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All Journal Infotech Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Electrical Technology IT JOURNAL RESEARCH AND DEVELOPMENT INTECOMS: Journal of Information Technology and Computer Science KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Teknik dan Informatika Building of Informatics, Technology and Science Jurnal Mantik Jurnal Sains dan Teknologi Community Engagement and Emergence Journal (CEEJ) Jurnal Tekinkom (Teknik Informasi dan Komputer) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Journal of Computer System and Informatics (JoSYC) INFOKUM Jurnal Darma Agung Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) International Journal Of Science, Technology & Management (IJSTM) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) KLIK: Kajian Ilmiah Informatika dan Komputer Instal : Jurnal Komputer Jurnal Info Sains : Informatika dan Sains Bulletin of Information Technology (BIT) International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Jurnal Fokus Manajemen Jurnal Minfo Polgan (JMP) Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN) Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Nasional Teknologi Komputer Jurnal Pengabdian Masyarakat Gemilang (JPMG) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Data Sciences Indonesia (DSI) DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Best Journal of Administration and Management Bulletin of Engineering Science, Technology and Industry Jurnal Pengabdian Masyarakat Variasi The Journal of Information Technology, Computer Science, and Electrical Engineering
Sitorus, Zulham
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Analysis Of Item-Based Collaborative Filtering For Sales Of Processed Oil Palm Products Sitorus, Zulham; Sri Wahyuni, Meri; Rika Uli Samosir, Siska
Bulletin of Information Technology (BIT) Vol 6 No 3: September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i3.2195

Abstract

Abstract- The sales system for processed palm oil necessitates a recommendation system that offers product suggestions to users, facilitating their selection of sales items for processed palm oil products. This study used the Item-Based Collaborative Filtering approach, which identifies the similarity between items. The system will assess the rating of each item and compute the similarity value utilising the Pearson correlation-based similarity formula. Companies will exhibit greater interest in product sales that possess identical similarity values. This article presents recommendations for system development concerning processed products intended for the sale of technology-based items that employ item-based collaborative filtering methods. It specifies a recommended selling value for processed palm oil products, with a Mean Absolute Error (MAE) of 10.463126965591, derived from the equation 5/1, yielding a final result of -5. The execution of the sales suggestions for processed palm oil products indicates that the items with the highest similarity value calculations are i4 and i5. PT. Sugih Riesta Jaya employs the Item-Based collaborative filtering method to enhance sales of refined palm oil products, thereby facilitating sales assistance and providing the public with sales information and recommendations for essential refined palm oil products..
Sistem Pakar untuk Menentukan Konsentrasi Mahasiswa Prodi Sistem Komputer Menggunakan Metode Forward Chaining Boy Rizki Akbar; Muhammad Fahriza; , Fery Anugerah; Chelfina Utami; Laila Maghfirah; Rian Farta Wijaya; Zulham Sitorus
Jurnal Nasional Teknologi Komputer Vol 5 No 4 (2025): Oktober 2025
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v5i4.319

Abstract

Determining a concentration is one of the obligations for computer systems students because it has a direct impact on their careers and individual potential development. However, most students determine their concentration based on general perceptions without understanding their personal interests and talents, job trends, or even following their friends' choices. Therefore, this study aims to help students recognize their potential, interests, and abilities in choosing the right concentration. This study uses the Forward Chaining method, which is an expert system-based approach oriented towards factual data to draw conclusions and provide relevant recommendations. In its implementation, the system will process inputs in the form of interests, talents, and ability assessment results, then trace logical rules to determine the most suitable concentration. The final result of the research is expected to produce visual recommendations in the form of percentages, which show the level of compatibility between students' interests and talents with various concentration options. The system is expected to support a more objective, systematic, and data-driven academic decision-making process so that students can optimize their potential to the maximum.
Analisis Deteksi Kecurangan Ujian Online Dengan Sistem Pakar Berbasis Event Browser Tanpa Kamera : Studi Kasus Penyelenggara Olimpiade Sains Digniti Ade Surya Bakti Pane; Harmiati Bungsu Bangun; Astri Mutia Rahma; Erbin Sitorus; Rian Farta Wijaya; Zulham Sitorus
Jurnal Nasional Teknologi Komputer Vol 6 No 1 (2026): Januari 2026
Publisher : CV. Hawari

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Abstract

The implementation of online exams differs significantly from exams supervised directly by proctors. Participants' body movements and interactions can be easily read. Even with camera monitoring, participants can still easily switch browser tabs or open applications on their devices. On the other hand, camera-based proctoring solutions can also raise invasive privacy concerns. Furthermore, device limitations can hinder someone from taking the exam due to the lack of a camera, and bandwidth limitations can result in unstable connections during the exam. Sample log activity data from test participants for log_id ranges from 44790 to 46883, while the actions consist of fullscreen_exit, context_switch, standby_log, focus_return, fullscreen_enter, and devtools_open. The results and rules of this test have scores, with each result having an average value of 1, and the rules having an average value of 1-10. In this process, experts assess each detection result using a binary scheme: a score of 1 is given if the expert system's conclusion matches the expert's decision, and a score of 0 is given if the expert system's decision does not match the expert's decision. This study proposes an expert system framework with a cameraless forward chaining method running on the Google Chrome browser (desktop and Android) as decision support. The system utilizes browser event telemetry (visibility changes, fullscreen, standby) that aligns with existing policies, such as mandatory fullscreen mode, copy/paste blocking, and question blurring when exiting fullscreen or a visibility change occurs. This can generate an audit trail that can be audited by decision makers.
Analisis Sentimen Publik Berbasis KNN Terhadap Kinerja Purbaya dan Sri Mulyani Selama Sebulan Anshari, Ari; Diva, Krisna; Azwan, M; Nainggolan, Irfan; Wijaya, Rian Farta; Sitorus, Zulham
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 3 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i3.1461

Abstract

Masyarakat selalu khawatir tentang perubahan staf Menteri Keuangan karena mereka memainkan peran penting dalam menjaga stabilitas ekonomi negara. Purbaya Yudhi Sadewa menggantikan Sri Mulyani sebagai Menteri Keuangan dalam Kabinet Merah Putih. Karena mungkin mengungkapkan tingkat penerimaan publik dan kepercayaan terhadap kebijakan yang akan diterapkan, persepsi publik selama bulan pertama menjabat sangat penting. Studi ini menggunakan algoritma K-Nearest Neighbor (KNN) untuk menguji sentimen publik terhadap pemikiran di platform media sosial X selama bulan pertama masa jabatan kedua kedua tokoh tersebut. Dataset ini mencakup 2.071 cuitan dari Sri Mulyani (21 Oktober–20 November 2024) dan 2.960 cuitan dari Purbaya Yudhi Sadewa (8 September–7 Oktober 2025). Setelah praproses dan pelabelan data dengan IndoBERT, distribusi sentimen untuk Purbaya adalah 57,80% positif dan 42,20% negatif, sedangkan untuk Sri Mulyani adalah 46,74% positif dan 53,26% negatif. TF-IDF kemudian akan digunakan untuk ekstraksi fitur, sementara KNN dengan K=5 dan metrik jarak kesamaan kosinus akan digunakan untuk klasifikasi. Menurut hasil evaluasi, model KNN Sri Mulyani memiliki akurasi 77% dengan presisi 78%, recall 77%, dan skor F1 77%, sedangkan model KNN Purbaya memiliki akurasi 80% dengan presisi 80%, recall 78%, dan skor F1 79%.
ANALISIS SENTIMEN TERHADAP ULASAN PENGGUNA APLIKASI RUMAH PENDIDIKAN DI PLAYSTORE MENGGUNAKAN ALGORITMA NAIVE BAYES Razaq, Abdul; Hidayat, Rahmat; Pasaribu, Ryan Fahreza; Sitorus, Zulham
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4935

Abstract

Abstract: The development of digital technology has led to the emergence of various educational applications that play an important role in supporting the teaching and learning process. One of the most widely used applications is Rumah Pendidikan, which provides a variety of online learning features. However, user perceptions and experiences of this application vary greatly, requiring a systematic analysis to determine public sentiment towards the application. This study aims to apply the Naive Bayes Classifier algorithm in analyzing user sentiment reviews of the Rumah Pendidikan application on the Google Playstore platform. The data, consisting of 284 reviews, was classified into three sentiment categories, namely positive, negative, and neutral. The analysis process included data cleaning, tokenization, stopword removal, and TF-IDF vectorization before classification. The results show that the Naive Bayes algorithm is capable of classifying sentiment with an accuracy of 78.95%, a precision value of 72%, a recall of 67%, and an F1-score of 70%. These findings indicate that the Naive Bayes approach is effective in identifying user sentiment towards the Rumah Pendidikan application. The findings of this analysis can be used by developers as a basis for consideration in improving the quality of the application and user satisfactioninthefuture.Keyword: Sentiment Analysis, Naive Bayes, Rumah Pendidikan Application, Google Play StoreAbstrak: Perkembangan teknologi digital telah mendorong munculnya berbagai aplikasi pendidikan yang berperan penting dalam mendukung proses belajar-mengajar. Salah satu aplikasi yang banyak digunakan adalah Rumah Pendidikan, yang menyediakan beragam fitur pembelajaran daring. Meskipun demikian, persepsi dan pengalaman pengguna terhadap aplikasi ini sangat beragam, sehingga diperlukan analisis yang sistematis untuk mengetahui sentimen masyarakat terhadap aplikasi tersebut. Penelitian ini bertujuan untuk menerapkan algoritma Naive Bayes Classifier dalam menganalisis sentimen ulasan pengguna terhadap aplikasi Rumah Pendidikan pada platform Google Playstore. Data yang terdiri atas 284 ulasan diklasifikasikan ke dalam tiga kategori sentimen, yaitu positif, negatif, dan netral. Proses analisis meliputi tahap data cleaning, tokenization, stopword removal, serta TF-IDF vectorization sebelum dilakukan klasifikasi. Hasil penelitian menunjukkan bahwa algoritma Naive Bayes mampu mengklasifikasikan sentimen dengan akurasi sebesar 78.95%, nilai precision sebesar 72%, recall sebesar 67%, dan F1-score sebesar 70%. Temuan ini menunjukkan bahwa pendekatan Naive Bayes mampu bekerja secara efektif dalam mengidentifikasi sentimen pengguna terhadap aplikasi Rumah Pendidikan. Temuan analisis tersebut dapat dimanfaatkan oleh pengembang sebagai dasar pertimbangan dalam meningkatkan kualitas aplikasi dan tingkat kepuasan pengguna dimasamendatang.Kata kunci: Analisis Sentimen, Naive Bayes, Rumah Pendidikan, Playstore
SENTIMENT ANALYSIS OF INDONESIAN COMMUNITY TOWARDS ELECTRIC MOTORCYCLES ON TWITTER USING ORANGE DATA MINING Zulham Sitorus; Maulian Saputra; Siti Nurhaliza Sofyan; Susilawati
INFOTECH journal Vol. 10 No. 1 (2024)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v10i1.9374

Abstract

This study explores sentiment analysis of the Indonesian community towards electric motorcycles on Twitter using Orange Data Mining. In the context of the increasing popularity of electric vehicles, especially electric motorcycles, understanding public sentiment becomes crucial for various stakeholders. Twitter, as a leading social media platform, serves as a rich source of opinions and discussions on various topics, including electric motorcycles. This research utilizes Orange Data Mining with multilingual sentiment analysis techniques to analyze the sentiment of the Indonesian community regarding electric motorcycles. The results of sentiment analysis are visualized through box plots and scatter plots, aiming to classify Twitter users based on their emotional responses. The findings of this study provide valuable insights into the sentiment landscape surrounding electric motorcycles in Indonesia, benefiting policymakers, manufacturers, and marketers in understanding public perception and making informed decisions.
Rancang Bangun Sistem Informasi Penjualan di The LDR Coffee Berbasis Web Menggunakan Native Programming Retno Mutiara; Zulham Sitorus; Andysah Putera Utama Siahaan
Jurnal Nasional Teknologi Komputer Vol 6 No 2 (2026): April 2026
Publisher : CV. Hawari

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

Abstract

Perkembangan teknologi informasi mendorong kebutuhan akan sistem yang efisien dalam mengelola operasional bisnis, termasuk pada kafe. Penelitian ini bertujuan untuk merancang dan mengimplementasikan Sistem Informasi Penjualan Berbasis Web di The LDR Coffee menggunakan pengembangan native programming. Sistem dirancang untuk mempermudah pencatatan transaksi, pengelolaan data minuman dan meja, serta penyusunan laporan penjualan secara real-time. Metode penelitian yang digunakan adalah Waterfall, dimulai dari analisis kebutuhan hingga implementasi, dan pengujian dilakukan menggunakan Black Box Testing untuk memastikan semua fitur berjalan sesuai kebutuhan pengguna. Hasil penelitian menunjukkan bahwa sistem mampu meningkatkan kecepatan, akurasi, dan efisiensi proses penjualan, serta meminimalkan kesalahan yang umum terjadi pada metode manual. Sistem ini juga memudahkan monitoring penjualan dan mendukung pengambilan keputusan manajerial secara lebih tepat.
Perancangan Platform Web Marketplace Personal Untuk Freelance Digital Artist Dengan Metode Research & Development (R&D) Hilal Prayogi; Zulham Sitorus; Zulfahmi Syahputra
Jurnal Nasional Teknologi Komputer Vol 6 No 2 (2026): April 2026
Publisher : CV. Hawari

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Abstract

Penelitian ini bertujuan untuk merancang dan mengembangkan platform marketplace berbasis web personal untuk freelance digital artist menggunakan metode Research and Development (R&D) dengan model ADDIE. Berdasarkan survei terhadap 17 digital artist, teridentifikasi pain points signifikan pada platform existing seperti biaya komisi tinggi (41,2%), proses verifikasi rumit (47,1%), dan persaingan tidak sehat dengan karya AI (Artificial Intelligence) (52,9%). Platform dikembangkan menggunakan Laravel 12, PHP, MySQL, HTML, CSS, dan JavaScript, dengan delapan fitur utama: Profile, Link, Portfolio, Commission Management, Review & Rating, Management Review, Real-time Chat, FAQ, dan Contact. Evaluasi yang melibatkan 15 responden menunjukkan bahwa platform mencapai tingkat kelayakan 85,6% dengan kategori "Sangat Layak". Aspek Usability memperoleh skor tertinggi yaitu 88,8%, sedangkan Functionality memperoleh skor 88,0%, yang menunjukkan bahwa fitur yang disediakan sesuai dengan kebutuhan digital artist Indonesia. Platform ini berhasil menjawab tantangan pemasaran yang dihadapi digital artist dengan menyediakan solusi terintegrasi dengan model komisi 0%, sehingga artist dapat memperoleh margin keuntungan maksimal.
Development of a distribution network electricity supply regulation system using machine learning at PT. PLN (Persero) UP2D North Sumatra Yasri, Afif; Zulham Sitorus
Bahasa Indonesia Vol 18 No 02 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i02.491

Abstract

The regulation of efficient energy supply within the distribution network is a significant difficulty for PT. PLN (Persero) UP2D North Sumatra in delivering dependable and stable services. This project seeks to create an automated system for regulating power supply in the distribution network through machine learning techniques. This system aims to forecast and enhance the allocation of electrical energy utilising historical data and current network circumstances. The dataset is partitioned into training data (training set) and testing data (testing/validation set) in specific ratios, such as 70%:30% or 80%:20%. The division is executed to preserve the temporal sequence (in time series scenarios) or to ensure a balanced representation (in classification scenarios). The employed machine learning approach is K-Means Clustering, utilised to analyse electricity consumption patterns and identify probable problems in the distribution network. The new centroid computation is based on the fact that each cluster contains a single data point, specifically C1 = (193, 205, 213), C2 = (153, 167, 170), and C3 = (179, 196, 200). This study's findings are anticipated to enhance the efficiency of energy distribution management, minimise downtime, and elevate the quality of service for consumers. By using a machine learning-driven automation system, PT. PLN UP2D North Sumatra can enhance its adaptability to load fluctuations and optimise the utilisation of current power supplies. The division is executed to preserve the temporal sequence (in the case of time series) or to ensure a balanced representation (in the case of classification).
Fault Detection And Recovery System On 20 Kv Distribution Network Using Real-Time Analysis With Support Vector Machine Algorithm Afrizal, Henri; Zulham Sitorus; Muhammad Syahputra Novelan
Bahasa Indonesia Vol 18 No 02 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i02.494

Abstract

The 20 kV distribution network is crucial for ensuring the uninterrupted supply of power to users in the PT. PLN UP2D North Sumatra Region. Disruptions in this network, including short circuits, overloads, and transient disturbances, can diminish system reliability and prolong outage durations if not promptly identified and rectified. This study seeks to develop a disturbance detection and recovery system for a 20 kV distribution network utilizing real-time analysis through the Support Vector Machine (SVM) algorithm. The system is designed by utilizing real-time electrical parameter data, including current, voltage, and network operational conditions, sourced from monitoring devices. The data undergoes preprocessing, feature extraction, and classification stages utilizing SVM to differentiate between normal and fault circumstances. The classification outcomes serve as the foundation for decision-making in isolating the fault zone and restoring supply to the unaffected segments of the network. The system's performance is assessed according to detection accuracy, response speed, and its capacity to facilitate the disturbance recovery process both automatically and semi-automatically. This research aims to enhance the dependability of the 20 kV distribution network, expedite fault resolution, and facilitate the advancement of a more intelligent, efficient, and responsive electrical distribution system.
Co-Authors , Arpan , Fery Anugerah A.A. Ketut Agung Cahyawan W Abda Abda Abdul Razaq Ade Alma Yuni Ade Guna Suteja Ade Surya Bakti Pane Aditya Ramadhani Afrizal, Henri Afrizal, Sandi Aldi Kesuma Alvian Alvian Alviona Marsya Ami Abdul Jabar Ami Abdul Jabar Amnisuhaila Abarahan Ananda Aulia Ananda Aulia Andi Ernawati Andi Ernawati Andi Ernawati Andysah Putera Utama Siahaan Angkat, Chairul Indra Anshari, Ari Antoni, Robin Anzas Ibezato Zalukhu Ardya, Dwika Arief, Muhammad Arif Rahman Astri Mutia Rahma Aulia, Ananda Ayu Ofta Ayu Ofta Sari Ayumi Kartika Sari azwan, m Baehaqi Bambang Sugito Bambang Sugito Batubara, Supina Boy Rizki Akbar Boy Rizki Akbar Br Tarigan, Sella Monika Chelfina Utami Chelfina Utami Daniel Happy Putra Danu Wardhana Azhari Darmeli Nasution Desy Ramatika DEWI SARTIKA Dhimas Prayogi diansyah, Suhar Didi Riswan`` Diva, Krisna Dwina Pri Indini Eko Hariyanto Eko Hariyanto Eko Hariyanto Eko Wahyudi Erbin Sitorus Fachri, Barany Fahmi Izhari Fahmi Kurniawan Fajar Aulia Lubis Feby Wulandari Sembirinng Fery Anugerah Fikri Zuhaili Simbolon Gilang Ramadhan Gultom, Ananda Christianto Hafiz Rodhiy Haliza, Siti Nur Hamzah, Iswadi Harmiati Bungsu Bangun Hartono Sinambela, Sugi Helmy, Ahmad Hendra Harnanda Hendra Utama Heni Wulandari Heri Eko Rahmadi Putra Heri Kurniawan Hilal Prayogi Hindra Syahputra Hrp, Abdul Chaidir Ibezato Zalukhu, Anzas Ibrahim Ika Devi Perwitasari Indra Angkat, Chairul IQBAL , MUHAMMAD Irwan Syahputra Irwan Syahputra, Irwan Josua M.H Simaremare Khairul Khairul Khairul Khairul, Khairul Kiki Artika Kurniawan, Fahmi Laila Maghfirah Laila Maghfirah Larius Ambasador Parlindungan Leni Marlina Leni Marlina Lia Nazliana Nasution Limbong, Yohannes France M Imam Santoso M. Azhari Rizko M. Rasyid M.Rizki Khadafi Maida Indrayani Mardiah, Nia Marzuki Sianturi, Ismail Maulian Saputra Meiarni Situkkir Melva Sari Panjaitan Meri Sri Wahyuni Mhd Arfan Sitorus Mhd Arie Akbar Mhd Ihsan Abidi Mohammad Yusuf Mohammad Yusuf, Mohammad Muhammad Fahriza Muhammad Fahriza Muhammad Hafizh Al-Ghifari Rangkuti Muhammad Iqbal Muhammad Iqbal Muhammad Irfan Sarif Muhammad Raihan Harahap Muhammad Syahputra Novelan Muhammad Wahyudi Nahampun, Natalia Nainggolan, Andreas Ghanneson Nainggolan, Irfan Nazar Saputra, Risfan Nelviony Parhusip Nurwijayanti Oktavia Tumangger Parhusip, Nelviony Pasaribu, Ryan Fahreza Pebri Ramadani Pranoto, Sugeng Putra, Khairil Ragil Satya Adi W Rahima Br Purba Rahmat Hidayat Rahmat Hidayat Raihan Risky Ramadani, Pebri Ramadhan, Aditya Ramadhani, Aditya Ramli S Siburian Rangga Rafandi Razaq, Abdul Retno Mutiara Rezkinah Rambe Rian Farta Wijaya Rian Farta Wijaya Rian Putra, Randi Rika Uli Samosir, Siska Risky, Raihan Robin Antoni Rowiyah Asengbaramae Rusydi Tanjung , Miftah Ryan Fahreza Pasaribu Sahputra, Fajar Said Oktaviandi Sarifuddin Septia Harliansyah Septiani, Nadya Sianturi, Ismail Sibarani, Dina Marsauli Simamora, Siska Simorangkir, Elsya Sabrina Asmita Sinambela, Sugi Hartono Sinyo Andika Nasution, Ahmad Sipra Barutu Sipra Barutu Siregar, Andree Risky Yuliansyah Sitepu, Fernando Siti Nurhaliza Sofyan Siti Nurhaliza Sofyan Sitinur, Siti Nurhaliza Sofyan Sitompul, Jelly Rolley Sofyan, Siti Nurhaliza Solahuddin Asri Ritonga Solly Ariza Solly Ariza Lubis Solly Aryza Sri Wahyuni, Meri Sugeng Pranoto Suhardiansyah Suhardiansyah Suhardiansyah Suherman Suherman Sukrianto, Sukrianto Sulis Sutiono Susilawati Yahya Sutiono, Sulis Syahputri, Maulisa Syamsiar, Syamsiar T, Siti Isna Syahri Tanjung, Miftah Rusydi Tiara Aninditha Utama, Hendra Vina Arnita Vivin Yulfia Sarah Wahyu Agung Pratama Wahyuni, Meri Sri Wijaya, Rian Farta Wirda Fitriani Yasri, Afif Yulianus Zai Zulfahmi Syahputera Zulfahmi Syahputra Zulfahmi Zulfahmi Zulfahmi Zulfahmi