yang penting, yaitu klorofil, kartenoid, dan antosianin. Analisis pigmen dapat dilakukan dengan Kromatorafi Cair Kinerja Tinggi (KCKT) dan spektrofotometer. Namun, KCKT dan spektrofotometer membutuhkan sumber daya dan waktu yang tinggi. Sehingga, aplikasi Android Fuzzy Piction yang dibangun menggunakan model FP3Net mejadi pilihan dalam prediksi pigmen dengan biaya murah dan mudah. Akan tetapi, aplikasi Android Fuzzy Piction menghasilkan kinerja yang berbeda-beda yang dipengaruhi oleh kondisi cahaya dan spesifikasi kamera. Dilakukan percobaan dengan mengambil sepuluh sampel citra daun dari empat varietas tanaman yaitu, pucuk merah, daun ungu, melati, dan sirih. Citra diambil dengan tiga kamera smartphone dan tiga tingkat pencahayaan yang berbeda. Perbaikan yang dilakukan menggunakan algoritma 3D-TPS menghasilkan nilai SSIM terbaik pada rentang 0.9191 – 0.9797 untuk citra daun pucuk merahdan nilai MAE prediksi pigmen sebesar 0.0296 –0.0492.Kata Kunci: 3D – TPS, daun tanaman, pigmen, perbaikan kualitas citra
Potential Cytotoxic Activity of Methanol Extract, Ethyl Acetate, and n-Hexane Fraction from Clitoria ternatea L. on MCF-7 Breast Cancer Cell Line and Molecular Docking Study to P53 Rollando Rollando; Marsha Anggita Amelia; Muhammad Hilmi Afthoni; Kestrilia Rega Prilianti
The Journal of Pure and Applied Chemistry Research Vol 12, No 1 (2023): Edition January-April 2023
Publisher : Chemistry Department, The University of Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jpacr.2023.012.01.705

Abstract

Breast cancer is a condition where the cells in breast tissue lose control and multiply uncontrollably. In this study, MCF-7 breast cancer cells were tested for cytotoxic activity using the MTT assay and the active compound's interaction with the p53 protein was tested in silico. The most active fraction was found to be the ethyl acetate fraction, with an IC50 value of 1.730 μg/mL and a selectivity index of 2.485. However, the selectivity index was less than 3, and Vero cells showed changes in morphology with the addition of the ethyl acetate fraction. GC-MS was used to identify 19 compounds in the ethyl acetate fraction, and in-silico tests were performed on 5 potential anticancer compounds. Lipinski's Rule of Five test showed that only 3 of these compounds could undergo molecular docking. The results indicated that Anethole compound can interact with p53 protein, while Cinnamaldehyde, (E)- can interact with p21 protein.
Metode Perbaikan Citra Tanaman atas Variasi Iluminasi dengan Metode KNN (K-Nearest Neighbour) dan ANN (Artificial Neural Network) pada Sistem Prediksi Pigmen Fotosintesis secara Non Destruktif Marcelino Centauri Dwi Prasetyo; Kestrilia Rega Prilianti; Hendry Setiawan
Journal of Embedded Systems, Security and Intelligent Systems Vol. 3 No. 2 (2022): Vol 3, No 2 (2022): November 2022
Publisher : Program Studi Teknik Komputer

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Abstract

Aplikasi Fuzzy Piction adalah aplikasi prediksi kandungan pigmen tanaman berbasis android yang dikembangkan oleh kelompok riset Precision Agriculture, Universitas Ma Chung Malang. Aplikasi mampu memprediksi kandungan 3 macam pigmen fotosintesis utama yaitu klorofil, karotenoid, dan antosianin secara non destruktif melalui citra daun tanaman yang sedang dievaluasi. Model prediksi dikembangkan dengan metode Convolutional Neural Network (CNN). Aplikasi menghadapi permasalahan akurasi yang terjadi karena variasi iluminasi di lapangan saat evaluasi terhadap tanaman dilakukan secara in-situ. Untuk menyelesaikan permasalahan tersebut, pada penelitian ini diimplementasikan metode perbaikan citra berbasis kecerdasan buatan yaitu KNN (K-Nearest Neighbor) dan ANN (Artificial Neural Network). Hasil eksperimen menunjukan bahwa metode KNN mampu memberikan perbaikan citra yang lebih baik. Indikator lebih baik dilihat dari presisi nilai prediksi pigmen dari beberapa citra pada iluminasi yang berbeda untuk objek tanaman yang sama. Nilai standar deviasi prediksi pigmen pada citra-citra hasil perbaikan dengan KNN berada pada kisaran 0,001 hingga 0,026 sedangkan dengan ANN berada pada kisaran 0,005 hingga 0,557. Sampel tanaman yang digunakan pada penelitian ini adalah Duranta Erecta dan Piper Betle.
Metode Deteksi Cepat Serangan Ganoderma pada Perkebunan Kelapa Sawit dengan Penginderaan Jauh William Wicaksono; Kestrilia Rega Prilianti; Hendry Setiawan; Prasetyo Mimboro
Journal of Embedded Systems, Security and Intelligent Systems Vol. 3 No. 2 (2022): Vol 3, No 2 (2022): November 2022
Publisher : Program Studi Teknik Komputer

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Abstract

Di tengah krisis ekonomi dunia saat ini, industri sawit masih mampu menopang perekonomian domestik Indonesia. Oleh karena itu, potensi kerugian akibat penyakit yang terlambat terdeteksi dan mengakibatkan gagal panen harus diantisipasi sejak dini. Penyakit yang paling sering menyerang perkebunan kelapa sawit adalah Ganoderma. Luas area perkebunan kelapa sawit di Indonesia yang sangat besar merupakan tantangan bagi pengelola untuk dapat melakukan monitoring terhadap serangan Ganoderma secara komprehensif. Teknologi penginderaan jauh merupakan salah satu solusinya. Dengan menggunakan Unmanned Aerial Vehicle (UAV) seperti drone citra perkebunan kelapa sawit dapat direkam dengan cepat. Pada penelitian ini, citra UAV dari perkebunan kelapa sawit diproses menggunakan algoritma Convolutional Neural Network (CNN) dengan arsitektur ResNet-34 untuk deteksi pokok pohon kelapa sawit. Pada hasil deteksi kemudian dilakukan ekstraksi nilai rerata RGB (Red, Green, dan Blue) dari setiap pokok pohon. Nilai rerata RGB kemudian digunakan sebagai input pada custom model Artificial Neural Network (ANN) untuk memprediksi status serangan Ganoderma (terinfeksi atau tidak terinfeksi) pada tiap pokok pohon. Akurasi model CNN deteksi pokok pohon (diukur dengan F1-Score) mencapai 84,61% untuk training dan 73,83% untuk testing. Sedangkan akurasi model ANN status serangan Ganoderma mencapai 91% untuk training dan 94% untuk testing. Dengan metode ini pengelolaan lahan terkait serangan Ganoderma dapat dilakukan dengan efektif dan efisien.
Characterization of Ralstonia solanacearum Using Fourier Transform Infrared (FTIR) Spectroscopy Ma'alifah, Nur; Aini, Luqman Qurata; Abadi, Abdul Latief; Prillianti, Kestrilia Rega; Prabowo, Matheus Randy
Research Journal of Life Science Vol 9, No 2 (2022)
Publisher : Direktorat Riset dan Pengabdian Masyarakat, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.rjls.2022.009.02.2

Abstract

Ralstonia solanacearum, the causal agent of bacterial wilt disease is worldwide in distribution, and results in serious economic losses, particularly in the tropics. Detection and characterization of microorganisms by Fourier transform infrared spectroscopy (FTIR) technique promises to be of great value because of the method’s inherent sensitivity, small sample size, rapidity, and simplicity. In this study, we used FTIR spectroscopy for the characterization of Ralstonia solanacearum. The bacteria were grown on Nutrient Agar (NA) at 28°C for 48 hours. The colonies of Ralstonia solanacearum on nutrient agar medium were smooth circular, raised, and dirty white. Cultures of bacteria were identified by molecular methods using PCR techniques. The DNA was amplified using a specific primer pair, 759f/760r (forward primer: 5'- GTCGCCGTCAACTCACTTTCC 3’, reverse primer: 5'-GTCGCCGTAGCAATGCGGAATCG-3’). The PCR produced a single band of 280 bp from the isolated DNA of cultured bacteria.  Bacterial spectra were obtained in the wavenumber range of 4000–400 cm-1 using FTIR spectroscopy. The identification of cell wall constituents in region 3000–2800 cm-1, the proteinaceous structure of bacteria in region 1665–1200 cm-1, and the fingerprint of bacteria in region 1200-800 cm-1 are all part of the spectra analysis in this study. Absorption bands obtained from bacteria Ralstonia solanacearum samples associated with protein, phospholipids, nucleic acids, and carbohydrates appear in the bacterial IR absorption spectra.
Fourier Transform Infrared (FTIR) Spectroscopy Method for Fusarium solani Characterization Hasanah, Ifa Maulidah; Martosudiro, Mintarto; Aini, Luqman Qurata; Prillianti, Kestrilia Rega; Prabowo, Matheus Randy
Research Journal of Life Science Vol 9, No 1 (2022)
Publisher : Direktorat Riset dan Pengabdian Masyarakat, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.rjls.2022.009.01.3

Abstract

The detection and identification of microorganisms using spectroscopy techniques promise to be of great value because of their sensitivity, rapidity, low expense, and simplicity.  In this study, we used FTIR spectroscopy for the characterization of Fusarium solani. PCR amplification of DNA extracted from these isolates showed the possibility of amplifying PCR products with sizes 559 bp using the ITS1-ITS4 primers. Based on phylogenetic tree analysis, the isolate of F. solani showed a closely relationship to Fusarium solani isolate MN (MH300495.1) with 99.63% similarity.  The study is focused on the carbohydrate structure which can be analyzed in the range of 900 to 1200 cm-1 of FTIR wavenumber.  The spectra of our samples share similarities with one another, although small differences occur in the absorbance value. The band at 1027 cm-1 is assigned to the C-O stretching of glycogen. Meanwhile, at 1042 cm-1 is interpreted as carbohydrate C-O stretching as well. The band around 1073 cm-1 might arise from both chitin C-C stretching and phosphate stretching of nucleic acids. Other vibrations associated with chitin are also found at 1115 cm-1 and 1151 cm-1 which are assigned to C-O-C symmetric stretching and C-O-C asymmetric stretching, respectively.
Implementasi Convolutional Neural Network untuk Sistem Prediksi Pigmen Fotosintesis pada Tanaman Secara Real Time Kestrilia Rega P; Ivan Christianto O; Hendry Setiawan
Jurnal Teknik Informatika dan Sistem Informasi Vol 4 No 2 (2018): JuTISI
Publisher : Maranatha University Press

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Abstract

It is common that evaluation on plant health is done by conducting measurement on photosynthetic pigments. Analysis of the presence or absence of some particular pigments could reveal any information about plant responses to the environment or climate changes. This is due to the fact that relative pigment concentrations are influenced by environmental factors such as light and nutrient availability. In this research, a non-destructive and rapid method was developed to identify the existence of photosynthetic pigments in plant leaf i.e. chlorophyll, carotenoid, and anthocyanin. The method used leaf’s RGB digital image as the color representation of the pigments contained in the plant being evaluated. The intelligence agent which is responsible to learn the data and provide information about the pigments was developed based on convolutional neural network (CNN) model. This model was chosen due to its capability to receive a digital image and automatically search for the best feature to learn it. Therefore, plant evaluation could run in real time. The result of the experiment reveals that CNN model could learn the color-pigment relationship very well. The best architecture is ShallowNet using Adam optimizer, batch size 30 and trained with 15 epoch. The MSE of the pigments prediction reaches 0.0055 (actual data range -0.2 up to 2.2) for training and 0.029 for testing.
Comparative analysis of random forest and deep learning approaches for automated acute lymphoblastic leukemia detection using morphologicaland textural features Swastika, Windra; Prilianti, Kestrilia Rega; Irawan, Paulus Lucky Tirma; Setiawan, Hendry
Jurnal Ilmiah Kursor Vol. 13 No. 1 (2025)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i1.427

Abstract

Acute Lymphoblastic Leukemia (ALL) is a type of blood cancer that requires early and accurate detection for effective treatment. Current diagnostic approaches face significant challenges including time-consuming manual examination, inter-observervariability, and difficulty in balancing sensitivity with specificity. This study aims to develop and compare two automated ALL detection methodologies to overcome these limitations. We propose: (1) a Random Forest classifier using carefully engineered morphological and textural features, and (2) a Convolutional Neural Network (CNN)architecture for automated feature learning from microscopic blood cell images. Using 10,661 images from the ALL Challenge dataset, we evaluated both approaches on training (70%), validation (15%), and test (15%) sets. Feature importance analysis revealed cell area (10.71%), energy (10.67%), and skewness (10.50%) as the mostsignificant discriminative features. The Random Forest achieved 85% accuracy withnotable sensitivity for ALL detection (93%), while the deep learning approachdemonstrated superior performance with 87% accuracy and better false positive control(27.50% vs. 35.76%). Our comparative analysis shows that while both methodsdemonstrate clinical viability for automated ALL screening, the deep learning approachoffers advantages in reducing false positives while maintaining high detectionsensitivity. This research contributes to the advancement of computer-aideddiagnostic tools that can support pathologists in early ALL detection,potentially reducingdiagnostic time and improving consistency.
Rancang Bangun Aplikasi Berbasis Android untuk Perbaikan Kualitas Citra Tanaman Atas Perbedaan Spesifikasi Kamera Smartphone pada Prediksi Kandungan Pigmen Fotosintesis Secara Real-Time Tjokro Atmodjo, Felix Adrian; Prilianti, Kestrilia Rega; Setiawan, Hendry
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 7: Spesial Issue Seminar Nasional Teknologi dan Rekayasa Informasi (SENTRIN) 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022976771

Abstract

Pigmen utama yang berperan penting pada fotosintesis, yaitu klorofil, karotenoid dan antosianin dapat dianalisis kandungannya untuk menentukan status kesehatan tanaman. Metode analisis kandungan pigmen yang dilakukan secara destruktif memerlukan penanganan khusus dan biaya yang tinggi. Fuzzy Piction adalah aplikasi Android yang telah dikembangkan sebelumnya untuk prediksi kandungan pigmen utama pada tanaman. Aplikasi tersebut mempunyai kemampuan untuk melakukan prediksi kandungan pigmen pada citra daun secara non-destruktif dengan menggunakan model Convolutional Neural Network (CNN) FP3Net. Namun, Fuzzy Piction masih belum invarian terhadap perbedaan kualitas citra yang dapat terjadi karena perbedaan kualitas atau spesifikasi kamera smartphone. Hal ini ditunjukkan dengan adanya perbedaan hasil prediksi kandungan pigmen pada beberapa smartphone untuk objek daun yang sama. Pada penelitian ini dikembangkan metode perbaikan citra dengan algoritma Partial Least Square Regression (PLSR) sebagai solusi atas permasalahan tersebut. Dengan penambahan metode perbaikan citra, aplikasi Fuzzy Piction dapat memberikan prediksi kandungan pigmen dengan tingkat presisi yang lebih baik. Aplikasi Fuzzy Piction difasilitasi dengan layanan cloud yang dikembangkan menggunakan Flask web service sehingga model perbaikan citra dan prediksi pigmen lebih mudah diperbarui. Hasil perbaikan warna oleh PLSR berhasil menyeragamkan warna citra serta dapat memberikan hasil prediksi kandungan pigmen dengan standar deviasi yang lebih kecil. Variasi prediksi kandungan pigmen dengan 3 jenis smartphone yang berbeda pada objek daun yang sama  dapat diturunkan sebesar 87% setelah citra asal diperbaiki dengan PLSR.AbstractChlorophyll, carotenoids, and anthocyanins are three main pigments that are important for photosynthesis process. Its content can be examined to determine the status of plants health. The destructive approach of evaluating pigment content is expensive and necessitates specialized handling. An Android based application called Fuzzy Piction could predict the content of those pigments nondestructively using the FP3Net, a Convolutional Neural Network (CNN) model. This application predicts the pigment content in plant leaf by its digital images. However, Fuzzy Piction is still not invariant to differences in image quality that can occur due to differences in smartphone camera specifications. This is indicated by the difference in the prediction results of the pigment content on several smartphones for the same leaf object. Therefore, the Partial Least Square Regression (PLSR) technique was used in this work as an image enhancement method to resolve the issue. Eventually, Fuzzy Piction may provide precise predictions of pigment content by embedding PLSR in it. A cloud service made with the Flask web service makes it easy to update the image enhancement and pigment prediction models for the Fuzzy Piction application. The results of color correction by PLSR succeeded in uniforming the color of the image and could provide predictive results of pigment content with a smaller standard deviation. The variation of pigment content prediction with 3 different smartphone types on the same leaf object can be reduced by 87% after the original image is corrected with PLSR.
Co-Authors A.S. Adhiwibawa, Marcelinus Abdul Latief Abadi Agus Suryanto Alfanaar, Rokiy Anam, Syaiful Anitarakhmi Handaratri Christianto O, Ivan Dewi Fandelia Tan Dion Notario Edward Hendryawan Michael Endhirayanti Caesarika Eva Monica Felix Adrian Tjokro Atmodjo Hasanah, Ifa Maulidah Hendry Setiawan Hendry Setiawan Hendry Setiawan Immanuel Olive DjajaPutra Indah Lestari Irawan, Paulus Lucky Tirma Irawan, Paulus Lucky Tirma Ivan C. Onggara Ivan Christianto O Lo Hanjaya Hanafi Lo Hanjaya Hanafi, Lo Hanjaya Luqman Qurata Aini Ma'alifah, Nur Marcelino Centauri Dwi Prasetyo Marcelinus A.S. Adhiwibawa Marsha Anggita Amelia Martanty Aditya, Martanty Melany Melany Agustina Melany Melany Mimboro, Prasetyo Mintarto Martosudiro Mochamad Subianto Muhammad Hilmi Afthoni Nugroho, Vincentius Adhien Oktariyanto, Vidian Vito Onggara, Ivan C. Paulus Lucky Tirma Irawan Paulus Lucky Tirma Irawan Paulus Lucky Tirma Irawan Paulus Lucky Tirma Irawan Prabowo, Matheus Randy Prasetyo Mimboro Rollando, Rollando Romy Budhi Widodo Samuel Samuel Setiawan, Hendry Sultan Arif Rahmadianto Suryanto, Agus Tan, Dewi Fandelia Tatas H.P. Brotosudarmo Theodore Zakharia Widjaja Theodore Zakharia Widjaja Tjokro Atmodjo, Felix Adrian Vincentius Adhien Nugroho William Wicaksono Windra Swastika Yohanes Hans Kristian Yuyun Yuniati