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All Journal International Journal of Electrical and Computer Engineering Majalah Forum Teknik UGM TEKNIK JURNAL SISTEM INFORMASI BISNIS Jurnal Sistem Komputer Jurnal Sains dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Sisfokom (Sistem Informasi dan Komputer) Aptisi Transactions on Technopreneurship (ATT) Journal of Information Systems and Informatics Jurnal Ilmu Teknik dan Komputer Jurnal Nasional Teknik Elektro dan Teknologi Informasi Buletin Abdi Masyarakat (BAM) Jurnal INFOTEL Prosiding Seminar Nasional Unimus Scientific Journal of Informatics
Oky Dwi Nurhayati
Departemen Teknik Komputer, Fakultas Teknik, Universitas Diponegoro
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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SISTEM PAKAR MENDETEKSI GANGGUAN OBSESSIVE COMPULSIVE DISORDER MENGGUNAKAN METODE BACKWARD CHAINING Ikhsan, Hammas Zulfikar; Nurhayati, Oky Dwi; Windarto, Yudi Eko
Jurnal Transformatika Vol. 17 No. 1 (2019): July 2019
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v17i1.1276

Abstract

Obsessive Compulsive Disorder (OCD) adalah salah satu jenis gangguan psikologi yang berasal dari rasa cemas dan takut yang muncul secara tiba-tiba dan tidak dapat dikendalikan oleh penderitanya. Jika gangguan ini dibiarkan maka akan mengganggu aktivitas sehari-hari penderita dan menyebabkan depresi. Dalam proses diagnosa oleh dokter, pasien menggunakan kata yang tidak pasti seperti jarang , lumayan, dan cukup dalam menjawab pertanyaan dari dokter. Hal ini menyebabkan dokter kesulitan dalam melakukan diagnosa. Dari permasalahan diatas, maka dibuatlah aplikasi sistem pakar mendeteksi gangguan obsessive compulsive disorder menggunakan metode backward chaining dan certainty factor untuk memudahkan dan meningkatkan tingkat kepercayaan dokter dalam mendeteksi gangguan obsessive compulsive disorder pasien. Aplikasi ini menggunakan ilmu kecerdasan buatan yaitu metode backward chaining dan certainty factor yang digunakan dalam perancangan dan pembuatan aplikasi sistem pakar. Aplikasi yang dibuat berbasis website menggunakan bahasa PHP dan MySQL. Dari penelitian ini, dihasilkan sistem pakar yang dapat menampilkan kemungkinan dideritanya tipe gangguan OCD. Hasil pengujian aplikasi telah sesuai dengan pengetahuan dari pakar. Pengujian sistem aplikasi menggunakan pengujian black box yang menunjukan semua fungsi yang ada pada aplikasi dapat berjalan sesuai yang diharapkan.  
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN LAHAN KRITIS MENGGUNAKAN PREFERENCE RANKING ORGANIZATION METHOD FOR ENRICHMENT EVALUATION (PROMETHEE) Windarto, Yudi Eko; Fathuddin, Harits; Nurhayati, Oky Dwi
Jurnal Pengembangan Rekayasa dan Teknologi Vol. 3 No. 2 (2019): November (2019)
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/jprt.v15i2.1644

Abstract

Critical land becomes a specific problem in data processing in the environmental field. Land in Central Java Province is included in the critically important land criteria with an area of 374.000 hectares. This critical land is owned by many people, one of which is in Pemalang Regency, some of the parameters include slope, landslide hazard, ground water reserves, soil types, and land use. Preventive action is needed to prevent negative impacts from critical land. Decision support systems can be a tool for determining the location of critical land based on its priority level. Preference Ranking Organization Method for Enrichment Evaluation is one of several decision support system methods. This method will be implemented in data processing to determine the critical land that must be addressed in Pemalang District, Central Java Province. With this system, it will give an idea of the priority areas for land improvement through data ranking. This system was built using PHP programming language and MySQL database. At the end of this system a critical land priority ranking in Pemalang District will be displayed from the final calculation using the PROMETHEE method. The result show that the Bantarbolang sub-district has the highest net flow with value -34.10 as the region with the highest critical land priority.
Global Research Trends and Map on Machine Learning Applications in Stunting Detection in Vulnerable Populations: A Bibliometric Analysis Bachri, Otong Saeful; Widodo, Catur Edi; Nurhayati, Oky Dwi
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1248

Abstract

Stunting and malnutrition continue to be significant public health challenges, particularly in low-income and rural populations. With the growing reliance on data-driven strategies in public health, machine learning (ML) has emerged as a promising tool for identifying, classifying, and predicting conditions related to undernutrition. This study presents a bibliometric analysis of global research from 2019 to 2025, focusing on the application of ML techniques—such as clustering, support vector machines (SVM), and random forest—in addressing malnutrition and stunting. A total of 417 Scopus-indexed publications were analyzed using Biblioshiny (R) to assess research trends, key themes, influential authors, prominent journals, and thematic evolution. The analysis reveals a consistent growth rate of 10.72% in publications, with notable contributions from China and other low- and middle-income countries. Keyword mapping highlights that “machine learning,” “spatial analysis,” and “stunting” are central to the research, although they remain areas for further development. Thematic evolution indicates a shift towards more integrated, context-aware approaches, with a growing focus on built environments and vulnerable populations. The study concludes that while ML holds significant promise for advancing decision-making in child health and nutrition, its impact will depend on continued methodological refinement and effective implementation within public health systems.
Model Prediksi Kinerja Siswa Berdasarkan Data Log LMS Menggunakan Ensemble Machine Learning Ardianti, Mifta; Nurhayati, Oky Dwi; Warsito, Budi
JST (Jurnal Sains dan Teknologi) Vol. 12 No. 3 (2023): Oktober
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v12i3.59816

Abstract

Institusi pendidikan saat ini menerapkan Learning Management System (LMS) sebagai sarana pembelajaran online. LMS dapat merekam sejumlah besar data perilaku siswa pada log LMS. Data perilaku ini dapat dikumpulkan dan digunakan untuk memprediksi kinerj belajar siswa. Sehingga, diperlukan analisis yang dapat mengubah sejumlah data yang tersimpan tersebut menjadi sebuah pengetahuan yang dapat meningkatkan kualitas pengajaran pada institusi pendidikan. Pada penelitian ini, mengusulkan model prediksi kinerja belajar siswa menggunakan ensemble machine learning berdasarkan ekstraksi ciri yang berhubungan dengan interaksi siswa pada LMS. Pemodelan dilakukan dengan menerapkan tiga jenis ensemble machine learning yaitu ; bagging, boosting dan voting. Hasil penelitian menunjukkan bahwa model ensemble machine learning yaitu bagging, boosting dan voting berhasil digunakan untuk memprediksi kinerja siswa dengan accuracy sebesar 81.25% dengan percision 0.810, recall 0.812 dan f-measure 0.809 yang diperoleh model bagging. Temuan pada penelitian ini adalah ensemble machine learning dapat diterapkan sebagai model prediks kinerja siswa berdasarkan data Log LMS. Institusi pendidikan baik sekolah maupun perguruan tinggi diharapkan dapat merancang sebuah kurikulum LMS untuk meningkatkan kualitas akademik institusi tersebut. Selain itu institusi pendidikan dapat memprediksi bagaimana kinerja siswanya, sehingga dapat meningkatkan prestasi akademik.
Analisis Pengaruh Model HOT-Fit Terhadap Pemanfaatan Sistem Informasi Kinerja Anggaran Gumay, Naretha Kawadha Pasemah; Gernowo, Rahmat; Nurhayati, Oky Dwi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 4: Agustus 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020743410

Abstract

Sistem informasi kinerja anggaran digunakan untuk memantau kinerja anggaran di fakultas Universitas Sriwijaya berdasarkan Indikator Kinerja Pelaksanaan Anggaran. Analisis pengaruh sistem menggunakan model Human, Organization, and Technology-Fit (HOT-Fit) dilakukan untuk menganalisis keberhasilan penerapan sistem, ketiga komponen penilaian tersebut mendapatkan net benefit berupa dampak sistem. Model HOT-Fit dalam penelitian ini memiliki delapan variabel, yaitu System Development (SD), System Use (SU), User Satisfaction (US), Structure (STR), Environment (LO), System Quality (SQ), Information Quality (IQ), dan Service Quality (SEQ). Jumlah sampel responden adalah 59, teknik analisis menggunakan PLS-SEM yang terdapat dua tahapan analisis. Pertama, measurement model digunakan untuk menguji reliabilitas dan validitas. Reliabilitas diambil dari nilai loading factor dan composite reliability yang memiliki nilai di atas 0,7, sedangkan validitas memiliki nilai di atas 0,5 dari AVE dan cross-loading indikator dimana nilai konstruk semua variabel lebih tinggi dari korelasi konstruk blok lain. Kedua, structural model diambil dari hasil uji path coefficient, coefficient of determination, dan t-test. Path coefficient terdapat empat jalur yang tidak signifikan (LO→SD, LO→SU, SD→SU, dan SQ→US) memiliki nilai dibawah 0,1. Coefficient of determination terdapat enam variabel dengan tingkat kuat dengan nilai sekitar 0,670 (LO, SD, SU, US, IQ, dan SQ) dan satu tingkat moderat dengan nilai sekitar 0,333 (STR). T-test terdapat dua belas hipotesis yang diterima dari sembilan belas hipotesis yang memiliki nilai lebih besar dari 1,96. Faktor-faktor yang paling kuat memengaruhi keberhasilan sistem adalah SU, US, STR, LO, dan SEQ. AbstractBudgeting performance information system is used to monitor budget performance at the faculty of Sriwijaya University based on Budget Implementation Performance Indicator. An analysis using Human, Organization, and Technology-Fit (HOT-Fit) model is conducted to analize the system implementation, those components get a net benefit as impact. The studied model has eight variables, System Development (SD), System Use (SU), User Satisfaction (US), Structure (STR), Environment (LO), System Quality (SQ), Information Quality (IQ), and Service Quality (SEQ). With 59 respondents, two stage of PLS-SEM technique is used for analysis. Firstly, measurement models for reliability and validity. Reliability is set from loading factor and composite reliability which values above 0.7, while the validity from AVE which values above 0.5 and cross-loading indicators where the block constructs from all variables higher than the correlation with others. Secondly, structural model, taken from the path coefficient, coefficient of determination, and t-test results, which have four insignificant pathways (LO→SD, LO→SU, SD→SU, SQ→US) which values below 0,1. The Coefficient of determination test has six variables with strong levels which values about 0,670 (LO, SD, SU, US, IQ, and SQ) and one moderate levels which values about 0,333 (STR). The T-test contained twelve accepted hypotheses from the nineteen hypotheses which values bigger than 1,96. The factors that strongly affect the success of the system are SU, US, STR, LO, and SEQ. 
Sistem Isyarat Bahasa Indonesia (SIBI) Metode Convolutional Neural Network Sequential secara Real Time Nurhayati, Oky Dwi; Eridani, Dania; Tsalavin, Muhammad Hafiz
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 4: Agustus 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022944787

Abstract

Bahasa isyarat dengan menggunakan gerakan tangan biasanya dilakukan oleh tuna rungu dan tuna wicara. Bahasa isyarat yang digunakan di Indonesia adalah SIBI (Sistem Isyarat Bahasa Indonesia). Namun, penggunaan bahasa isyarat tangan tidak selalu di mengerti oleh manusia normal sehingga dibutuhkan perangkat tambahan yang dapat mempermudah dalam menerjemahkan suatu isyarat. Perangkat tambahan yang dikembangkan dalam penelitian ini melibatkan teknologi visi komputer deep learning sehingga menghasilkan tools untuk menerjemahkan bahasa isyarat tangan. Dalam penelitian ini, gambar isyarat tangan di capture menggunakan webcam kemudian dilakukan pre-processing dengan mengubah gambar ke dalam bentuk HSV. Gambar yang digunakan dalam penelitian berupa citra sebanyak 26 kelas huruf alfabet SIBI dan 3 kelas tambahan, dengan masing-masing kelas memiliki 1000 gambar. Kemudian dilakukan cropping dan thresholding dengan menempatkan isyarat tangan yang berbentuk huruf  kedalam kotak yang merupakan area ROI untuk memudahkan pengenalan. Teknologi visi komputer deep learning convolutional neural network (CNN) digunakan untuk feature learning dan mengklasifikasi isyarat tangan pada sebuah obyek. Untuk menguji metode CNN, digunakan berbagai variasi cahaya sebesar 10-200 lux, serta jarak dari tangan ke webcam 50-200 cm. Hasil penelitian dengan metode CNN pada citra isyarat tangan memberikan akurasi sebesar 92%, presisi 91,96%, sensitivitas 91,9%, spesivisitas 91,96% dan f1 score 91,9%. AbstractSign language is usually used by deaf and speech impaired persons. The Sistem Isyarat Bahasa Indonesia (SIBI) is a hand signal language used in Indonesia. The use of hand signals is not always understood by normal humans, such that additional devices are needed to make sign translation easier. The additional device in this study is developed using deep learning and computer vision technology to produce a hand signal translation tool. This study uses 29 sign images for a dataset, consisting of 26 letters of the alphabet and 3 additional signs, namely space, delete, and unclassified. Pre-processing is performed by converting the image into HSV, cropping, and thresholding to make easy recognition. The convolutional neural network (CNN) method is then used as a learning feature and hand signals classifier on an object. The testing phase is performed on various lights ranging from 10-200 lux and the hand distance to the webcam is about 50-200 cm. Experimental results show that the CNN method on the hand signal image could provide an accuracy of 97.2%, precision of 91.96%, sensitivity of 91.9%, specificity of 91.96%, and F1 score of 91.9%, respectively.
Klasifikasi Jenis Ikan Laut K-Nearest Neighbor Berdasarkan Ekstraksi Ciri 2-Dimensional Linear Discriminant Analysis Al Iman, Yusraka Dimas; Isnanto, R Rizal; Nurhayati, Oky Dwi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 4: Agustus 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024106787

Abstract

Indonesia adalah suatu negara kepulaun yang memiliki 2/3 wilayah lautan, secara sektor indonesia memiliki potensi pangan yang sangan besar dalam sektor perikanan. Ikan di dunia yang berhasil diuraikan sebanyak 27.000 terutama paling banyak dilaut indonesai. Ikan adalah salah satu keanekaragaman biologi yang menyusun ekosistem bahari. Ikan mempunyai bentuk serta ukuran eksklusif yang berbeda jenis yang satu dangan jenis yang lain. Pengenalan spesies ikan umumnya dilakukan secara manual dengan pengamatan mata. Tujuan penelitian ini untuk mengenali spesies ikan laut. 2-Dimensional Linear Discriminant Analysis (2D-LDA) dipergunakan untuk ekstraksi ciri dan K-Nearest Neighbor (K-NN) dipergunakan untuk klasifikasi jenis ikan laut. Fitur 2-Dimensional Linear Discriminant Analysis (2D-LDA) yang diekstraksi untuk menghasilkan dua matrik baru yaitu matrik score. Klasifikasi menggunakan metode K-Nearest Neighbor (K-NN) dengan membandingkan nilai k-n. Penelitian ini menggunakan 5 jenis ikan laut, dengan total data latih 800 gambar dan data uji 160 gambar. Hasil percobaan tebaik diperoleh k-9 dengan tingkat akurasi terbaik sebesar 93,12%, presisi 82,05%, recall 100%, dan F-1 score 90,14%.AbstractIndonesia is an archipelagic country which has 2/3 of the sea area, in terms of sector Indonesia has enormous food potential in the fisheries sector. There are 27,000 fish in the world that have been successfully described, especially in the Indonesian seas. Fish is one of the biological diversity that makes up the marine ecosystem. Fish have specific shapes and sizes that differ from one type to another. The identification of fish species is generally done manually by eye observation. The purpose of this research is to identify marine fish species. 2-Dimensional Linear Discriminant Analysis (2D-LDA) is used for feature extraction and K-Nearest Neighbor (K-NN) is used for classification of marine fish species. The 2-Dimensional Linear Discriminant Analysis (2D-LDA) features were extracted to produce two new matrices, namely the score matrix. The classification uses the K-Nearest Neighbor (K-NN) method by comparing the k-n values. This study used 5 types of marine fish, with a total of 800 images of training data and 160 images of test data. The best experimental results were obtained by k-9 with the best accuracy rate of 93.12%, precision of 82.05%, recall of 100%, and F-1 score of 90.14%.
User Experience Improvement (MSMEs and Buyers) Mobile AR Using Design Thinking Methods Dwiyanasari, Desty; Nurhayati, Oky Dwi; Surarso, Bayu; Nugraheni, Dinar
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.v12i2.24088

Abstract

Purpose: This research aims to improve the User Experience (UX) of Augmented Reality (AR) mobile applications for MSMEs and buyers through the Design Thinking method. This research solves the problem of suboptimal UX in AR-based mobile applications. This study hypothesizes that the application of Design Thinking can result in significant improvements in the UX of AR mobile applications, which is evidenced by an increase in heuristic evaluation scores. Methods: The Design Thinking approach (Empathize, Define, Ideate, Prototype, Test) is implemented. Data were collected through interviews, observations, and heuristic evaluation questionnaires. Result: Initial heuristic testing showed several usability problems in the developed AR mobile applications, such as Help and Documentation (H10), Recognition Rather than Recall (H6), and Error Prevention (H5). After the application of the Design Thinking method and design iteration, the heuristic testing showed that the results of the evaluation comparison before and after the improvement showed a high effectiveness of the corrective actions taken, with an average decrease in severity score of 37% based on the Nielsen scale (0–4), indicating that the most critical and major issues were successfully reduced to cosmetic or minor levels. Novelty: This research contributes in the form of a practical framework to improve the UX of AR mobile applications for MSMEs and buyers by utilizing the Design Thinking method. The results of this research can be a reference for developers in designing user-friendly AR mobile applications.
Analisis Penerimaan dan Kesuksesan Aplikasi M-health pada Lansia menggunakan Model UTAUT dan Delone & McLean Merdekawati, Utami; Nugraheni, Dinar Mutiara Kusumo; Nurhayati, Oky Dwi
Jurnal Sistem Informasi Bisnis Vol 14, No 3 (2024): Volume 14 Nomor 3 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss3pp267-276

Abstract

M-health plays a crucial role in providing medical services through features like online doctor appointments. While it offers convenience, there are challenges in its adoption among the elderly. The success of M-health depends on user acceptance and continued use. Therefore, an evaluation of information technology focused on the elderly in Indonesia is necessary. Technology accepted by users is not necessarily successful, and vice versa. This study aims to identify factors influencing the acceptance and success of M-health applications among the elderly. It combines the UTAUT and Delone & McLean models to investigate acceptance and success factors. The variables used are performance expectancy, effort expectancy, information quality, system quality, service quality, user satisfaction, and continuance intention. The PLS-SEM method is used to process respondent data. Analysis result shows that 61.6% of elderly users' satisfaction with M-health is influenced by information quality, service quality, performance expectancy, and effort expectancy. Meanwhile, 59.4% of the continuance intention is influenced by user satisfaction. This indicates that the application is well received and successful because it provides a satisfying experience. This study confirms that the combination of the UTAUT and Delone & McLean models is adequate.
Analysis of Naïve Bayes and K-Nearest Neighbors Algorithms for Classifying Fishermen Aid Eligibility Nasrullah, Muhammad; Surarso, Bayu; Nurhayati, Oky Dwi
Jurnal Penelitian Pendidikan IPA Vol 10 No 10 (2024): October
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i10.8818

Abstract

This article analyzes the use of data mining with Naïve Bayes and K-Nearest Neighbor (KNN) algorithms to build classification models and evaluate their performance in identifying fishermen eligible for aid. The study aims to compare the effectiveness of these algorithms in handling imbalanced datasets using the Synthetic Minority Over-sampling Technique (SMOTE). The research applies SMOTE to improve the balance of the dataset before classification. Without SMOTE, Naïve Bayes achieved an accuracy of 97.01%, precision of 94.16%, recall of 96.67%, and F1-score of 95.39%. KNN, on the other hand, reached an accuracy of 94.04%, precision of 94.53%, recall of 86.00%, and F1-score of 90.06%. After applying SMOTE, both algorithms improved: Naïve Bayes attained an accuracy of 98.33%, precision of 96.86%, recall of 100.00%, and F1-score of 98.49%, while KNN reached an accuracy of 96.90%, precision of 97.72%, recall of 96.19%, and F1-score of 96.94%. The results show that Naïve Bayes, with SMOTE, outperforms KNN in managing data imbalance and accurately classifying eligible fishermen for aid.
Co-Authors Achmad Hidayatno Adhi Susanto Adi Mora Tunggul Adi, Yudi Restu Agung Budi Prasetijo Agung Budi Prasetijo Agus Subhan Akbar, Agus Subhan Agus Subkhi Hermawan Agus Supriyanto Ahmad Aviv Mahmudi Ahmad Muzami Aji Yudha Al Iman, Yusraka Dimas Alim Muadzani Ambrina Kundyanirum Amrina Rosyada Anggi Anugraha Putra Anggit Sri Herlambang Anggoro Mukti Anisa Eka Utami Annisa Hedlina Hendraputri Aria Hendrawan, Aria Arief Puji Eka Prasetya Atik Zilziana Muflihati Noor Aulia Medisina Ramadhan Bayu Surarso Budi Warsito Catur Edi Widodo Christine Dewi Damar Wicaksono Danal Meizantaka Daeanza Dania Eridani Dania Eridani Deryan Gelrandy Diana Nur Afifah, Diana Nur Dinar Mutiara Kusumo Nugraheni Dwiana Okviandini Dwiyanasari, Desty Eggy Listya Sutigno Eko Didik Widianto Eko Sediyono Fardana, Nouvel Izza Fathuddin, Harits Febi Andrea Renatha Galuh Boy Hertantyo Gayuh Nurul Huda Gumay, Naretha Kawadha Pasemah Hadi Hilmawan Hanna Mariana Baun, Hanna Mariana hastuti, Isti Pudji Hendra Pria Utama Hengki Hengki Ike Pertiwi Ike Pertiwi Windasari Ike Pertiwi Windasari Ikhsan, Hammas Zulfikar Imaduddin Abdul Rahim Indra Aditia Indra Permana Isti Pudjihastuti Julce Adiana Sidette, Julce Adiana Juwanda, Farikhin Keszya Wabang Kurniawan Teguh Martono Kusworo Adi Lazuardi Arsy Lia Dorothy M Irfan Syarif Hidayatullah M. Rizki Kurniawan Maesadji Tjokronagoro Menur Wahyu Pangestika, Menur Wahyu Merdekawati, Utami Mey Fenny Wati Simanjuntak Mifta Ardianti Migunani Migunani Muhammad Nasrullah Muhammad Naufal Prasetyo Muhammad Ridwan Asad Mustafid Mustafid Ningrum, Alifvia Arvi Ninik Rustanti Nofiyati Nofiyati, Nofiyati Nugraheni, Dinar Nugroho Adhi Santoso Nurazizah Nurazizah Nurhuda Maulana Nurul Arifa Nuryanto . Otong Saeful Bachri Prio Pambudi R Rizal Isnanto R. Rizal Isnanto R. Rizal Isnanto Rahmat Gernowo Rahmawati, Nurhita Reza Najib Hidayat Reza Setiawan Rian Haris Muda Nasution Rinta Kridalukmana Risma Septiana Rismawan Fajril Falah Riyadhi Sholikhin Rizki Galang Rahmadani Satriaji Cahyo Nugroho Siswo Sumardiono Sri Widodo, Thomas Suryo Mulyawan Raharjo Suryono Suryono Teguh Hananto Widodo Thomas Sri Widodo Tristy Meinawati Tsalavin, Muhammad Hafiz Tyas Panorama Nan Cerah Ulinuha, Ajik Wahyul Amien Syafei Wijaya Wahyudi Akbar Yessy Kurniasari Yudhi Kasih Pasaribu Yudi Eko Windarto Yudi Restu Adi Yusuf Arya Yudanto Zaskia Wiedya Sahardevi