Article Per Year (5 Year)
p-Index From 2021 - 2026
2.595
P-Index
This Author published in this journals
All Journal TEKNIK Transmisi: Jurnal Ilmiah Teknik Elektro TRANSIENT: Jurnal Ilmiah Teknik Elektro Jurnal Sains dan Teknologi Sainteknol : Jurnal Sains dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Scientific Journal of Informatics JOIV : International Journal on Informatics Visualization Journal of Robotics and Control (JRC) Jurnal Ilmu Teknik dan Komputer Jurnal Abdi Insani Journal of Applied Data Sciences Jurnal Algoritma Jurnal Profesi Insinyur Indonesia Scientific Journal of Informatics Greenation International Journal of Tourism and Management Greenation International Journal of Economics and Accounting Sainteknol : Jurnal Sains dan Teknologi
R. Rizal Isnanto
Computer Science Department, Semarang State University
Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Transportation Other

Published : 104 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 104 Documents
Search

 7   8   9   10   11 
Applied Data Science and Artificial Intelligence for Tourism and Hospitality Industry in Society 5.0: A Review Hartatik, Hartatik; Isnanto, R. Rizal; Warsito, Budi
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.300

Abstract

The primary purpose of this research is to delve into the emerging trends of artificial intelligence and data science with a specific focus on the tourism and hospitality sectors. A comprehensive methodology used to conduct this research includes collecting article data, conducting analysis and then conducting a review study on data science and artificial intelligence trends. These articles were selected based on metadata sourced from web of science and Scopus metadata. In particular, the research scrutinized and assessed the evolving trends in data science and artificial intelligence   within the hotel and tourism category. This analysis drew data from two prominent databases, Web of Science and Scopus, obtained a total of 4155 articles identified using the software and generated 124 terms in the articles with at least ten co-occurrence relationships. The findings of this study explain the huge potential, namely the trend of data application of science and artificial intelligence   in the tourism sector which is categorized in five distinct areas: forecasting tourist demand, implementing customized service recommender systems for the tourism industry, classifying tourist behavior patterns in automation, analyzing and understanding tourist behavior, developing tourist destinations, and planning itineraries. Additionally, the research anticipates a heavy emphasis on future studies on predicting travel demand. Looking ahead, this research extends the foundations laid by previous review studies primarily focusing on knowledge and forecasting methodologies in the tourism sector. The conclusions drawn in this research are well-supported by the evolving landscape of knowledge in this field. Furthermore, contributions of this research it offers valuable insights into the future directions of apllied data science and artificial intelligent research are represents the pioneering effort to analyze of applying machine learning to advance artificial intelligence and big data within the hotel and travel industries. The authors propose several avenues for future research in this domain based on the data unearthed.Additionally, the research anticipates a heavy emphasis on future studies on predicting travel demand. Looking ahead, this research extends the foundations laid by previous review studies primarily focusing on knowledge and forecasting methodologies in the tourism sector. The conclusions drawn in this research are well-supported by the evolving landscape of knowledge in this field. Furthermore, it offers valuable insights into the future directions of sentiment analysis research. Notably, this paper represents the pioneering effort to comprehensively analyze the methodology of applying machine learning to advance AI and big data within the hotel and travel industries. The authors propose several avenues for future research in this domain based on the data unearthed.
Metode Analisis Integrasi Aset Konektivitas Proyek untuk Mencapai Target Penyelesaian Proyek Kholid, Kholid; Sumardi, Sumardi; Isnanto, R. Rizal
Jurnal Profesi Insinyur Indonesia Vol 2, No 4 (2024): JPII
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jpii.2024.24572

Abstract

PT PLN (Persero) bertugas untuk meningkatkan kapasitas penyediaan tenaga listrik, tantangan terbesarnya adalah keterlambatan penyelesaian proyek pembangkit, transmisi dan gardu induk. Sesuai dengan perencanaan RUPTL 2021-2030, pada proyek PLTU Sumsel-8 evakuasi dayanya direncanakan melalui sistem PLN jaringan transmisi 500 kV sistem Sumatera yaitu SUTET 500 kV Muara Enim-Aur Duri, SUTET 500 kV Aur Duri-Peranap dan SUTET 500 kV Peranap-Perawang. Sehingga perlu dilakukan analisis terhadap proyek PLTU Sumsel-8. Hasil dari analisis integrasi aset konektivitas proyek yang menggunakan integrasi jalur 500 kV SUTET dan GITET adalah terkait schedule key date untuk back feeding (BF) yaitu pada Maret 2021 dan target penyelesaian proyek yaitu Commercial Operation Date (COD) pada Desember 2021 tidak akan tercapai karena pada jaringan transmisi SUTET 500 kV Muara Enim-Aur Duri kontrak proyek yang belum efektif dan direncanakan baru selesai pada bulan Desember 2022 dan kendala permasalahan pembebasan lahan, ROW dan IPPKH (Izin Pinjam Pakai Kawasan Hutan). Sebagai alternatif proyek PLTU Sumsel-8 menggunakan jalur 275kV dengan menganalisis terkait jalur interkoneksinya (single line diagram), analisis aliran daya menggunakan kriteria keandalan keamanan N-1 dapat diketahui terkait kapasitas dari jalur tersebut dan aset konektivitas mana saja yang perlu mendapat perkuatan pada jaringan tersebut. Sesuai dengan hasil analisis integrasi aset konektivitas proyek pada jalur alternatif 275 kV optimis selesai sesuai schedule key date PLTU Sumsel-8 untuk back feeding (BF) dan mendukung target penyelesaian proyek yaitu Commercial Operation Date (COD). Kata kunci: analisis integrasi aset konektivitas proyek, single line diagram, schedule key dates, project review meeting
Desain Proteksi Kubikel 20 kV pada Gardu Distribusi Pelanggan Tegangan Menengah di PT PLN (Persero) Unit Pelaksana Pelayanan Pelanggan (UP3) Bali Utara Saputra, I Gede Dharma; Sofwan, Aghus; Isnanto, R. Rizal
Jurnal Profesi Insinyur Indonesia Vol 2, No 4 (2024): JPII
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jpii.2024.24575

Abstract

PLN adakalanya dalam menyalurkan tenaga listrik ke pelanggan rumah tangga dan industri menggunakan jaringan distribusi yang sama, sehingga keandalan jaringan harus terjaga dengan baik. Pada pelanggan tegangan menengah (TM) menggunakan level tegangan yang sama dengan jaringan distribusi yaitu 20 kV. Gangguan pada instalasi milik pelanggan TM dapat menyebabkan proteksi pada jaringan distribusi bekerja atau membuat jaringan trip (terputus), sehingga penyaluran tenaga listrik terhenti, baik kepada pelanggan rumah tangga dan industri. Maka dari itu pada pelanggan TM, tenaga listrik disalurkan melalui kubikel 20 kV yang dilengkapi dengan relay proteksi. Dengan relay tersebut, gangguan pada instalasi milik pelanggan TM dapat dilokalisir. Agar relay dapat berfungsi dengan baik perlu dibuat desain dan perhitungan proteksi yang tepat. Proteksi didesain untuk bekerja pada gangguan overcurrent (arus lebih), ground fault (gangguan ke tanah) dan proteksi thermal (overload). Perhitungan proteksi akan menggunakan data-data daya pelanggan, kapasitas trafo pelanggan dan rasio CT yang digunakan, kemudian menggunakan beberapa persamaan untuk menentukan nilai setting yang tepat. Dari daya pelanggan akan didapatkan nilai arus nominal (In), dan dari hasil desain untuk In pada nilai 105%, 120% dan 150% termasuk kategori beban pemakaian lebih, sedangkan untuk In dengan nilai 400% masuk kategori gangguan overcurrent. Untuk setting ground fault, mengacu besar nilai arus maksimum trafo pelanggan (Ine), dengan cara membatasi arus netral sebesar 20% dari Ine. Kata kunci: kubikel 20 kV, relay proteksi, overcurrent, ground fault, proteksi thermal
Comparing Customer Satisfaction: The Role of User Experience in Online vs Offline Shopping Sompura, Jayesh; Ramchandani, Paras; Shriyal, Harsh; Raithatha, Bhavya; Chauhan, Rahul; Maseleno, Andino; Isnanto, R. Rizal
Greenation International Journal of Tourism and Management Vol. 2 No. 4 (2024): (GIJTM) Greenation International Journal of Tourism and Management (December 20
Publisher : Greenation Research & Yayasan Global Resarch National

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/gijtm.v2i4.285

Abstract

This research explores the influence of demographic factors, specifically age and income, on consumer preferences and behavior regarding online and offline shopping. Using a sample of 114 respondents from Ahmedabad, the study assesses the satisfaction, convenience, and trust associated with both shopping modes. ANOVA analysis reveals that while age and income significantly affect online enhancements and offline convenience, other factors like trust and barriers remain relatively consistent across demographics. The results emphasize the importance of omnichannel strategies, blending the convenience of online shopping with the tactile and trust-based advantages of physical stores. Future research should explore additional demographic and psychological variables, as well as the adoption of advanced technologies like augmented reality (AR) in shopping, to better understand the evolving consumer landscape. The findings offer global insights relevant to both emerging and developed markets.
Pneumothorax Detection System in Thoracic Radiography Images Using CNN Method Fardana, Nouvel Izza; Isnanto, R. Rizal; Nurhayati, Oky Dwi
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.16635

Abstract

Purpose: This research aims to develop an automatic pneumothorax detection system using Convolutional Neural Networks (CNN) to classify thoracic radiography images. By leveraging CNN's effectiveness in identifying medical abnormalities, the system seeks to enhance diagnostic accuracy, reduce evaluation time, and minimize subjective interpretation errors. The output will provide a predicted label of "pneumothorax" or "non-pneumothorax," facilitating faster clinical treatment and improving diagnostic services while supporting radiologists in making more accurate and efficient decisions for this critical condition. Methods: This research employs an experimental deep learning approach using Convolutional Neural Networks (CNN) to detect pneumothorax in thoracic radiography images. The CNN model is trained on an annotated dataset with preprocessing steps, including zooming, brightness adjustment, flipping and format adjustment, followed by performance evaluation using accuracy, precision, recall, and F1 score metrics. Result: The results showed that the CNN model detected pneumothorax with 79.59% accuracy, a loss of 1.3056, and 1,092 correct predictions out of 1,372 test data. Precision was 51.12%, recall 78.62%, and F1 score 61.96%, confirming the system's potential, though further optimization is needed. Novelty: The novelty of this research lies in developing an automated pneumothorax detection system using a CNN architecture, improving diagnostic accuracy and efficiency. Despite high accuracy, precision and recall can be improved. Future research can focus on optimizing the model and applying data augmentation techniques.
Sistem Informasi Uji Forensik Proses Klasterisasi Protektil Amunisi Senjata Api Menggunakan Algoritma Gray Level Co-occurance Matrix dan K-Mean Clustering Supriyadi, Didik; Widodo, Catur Edi; Isnanto, R. Rizal
Jurnal Algoritma Vol 21 No 2 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-2.2119

Abstract

Pemanfaatan teknologi menjadi solusi saat perkembangan jaman terus meningkat dan berkembang. Tidak terkecuali keterkaitan teknologi untuk bidang keamanan negara. Metode yang mendukung klaterisasi adalah ekstraksi ciri menggunakan Gray Level Co-occurence Matrix (GLCM) yang dilakukan sebelum proses klaterisasi itu sendiri. GLCM sangat cocok digunakan untuk melakukan ekstraksi fitur atau ciri-ciri pada citra yang memiliki pola-pola khusus seperti penelitian pengenalan pola wayang. Prosedur penelitian ini merupakan alur dari flowchat untuk membangun sistem informasi untuk uji forensik proses klasterisasi proyektil amunisi senjata api menggunakan algoritma Gray Level Co-occurrene Matrices (GLCM) dan K-Means clustering. Pada Gambar 3.1 berikut merupakan kerangka sistem informasi sebagai penjelas setiap alur input, proses dan output diilustrasikan.Hasil penelitian menunjukkan bahwa penggunaan metode GLCM sebagai ekstraksi fitur dari image grayscale dan metode K-Means untuk clustering memberikan hasil dan akurasi yang cukup baik. Performa model mencapai 71.14% meski dengan keterbatasan data yang dimiliki. Model tersebut dapat digunakan tidak hanya pada aplikasi console seperti Google Collabs, tetapi juga dapat digunakan pada aplikasi yang memiliki GUI dengan performa aplikasi yang cukup stabil.
Implementation of Hierarchy Attribute-Based Encryption (HABE) to Secure Information Stored in Cloud Storage Arfriandi, Arief; Gernowo, Rahmat; Isnanto, R. Rizal
Sainteknol : Jurnal Sains dan Teknologi Vol. 23 No. 1 (2025): June 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sainteknol.v23i1.22239

Abstract

The usage of cloud storage is currently growing, particularly for media used for information storage. Information saved in cloud storage makes it possible for the owner of the cloud storage to access the Information without the owner's permission. The privacy of access and management of access to the information kept in the cloud storage becomes a problem and security risk when using that cloud storage because the owner cannot ensure that the stored information is only accessed by authorized parties. Access control management is necessary for information owners to guarantee the safety of the information they hold. To regulate access to information kept in cloud storage, this study intends to apply the Hierarchy Attribute-Based Encryption (HABE) technique. A hierarchical attribute base is used to encrypt and decode information saved in cloud storage through the application of HABE, making hierarchical attributes the essential element in controlling access to the information. The hierarchical structure utilized to manage access control for information security will also be detailed in this research. This implementation procedure is completed before the information is stored in cloud storage. The mathematical computations used to secure text information during the encryption and decryption operations are described in the HABE implementation method and debate. By using Hierarchy Attribute-Based Encryption, security concerns with cloud storage can be resolved and flexible access control management in compliance with corporate standards is made possible.  
Analisis Pengenalan Pola Daun Menggunakan Metode Linear Discriminant Analysis (LDA) dan Jarak Minkowski Widyati, Dian Ami; Isnanto, R. Rizal; Riyadi, Munawar Agus
Jurnal Transformatika Vol. 18 No. 2 (2021): January, 2021
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v18i2.2975

Abstract

Indonesia adalah negara tropis yang memiliki keanekaragaman jenis tumbuhan. Tumbuhan terdiri atas tiga organ dasar yaitu akar, batang dan daun. Daun merupakan salah satu bagian yang sering digunakan untuk mengklasifikasikan tanaman, karena setiap jenis tanaman memiliki ciri yang berbeda. Bentuk tepian daun bisa digunakan untuk acuan klasifikasi daun. Otak manusia memiliki keterbatasan dalam mengolah atau mengignat informasi jenis-jenis tanaman yang berdasarkan daun. Oleh karena itu dibutuhkan peralihan pengetahuan manual ke suatu sistem digital. Maka dalam penelitian ini dibuat sistem yang mampu melakukan pengenalan daun menggunakan ekstraksi ciri pada daun menggunakan metide Linear Discriminant Analysis ( LDA ) dan jarak Minkowski.Proses pengenalan pola citra daun diawali dengan pengambilan citra daun, kemudian masuk ke tahap prapengolahan untuk membedakan objek dengan latar belakang. Setelah itu masuk ke tahap ekstraksi ciri menggunakan Linear Discriminant Analysis (LDA ) untuk mendapatkan karakteristik ciri dari citra dan Jarak Minkowski untuk melakukan pengenalan dari pola daun.                       Berdasarkan hasil penelitian dengan jumlah data sebanyak 40 kelas dengan masing-masing kelas sebanyak 6 citra, dengan citra latih sebanyak 160 citra daun dan citra uji sebanyak 80 citra daun. Saat pengenalan menggunakan jarak minkowski menggunakan 3 koefisien yaitu koefisien minkowski 1, 2, dan 3. Dari ­ ­masing-masing koefisien minkowski didapatkan persentase keakurasian. Persentasi keakurasian saat menggunakan koefisien minkowski 1 sebesar 41,25%, koefisien minkowski 2 sebesar 33,75%, dan koefisien minkowski 3 sebesar 30%. Persentase keakurasian pada penelitian ini tidak dapat menghasilkan diangka 80% dikarenakan jumlah data sangat mempengaruhi hasil persentase, semakin banyak data yang ada maka nilai persentase juga akan semakin kecil.
Optimization of Coronary Heart Disease Risk Prediction Using Extreme Learning Machine Algorithm (Case Study: Patients of Dr. Soeselo Hospital) Iswanti, Arie; Isnanto, R. Rizal; Widodo, Catur Edi
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24746

Abstract

Purpose: Coronary heart disease (CHD) is the leading cause of death globally, with 17.8 million deaths reported by the WHO in 2021. Early detection remains a major challenge due to low public awareness and dependence on manual diagnostic procedures. These limitations necessitate the development of automated and accurate predictive models. This study aims to construct a CHD risk prediction model using the Extreme Learning Machine (ELM) algorithm. The research addresses a critical limitation in existing models, namely, poor performance on minority classes (CHD stages 2–4), caused by data imbalance. To overcome this, oversampling techniques such as Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) are applied. The objective is to improve classification performance, particularly in high-risk categories, and to enhance the model’s generalisation capability for real-world implementation. Methods: This research implements the Extreme Learning Machine (ELM) algorithm to achieve optimal prediction results. The data used in this study as the initial database of patients consists of gender, age, height, weight, whether they have diabetes or not, the number of cigarettes consumed daily, and blood pressure. The data will be the main component in building the heart disease prediction system. The prediction classes are: no heart disease, stage 1 heart disease, stage 2 heart disease, stage 3 heart disease, and stage 4 heart disease. The total number of dataset are 521 data points, with 70% of the training data amounting to 364 patients, and 30% of the test data amounting to 157 patients. The data collection process uses patient data from RSUD Dr. Soeselo, Tegal Regency, Central Java, for the years 2023 and 2024. Result: The research successfully developed and evaluated an Extreme Learning Machine (ELM) algorithm for Coronary Heart Disease (CHD) risk prediction using patient data from Dr. Soeselo Hospital. The model achieved an overall accuracy of 82% on the dataset of 157 patients, demonstrating a promising capability for automated risk assessment. Novelty: This predictive model can be utilised in the medical field to facilitate the early detection of heart disease or other risks. This model will soon be introduced in hospitals in the Tegal Regency and City area, Central Java.
Computer Vision for Food Nutrition Assessment: A Bibliometric Analysis and Technical Review Purwati, Nani; Isnanto, R. Rizal; Kartasurya, Martha Irene
Journal of Robotics and Control (JRC) Vol. 6 No. 5 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i5.27525

Abstract

This study examines the latest trends, challenges, and advances in food image segmentation and computer vision-based nutritional analysis. Traditional nutritional assessment methods such as food diaries and questionnaires are limited by their reliance on participant recall and manual processing, which reduces their accuracy and efficiency. As an alternative, advances in machine learning and deep learning have shown potential in automating food identification and estimating nutrient content, such as calories, protein, carbohydrates, and fat. This study was conducted through bibliometric analysis and technical review of publications from the Scopus database, using a structured search strategy and applying inclusion and exclusion criteria. Articles were selected based on topic relevance, use of machine learning or deep learning methods, publication in English, and publication between 2020 and 2024. The review identified key research trends, key contributors, popular methods such as CNN and YOLO, and the most frequently reported limitations, including lack of dataset diversity, inaccuracy in food volume estimation, and the need for real-time integrated systems. These limitations were analyzed based on the methodology and findings of the reviewed studies. This review is expected to be a comprehensive reference for researchers and practitioners in developing food image segmentation technology for more accurate and applicable nutritional assessment.
Co-Authors Achmad Hidayatno Adi Dhama Kameswara Adi Mora Tunggul Adian Fatchur R Adian Fatchur Rochim Adrian Khoirul Haq Adrianus Stephen, Adrianus Afrizal Mohamad Riand Aghus Sofwan agung setiawan Agung Wicaksono Ahmad Bahauddin Ahmad Fashiha Hastawan Ajub Ajulian Zahra Macrina Ali, Sarifa Isna Ali, Sarifa Isna Alwin Indra Fatra Aminullah Ruhul Aflah Anang Paramita Wahyadyatmika Andino Maseleno Andre Lukito Kurniawan, Andre Lukito Angga Setiawan Anggie Salsa Saputra Antonius Dwi Hartanto Antonius Hendry Setyawan Ardian Wijaya Arfriandi, Arief Arie Firmansyah Permana Aris Triwiyanto Aris Triwiyatno Bagus Hario Setiadji Basuki Rahmat Masdi Siduppa Bondhan Tunjung Bowo Leksono Budi Setiyono Budi Warsito Candra Laksono Catur Edi Widodo Causa Prima Wijaya Chairunnisa Adhisti Prasetiorini Chandra Yogatama Chauhan, Rahul Darmawan Surya Kusuma Dela Nurlaila Dewi Lestari Dian Wijayanto Dictosendo Noor Pambudi Rahayu Didik Supriyadi, Didik Djoko Windarto Donny Zaviar Rizky Dony Bagus Rudiyanto Dyah Kusuma Mauliyani, Dyah Kusuma Eko Didik Widianto Eliezer, Petrick Jubel Enda Wista Sinuraya Endang Purbowati Endriawan Endriawan Eskanesiari Eskanesiari Fachrul Rozy Fachry Abda El Rahman Fajar Adi Nugroho Fara Mantika Dian Febriana, Fara Mantika Fardana, Nouvel Izza Febry Santo Ferry Hadi Fifiana Wisnaeni Fikri Ahmad Affandi Habiba, A. Herdhian Cahya Novanto Herjuna Dony Anggara Putra, Herjuna Dony Anggara Heru Prastawa Ilina Khoirotun Khisan Iskandar Imam Santoso Irwan Andaltria Iswanti, Arie Kholid, Kholid Kodrat Imam Satoto Kurnia, Dita Juni Lasmedi Afuan Lathifah Alfat, Lathifah Lukas Aditratika M. Azwar A. G. N. M. Ikhsan Mulyadi M. Wirdan Syahrial Maman Somantri Maria Fitriana Mario Christy Sinuraya Martha Irene Kartasurya Meet Shah, Meet Meidiana Dwidiyanti Melly Arisandi Muhammad Satriya Utama Mukharrom Edisuryana Munawar Agus Riyadi Mutiara Shabrina Nanang Trisnadik Nani Purwati Natanael Benino Tampubolon, Natanael Benino Novettralita, Ucky Pradestha Nugroho Arif Widodo Nur Arifin Akbar Nur Rizky Rosna Putra Nurul Ifan Purba Oky Dwi Nurhayati Patel, Raj Praseti, Agung Budi Praseti, Agung Budi Prasetijo, Aging Budi Pringgo Budi Utomo R. Edith Indera Bagaskara R. G Alam Nusantara P.H, R. G Alam R. Mh. Rheza Kharis Rachmad Arief Setiawan Ragil Aji Prastomo Rahmat Gernowo Raidah Hanifah Raithatha, Bhavya Ramchandani, Paras Relung Satria D Rico Eko Wibowo Rizky Parlika, Rizky Rody Verdika Cahyadi RR. Ella Evrita Hestiandari Saputra, I Gede Dharma Setyowati, Ro'fah Shabrina Mihanora Sharma, Ansh Shriyal, Harsh Siboro, Septihadi Klinsman Sompura, Jayesh Sudjadi Sudjadi Sumardi . Suseno, J.Endro Teguh Dwi Prihartono Theodora Anita Fidelia Tito Tri Pamungkas Tri Murwanto Tri Prasetyo Wahyul Amien Syafei Widyati, Dian Ami Yuli Christiyono Yuli Christyono Yuli Syarif Zaka Bil Fiqhi