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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Buana Informatika Dinamika Informatika Jurnal Teknologi MAGISTRA Sinergi Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Seminar Nasional Informatika (SEMNASIF) INFORMATIKA Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Ilmiah KOMPUTASI Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Creative Information Technology Journal Jurnal Sains dan Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ CSRID (Computer Science Research and Its Development Journal) Informasi Interaktif JOISIE (Journal Of Information Systems And Informatics Engineering) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Suara Keadilan Technologia: Jurnal Ilmiah KURVATEK Jurnal Tecnoscienza Respati Jurnal Sistem Komputer & Kecerdasan Buatan Journal of Computer System and Informatics (JoSYC) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Madani : Indonesian Journal of Civil Society JURNAL PENDIDIKAN, SAINS DAN TEKNOLOGI Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Electrical Engineering and Computer (JEECOM) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Senopati : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering Journal of Applied Sciences, Management and Engineering Technology (JASMET) Jurnal Ekonomi dan Teknik Informatika Transformasi Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Dinamika Informatika (JDI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) PELS (Procedia of Engineering and Life Science) JAIA - Journal of Artificial Intelligence and Applications Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi EXPLORE Prosiding University Research Colloquium COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Journal of Information Technology and society (JITS) Teknomatika: Jurnal Informatika dan Komputer Explore Jurnal Teknologi SWAGATI: Journal of Community Service
Andi Sunyoto
Universitas Amikom Yogyakarta
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Transportation Other

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Breast Cancer Detection in Histopathology Images using ResNet101 Architecture Istighosah, Maie; Sunyoto, Andi; Hidayat, Tonny
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12948

Abstract

Cancer is a significant challenge in many fields, especially health and medicine. Breast cancer is among the most common and frequent cancers in women worldwide. Early detection of cancer is the main step for early treatment and increasing the chances of patient survival. As the convolutional neural network method has grown in popularity, breast cancer can be easily identified without the help of experts. Using BreaKHis histopathology data, this project will assess the efficacy of the CNN architecture ResNet101 for breast cancer image classification. The dataset is divided into two classes, namely 1146 malignant and 547 benign. The treatment of data preprocessing is considered. The implementation of data augmentation in the benign class to obtain data balance between the two classes and prevent overfitting. The BreaKHis dataset has noise and uneven color distribution. Approaches such as bilateral filtering, image enhancement, and color normalization were chosen to enhance image quality. Adding flatten, dense, and dropout layers to the ResNet101 architecture is applied to improve the model performance. Parameters were modified during the training stage to achieve optimal model performance. The Adam optimizer was used with a learning rate 0.0001 and a batch size of 32. Furthermore, the model was trained for 100 epochs. The accuracy, precision, recall, and f1-score results are 98.7%, 98.73%, 98.7%, and 98.7%, respectively. According to the results, the proposed ResNet101 model outperforms the standard technique as well as other architectures.
URGENSI PERLINDUNGAN MOTIF BATIK KUDUS MELALUI PENDAFTARAN HAK CIPTA Sunyoto, Andi; Sulistyowati, Sulistyowati; Sukresno, Sukresno
Jurnal Suara Keadilan Vol 21, No 1 (2020): Jurnal Suara Keadilan Vol. 21 No 1 (2020)
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/sk.v21i1.5682

Abstract

ABSTRAKPenelitian ini dengan judul Urgensi Perlindungan Motif Batik Kudus Melalui Pendaftaran Hak Cipta. Batik Kudus merupakan salah satu warisan kebudayaan tradisional rakyat Indonesia yang dilindungi oleh Undang-Undang Hak Cipta. Pasal 40 ayat (1) huruf j Undang-undang Hak Cipta menetapkan bahwa Hak Cipta atas karya seni batik yang ada di Indonesia dilindungi oleh negara. Namun sayangnya, kelahiran Undang-Undang Nomor 28 Tahun 2014 tentang Hak Cipta ternyata belum optimal untuk perlindungan Hak Cipta terhadap seni batik itu sendiri. Penelitian ini bertujuan untuk memahami urgensi perlindungan motif batik khas daerah dan implementasi perlindungan motif batik Kudus di Kabupaten Kudus.Metode pendekatan yang digunakan adalah yuridis sosiologis dan pengambilan datan menggunakan metode wawancara dengan Dinas Tenaga Kerja, Perindustrian, Koperasi, Usaha Kecil, dan Menengah Kudus, Alfa Shoofa Batik Kudus dan Muria Batik Kudus. Metode penentuan sampel digunakan dengan purposive sampling, sedangkan hasil pengolahan datanya disajikan dalam bentuk deskriptif analisis.Hasil penelitian ini menunjukkan bahwa perlindungan motif batik khas daerah termasuk motif batik Kudus sangat penting untuk dilakukan karena potensi batik Kudus yang perkembangannya semakin meningkat dan memberikan perlindungan seni budaya Kudus yang tertuang di dalam motif batik Kudus serta memberikan jaminan secara hukum akan perlindungan karya cipta terutama motif batik Kudus kepada para pengrajin. Implementasi perlindungan motif batik Kudus di Kabupaten Kudus dalam praktiknya belum secara optimal dapat diterapkan. Bukan saja masalah kaidah hukum yang diterapkan jauh dalam ranah berpikir masyarakat sebagai subjek hukum, aparat dan perangkat penegak hukum juga dinilai kurang responsif untuk bisa secara aktif melakukan perlindungan terhadap hak-hak pengrajin. Selain itu budaya masyarakat yang komunal menjadikan penerapan sistem hukum hak cipta yang bersifat individual. Kata Kunci : Perlindungan Hukum, Motif Batik Kudus, Hak Cipta
Detection of Palm Fruit Maturity Using Convolutional Neural Network Method kurniawan, Ade Kurniawan; Andi Sunyoto; Alva Hendi Muhammad
JAIA - Journal of Artificial Intelligence and Applications Vol. 2 No. 2 (2022): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v2i2.859

Abstract

Palm oil has an important role as a source of foreign exchange in the economy in Indonesia. Oil palm is one of the vegetable oil-producing plants that has the highest economic value compared to other crops such as soybeans, olives, coconuts or sunflowers. Palm oil quality is also influenced by water content, dirt content, free fatty acid content and the level of maturity of the palm fruit. Maturity of palm fruit is a very important factor in determining the quality of crude oil produced by palm fruit. In determining the maturity of oil palm, sorting is necessary to get quality palm fruit with the appropriate level of maturity. The use of image processing technology (ImageProcessing) can facilitate the process of analyzing objects. Meanwhile, the implementation of deep learning using the Convolutional Neural Network method can help identify the maturity level of oil palm fruit with a high level of accuracy. The results showed a very good effectiveness with an accuracy reaching 99% and a precision level reaching 99.8%.
AUGMENTASI DATA MENGGUNAKAN DCGAN PADA GAMBAR TANAH PATMAWATI, PATMAWATI; Andi Sunyoto; Emha Taufiq Luthfi
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 1 (2023): Juni 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i1.100

Abstract

Several studies related to soil type classification have been conducted. However, each of these studies uses different datasets. Only a small number of researchers share soil image datasets publicly. In addition, published datasets have an imbalance in the amount of data in each class which will result in poor model performance or over fit, especially deep learning. With data augmentation, new data variations can be formed that can handle the limited number of datasets. One of the modern augmentation models is DCGAN which is an extension of GAN. DCGAN is considered a good model in improving the stability of GAN training and the quality of image results. The resulting synthesized image is the result of mapping randomized latent vectors in an n-dimensional latent space. A meaningful image transformation is generated from the latent vector through arithmetic operations in the latent space dimension. The size of the latent space dimension is very important in enabling accurate reconstruction of the training data. To test the effect of latent space dimension on images, a quantitative evaluation using Fre'chet Inception Distance (FID) is used. The following results were obtained, for the best image quality in the alluvial soil category using latent space dimension 10 with FID score = 322.0. For clay soil category, the best image quality is generated using latent space dimension 100 with score FID = 332.84 and 512 with score FID = 322.08. In the black soil category, the best latent space dimension is 128 with FID score = 360.80. And for red soil, the best image quality is generated with the use of 512 latent spaces that have a FID score = 256.67.
PENGARUH ARSITEKTUR CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT DAUN TOMAT Rizky Arya Kurniawan; Sunyoto, Andi; Nasiri, Asro
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 2 (2023): Desember 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i2.111

Abstract

Plant disease is one of the crucial factors in plant survival. Tomato plants also need early help to be able to deal with disease problems. One of the organs of the tomato plant that is commonly attacked by diseases is the leaves. By providing assistance early on, it can prevent crop failure. Of course, having a trained system can reduce the cost of a farmer in dealing with diseases without expert assistance. In this research, we will test the ability of the CNN architecture to classify tomato leaf disease images. The dataset used is 4079 image data which are divided into 3 disease classes. From the results of experiments that have been carried out the InceptionV3 architecture gets the best results with an accuracy rate of 100%, ResNet50 has 97,36% accuracy and MobileNet 85,81%.
PEMANFAATAN DEEP LEARNING UNTUK SEGMENTASI PARU-PARU DARI CITRA X-RAY DADA Putranto, Dinar Wakhid; Andi Sunyoto; Asro Nasiri
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 2 (2023): Desember 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i2.114

Abstract

Chest or thoracic X-ray examination is the most commonly used supporting examination in the diagnosis of lung diseases. In addition to being quick, X-rays are more economical than CT scans or laboratory blood tests. Before determining the disease that appears from the lung image in the chest X-ray image, the doctor first determines the boundaries of the lung area. Not every chest X-Ray image has a normal lung image, some display abnormal images that look white haze or morphological changes due to lung disease processes. As one of the CNN architectures that can be used in segmenting the lungs is UNET which is an encode-decoder architecture. This research tries to train a deep learning model with U-Net CNN architecture. From the results of our experiments, the proposed model can show the ability to recognize lung boundaries even though there are abnormal or foggy lung images. The performance of the model is calculated by measuring the pixel accuracy value and the overlap value with Jaccard Index (IoU), the values of both are 98.25% and 94.54% respectively.
Klasifikasi Penyakit Alzheimer pada Citra Medis Magnetic Resonance Images dengan Arsitektur DenseNet121 Ismail, Muhamad Yusuf; Sunyoto, Andi; Purwanto, Agus
Jurnal Ilmiah Komputasi Vol. 23 No. 2 (2024): Jurnal Ilmiah Komputasi : Vol. 23 No 2, Juni 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.23.2.3600

Abstract

Penyakit Alzheimer merupakan penyakit neurodegeneratif yang paling umum terjadi pada manusia, terutama pada lansia, dan merupakan masalah kesehatan global yang serius. Citra Magnetic Resonance Imaging (MRI) ini dapat membantu membedakan kondisi normal pada pasien penyakit Alzheimer. Tetapi interpretasi MRI secara manual gambar oleh profesional medis masih memiliki keterbatasan tertentu. Proses interpretasi manual memakan waktu dan bergantung pada keterampilan dan pengalaman individu. Selain itu, kemungkinan kesalahan manusia juga meningkatkan risiko kesalahan diagnostik. Masalah tersebut bisa diatasi dengan menggunakan kecerdasan buatan, yaitu dengan menerapkan metode Convolutional Neural Network (CNN) dengan model Densenet121 yang dilatih untuk membedakan suatu objek bedasarkan gambar. Untuk tujuan tersebut, penelitian ini menerapkan metode Densenet121 untuk melakukan proses klasifikasi jenis penyakit Alzheimer melalui citra MRI otak. Hasil dari penelitian tersebut adalah metode Densenet 121 dapat digunakan untuk proses klasifikasi penyakit Alzheimer melalui citra MRI otak dengan tingkat akurasi 97,83%.
Preprocessing Data dan Klasifikasi untuk Prediksi Kinerja Akademik Siswa Gori, Takhamo; Sunyoto, Andi; Al Fatta, Hanif
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 1: Februari 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241118074

Abstract

Pendidikan merupakan aspek penting dalam kehidupan masyarakat dan memiliki peran yang sangat vital untuk menciptakan sumber daya manusia yang handal dan berkualitas dalam menghadapi berbagai tantangan pada era modernisasi. Namun, putus sekolah dan retensi siswa menjadi tantangan serius bagi perkembangan pendidikan saat ini. Salah satu faktor pemicu putus sekolah adalah kinerja akademik siswa yang rendah, mendorong perlunya tindakan pencegahan yang efektif untuk mengurangi tingkat kegagalan pendidikan. Penelitian ini bertujuan untuk memprediksi kinerja akademik siswa dengan mengintegrasikan metode Correlation-Based Feature Selection (CFS) dan Algoritma Naïve Nayes pada gabungan dataset pelajaran Matematika dan Bahasa Portugis dua sekolah menengah di Portugal. Proses preprocessing data melibatkan integrasi data, pelabelan data, transformasi data, dan pembersihan data diterapkan pada tahap awal penelitian. Hasil penelitian menunjukkan bahwa atribut signifikan yang mempengaruhi kinerja akademik siswa meliputi G2, G1, Higher, Medu, Studytime, goout, Absences, dan Failures. Melalui pemodelan algoritma Naïve Bayes, metode CFS terbukti meningkatkan nilai accuracy, recall, precision, dan f1-score dalam memprediksi kinerja akademik siswa. Sebelum CFS, model Naïve Bayes menunjukkan accuracy sebesar 89.27%, dengan recall, precision, danf1-score masing-masing sebesar 89.27%, 89.86%, dan 89.47%. Setelah implementasi CFS, evaluasi model prediksi mengalami peningkatan signifikan menjadi 91.22%, 91.22%, 92.24%, dan 91.48%.
Implementasi Metode Deep Learning CNN Dalam Klasifikasi Tajong (Sarung) Samarinda Muhartini, Sitti; Sunyoto, Andi; Muhammad, Anva Hendi
Jurnal SENOPATI : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering Vol 6, No 1 (2024): Jurnal SENOPATI Vol 6, No 1
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.senopati.2024.v6i1.6573

Abstract

Samarinda sarongs are one of Indonesia's traditional fabrics that are famous for their beautiful motifs and textures. This fabric is made using traditional weaving techniques using non-machine looms (ATBMs), resulting in a unique and distinctive diversity of textures. The difference between the loom, namely the machine and the non-machine, resulting in a difference in the texture of the Samarinda sarong. This difference can be seen from the thread density, texture smoothness, and sharpness of the motif. On certain Samarinda sarong motifs that do not require special details. This study aims to develop a classification model of Samarinda sarong texture based on the loom (machine and non-machine) using the Deep Learning method. This model is expected to help, increase the selling value of Samarinda sarongs, preserve and promote traditional fabrics In this context, the choice between DenseNet121 and VGG16 can depend on user preferences or specific needs, such as computing speed or model size.
Knowledge-Based Decision Support System for Determining Types of Agricultural Crops According to Soil Conditions Wibowo, Ferry Wahyu; Sunyoto, Andi; Setiaji, Bayu; Wihayati, Wihayati
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6254

Abstract

Selecting the right crop for a particular land condition is one of the significant challenges in the agricultural sector. Each crop type has specific needs related to environmental factors such as soil type, pH, humidity, rainfall, and temperature. Mistakes in determining the appropriate crop type can result in decreased production, wastage of resources, and losses for farmers. This paper aims to determine the best model for use as a knowledge base to choose suitable plants for soil conditions. Machine learning algorithms were used to identify patterns of relationships between land conditions and the success of certain crop types to assist in selecting suitable crops and then made a knowledge-based decision support system. Algorithms such as Decision Tree, Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN) have been applied to solve this problem. In this paper, 30 experiments were conducted to test the stability of the model in determining suitable crops based on land conditions. The results of the experiments showed that the Support Vector Machine (SVM) has a more stable performance than other algorithms, with accuracy values of mean and standard deviation of 1 and 0, respectively.
Co-Authors *, Pramono A.A. Ketut Agung Cahyawan W Aam Shodiqul Munir Abdul Jalil Rozaqi Abdul Jalil Rozaqi Abdul Mizwar A. Rahim Abidarin Rosidi Abidarin Rosidi Ade Kurniawan kurniawan Ade Pujianto Afis Julianto Afis Julianto Agus Harjoko Agus Harjoko AGUS PURWANTO Ahmad Sanusi Mashuri Aidina Ristyawan, Aidina Alva Hendi Muhammad Alva Hendi Muhammad Alva Hendi Muhammad Muhammad Ana Wati Ndarbeni Anna Baita Annas Al Amin Arif Sutikno Asro Nasiri Asro Nasiri Asro Nasiri Astria, Kadek Kiki B, Arijal Bambang Soedijono Bambang Soedijono WA Banu Dwi Putranto Bayu Anugerah Putra Bayu Setiaji Bonifacius Vicky Indriyono Bonifacius Vicky Indriyono, Bonifacius Vicky Cahyo, D. Diffran Nur Dhanar Intan Surya Saputra Diansyah, Ahmad Febri Dwi Sari Widyowaty Dwi Yuli Prasetyo Eka Yulia Sari Eko Pramono Eko Pramono Ema Utami Emha Taufiq Luthfi Emha Taufiq Luthfi Ferry Wahyu Wibowo Ferry Wahyu Wibowo Firdiyan Syah Fitri Handayani Gagah Gumelar Gori, Takhamo Hani Atun Mumtahana Hani Atun Mumtahana Hani Setiani Hanif Al Fatta Hanif Al Fatta Hanif Al Fatta Hanif Al Fatta Harianto, Harianto Hidayat Hidayat Ibnu Hadi Purwanto Ikhwan Baidlowi Sumafta Ikmah Ikmah Indah Nofikasari Indra Irawanto Ismail, Muhamad Yusuf Isthigosah, Maie K Kusrini Kapti . kurniawan, Ade Kurniawan Kurniawan, Mei P Kusnawi Kusnawi Kusnawi Kusnawi Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Liana Trihardianingsih Licantik M rudyanto Arief M. Afriansyah M. Hanafi M. Rudyanto Arief M. Suyanto M. Suyanto, M. M. Syukri Mustafa Maie Istighosah Mei P. Kurniawan Mohammad Suyanto Mudawil Qulub Muhammad Rudyanto Arief Muhammad Setiyawan Muhammad, Anva Hendi Muhartini, Sitti Mumtahana, Hani Atun Mursyid Ardiansyah Nalendra, Adimas Ketut Nasiri, Asro Nizar Haris Masruri Ni’matur Rohim Noordin Asnawi Norlaila2 Nugraha, Anggit Ferdita Nulngafan, Nulngafan Nur Arifin Akbar Parsiyono Parsiyono Patmawati Patmawati, Patmawati Pramono * Putranto, Dinar Wakhid Quratul Ain Rafli Junaidi Kasim Rahim Jamal Rahmat Hidayat Raynaldi Fatih Amanullah Ria Andriani Rifda Faticha Alfa Aziza Rifqi Mulyawan Riyanto, Thomas Pramuji Singgih Riza Marsuciati Rizfi Syarif Rizky Arya Kurniawan Rudi Prietno Sahirul Muklis Saiful Bahri Salmuasih - Samsul Bahri Sari, Rita Novita Setiawan Budiman Sholihin, Iasya Silvi Agustanti Bambang Singgih Arif Widodo Slamet Triyanto Soedijono W A, Bambang Sudarmawan Sudarmawan Sudarmawan, Sudarmawan Sudiana Sudiana Sukresno Sukresno Sulistyowati Sulistyowati Suliswaningsih Suliswaningsih Supomo, Eko Sutejo, Danang Syah, Firdiyan Syah, Firdiyan Syukirman Amir TONNY HIDAYAT Tribiakto, Herlandro Ulinuha, Hinova Rezha W., Bambang Soedijono WA, Bambang Soedijono Wahyu Caesarendra Wahyu Hidayat Wihayati, Wihayati Windarni, Vikky Aprelia Windha Mega Pradnya Dhuhita Wing Wahyu Winarno Yoga Dwi Pambudi Yoga Pristyanto Yohanes Setyo Prabowo, Yohanes Setyo Yusuf Sutanto Zaipin, Zaipin