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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal Information System Development METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Informatika Kaputama (JIK) Jurnal Sistem Informasi Kaputama (JSIK) Majalah Ilmiah Kaputama JTIK (Jurnal Teknik Informatika Kaputama) KAKIFIKOM : Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer Journal of Intelligent Decision Support System (IDSS) JUKI : Jurnal Komputer dan Informatika MEANS (Media Informasi Analisa dan Sistem) El-Mujtama: Jurnal Pengabdian Masyarakat Journal of Vision and Ideas (VISA) Bulletin of Multi-Disciplinary Science and Applied Technology TECHSI - Jurnal Teknik Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) International Journal of Informatics, Economics, Management and Science Journal of Engineering, Technology and Computing (JETCom) Journal of Mathematics and Technology (MATECH) International Review of Practical Innovation, Technology and Green Energy (IRPITAGE) International Journal of Health, Engineering and Technology Jurnal Kajian dan Penelitian Umum Indonesian Journal of Education And Computer Science Indonesian Journal of Science, Technology, and Humanities Jurnal Penelitian Teknologi Informasi dan Sains Journal of Data Analytics, Information, and Computer Science (JDAICS) Router : Jurnal Teknik Informatika dan Terapan Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Modem : Jurnal Informatika dan Sains Teknologi Repeater: Publikasi Teknik Informatika dan Jaringan Switch: Jurnal Sains dan Teknologi Informasi Saturnus: Jurnal Teknologi dan Sistem Informasi Router : Jurnal Teknik Informatika dan Terapan Jurnal Inovasi Informatika dan Bisnis Digital (JIIBD)
Pardede, Akim Manaor Hara
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Diagnosis of Kidney Disease Using Fuzzy Logic Method Andini, Anggun; Hara Pardede, Akim Manaor; Sihombing, Marto
International Journal of Informatics, Economics, Management and Science Vol 3 No 1 (2024): IJIEMS (January 2024)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/ijiems.v3i1.1234

Abstract

Kidney disease is a condition in which the kidneys cannot function properly. Kidney disease is one of the diseases that can cause death and disability. Ignorance and lack of information about kidney disease causes the death rate of people with kidney disease to increase every year. Limited special kidney facilities in Indonesia are one of the factors slowing treatment. To distinguish what type of kidney disease sufferers of symptoms need to bring in an expert or specialist in internal medicine for a consultation. However, there are some problems with an expert such as an expert who is not always in place, the cost of consulting an expert is quite expensive, and still limited. Unhealthy lifestyle is also a factor in the increase in kidney disease sufferers. With these problems it can be concluded that it is necessary to create a system that can diagnose diseases without having to consult an expert with an expert system. An expert system is a system that can store expert information in a computer. In the Fuzzy Logic method, the existing data will be inferred by creating a rule in the form of IF_THEN and using the AND operation, then the minimum value (MIN) of several existing variables will be selected. From the results of trials conducted in this study it was found that based on the symptoms that occur the patient suffers from Acute Renal Failure.
Security of Employee Salary Data Using the ElGamal Algorithm By Utilizing the Diffie-Hellman Algorithm Key Generator Indakholasy, Barru; Hara Pardede, Akim Manaor; Khair, Husnul
International Journal of Informatics, Economics, Management and Science Vol 3 No 1 (2024): IJIEMS (January 2024)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/ijiems.v3i1.1221

Abstract

Data security is something that is very important for companies or organizations, one of which is the South Binjai District Office. Almost all work in the South Binjai sub-district office uses a computer, especially in processing data stored in the form of data files on storage media without encoding. In today's digital era, many irresponsible parties find it easy to tap company data so that it can be easily accessed and there is also some software that can be easily used to crack passwords. One of the data that needs to be kept confidential is employee salary data. To overcome these problems, a security system is needed to secure text data. The cryptographic method is regarding encryption techniques in which plaintext is scrambled using an encryption key to become ciphertext which is applied in data security systems. The Diffie-Hellman algorithm is a key exchange algorithm, this algorithm is limited to the key exchange process, so it must require another algorithm for the encryption and decryption process such as the ElGamal algorithm. The use of these two algorithms can strengthen the level of data security. From the results of trials conducted, the sample data used containing the word "KARYAWAN" was successfully secured with both diffie-helman and ElGamal algorithms.
Penerapan Metode Fuzzy untuk Mengetahui Penyakit Radang Kelopak Mata (Blepharitis) Bayu Juliansyah; Akim Manaor Hara Pardede; Husnul Khair
Router : Jurnal Teknik Informatika dan Terapan Vol. 3 No. 2 (2025): Juni: Router : Jurnal Teknik Informatika dan Terapan 
Publisher : Asosiasi Profesi Telekomunikasi dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v3i2.614

Abstract

Blepharitis or inflammation of the eyelids is one of the common eye diseases, characterized by inflammation of the edges of the eyelids that can cause discomfort, irritation, and even visual disturbances. This disease can be chronic with recurrent symptoms such as red eyes, itching, watering, and the appearance of crust on the eyelashes. Proper and prompt diagnosis is necessary so that medical treatment can be carried out effectively and further complications can be prevented. This study aims to design and build an expert system based on the Fuzzy Logic method in helping diagnose blepharitis. The fuzzy method was chosen because it is able to handle the uncertainty of symptom data that often arises in the medical diagnosis process. This system is developed through the identification of the common symptoms of blepharitis, then processed using the fuzzy membership function to determine the type of disease based on the degree of symptom onset. The output of the system is in the form of the results of the diagnosis of blepharitis along with initial treatment recommendations that can be used as a reference for users. The results of the system test show that the application of fuzzy logic is able to provide diagnosis results that are quite accurate, fast, and easy to understand both medical personnel and the general public. This system is expected to help increase public awareness about the importance of early detection of blepharitis, as well as being a tool in the initial medical decision-making process. However, the limitations of this study lie in the limited amount of data and coverage of the type of blepharitis, so further development is needed, both in expanding the knowledge base, increasing the variety of symptoms, and improving system interaction with users.
Optimizing brain tumor MRI classification using advanced preprocessing techniques and ensemble learning methods Pardede, Akim Manaor Hara; Zamsuri, Ahmad; Nuroini, Indi; Alkhairi, Putrama
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5106-5119

Abstract

Brain tumor classification is a critical task in medical imaging that directly impacts the accuracy of diagnosis and treatment planning. However, the complexity and variability of magnetic resonance imaging (MRI) images pose significant challenges, often resulting in reduced model reliability and generalization. This study addresses these limitations by proposing a novel ResNet+Bagging model, leveraging the strengths of residual networks and ensemble learning to enhance classification performance. Using publicly available brain tumor MRI datasets, including images labeled as benign, malignant, and normal, the study employs advanced preprocessing techniques such as normalization, data augmentation, and noise reduction to ensure high-quality inputs. The proposed model demonstrated significant improvements, achieving the highest testing accuracy of 72%, outperforming other tested models such as LeNet, standard ResNet, GoogleNet, and VGGNet. Precision (0.6010), recall (0.6000), and F1-score (0.5990) metrics further highlight its superior balance in detecting positive and negative classes. The novelty of this research lies in the application of Bagging to ResNet, which effectively mitigates overfitting and enhances predictive stability in complex medical datasets. These findings underscore the proposed model's potential as a robust solution for brain tumor classification, contributing to more accurate and reliable diagnostics.
RANCANGAN SISTEM NOTIFIKASI KEDATANGAN PEMBELI DENGAN SUARA MENGGUNAKAN ARDUINO Subakti, Al Haby Pratama; Pardede, Akim Manaor Hara; Syari, Mili Alfhi Syari
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 7 No. 1 (2023): Volume 7, Nomor 1, Januari 2023
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v7i1.24

Abstract

Toko kelontong merupakan tempat untuk menjual berbagai kebutuhan rumah tangga. Yang mana menyediakan kebutuhan seperti kebutuhan dapur, kebutuhan mandi, peralatan sekolah, makanan ringan, dan yang lainnya. Tempat yang menyediakan segitu banyak barang tentu tidak luput dari tindak pencurian yang dilakukan oleh beberapa oknum masyarakat. Pada penelitian sebelumnya, adalah sistem untuk keamanan rumah. Pada penelitian tersebut berfokus pada pemasangan multisensor pada setiap jalan masuk yang ada pada rumah, yang menggunakans sensor buzzer dan SMS sebagai output. Oleh karena itu, untuk menyesuaikan kondisi pada toko kelontong. Komponen yang terdiri dari Arduino uno R3, sensor ultrasonik, DF Player, LCD, I2C, Speaker, dan ESP32-CAM. Sistem ini dilengkapi dengan fitur mengambil foto yang dikirimkan ke telegram dengan menggunakan ESP32-CAM. Hasil dari penelitian ini adalah jarak yang bisa dideteksi sensor cukup jauh, namun sebagai bentuk percobaan penulis membatasi jarak 12cm dan akan mendeteksi objek ketika berada di jarak 5cm. Ujicoba membuktikan ketika sensor mendeteksi objek dengan status objek datang, maka speaker berbunyi yang terhubung pada DF Player dengan memutar audio yang tersimpan di sdcard. LCD akan menampilkan status “ada” ketika terdapat objek yang berada didalam toko. Disaat itu juga ESP32-CAM akan mengambil gambar dan mengirimnya ke telegram. Ketika objek keluar melewati objek maka tidak ada respon dari komponen lain selain LCD, yang menampilkan status “tidak ada” yang berarti tidak ada objek yang berada didalam toko.
RANCANGAN PENELITIAN MODEL AI-OPTIMASI HIBRIDA MENGGUNAKAN PEMROGRAMAN LINIER UNTUK KOMPUTASI TEPI HEMAT ENERGI DI INDUSTRI 5.0 Pardede, A M H
Jurnal Informatika Kaputama (JIK) Vol 10 No 1 (2026): Volume 10, Nomor 1, Januari 2026
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v10i1.1231

Abstract

The development of Industry 5.0 demands intelligent, adaptive, sustainable computing systems oriented toward human-machine collaboration. In this context, edge computing plays a crucial role because it can process data in real time near the data source, supporting industrial applications such as smart manufacturing, predictive maintenance, and cyber-physical systems. However, the increasing use of edge devices results in high energy consumption, limited computing resources, increased operational costs, and an increased carbon footprint of digital industrial systems. Therefore, energy efficiency in edge computing has become a strategic and pressing issue. The problem is further complicated by the fact that most edge resource management currently relies on static or heuristic approaches that are less adaptable to the dynamics of industrial workloads. Artificial Intelligence (AI)-based approaches have the advantage of accurately predicting workloads, but generally fail to guarantee optimal resource allocation. In contrast, mathematical optimization methods such as Linear Programming (LP) are capable of producing optimal solutions but are less adaptable to changing dynamic system conditions. This research aims to develop a hybrid AI-Optimization model based on Linear Programming to improve the energy efficiency of edge computing in Industry 5.0. AI models are used to predict workloads and computing demands, while LP is utilized to determine optimal resource allocation by minimizing energy consumption without violating the Service Level Agreement (SLA). The research methods include collecting workload datasets, developing machine learning prediction models, formulating LP models, and integrating the two into an adaptive system
RANCANGAN PENELITIAN PENGEMBANGAN MODEL OPTIMASI PELAYANAN PUBLIK DARURAT PADA BENCANA BANJIR MENGGUNAKAN PEMROGRAMAN LINIER DALAM KERANGKA SMART CITY Pardede, A M H
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 10 No. 1 (2026): Volume 10, Nomor 1, Januari 2026
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v10i1.1233

Abstract

Recurrent flooding in Aceh, North Sumatra, and West Sumatra reflects the increasingly complex challenges of providing public services in emergency situations. In large-scale floods, service disruptions are triggered not only by the increasing number of victims and the need for assistance, but also by disruptions to the operational capacity of public service facilities, such as hospitals, police, fire departments, and transportation access. These disruptions result in slow responses and low resource utilization efficiency, ultimately increasing the risk of loss of life. This research aims to formulate a mathematical optimization model for emergency public services during floods, taking into account resource limitations and the simultaneous reduction in the capacity of service centers. The model was developed to minimize response times and delays in victim handling, while also providing a conceptual contribution to the development of public service optimization studies and strengthening healthcare resilience within a Smart City framework. The research approach used is fundamental research through linear programming-based mathematical modeling. The emergency public service problem is formulated as a multi-agency optimization model that includes an objective function and various constraints, such as service capacity, victim priorities, resource limitations, and access barriers due to flooding. The analysis is conducted through conceptual simulations on various service capacity reduction scenarios to examine the solution characteristics and theoretical model consistency.
Pengembangan Sistem Notifikasi Audio untuk Deteksi Kedatangan Pembeli Berbasis Arduino Al Haby Pratama Subakti; A M H Pardede; Mili Alfhi Syari
Jurnal Inovasi Informatika dan Bisnis Digital (JIIBD) Vol 1 No 1 (2025): November 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jiibd.v1i1.1822

Abstract

Toko kelontong merupakan unit usaha ritel yang menyediakan berbagai kebutuhan sehari-hari masyarakat, seperti perlengkapan dapur, kebutuhan mandi, alat tulis sekolah, makanan ringan, serta produk rumah tangga lainnya. Dengan banyaknya jenis barang yang tersedia, toko kelontong berpotensi mengalami risiko tindak pencurian oleh pihak-pihak yang tidak bertanggung jawab. Oleh karena itu, diperlukan suatu sistem pendukung keamanan yang mampu membantu pemilik toko dalam melakukan pemantauan secara lebih efektif. Penelitian sebelumnya mengembangkan sistem keamanan rumah berbasis multisensor yang dipasang pada setiap akses masuk, dengan keluaran berupa bunyi buzzer dan notifikasi SMS. Berbeda dengan penelitian tersebut, studi ini menyesuaikan desain sistem agar relevan dengan kebutuhan lingkungan toko kelontong. Sistem yang dikembangkan memanfaatkan beberapa komponen utama, yaitu Arduino Uno R3 sebagai pengendali utama, sensor ultrasonik sebagai pendeteksi objek, DF Player sebagai modul pemutar audio, LCD dengan modul I2C sebagai penampil informasi, speaker sebagai output suara, serta ESP32-CAM sebagai perangkat pengambil gambar. Selain menghasilkan notifikasi suara, sistem ini dilengkapi fitur dokumentasi visual yang memungkinkan pengambilan gambar secara otomatis menggunakan ESP32-CAM, kemudian mengirimkannya melalui platform Telegram. Berdasarkan hasil pengujian, sensor ultrasonik memiliki kemampuan deteksi pada jarak yang relatif jauh, namun dalam implementasinya jarak deteksi dibatasi hingga 12 cm, dengan ambang aktivasi sistem pada jarak 5 cm dari sensor. Ketika objek terdeteksi memasuki area toko, speaker akan mengeluarkan suara melalui DF Player yang memutar file audio dari kartu SD, sementara LCD menampilkan status “ada”. Pada saat yang sama, kamera akan mengambil gambar dan mengirimkannya secara otomatis melalui Telegram. Sebaliknya, ketika objek keluar dari area deteksi, tidak ada respons suara maupun pengambilan gambar, dan sistem hanya memperbarui tampilan LCD menjadi “tidak ada”, yang menunjukkan bahwa tidak terdapat objek di dalam area pemantauan toko.
HYBRID TRANSFER LEARNING AND ADVANCED DATA AUGMENTATION FOR MULTICLASS BRAIN TUMOR CLASSIFICATION USING EFFICIENTNET A M H Pardede; Riki Winanjaya; Juni Ismail
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7524

Abstract

Accurate Accurate brain tumor diagnosis from MRI images remains challenging due to dataset limitations, class imbalance, and high morphological variability across tumor types. Existing deep learning approaches often yield suboptimal results when trained on small or imbalanced datasets. This study proposes a hybrid learning strategy that integrates transfer learning with advanced data augmentation to classify four brain tumor categories: glioma, meningioma, pituitary adenoma, and normal tissue. Using a large-scale dataset of 7,023 MRI images, the proposed framework incorporates Mixup, CutMix, and a comprehensive augmentation pipeline with an optimized EfficientNet-B0 architecture. The model achieves a test accuracy of 99.05% with F1-scores of 0.99, representing a 4.05 percentage point improvement over a baseline InceptionV3 model (95.00%) and outperforming ResNet-based approaches (93.80%) reported in previous studies. This quantitative improvement demonstrates the effectiveness of combining modern CNN architectures with advanced augmentation strategies. The streamlined architecture and high accuracy make the method suitable for deployment in resource-constrained healthcare environments. These results indicate that hybrid augmentation and transfer learning can deliver clinically meaningful performance for early brain tumor identification, offering a scalable and practical solution for computer-aided medical diagnosis
Implementation of Random Forest Algorithm for Classifying Land and Building Tax Arrears and Risk Factor Analysis Dashboard Risky Firmansyah Manik; A M H Pardede; Anton Sihombing
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2326

Abstract

This study aims to develop a predictive model to identify the potential for land and building tax arrears and analyze the dominant risk factors contributing to non-compliance. The research utilizes the Random Forest classification algorithm applied to historical tax data from the Regional Financial and Revenue Management Agency of Binjai City. The approach involves data preprocessing, feature engineering including target encoding for geographical areas, and model training with hyperparameter tuning to optimize classification performance. Furthermore, a web-based interactive dashboard is developed using the Flask framework to visualize the predictions and risk factors. The results demonstrate that the Random Forest model achieves a robust and consistent accuracy of approximately 85% in classifying compliant and non-compliant taxpayers. Feature importance analysis reveals that land area is the most dominant risk factor influencing tax arrears, significantly outweighing other variables. In conclusion, the integration of the Random Forest algorithm with an interactive dashboard provides a highly accurate, efficient, and scalable solution for local governments to transition from reactive tax collection to proactive, data-driven risk management.
Co-Authors ., Novriyenni ., Novriyenni Abdillah, Afif Abdullah Rayni Achmad Fauzi Ade Dharma Agung Prayogi Agus Muji Santoso Agus Syahrani Agus Syahrani Ahmad Zamsuri, Ahmad Al Haby Pratama Subakti al Haby Pratama Subakti Alia Bihrajihant Raya Alia Bihrajihant Raya Alkhairi, Putrama Alya Fadillah Amanda Putri Ardana Ambarita, Indah Amin Hou Anang Wahid M.Diah ANDIKA Andika Andika Andini, Anggun Anggi Pratiwi, Anggi Anggraeni Dyah S Anggraeni Dyah Sulistiowati Anggun Anggraini Anggun Angraini Anita Shintya Devi Apriandi Alfa Reza Saragih Arifin Dwi Saputro Aris Wahyono, Aris Wahyono Artika Dini Anggriani Asrofi Liza Nasution Astrika, Rahayu Aula, Nurhasanah Azmy, Maulida Bachtiar, Willy Bagus Aziz Bagus Azizi Bayu Juliansyah Boyke Gunawan Manurung Br Bangun, Cindy Arsita Buaton, Relita Budi Purnomo Siahaan Budi Saragi Ginting budi saragih budi saragih Budi Serasi Ginting Budi Serasi Ginting Christian Adi Pratama Saragih Debby Ade prastiwi Deby Siska Oktavia Pasaribu Destiara, Destiara Desty Dwi Putri Diana Lestari Simamora Diaz Kuncoro Dina Filina Eddu Dwi Rizki Eddu Dwi Rizki Efendi, Sutris Eka Rahamadi Susilo Elni Arbaeti, Endang Evi Rinata Fahmi Aulia Sirait Fahri Husaini Farid Reza Malau Fatmaira, Zira Fauzi, Achmad Fazar Syahfitra Ferry Rahmadani fika lestika Fika Lestika Br. Tarigan Filina, Dina Finda Meyditia Finda Meyditia Fuzy Yustika Manik Fuzy Yustika Manik, Fuzy Yustika Ginting, Budi Saragi Gultom, Imeldawaty H Khair Hamdani, Diky Hamdani, Diky Harahap, Khairunnisa Hari Sabana Hasanul Khair Hasdari Helmi Hasdari Helmi Hendy Setiawan Hendy Setiawan Herdianta Herdianta Herlina, Lien Hermansyah Sembiring Hotler Manurung Husnul Kahir I G Prahmana I Gusti Prahmana I Gusti Prahmana igusti prahmana Iis Joice Susanti Marpaung Ilham Ilham Indah Haziziyah Indakholasy, Barru Indriana, Nurita Jen Peng Huang John Darwis Martuadi Saragih John Darwis Martuadi Saragih Jonathan Setiadi Pasaribu Juni Ismail Kadim, Lina Arliana Nur Katen Lumbanbatu Katen Lumbanbatu Katen Lumbanbatu Khadafi, Muhamad Khadapi, Muammar Khair , Husnul Khair, Husnul Khairunnisa Harahap Lestari Riawan, Putri Lita, Catlya Novera Lumbanbatu, Katen M Fakhrul Hirzi M Ilham Nst M. Rizki Auliansyah Ginting M. Taufik Magdalena Simanjuntak Maida Andriani MANURUNG, HOTLER Marto Sihombing Marto Sihombing Marto Sihombing Marto Sihombing Maulida Azmy maulida azmy Melda Pita Uli Sitompul MHD Micho Januar Prananta Mili Alfhi Syari Mili Alfhi Syari Syari Muhammad Arie Afryanda Muhammad Arie Afryanda Muhammad Taufik Nanang Wahyudin Nanang Wahyudin Naufal Falaah Nduru Nidya Banuari Nila Damayanti Ningsih, Nur Ema Tiana Nisa R, Ajeng Arina Novriyenni Novriyenni - Novriyenni Novriyenni Novriyenni Novriyenni Novriyenni Novriyenni Novriyenni Novriyenni, Novriyenni Nur Ema Tiana Ningsih nurema tiana ningsih Nurhayati Nurhayati Nurhayati Nuroini, Indi PASARIBU, TIO RIA Pasaribu, Tioria Paul Saut Marganda L Tobing Permana Putra Permana Putra Pitaloka, Nadila Prahmana, I Gusti Prastya, Reno Preddy Marpaung Rafael R.F. Sijabat Rahmad Syahputra Rahmadani Rahmadani, Feri Ramadani, Suci Ramadhani Rangkuti, Nazwa Nurul Hafizah Refirda Agtalia Rendi Eka Pratama Rendi Eka Pratama Resti Afrelia Sibuea Richa Orellia Rika Hedy Anggraini Prastio Riki Winanjaya rima siburian Riski Ramadhansyah Risky Firmansyah Manik Ronny Lesmana Rusmin Saragih, Rusmin Saragih, Christian Adi Pratama Saragih, John Darwis Martuadi Sari, Indah Kelana Sawitri Sawitri Selfira Selfira Sembiring, Hermansyah Sihombing, Anton Sihombing, Marto Sijabat, Maruba Simanjorang, Armadani Simanjuntak, Magdalena Simanjuntak, Magdalena Simanjuntak, Ruth Rani Siswan Syahputra Sitepu, Cindy Yohana Siti Alyunita Mega Lestari Siti Mutoharoh Permata Ayunda Subakti, Al Haby Pratama Suhada Suhada Suhada Suhada Suhada Suhada Suhada Suhada, Suhada Surja Arafat Surtan Hasibuan Surya Siswoyo Surya Siswoyo Sutris Efendi Syahputra , Siswan Syahputra, Siswan Syari, Mili Alfhi Syari Syari, Milli Alfhi Tanjung, Anggi Muhammad Tarigan, Fika Lestika Br. Tengku Syahdina Riyan Thalia, Novi Tio Ria Pasaribu Tria Pusvita Dewi Triana Dewi lestari Wahyu Surya Nanda Wardani, Sisca Willy Bachtiar Wisnu Satriyo Jati Wisnu Satriyo Jati Yani Maulita Yanuar Dwi Prastyo Yehezkiel Wibisono Sagala Yourman Doni Siddik Yuda Washita