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Syahroni Hidayat
Universitas Negeri Semarang
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EVALUASI SISTEM INFORMASI PENGGUNAAN E-LEARNING SEBAGAI SISTEM PERKULIAHAN PERGURUAN TINGGI Uswatun Hasanah; Syahroni Hidayat; Danang Tejo Kumoro
JURNAL INFOTEL Vol 12 No 4 (2020): November 2020
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v12i4.475

Abstract

This study aims to evaluate the use of technology to support teaching and learning activities. Lecturers and students have applied e-learning to teach subjects. The purpose of this evaluation is to measure the success of the use of STMIK Bumigora e-learning by using the Technology Acceptance Model (TAM) approach, which is an approach that can explain user behavior towards the use of technology. Evaluation of the use of e-learning is formulated into a model based on the TAM model, while SEM (Structural Equation Modelling) is used for data analysis. Based on the measurement analysis in this study, several factors most influenced the effectiveness of e-learning, namely the usage tutorial for users, ICT facilities related to the Ease of accessing the internet network. Meanwhile, in structural analysis, it was found that attitudes toward the use and perceived usefulness were strongly correlated with real use factors. The actual use is a real condition of the use of e-learning measured by the frequency and duration of time in using the technology, which is influenced by the user's belief in accepting the existence of e-learning in STMIK Bumigora and user beliefs related to the benefits when using it. Therefore, attitudes toward the use and perception of usefulness are the main determining factors in measuring the frequency and duration of e-learning use.
IMPLEMENTASI PERTANIAN CERDAS BERBASIS IOT PADA KELOMPOK TANI TEGER 02 DESA MANGUNSARI: Implementation of IoT-Based Smart Farming in the TEGER 02 Farmer Group in Mangunsari Village Anan Nugroho; Feddy Setio Pribadi; Mona Subagja; Syahroni Hidayat; Ahmad Zein Al Wafi; Muhammad Fathurrahman; Zidan Vieri Wijaya; Agus Ardiyanto; Haikal Abror
Jurnal Abdi Insani Vol 10 No 4 (2023): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v10i4.1267

Abstract

Plantation is a very important sector to meet food needs in Indonesia. However, plantations in Indonesia do not always increase over time. There are still many plantation sectors whose productivity is still low, especially in rural areas. Factors such as lack of capital and technology, lack of market access, and social problems such as land conflicts often become obstacles to the development of a productive and sustainable plantation sector. One example of a farmer group experiencing problems in water distribution is the TEGER 02 farmer group in Mangunsari Village, Semarang City, Central Java. This group has 7 hectares of land and 20 workers. However, the biggest obstacle faced is ineffective water distribution, especially during the dry season. Currently, to water 3000 𝑚2 of cultivated land, the group needs a full day involving 3 workers. Of course, the ratio between a few workers and a large area of land is inversely proportional, so it takes workers a long time to water the plantation land. To overcome this problem, a tool has been developed that can help farmers distribute water that utilizes strong internet access, the Internet of Things (IoT). IoT is very suitable to be applied to Mangunsari Village plantations. An IoT-based sprinkler that can help farmers in watering automatically which is connected to a website application so that it can be accessed directly using the farmer's smartphone. As an electricity supply, photovoltaics are used as an environmentally friendly energy source with a conservation perspective. The results of the service show that this activity makes it very easy for farmers to overcome watering problems which are difficult to control because the number of workers is not proportional to the size of the plantation land. However, adjustments to the website need to be made to produce an application that is more responsive when accessed via farmers' smartphones.
Comparison of Ensemble Learning Methods for Mining the Implementation of the 7 Ps Marketing Mix on TripAdvisor Restaurant Customer Review Data Sunarko, Budi; Hasanah, Uswatun; Hidayat, Syahroni
International Journal of Artificial Intelligence Research Vol 7, No 2 (2023): December 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i2.1096

Abstract

The 7P marketing mix encompasses various business facets, notably the Process element governing internal operations from production to customer service. With the surge in online customer feedback, assessing machine learning efficacy, especially ensemble learning, in classifying 7P-related customer review data has gained prominence. This research aims to fill a gap in existing literature by evaluating ensemble learning’s performance on 7P classification, an area not extensively explored despite prior sentiment analysis studies. Employing a methodology merging Natural Language Processing (NLP) with ensemble learning, the study processes restaurant reviews using NLP techniques and employs ensemble learning for precision and accuracy. Findings demonstrate that DESMI yielded the highest performance metrics with accuracy at 0.697, precision at 0.699, recall at 0.697, and an F1-score of 0.684. These outcomes underscore ensemble learning's potential in handling complex datasets, signifying its relevance for marketers and researchers seeking comprehensive insights from customer reviews within the 7P marketing mix domain. This study sheds light on how ensemble learning outperforms its foundational methods, indicating its prowess in extracting meaningful insights from diverse and intricate customer feedback.
Penerapan Stacking Ensemble Learning untuk Klasifikasi Efek Kesehatan Akibat Pencemaran Udara Sunarko, Budi; Hasanah, Uswatun; Hidayat, Syahroni; Muhammad, Naufal; Ardiansyah, Muhammad Irfan; Ananda, Briska Putra; Hakiki, Muhammad Khikam; Baroroh, Luluk Taufiqul
Edu Komputika Journal Vol 10 No 1 (2023): Edu Komputika Journal
Publisher : Jurusan Teknik Elektro Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukomputika.v10i1.72080

Abstract

Pencemaran udara merupakan masalah serius yang berdampak negatif pada kesehatan manusia. Berbagai jenis polutan udara seperti partikel halus, sulfur dioksida, nitrogen oksida, dan ozon dapat menyebabkan gangguan pernapasan, penyakit jantung, kanker paru-paru, dan masalah kesehatan lainnya. Untuk memahami dampak kesehatan pencemaran udara, klasifikasi efek kesehatan akibat pencemaran udara menjadi penting. Metode klasifikasi ini membagi efek kesehatan berdasarkan jenis polutan, dosis, dan waktu paparan. Penelitian ini mengusulkan penerapan metode klasifikasi dengan ensemble learning untuk mengidentifikasi polutan berdampak dan tingkat risiko kesehatannya. Ensemble learning adalah teknik pembelajaran mesin yang menggabungkan beberapa model untuk meningkatkan akurasi prediksi. Stacking ensemble learning merupakan salah satu metode yang digunakan dalam klasifikasi efek kesehatan pencemaran udara dengan mengintegrasikan beberapa model dasar seperti Logistic Regression, Decision Tree, K-Nearest Neighbor, Support Vector Machine, dan Multi-Layer Perceptron. Hasil penelitian menunjukkan bahwa model Stacking memberikan performa tertinggi dengan akurasi sekitar 99,9% pada dataset baik yang seimbang maupun tidak seimbang. Namun, model Decision Tree dan K-Nearest Neighbor juga berhasil memberikan performa yang sangat baik. Waktu pelatihan model menjadi pertimbangan penting, di mana K-Nearest Neighbor dan Decision Tree memiliki waktu yang jauh lebih singkat dibandingkan dengan model Stacking.
Integration of Sentiment Analysis and RFM in Restaurant Customer Segmentation: A 7P-Based CRM Model with Clustering Sunarko, Budi; Hasanah, Uswatun; Hidayat, Syahroni; Rachmawati, Rina
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.633

Abstract

The increasing use of digital platforms like Tripadvisor has created opportunities to transform customer review data into strategic insights for Customer Relationship Management (CRM). This study proposes a novel CRM model by integrating the Recency, Frequency, Monetary (RFM) framework with the 7P marketing mix to segment restaurant customers more effectively. Using 3,716 Tripadvisor reviews, annotated based on 7P elements and clustered through unsupervised learning, three key customer segments were identified: acquisition, retention, and win-back. Evaluation metrics show strong clustering performance with a Silhouette Score of 0.73 and a Davies-Bouldin Score of 0.08. The acquisition cluster (Product) demonstrates the highest Frequency (37,664) and Monetary value (64.94), signifying high engagement and revenue potential. The retention cluster (Physical Evidence, Place, Process, Promotion, Traveler) shows stable interaction patterns with Recency values of 1261–1262 and moderate Frequency (378–2,079). The win-back cluster (Price, People) reflects lower Frequency (198–946) but equal Recency (1259), indicating recent but infrequent activity, which is ideal for reactivation strategies. By mapping customer reviews to 7P labels and analyzing them using RFM, the model uncovers specific behavioral patterns tied to service quality, pricing, and promotions. This integration allows restaurants to apply tailored strategies: offering loyalty rewards to high-frequency customers, promotional incentives for those with high Recency, and prioritizing high-monetary customers for exclusive programs. The novelty of this research lies in its combined use of sentiment-based review analysis and RFM–7P segmentation, offering a scalable, data-driven framework for enhancing customer satisfaction, loyalty, and long-term business growth in the restaurant industry.
Penentuan Filterbank Wavelet Menggunakan Algoritma Mean Best Basis untuk Ekstraksi Ciri Sinyal Suara Ber-Noise Abdurahim, Abdurahim; Hidayat, Syahroni
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 1: Februari 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Belakangan ini filterbank berbasis wavelet sebagai ekstraktor ciri mulai banyak dikembangkan untuk dapat menggantikan peran ciri Mel Frequency Cepstral Coefficient (MFCC) dalam sistem pengenalan suara otomatis. Salah satu filterbank ciri wavelet yang dikembangkan adalah Wavelet-Packet Cepstral Coefficient (WPCC). Namun sejauh ini pengembangannya hanya difokuskan untuk suara tanpa noise. Sehingga penelitian ini bertujuan untuk mendesain WPCC untuk suara yang mengandung noise. Algoritma Mean Best Basis (MBB) dan fungsi wavelet db44 dan db45 digunakan untuk memperoleh desain filterbank WPCC. Suara yang digunakan adalah rekaman suara vokal bahasa Indonesia a, i, u, e, é, o, dan ó yang mengandung noise. Hasil menunjukkan telah terbentuk dua buah desain filterbank WPCC. Masing-masing merupakan hasil penerapan fungsi daubechies db44 dan db45. Noise tidak memberikan pengaruh terhadap pembentukan kedua filterbank WPCC tersebut. Kedua bentuk filterbank telah memenuhi standar bentuk filter MFCC terutama untuk variabel range dan skala frekuensinya. Range frekuensinya berkisar antara 125 Hz - 1000 Hz dengan bentuk skala yang linier untuk frekuensi di bawah 1000 Hz. Sehingga dapat disimpulkan kedua bentuk filterbank WPCC ini dapat dipertimbangkan untuk digunakan sebagai ekstraktor ciri suara ber-noise. AbstractRecently wavelet-based filterbanks as feature start extractors have been widely developed to replace the role of the Mel Frequency Cepstral Coefficient (MFCC) feature in automatic speech recognition systems. One of the wavelet feature filterbanks developed is Wavelet-Packet Cepstral Coefficient (WPCC). But so far the development has only been focused on clean speech signal. So, the aim of this study is designing WPCC for a noisy speech signal. The Mean Best Basis (MBB) algorithm and db44 and db45 wavelet functions are applied to obtain the WPCC filterbank design. The noisy speech signal used is the recorded utterance Indonesian vowels a, i, u, e, é, o, and ó. The results show that two WPCC filterbank designs have been formed. Each of them is the result of applying the daubechies db44 and db45 functions. Noise has no effect on the establishment of both the WPCC filterbanks. Both fiterbank designs have met MFCC filter form standards, especially for its range of frequency and frequency scale. Its range of frequency is between 125 Hz - 1000 Hz with a linear scale for frequencies below 1000 Hz. Therefore it can be concluded that the two forms of WPCC filterbank can be considered to be used as a feature extractor for a noisy speech signal.
Sistem Pengenalan Pembicara dengan Metode Wavelet-MCFF dan Pengklasifikasi Hidden Markov Models (HMM) Hidayat, Syahroni; Anas, Andi Sofyan; Yusuf, Siti Agrippina Alodia; Tajuddin, Muhammad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0813284

Abstract

Penelitian pengolahan sinyal digital yang berfokus pada pengenalan pembicara telah dimulai sejak beberapa dekade yang lalu, dan telah menghasilkan banyak metode-metode pengenalan pembicara. Di antara algoritma pembentukan koefisien ciri yang telah dikembangkan tersebut, ada dua algoritma yang dapat memberikan akurasi yang tinggi jika diterapkan pada sistem, yaitu Mel Frequency Cepstral Coefficient (MFCC) dan Wavelet. Penelitian ini bertujuan untuk menguji dan memilih kanal terbaik dari proses wavelet-MFCC yang dapat dijadikan sebagai koefisien ciri baru untuk diterapkan pada sistem pengenal pembicara. Koefisien ciri baru tersebut kemudian disebut dengan koefisien ciri Wavelet-MFCC. Kofisien ini dibentuk dari merubah kanal hasil dekomposisi wavelet, yaitu kanal aproksimasi (cA), kanal detail (cD), dan penggabungannya (cAcD), menjadi koefisien MFCC. Metode dekomposisi wavelet yang digunakan adalah metode dyadic dengan menerapkan level dekomposisi level 1 dan level 2. Setiap koefisien ciri kemudian menjadi inputan pada sistem pengklasifikasi Hidden Markov Models (HMM). Keluaran dari HMM kemudian dihitung akurasinya dan dianalisis. Dari pengujian yang dilakukan, diperoleh bahwa kanal detail (cD) sebagai ciri dapat memberikan akurasi yang sama dengan menggunakan kanal gabungan (cAcD) dan lebih tinggi dari kanal aproksimasi (cA), dengan akurasi sebesar 95%. Hal ini menunjukkan bahwa, kanal detail pada dekomposisi level 1 menyimpan ciri suara dari setiap pembicara sehingga sudah cukup untuk dijadikan sebagai koefisien ciri. Maka, penggunaan dekomposisi level 1 dan kanal detail cD sebagai ciri Wavelet-MFCC pada sistem pengenalan pembicara dapat meringankan dan mempercepat proses komputasi. AbstractResearch in digital signal that focused on speaker recognition has begun since decades ago, and has resulted many speaker recognition methods. there are two algorithms that can provide high accuracy in recognition system, which are Mel Frequency Cepstral Coefficient (MFCC) and Wavelet. the aims of this study is to examine and chose the best channel from wavelet-MFCC process that can be used as new feature coefficient, then called as Wavelet-MFCC features coefficient. The coefficient is built by converting the wavelet decomposition channels, which are approximation (cA), detail (cD), and its combination (cAcD), into the MFCC coefficient. Wavelet dyadic decomposition with level 1 and level 2 of decomposition is applied. Each feature coefficient acts as an input to the HMM classifier. The accuracy of the HMM output is calculated, then analyzed. The obtained results show that the detail chanel (cD) achieve equal accuracy as the combination chanel (cAcD), and higher accuracy compared to aproximation channel (cA), with accuracy 95%. Thus, it can be conclude that the detail channel on level 1 decomposition contains features of each speaker's. Then, cD is enough to be used as a Wavelet-MFCC feature. Thus, its implementation in the SRS can ease and speed up the computing process.
Transformasi Lontar Babad Lombok Menuju Digitalisasi Berbasis Natural Gradient Flexible (NGF) Anwar, Muhammad Tajuddin; Hidayat, Syahroni; Adil, Ahmat
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 2: April 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021824088

Abstract

Suku Sasak, yang tinggal di pulau Lombok Nusa Tenggara Barat, memiliki tradisi penulisan di daun lontar (Borassus Flabellifer) kering, salah satunya adalah naskah Lontar Babad Lombok. Naskah Lontar Babad Lombok seiring berlalunya waktu, menjadi rapuh dan mudah patah sehingga memerlukan perawatan. Keadaan ini mendorongnya perlu dilakukan digitalisasi naskah lontar babad lombok sebagai bentuk pelestarian sehingga para generasi Milenial, khususnya di Lombok, dapat menikmati lontar babad lombok. Digitalisasi citra tersebut tantangan utama adalah tepi kabur teks dan perbedaan minimum antara teks dan bagian non-tekssebagai akibat dari proses perawatan. Oleh karena itu, dibutuhkan proses peningkatan kualitas citra hasil digitalisasi agar tulisan dapat lebih jelas terbaca. Salah satu metode yang terbukti mampu untuk memisahkan teks dari latar belakang yang sangat berkorelasi adalah Natural Gradient Flexibel (NGF) berbasiskan Independent Component Analysis (ICA), NGF-ICA. Penelitian ini bertujuan untuk melakukan peningkatan kualitas citra digitalisasi sebelum diumpankan pada database dan sistem informasi yang telah dibangun. Kualitas citra yang telah ditingkatkan diukur menggunakan metode MSE dan PSNR untuk tingkat kemiripannya, dan metode Entropi dan SSIM untuk informasi dan perspektif visual. Hasil penelitian menunjukkan bahwa penerapan algoritma NGF-ICA dapat memberikan citra keluaran dengan kualitas yang tinggi dengan nilai rata-rata MSE, PSNR, SSIM dan peningkatan Entropi sebesar 708, 19.95 db, 0.87 dan 0.45, secara berturut-turut. AbstractSasak tribe, who lives on Lombok Island, West Nusa Tenggara, has been writing manuscripts on dry palm leaves (Borassus Flabellifer) as a tradition, one of the manuscripts is Lontar Babad Lombok. As time pass by, the manuscript becomes brittle and breaks easily, therefore maintenances are required. this situation force the need to digitalize the manuscript as an act of preservation, hence the millennial generation, especially on Lombok Island, can enjoy the manuscript. the main challenge is the blurry edge of the text and the slight difference between the text and non-text part caused by the treatment process. Hence, it is needed to enhance the quality of the digitalize image to make the manuscript can be more clearly read. One of the proven methods that able to separate text from highly correlated backgrounds is Natural Gradient Flexibel (NGF) based on Independent Component Analysis (ICA), NGF-ICA. The aim of this study is to improve the quality of the digitized images before they fed into the database and information system that has been built. The enhanced image quality was measured, MSE and PSNR methods were used to measure the similarity level, and the Entropy and SSIM method were used to measure the information and visual perspective. The results show that the application of the NGF-ICA algorithm can generate high-quality output images with average values of MSE, PSNR, SSIM, and increasing Entropy by 708, 19.95 dB, 0.87, and 0.45, respectively.
Pelatihan Konten E-Commerce bagi Kelompok Kerajinan Bambu Dusun Dasan Bare untuk Menyongsong Era Digital Market Budiarto, Jian; Hidayat, Syahroni; Rizal, Ahmad Ashril
Paradharma: Jurnal Aplikasi IPTEK Vol. 3 No. 2 (2019): Paradharma: Jurnal Aplikasi IPTEK
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Dhyana Pura – Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (677.076 KB) | DOI: 10.36002/jpd.v3i2.1045

Abstract

Tujuan dilaksanakannya Program Kemitraan Masyarakat (PKM) Kelompok Kerajinan Bambu Dasan Bare adalah untuk meningkatkan kegiatan ekonomi di tempat tersebut. Semakin meningkatnya jumlah pengguna teknologi internet sebagai media informasi saat ini dapat menjadi peluang bagi penjual agar berbondong-bondong untuk melakukan penjualan secara online. Pada umumnya pengusaha UKM sudah mulai menerapkan e-commerce karena banyaknya manfaat yang diperoleh oleh pengusaha dari e-commerce. Meskipun demikian penerapan e-commerce juga memiliki hambatan. Faktor-faktor penghambat tersebut di antaranya sumber daya yang kurang mampu untuk bersaing dalam dunia teknologi, kurangnya informasi mengenai e-commerce, kebiasaan masyarakat Indonesia yang melihat dan merasakan secara langsung apa yang dibeli, keamanan transaksi dan lainnya. Oleh karena itu, sangat perlu dilakukan pelatihan penggunaan e-commerce bagi pengusaha UKM kerajinan agar dapat melakukan promosi penjualan yang lebih efektif dan efisien. Sehingga, permasalahan seperti peningkatan pengetahuan mengenai teknologi informasi oleh UKM, aplikasi website yang kurang menarik dan penyiapan konten (teks dan gambar) dapat diatasi. Metode pelaksanaan dari kegiatan PKM dilaksanakan melalui tahap persiapan, pelaksanaan pelatihan, penyajian materi, penugasan praktik, evaluasi dan editing konten e-commerce hingga refleksi dan penutupan program.Kata kunci: e-commerce, kerajinan bambu-rotan, LombokABSTRACTThe aim of Public Partnership Program for bamboo craftsman in Dasan Bare is to improve economic activity. Nowadays, online transactions to selling their product is increasing. Seller has been implement e-commerce to promote their product. Some factor to restrict implementation of e-commerce is the people don’t have enough information how to use e-commerce. So that, we propose to give training how to prepare content and using e-commerce application. This training using some effective methods such as preparing tools, preparing content, training application, practical task, evaluation and editing ecommerce virtual shop.Keywords: e-commerce, bamboo-rattan crafts, Lombok
Ensemble Implementation for Predicting Student Graduation with Classification Algorithm Rismayati, Ria; Ismarmiaty, Ismarmiaty; Hidayat, Syahroni
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 1 No. 1 (2022): March 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v1i1.1805

Abstract

Graduating on time at the higher education level is one of the main targets of every student and university institution. Many factors can affect a student's length of study, the different character of each student is also an internal factor that affects their study period. These characters are used in this study to classify data groups of students who graduated on time or not. Classification was chosen because it is able to find a model or pattern that can describe and distinguish classes in a dataset. This research method uses the esemble learning method which aims to see student graduation predictions using a dataset from Kaggle, the data used is a IPK dataset collected from a university in Indonesia which consists of 1687 records and 5 attributes where this dataset is not balanced. The intended target is whether the student is predicted to graduate on time or not. The method proposed in this study is Ensemble Learning Different Contribution Sampling (DCS) and the algorithms used include Logistic Regression, Decision Tree Classifier, Gaussian, Random Forest Classifier, Ada Bost Classifier, Support Vector Coefficient, KNeighbors Classifier and MLP Classifier. From each classification algorithm used, the test value and accuracy are calculated which are then compared between the algorithms. Based on the results of research that has been carried out, it is concluded that the best accuracy results are owned by the MLPClassifier algorithm with the ability to predict student graduation on time of 91.87%. The classification model provided by the DCS-LCA used does not give better results than the basic classifier of its constituent, namely the MLPClassifier algorithm of 91.87%, SVC of 91.64%, Logistic Regression of 91.46%, AdaBost Classifier of 90.87%, Random Forest Classifier of 90.45% , and KNN of 89.80%.
Co-Authors Abdulloh Abdulloh Abdurahim, Abdurahim Achmadi, Taofan Ali Adam Bachtiar Maulachela Agung Budiwirawan Agus Ardiyanto Ahmad Zuli Amrullah Ahmat Adil Akbar Juliansyah Akmal Fikri Amrullah Anan Nugroho Anan Nugroho Ananda, Briska Putra Andi Sofyan Anas Ansar Ansar Ardiansyah, Muhammad Irfan Astri Iga Siska Ayu, Hanifah Baroroh, Luluk Taufiqul Budi Sunarko Budiarto, Jian Budisantoso, Heri Tri Luqman Danang Tejo Kumoro Danang Tejo Kumoro Darmawan, Joelianto Dian Syafitri Chani Saputri Dinata, Muhammad Imam Esa Apriaskar Febry Putra Rochim Feddy Setio Pribadi Habib Ratu Perwira Negara Haikal Abror Hakiki, Muhammad Khikam Hanif Ardhiansyah Hanif Hidayat Ida Ayu Widhiantari Intan Ermawati Irmayanti Irmayanti, Irmayanti Ismarmiaty Ismarmiaty, Ismarmiaty Jhonatur Stheven Simanjuntak Joko Sumarsono Khoiron, Ahmad Mustamil Khoirudin Fathoni, Khoirudin Kumoro, Danang Tejo Ledi Diyanasari Mahendra Adiastoro Mona Subagja Mona Subagja Muhammad Fathurrahman Muhammad Hilmy Herdiansyah Muhammad Muhammad MUHAMMAD TAJUDDIN Muhammad, Naufal Murad Murad Murad, Murad Ni Luh Putu Merawati Nur Azis Salim Nur Iksan Qudsi, Jihadil R Fanny Priniti Raden Fanny Printi Ardi Rahmat Sabani Rezky Ramdhaningsih Ria Rismayati Rian Febriyanto Rifki Lukman Satria Rina Rachmawati Risanuri Hidayat Rismayati, Ria Rizal, Ahmad Ashril Salim, Nur Azis Sandi Justitia Putra Satria, Rifki Lukman Sukmawaty Sukmawaty Sukmawaty Sukmawaty Sulistianingsih, Nani Sulistyawan, Vera Noviana Tajuddin, Muhammad Taofan Ali Achmadi Teguh Bharata Adji Tri Agus Wahyudi Uswatun Hasanah USWATUN HASANAH Uswatun Hasanah Vera Noviana Sulistyawan Wafi, Ahmad Zein Al Wardatullatifah S, Ince Siti Yusuf, Siti Agrippina Alodia Yusuf, Sitti Agripina Alodia Zaenal Abidin Zaurarista Dyarbirru Zidan Vieri Wijaya