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All Journal International Journal of Electrical and Computer Engineering Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sinkron : Jurnal dan Penelitian Teknik Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab CogITo Smart Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Journal of Education Technology Jurnal Tekno Insentif JURTEKSI EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Infotekmesin Journal of Information Systems and Informatics Jurnal Sistem Komputer dan Informatika (JSON) Journal of Innovation Information Technology and Application (JINITA) Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jurnal Algoritma Jurnal Pengabdian Mitra Masyarakat (JPMM) Padma: Jurnal Pengabdian Kepada Masyarakat Bulletin of Social Informatics Theory and Application Jurnal Riset Teknik Komputer CSRID Southeast Asia Journal on Open and Distance Learning
Rujianto Eko Saputro
Universitas Amikom Purwokerto
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry 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 Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Penerapan Model Ensemble Learning dengan Random Forest dan Multi-Layer Perceptron untuk Prediksi Gempa Turino, Turino; Saputro, Rujianto Eko; Karyono, Giat
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 2 (2025): JPTI - Februari 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.667

Abstract

Penelitian ini mengusulkan model hybrid yang menggabungkan metode Random Forest (RF) dan Multi-Layer Perceptron (MLPRegressor) untuk memprediksi magnitudo gempa bumi. Model ini bertujuan untuk meningkatkan akurasi prediksi dengan memanfaatkan kekuatan kedua algoritma tersebut, yang masing-masing memiliki keunggulan dalam menangani hubungan non-linier dan mengenali pola kompleks dalam data seismik. Evaluasi model menggunakan tiga metrik utama, yaitu Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan R-squared (R²). Hasil evaluasi menunjukkan bahwa model hibrida ini mampu memprediksi magnitudo gempa dengan akurasi yang cukup baik, dengan MAE sebesar 0,0738, RMSE 0,1078, dan R² 0,4204. Penerapan praktis dari model ini sangat relevan untuk sistem peringatan dini gempa bumi yang dapat membantu masyarakat untuk mengambil langkah-langkah pencegahan, seperti evakuasi dan penguatan infrastruktur di wilayah yang berisiko tinggi. Penelitian ini juga membuka peluang untuk mengembangkan model lebih lanjut dengan memperkenalkan data seismik real-time, algoritma pembelajaran mesin yang lebih canggih, dan penggunaan data geofisik serta pengamatan satelit untuk meningkatkan akurasi prediksi gempa bumi di masa depan. Dengan terus melakukan inovasi, ada potensi untuk mengembangkan sistem prediksi gempa bumi yang lebih akurat dan dapat diandalkan, yang pada akhirnya dapat meningkatkan kesiapsiagaan dan ketahanan terhadap bencana alam.
Elevating Student Motivation: Constructing a Gamified Massive Open Online Courses using the MARC Framework Saputro, Rujianto Eko; Salam, Sazilah
Journal of Education Technology Vol. 8 No. 1 (2024): February
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jet.v8i1.67624

Abstract

Education is undergoing a profound revolution in the current era of digital technology, primarily due to the implementation of Massive Open Online Courses (MOOCs). These courses have facilitated worldwide accessibility to education of exceptional quality, transcending geographical and institutional limitations. This study aims to evaluates the implementation of gamification in Massive Open Online Courses (MOOCs) to enhance students' intrinsic motivation and graduation rates. Facing the challenge of low graduation rates in MOOCs, this study designed and implemented a Gamified MOOC (GMOOC) platform using a new gamification framework, MARC. Through an experimental method, this study involved 101 students and collected data through a questionnaire based on the Instructional Materials Motivation Survey (IMMS) and ARCS Model. The data was analyzed using Structural Equation Modelling (SEM), and the results showed that all MARC variables have high reliability and positively impact students' intrinsic motivation, with the Autonomy variable having the most significant impact. This study underlines the importance of a gamification framework in enhancing motivation and graduation rates in MOOCs, as well as the importance of considering the correct design elements and gamification components in the development of MOOCs. This study provides significant implications for developing and implementing effective and engaging MOOCs.
Program pendampingan penulisan ilmiah dan eksplorasi kesenjangan penelitian menggunakan teknologi kecerdasan buatan bagi Dosen Fakultas Ilmu Komputer Universitas Amikom Purwokerto Widiawati, Chyntia Raras Ajeng; Utomo, Fandy Setyo; Saputro, Rujianto Eko; Sarmini, Sarmini; Adiya, Az Zahra Dwi Nur; Ilham, Rifqi Arifin; Hartini, Sri
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 8, No 4 (2024): December
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v8i4.26890

Abstract

AbstrakPermasalahan yang dihadapi oleh mitra sasaran Fakultas Ilmu Komputer (FIK) Universitas Amikom Purwokerto adalah rendahnya jumlah dosen yang memiliki gelar doktor yang berperan penting untuk meningkatkan kualitas pendidikan dan penelitian. Berdasarkan data FIK, dari 72 dosen, hanya 6 yang memiliki gelar doktor. Rendahnya jumlah doktor ini disebabkan karena dosen kesulitan dalam menentukan tema penelitian yang tepat dan relevan dengan kepakaran mereka, kesulitan menemukan dan merumuskan kesenjangan penelitian, serta kesulitan dalam merumuskan inovasi dan kebaruan riset. Berdasarkan permasalahan tersebut, kami memberikan solusi menyelenggarakan program Bootcamp Doktoral: Penulisan ilmiah dan identifikasi kesenjangan penelitian menggunakan teknologi kecerdasan buatan. Target yang diharapkan dari program ini, yakni dosen di lingkungan fakultas ilmu komputer dapat meningkatkan kompetensi akademik, mengembangkan jaringan profesional, meningkatkan keterampilan penelitian dan penulisan publikasi, memperoleh motivasi dan inspirasi untuk studi lanjut S3, mengembangkan soft skill, serta mampu beradaptasi dengan tren teknologi terbaru. Berdasarkan target luaran yang telah ditetapkan, metode pengabdian masyarakat yang digunanakan dalam program ini mencakup 3 tahap utama, yaitu tahap persiapan kegiatan, implementasi kegiatan, dan pelaporan kegiatan. Hasil evaluasi pelaksanaan program pendampingan melalui umpan balik peserta pada hari selasa, 20 Agustus 2024 secara daring diperoleh hasil bahwa seluruh peserta webinar dapat memahami pengoperasian tools berbasis teknologi kecerdasan buatan untuk penulisan ilmiah, dan memahami etika penggunaan teks atau data dari hasil tools kecerdasan buatan dalam konteks penelitian. Kata kunci: pendampingan; kecerdasan buatan; penulisan ilmiah; penelitian; doktoral AbstractThe problem faced by the target partners of the Faculty of Computer Science Universitas Amikom Purwokerto is the low number of lecturers who have doctoral degrees who play an essential role in improving the quality of education and research. The low number of doctors is caused by lecturers having difficulty determining the correct and relevant research themes with their expertise, difficulty finding and formulating research gaps, and difficulty formulating innovation and research novelty. Based on these problems, we provide a solution to organize a Doctoral Bootcamp program: Scientific writing and identifying research gaps using artificial intelligence technology. The expected target of this program is that lecturers in the faculty of computer science can improve their academic competence, develop professional networks, improve research skills and publication writing, gain motivation and inspiration for further doctoral studies, develop soft skills, and be able to adapt to the latest technological trends. Based on the set output targets, the community service method used in this program includes three main stages: the activity preparation stage, activity implementation, and activity reporting. The results of the evaluation of the implementation of the mentoring program through participant feedback showed that all webinar participants were able to understand the operation of artificial intelligence technology-based tools for scientific writing and understand the ethics of using text or data from the results of artificial intelligence tools in the context of research. Keywords: mentoring; artificial intelligence; scientific writing; research; doctoral
Optimizing Marketplace Registration Page Design with Predictive Heatmap Analysis Bagaskoro, Galih; Eko Saputro, Rujianto; Shouni Barkah, Azhari; Nanjar, Agi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14547

Abstract

Optimizing marketplace registration pages is crucial for improving user experience and conversion rates. This study evaluates the design of registration pages for four leading Indonesian marketplaces Tokopedia, Shopee, Blibli, and Lazada—using Predictive Heatmaps from UX Pilot alongside Heuristic Evaluation and Gestalt Principles. The analysis identifies key usability issues, such as distractions from branding elements, inconsistent visual hierarchy, and a lack of real-time validation and feedback mechanisms. Findings indicate that while branding elements effectively capture user attention, they often divert focus from essential features, a trend observed not only in these marketplaces but also in broader UI design contexts. such as Call-to-Action (CTA) buttons and registration forms. Shopee and Lazada successfully utilize high-contrast CTA buttons to direct user interaction, whereas Tokopedia and Blibli suffer from visual distractions caused by mascots and unnecessary decorative elements. Heatmap results also reveal inconsistent grouping of interface components, reducing page efficiency. To enhance user experience and conversion rates, recommendations include improving CTA button visibility through contrasting colors and strategic placement, minimizing decorative distractions, and implementing real-time validation and feedback. The application of Gestalt Principles further aids in optimizing interface organization by grouping related elements more effectively. This study underscores the importance of a structured design approach incorporating heuristic and predictive analytics to enhance the usability of online registration pages. Future research may explore the impact of interactive elements and A/B testing in refining registration interfaces.
Enhancing Sentiment Analysis Accuracy Using SVM and Slang Word Normalization on YouTube Comments Saputra, Alfin Nur Aziz; Saputro, Rujianto Eko; Saputra, Dhanar Intan Surya
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14613

Abstract

Sentiment analysis is a crucial technique in understanding public opinion, particularly on social media platforms such as YouTube. However, the presence of informal language, including slang words, poses significant challenges to accurate sentiment classification. This study aims to enhance sentiment analysis by implementing a Support Vector Machine (SVM) classifier combined with SMOTEENN data balancing techniques to address class imbalance issues. The research collects 3,375 YouTube comments on the movie Pengabdi Setan 2: Communion using the YouTube Data API. The preprocessing steps include text cleaning, tokenization, stopwords removal, stemming, and slang word normalization using kamusalay.csv to ensure standardization of informal expressions. The extracted features are represented using TF-IDF, and sentiment labeling is performed using VADER. Experimental results show that the SVM model achieves an accuracy of 98%, but struggles with detecting negative sentiments, as indicated by lower recall (38%) and F1-score (53%) for the negative class. Although the application of SMOTEENN improves data balance, further refinements, such as adjusting classification thresholds and integrating deep learning techniques, are necessary to enhance sentiment detection, particularly for informal and emotionally nuanced language. This study contributes to improving sentiment analysis models by demonstrating the effectiveness of slang word normalization in handling non-standard language variations. Future work will explore more sophisticated language models to enhance accuracy in sentiment classification.
Eksplorasi Model Hybrid Transformer-Latent Semantic Analysis (LSA) Untuk Pemahaman Konteks Teks Berita Berbahasa Indonesia Sofa, Nur; Utomo, Fandy Setyo; Saputro, Rujianto Eko
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 5 (2025): JPTI - Mei 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.662

Abstract

Kemajuan teknologi informasi meningkatkan konsumsi berita digital, menuntut sistem Natural Language Processing (NLP) yang efisien dalam memahami bahasa Indonesia. Namun, kompleksitas morfologi bahasa Indonesia menyulitkan model NLP konvensional dalam menangkap makna semantik secara akurat. Model deep learning seperti Transformer unggul dalam menangkap hubungan semantik lokal, sementara Latent Semantic Analysis  (LSA) memahami hubungan semantik global melalui reduksi dimensi. Namun, Transformer membutuhkan sumber daya komputasi besar, sedangkan LSA cenderung kehilangan konteks sintaksis. Penelitian ini mengusulkan model hybrid yang mengintegrasikan Transformer dan LSA untuk meningkatkan pemahaman teks berita Indonesia serta mengevaluasi performanya dibandingkan model individu dan deep learning yang lebih kompleks. Evaluasi menggunakan Accuracy, F1-Score, BLEU Score, ROUGE, dan Perplexity. Model hybrid mencapai akurasi 0.510760 dan F1-Score 0.520486, lebih baik dari LSA dan Transformer, tetapi masih tertinggal dari BERT dan GPT. Meski demikian, model hybrid lebih efisien secara komputasi dibandingkan model deep learning yang lebih kompleks. Penelitian ini berkontribusi pada pengembangan NLP bahasa Indonesia dengan pendekatan yang lebih ringan. Implikasi penelitian menunjukkan perlunya dataset lebih besar dan teknik embedding lebih maju. Penelitian selanjutnya dapat mengeksplorasi integrasi model hybrid dengan BERT atau GPT, serta teknik embedding lain seperti word2vec atau fastText untuk meningkatkan pemahaman semantik.
Penerapan Algoritma FP-Growth Untuk Menentukan Pola Penjualan Najah Mart Serli, Serli; Saputro, Rujianto Eko
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 1 (2025): Februari 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.1.2025.1-19

Abstract

This research aims to apply the FP-Growth algorithm at Najah Mart with a focus on identifying significant sales patterns. By recognizing products that are frequently purchased together, this study is expected to help Najah Mart optimize inventory management, enhance customer shopping experiences, and design more efficient marketing strategies. The methodology employed in this study includes collecting sales transaction data and preprocessing to ensure optimal data quality. Subsequently, the FP-Growth algorithm is applied to uncover patterns and associations within the sales data. The analysis focuses on identifying strong product correlations based on customer purchasing patterns. The research findings reveal relevant and significant purchasing patterns, such as combinations of specific products frequently bought together. These insights can be utilized to improve marketing strategies and inventory management. This study not only provides practical benefits for Najah Mart in enhancing operational effectiveness and supporting strategic decision-making but also contributes to the advancement of knowledge by expanding the application of the FP-Growth algorithm in the retail sector, particularly in sales data analysis to support business growth.
Optimalisasi UX Aplikasi Penyewaan Peralatan Bayi dengan Design Thinking dan Evaluasi SUS Ismail, Dimas Shafa Malik; Saputro, Rujianto Eko
Jurnal Sistem Komputer dan Informatika (JSON) Vol 6, No 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8657

Abstract

Penyewaan perlengkapan bayi menjadi solusi praktis bagi orang tua yang membutuhkan alat dalam jangka waktu terbatas. Namun, dalam praktiknya, masih banyak layanan penyewaan yang berjalan secara manual dan belum efisien, sehingga menimbulkan kendala seperti keterbatasan informasi, proses pemesanan yang rumit, serta waktu layanan yang tidak fleksibel. Penelitian ini bertujuan untuk merancang dan meningkatkan kualitas antarmuka pengguna (UI) serta pengalaman pengguna (UX) melalui pengembangan aplikasi mobile berbasis metode Design Thinking, guna meningkatkan efisiensi layanan dan kepuasan pengguna. Proses perancangan mengikuti lima tahap, yaitu Empathize, Define, Ideate, Prototype, dan Test, dengan diawali wawancara serta observasi untuk memahami kebutuhan pengguna, kemudian dilanjutkan dengan perancangan wireframe, alur navigasi, hingga pengujian prototipe low-fidelity dan high-fidelity. Evaluasi dilakukan dengan metode System Usability Scale (SUS) terhadap 15 responden yang merepresentasikan target pengguna. Hasil pengujian menunjukkan skor rata-rata SUS sebesar 77,17, yang menandakan aplikasi memiliki tingkat kegunaan tinggi dan respons positif dari pengguna. Temuan ini membuktikan bahwa pendekatan Design Thinking efektif dalam menghasilkan desain aplikasi penyewaan yang intuitif, ramah pengguna, dan mampu meningkatkan pengalaman digital pelanggan secara signifikan.
Effectiveness of The Gamified LMS Platform on The Level of Online Course Completion Rujianto Eko Saputro; Wiga Maulana Baihaqi; Sarmini
Southeast Asian Journal on Open and Distance Learning Vol. 1 No. 01 (2023): Strategies for Cultivating Active Learning in Online Environment
Publisher : SEAMEO SEAMOLEC

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

Abstract

Gamified Massive Open Online Courses (G-MOOCs) is a Learning Management System (LMS) platform built on the gamification framework (MARC Gamification Framework) that has been proposed in previous studies based on various aspects of game elements, social learning, motivation and interactive theory learning environment (ILE). G-MOOC is a background element that can motivate them when taking courses in online learning. This program is intended to increase the intrinsic motivation of participants in completing their courses. Tests are carried out using the experimental group method using two indicators, namely the level of mastery of the course (performance) and the status of learning courses (Done/Not Done). To produce data from used indicators, researchers gave four weeks to take the course. The courses are compared with the LMS platform which has no gamification element (SIMOOC), the performance indicators are tested in the participant values between the G-MOOCs platform and the SIMOOC platform. Based on the results of the test, the platform is a platform that is better and different compared to the SIMOOC platform. Judging from the status of the participants, out of 71 participants who took the course on the G-MOOC platform there were 46.5% which were declared completed, while on the SIMOOC platform only 7% had completed status. It can be concluded that the G- MOOC platform can increase the effectiveness of the value courses compared to the SIMOOC platform.
Analisis Tingkat Akurasi Metode Naive Bayes dan Random Forest dalam Prediksi Penjualan Emas Pandu W, Muhammad Arfianto; Saputro, Rujianto Eko; Purwadi, Purwadi; Rohmah, Umdah Aulia
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 7 (2025): JPTI - Juli 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.732

Abstract

Penelitian ini bertujuan untuk menganalisis tingkat akurasi metode Naive Bayes dan Random Forest dalam prediksi penjualan emas, yang memainkan peran penting dalam perencanaan investasi dan strategi bisnis di sektor pertambangan, terutama dalam menghadapi volatilitas pasar yang tinggi. Metode penelitian yang digunakan adalah narrative literature review, yang berfokus pada perbandingan dan analisis teori-teori yang ada sebelumnya. Pendekatan ini memungkinkan peneliti untuk mengevaluasi, mengidentifikasi, dan menganalisis literatur yang relevan serta menyarikan temuan-temuan penting yang dapat memberikan wawasan lebih dalam tentang topik yang dikaji. Dalam konteks ini, Naive Bayes dan Random Forest digunakan untuk meramalkan fluktuasi harga emas dan perilaku pembelian produk, dengan fokus pada pengoptimalan strategi pemasaran. Hasil analisis menunjukkan bahwa Naive Bayes efektif dalam mengidentifikasi produk yang diminati dan memfasilitasi perencanaan pemasaran. Namun, Random Forest menunjukkan keunggulan dalam prediksi yang lebih kompleks, seperti perilaku repeat order pelanggan, berkat kemampuannya untuk menangani data variatif dan mengurangi risiko overfitting melalui pendekatan ensemble yang menggabungkan banyak pohon keputusan. Meskipun terdapat sedikit penurunan akurasi pada data pengujian, Random Forest tetap dapat menghasilkan prediksi yang akurat dan robust. Oleh karena itu, kedua metode ini memberikan kontribusi signifikan dalam merancang strategi bisnis dan keputusan investasi yang lebih akurat, dengan Random Forest lebih unggul dalam menghadapi data yang lebih kompleks. Kontribusi penelitian ini yakni memberikan landasan teoretis tentang penerapan algoritma pembelajaran mesin di sektor pemasaran berbasis data, serta menjadi panduan bagi praktisi dan peneliti dalam memilih metode prediktif yang tepat.
Co-Authors Adam Prayogo Kuncoro Adam Prayogo Kuncoro Adiya, Az Zahra Dwi Nur Afriansyah, Fery Aimah, Samsul Anugerah Bagus Wijaya Aptana, Naufal Yogi Arif Mu'amar Wahid Aulia Hamdi Azhari Shouni Barkah Bagaskoro, Galih Baihaqi, Wiga Maulana Berlilana Berlilana Berlilana Bunga Asriandhini Chyntia Raras Ajeng Widiawati Damayanti, Wenti Risma Dani Arifudin Darmono Deasy Komarasary Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Dinar Mustofa Dwi Angesti Dinda Parameswara Ely Purnawati Ely Purnawati, Ely Embong Octavianto Fandy Setyo Hutomo Fandy Setyo Utomo Fandy Setyo, Utomo Fatudin, Arif Faturama, Rafi Febriansyah Husni Adiatma Febrianti, Diah Ratna Fery Afriansyah Fiby Nur Afiana Giat Karyono Hasna Salsa Dhia hidayatulloh, hanif Ikmah Ikmah Ikmah, Ikmah Ilham, Rifqi Arifin Indriyani, Ria Irwansyah Munandar Ismail, Dimas Shafa Malik Junianto, Haris Kusuma, Bagus Adhi Latif, Imam Sofarudin Linda Perdana Wanti Lughri Wijaya Pamungkas Maulana Baihaqi, Wiga Millatul Izza, Nia Mohamad, Siti Nurul Mahfuzah Mohd. Hafiz Zakaria Muhammad Nur Faiz Munandar, Irwansyah Nanjar, Agi Ndari, Arum Vika Nia Millatul Izza Novita Eka Ramadhani Nugroho, Lustiyono Prasetyo Nur Rahma Keysha Maharani Maharani Nurmalitasari, Gupita Octavianto, Embong Pandu W, Muhammad Arfianto Prasetyo, Agung Priaji Januardi Primandani Arsi Pungkas Subarkah Purwadi Purwadi Purwadi Purwadi R. Vitto Mahendra Putranto Radeta Tea Makdatuang Ramadhan, Rio Fadly Ranggi Praharaningtyas Aji Ria Indriyani Rida Purnama Sari Rizqi Aulia Widianto Rohmah, Umdah Aulia Rosana Fadila Sari safitri feriawan, Titi Salam, Sazilah Salsa Dhia, Hasna Samsul Aimah Saputra , Dhanar Intan Surya Saputra, Alfin Nur Aziz Saputri, Inka Sari, Rida Purnama Sarmini Sarmini - Sarmini Sarmini Sarmini Sazilah Salam Serli, Serli Shendy Filanzi Slamet Endro Prianto Sofa, Nur Sri Hartini Suliswaningsih, Suliswaningsih Syahputra, Akhmal Angga Tanzilla, Armeyta Putri Tea Makdatuang, Radeta Titi Safitri Maharani Toni Anwar Turino, Turino Wasihatun Hasanah Wenti Risma Damayanti Wiga Maulana Baihaqi Wijaya, Anugerah Bagus Yuli Purwati Yulianto, Koko Edy Yusoff, Azizul Mohd Zanuar Rifai