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All Journal Jurnal Informatika dCartesian: Jurnal Matematika dan Aplikasi Jurnal Simetris Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Informatika dan Teknik Elektro Terapan SMATIKA Sistemasi: Jurnal Sistem Informasi Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal EMT KITA JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Syntax Literate: Jurnal Ilmiah Indonesia JUTEI (Jurnal Terapan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi INTECOMS: Journal of Information Technology and Computer Science MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal ULTIMA Computing JURIKOM (Jurnal Riset Komputer) Jurnal Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) ComTech: Computer, Mathematics and Engineering Applications Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Zonasi: Jurnal Sistem Informasi Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Tekinkom (Teknik Informasi dan Komputer) Abdimasku : Jurnal Pengabdian Masyarakat INFOKUM Aiti: Jurnal Teknologi Informasi Jurnal Teknologi Informatika dan Komputer IJECS: Indonesian Journal of Empowerment and Community Services J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Ekonomi JUSTIN (Jurnal Sistem dan Teknologi Informasi) International Journal Software Engineering and Computer Science (IJSECS) Jurnal Media Computer Science Malcom: Indonesian Journal of Machine Learning and Computer Science STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi J-Icon : Jurnal Komputer dan Informatika Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) d'Cartesian: Jurnal Matematika dan Aplikasi JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Magdalena Ariance Ineke Pakereng
Universitas Kristen Satya Wacana
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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PERBANDINGAN METODE RANDOM FOREST DAN KNN DALAM MENDETEKSI PENYAKIT LIVER Jordan Johan Josafat Simanjuntak; Magdalena A. Ineke Pakereng
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 2 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/j9se3a69

Abstract

Penyakit hati merupakan masalah kesehatan global yang semakin diperburuk oleh gaya hidup tidak sehat, seperti konsumsi alkohol yang berlebihan dan deteksi dini yang tidak memadai. Oleh karena itu, pendekatan berbasis teknologi diperlukan untuk meningkatkan akurasi diagnosis. Penelitian ini bertujuan untuk membandingkan kinerja metode pembelajaran mesin Random Forest dan K-Nearest Neighbor dalam mengklasifikasikan penyakit hati berdasarkan faktor klinis dan gaya hidup pasien. Data diperoleh dengan menggabungkan dua dataset terbuka, yang totalnya berjumlah 2.283 entri dan 19 fitur, termasuk parameter laboratorium, usia, konsumsi alkohol, dan riwayat kesehatan. Tahapan penelitian meliputi pra-pemrosesan data, termasuk imputasi nilai yang hilang, pengkodean variabel kategorikal, normalisasi fitur numerik, rekayasa fitur, dan penyeimbangan kelas menggunakan Teknik Oversampling Minoritas Sintetis. Dataset kemudian dibagi menjadi 80% data pelatihan dan 20% data pengujian secara stratifikasi. Evaluasi model dilakukan menggunakan matriks kebingungan, akurasi, presisi, recall, F1-score, dan Area Di Bawah Kurva Receiver Operating Characteristic. Hasil menunjukkan bahwa Random Forest memiliki kinerja lebih baik dengan akurasi 82% dan nilai Area Di Bawah Kurva 0,91, dibandingkan dengan K-Nearest Neighbor yang mencapai akurasi 72% dan Area Di Bawah Kurva 0,83. Selain itu, Random Forest menghasilkan jumlah negatif palsu yang lebih rendah, menjadikannya lebih dapat diandalkan dalam mendeteksi pasien dengan penyakit hati. Oleh karena itu, Random Forest direkomendasikan sebagai metode yang lebih efektif dan stabil untuk sistem deteksi dini penyakit hati berbasis pembelajaran mesin. Kata kunci: Penyakit Hati, Klasifikasi Medis, Random Forest, K-Nearest Neighbor, Deteksi Dini
Sistem Pendukung Keputusan untuk Menentukan Peminatan Mahasiswa Menggunakan Metode Fuzzy Tsukamoto dan TOPSIS Juan Keinan thimothi Paparang; Magdalena A. Ineke Pakereng
J-Icon : Jurnal Komputer dan Informatika Vol 14 No 1 (2026): March 2026
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v14i1.27447

Abstract

This study aims to design a decision support system that helps students choose a specialization that suits their abilities and interests, thus supporting timely graduation. The method used is a combination of Fuzzy and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). This study was conducted to obtain the most appropriate specialization recommendations for students, based on existing criteria. The criteria used in this study include academic scores of compulsory courses relevant to each specialization, skills, and programming abilities. The results of the study will provide specialization recommendations that are sorted by the highest preference value, so that students can see the choices of specializations that best suit their abilities and interests. In addition, these results are also expected to help students make the right decisions, as well as increase their chances of graduating on time.
Benefit Analysis of Machine Learning Implementation for Data Engineering Automation in MSMEs Alexander Mario Saputra; Magdalena A. Ineke Pakereng
SISTEMASI Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6536

Abstract

Micro, Small, and Medium-Sized Enterprises (MSMEs) are facing increasing pressure to undergo digital transformation. However, many MSMEs continue to rely on manual data management processes, making them vulnerable to human error and operational inefficiencies. This study aims to examine the benefits of implementing machine learning to automate data engineering processes in MSMEs, while identifying the key barriers to adoption and highlighting unresolved research gaps in the existing literature. A Systematic Literature Review (SLR) was conducted using the Population, Intervention, Comparison, Outcome, and Context (PICOC) framework. A total of 25 peer-reviewed studies published between 2021 and 2026 were selected from six academic databases: Google Scholar, ResearchGate, DOAJ, Garuda, Scopus, and IEEE Xplore. The synthesis identified four major benefits of machine learning implementation: reduced dependence on manual data processing, improved business prediction accuracy, operational cost savings of up to 42% through more efficient data pipelines, and more responsive inventory and customer management. Despite these advantages, adoption remains constrained by limited digital capabilities of human resources, inadequate infrastructure and financial resources, and insufficient historical data for model development. Furthermore, the literature analysis revealed that approximately 80% of existing studies focus primarily on the operational use of machine learning outputs, whereas the design of automated upstream data engineering foundations tailored to the resource constraints of MSMEs has received very limited attention. This research gap represents the primary focus and contribution of the present study.
EVALUASI ANTARMUKA DAN PENGALAMAN PENGGUNA WEB OPAC LIBRARY UKSW MENGGUNAKAN PENDEKATAN USER-CENTERED DESIGN (UCD) Riza Jeheskiel N. Tarigan; Magdalena A. Ineke Pakereng
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 1 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/ddks1702

Abstract

Penelitian ini mengevaluasi antarmuka dan pengalaman pengguna (user experience) Web OPAC Perpustakaan O. Notohamidjojo UKSW dengan menerapkan pendekatan User-Centered Design (UCD). Metode yang digunakan adalah mixed methods yaitu observasi, task-based usability testing, dan kuesioner standar (SUS dan UEQ) kepada 100 responden mahasiswa. Hasil reliabilitas menunjukkan instrumen layak (Cronbach’s α SUS = 0,82, dimensi UEQ ≥ 0,71). Skor rata-rata SUS adalah 52,5 (SD = 2,92), tergolong marginal / poor, menandakan kebutuhan perbaikan kegunaan. Analisis UEQ memperlihatkan profil pragmatis (kejelasan 0,12, efisiensi 0,08, ketepatan 0,15 - netral) dan profil hedonis yang negatif (daya tarik -0,21, stimulasi -0,34, kebaruan -0,47). Task testing mengungkap tingkat keberhasilan: pencarian judul 92% (41 s), penggunaan filter 63% (78 s), melihat detail koleksi 71% (66 s). Temuan kualitatif menyorot navigasi yang tidak intuitif, visibilitas filter rendah, struktur detail koleksi kurang rapi, dan umpan balik sistem minim. Rekomendasi prioritas meliputi perbaikan visibilitas kontrol filter, peningkatan feedback sistem, optimasi performa, pembaruan visual language, dan strategi evaluasi berkelanjutan berbasis metrik ( target SUS ≥ 68, kenaikan dimensi UEQ hedonis ≥ +0,5). Implikasi praktis dan langkah implementasi diusulkan mengikuti siklus UCD iteratif. Kata Kunci: Antarmuka Pengguna, Evaluasi Usabilitas, OPAC, Pengalaman Pengguna (UX), User-Centered Design.
DETEKSI PENYAKIT GLAUKOMA PADA CITRA FUNDUS RETINA DENGAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) Rheyna Atalya Setiadi; Magdalena A. Ineke Pakereng
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 3 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/g5sztt66

Abstract

Penelitian ini membahas mengenai deteksi penyakit glaukoma pada citra fundus retina secara otomatis menggunakan metode Convolutional Neural Network dengan pendekatan transfer learning dan fine-tuning pada arsitektur EfficientNetB0, dan menggunakan dataset dari Standardized Multi-Channel Dataset for Glaucoma (SMDG-19) dengan total 12.316 citra fundus retina beresolusi 512×512 piksel. Proses penelitian ini meliputi tahap preprocessing berupa resizing, rescaling serta data augmentation untuk meningkatkan variasi dan mengurangi overfitting. Model ini dilatih dengan Adam optimizer. Hasil pelatihan menunjukkan akurasi validasi mencapai 89,02% dan evaluasi pada data uji menghasilkan akurasi 87,29%, presisi 87,04%, recall 78,04% dan F1-Score sebesar 82,54%. Hasil ini menunjukkan bahwa metode CNN dengan arsitektur EfficientNetB0 mampu mendeteksi glaukoma secara efektif dan berpotensi digunakan sebagai sistem pendukung diagnosis dini yang akurat di bidang oftalmologi. Kata Kunci: Glaukoma, Citra Fundus Retina, Convolutional Neural Network, EfficientNetB0, Transfer Learning
ANALISIS SENTIMEN MAHASISWA TERHADAP SIASAT UKSW BERDASARKAN KUESIONER MENGGUNAKAN METODE LOGISTIC REGRESSION Robby Adrian Fajar Sulistya Sulistya; Magdalena Ariance Ineke Pakereng
Jurnal Pendidikan Teknologi Informasi (JUKANTI) Vol 9 No 1 (2026): JURNAL PENDIDIKAN TEKNOLOGI INFORMASI (JUKANTI) EDISI APRIL 2026
Publisher : Universitas Citra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37792/jukanti.v9i1.1935

Abstract

SIASAT is the primary academic information system at Satya Wacana Christian University (UKSW) used by students for academic administrative activities. This study aims to analyze students’ sentiment toward SIASAT using a Logistic Regression classifier based on open-ended responses collected through an online questionnaire. A total of 58 textual responses were manually labeled into two classes, namely positive and negative. The research stages include text preprocessing (lowercasing, cleaning, tokenization, stopword removal, and stemming), feature weighting using TF-IDF, handling class imbalance through Random Oversampling, and classification using Logistic Regression. Evaluation was performed using Stratified 5-Fold Cross-Validation, with oversampling applied exclusively to the training data within each fold to prevent data leakage. The model achieved an average accuracy of 70% and a weighted F1-score of 70%. These results suggest that Logistic Regression provides a promising baseline performance for Indonesian text sentiment classification on a small-scale dataset, although its generalizability remains limited due to the small sample size and the manual labeling process.
Deteksi Objek Secara Real-Time Berbasis YOLOv8 dan Algoritma DeepSORT Daniel Satria Mahardhika; Magdalena A. Ineke Pakereng
Jurnal Media Computer Science Vol 5 No 1 (2026): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i1.10038

Abstract

Manual monitoring of traffic density and pedestrian movement is often inefficient and prone to errors. This study aims to develop and evaluate an automated real-time object detection and tracking system as a solution to these issues, focusing on four main classes: cars, motorcycles, trucks, and people. The method employed involves the implementation of the YOLOv8l deep learning architecture for high-precision object detection, combined with the DeepSORT algorithm for tracking and re-identification to maintain unique IDs for each observed object. The training data were collected from various CCTV recordings and enriched through augmentation techniques, after which the model was trained using the Google Colab platform. For functional testing, the system was implemented in a local Flask-based web application capable of processing input from videos, webcams, and YouTube live streams. The evaluation results indicate that the model achieved very high and balanced performance, with overall accuracy, precision, recall, and F1-score values reaching 0.98. Although challenges related to Frame Per Second (FPS) stability were identified due to heavy computational loads, the system as a whole proved to be functional, reliable, and effective as an automated solution for object monitoring in public environments.
Analisis Sentimen Pemilihan Presiden dan Wakil Presiden Tahun 2024 Di Twitter Menggunakan Metode Klasifikasi Naive Bayes Bhilton Mesianus Obidje; Magdalena A. Ineke Pakereng
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5836

Abstract

Pemilihan Umum (PEMILU) dalam sejarah negara Indonesia telah dilaksanakan beberapa kali, namun pemilihan umum yang dilakukan secara langsung oleh masyarakat Indonesia baru pertama kali dimulai pada tahun 2004. PEMILU yang telah diselanggarakan pada tahun 2024 adalah proses untuk mewujudkan demokrasi di Indonesia. Oleh karena itu penelitian ini bertujuan untuk menganalisis sentimen masyarakat Indonesia tentang calon presiden yang  dipilih pada PEMILU tahun 2024. Untuk menganalisis sentimen masyarakat pada penelitian ini, akan menggunakan metode klasifikais Naive Bayes untuk dikelompokkan menjadi sentimen positif, netral dan negatif. Oleh karena itu hasil penelitian ini adalah pengelompokkan sentimen masyarakat tentang PEMILU 2024 dan menarik kesimpulan dari pengelompokkan tersebut. Analisis sentimen yang didapatkan menggunakan metode Naive Bayes pada Calon Presiden (Capres) Anies Baswedan mendapatkan sentimen positif 58, netral 20, negatif 19 dengan accuracy = 0,75 dan missclass = 0,25, sedangkan pada capres Prabowo Subianto mendapatkan sentimen positif 53, netral 24, negatif 21 dengan accuracy = 0,5 dan missclass = 0,5 dan pada capres Ganjar Pranowo mendapatkan sentimen positif 77, netral 14, negatif 9 dengan accuracy = 0,8 dan missclass = 0,2.
DETEKSI JUMLAH KENDARAAN YANG MASUK DAN KELUAR JALAN TOL MENGGUNAKAN YOLOv5 DAN DEEP SORT Stevanus Januar Latuluma; Magdalena A. Ineke Pakereng
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 4 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/fetaqm71

Abstract

Perkembangan teknologi computer vision memungkinkan proses pemantauan lalu lintas dilakukan secara ototmatis melalui deteksi, pelacakan, dan perhitungan kendaraan. Penelitian ini bertujuan untuk membangun sistem deteksi dan pelacakan kendaraan menggunakan algortima YOLOv5 dan Deep SORT pada video lalu lintas jalan tol. Dataset yang digunakan diperoleh dari proses pengambilan gambar dan video secara mandiri, kemudian dilakukan anotasi menggunakan Roboflow dengan tiga kelas kendaraan yaitu car, bus, truck. Dataset terdiri dari 326 objek kendaraan yaitu meliputi 242 mobil, 70 truk, dan 14 bis. Model YOLOv5s dilatih menggunakan 100 epoch dengan ukuran citra 640x640 piksel dan batch size 16. Hasil pengujian menunjukan bahwa model memperoleh nilai precision sebesar 94,7%, recall sebesar 92,6% mAP@0.5 sebesar 98,1% dan mAP@0.5:0.95 sebesar 66,9%. Selanjutnya, hasil deteksi diintegrasikan dengan algoritma Deep SORT untuk memberikan identitas unik pada setiap kendaraan sehingga pergerakannya dapat dilacak secara konsisten antar frame. Pengujian dilakukan menggunakan video lalu lintas berdurasi 3 menit 38 detik. Hasil penghitungan menunjukan terdapat 16 mobil masuk dan 32 mobil keluar, 2 bus masuk tanpa kendaraan bus keluar, serta 7 truk keluar tanpa ditemukan truk yang masuk. Berdasarkan hasil tersebut, kombinasi YOLOv5 dan Deep SORT mampu melakukan deteksi, pelacakan, serta penghitungan kendaraan secara otomatis dengan hasil yang baik pada kondisi lalu lintas yang diamati. Kata Kunci : YOLOv5, Deep SORT, deteksi kendaraan, pelacakan objek, counting line, jalan tol.
Improving Marketing and Operational Efficiency in PT Rumekar Agribusiness through an Integrated E-Commerce based on a Design Thinking Approach Donny Octariyanto; Magdalena A. Ineke Pakereng
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6840

Abstract

The advancement of information technology has driven the need for digital transformation across various sectors, including the agribusiness industry. PT Rumekar currently faces several operational challenges due to the continued use of conventional methods, including manual stock recording, fragmented financial data reporting, slow information flows, and limited marketing reach. This study aims to design an integrated web-based agribusiness e-commerce information system for PT Rumekar to address these challenges. The novelty of this study lies in integrating a sales module (digital storefront) with real-time inventory management and financial reporting within a single platform specifically tailored to agribusiness workflows. The system was designed using the Design Thinking methodology, which adopts a user-centered approach through five iterative stages: Empathize, Define, Ideate, Prototype, and Test. The system was developed using the Laravel framework and provides interfaces and functional features tailored to customers (users) and system administrators (admins). System testing was conducted using two approaches: Black Box Testing to evaluate system functionality and the System Usability Scale (SUS), involving five respondents consisting of three users and two system administrators to assess usability. The Black Box Testing results indicate that all system features functioned as designed and met the specified requirements. Meanwhile, the SUS evaluation yielded scores of 78.0 from users and 78.5 from administrators. Both scores correspond to Grade B and fall within the “Acceptable” acceptability range. These findings indicate that the Rumekar e-commerce website is feasible as a solution for improving sales efficiency, report management, and information dissemination. The system also has potential for adoption by other agribusiness companies facing similar operational challenges.
Co-Authors Adam Belo Paembonan Afiyatar Asyer Asyer Afril Caesar Muhammad Hanif Agnes Meilosa Callysta Airlangga, Radithya Alexander Mario Saputra Alvira Karisma Putri Alz Danny Wowor Alz Danny Wowor Andeka Rocky Tanaamah Angela Putri Larasati Darakay Angellio Wattimena Anggara, Richardus Sapta Antonius Bintang Timur Aziiz, Anriza Kurnia Bhilton Mesianus Obidje Bramantya, Samuel Dwi Briandika, Jordan Christin Ngongoloy, Beststinsi Claudio Canavaro Daniel Satria Mahardhika Deasy Carolina Deni Supimum Jaya Devara Putra Aryasa Dewi, Syarafina Dimara, Indri Dio Yudha Perdana Diva Christalivea Donny Octariyanto Dwayne Jeremy Euagellino Prihanto Dwi Hosanna Bangkalang E.V. Sihombing, Kristina Eirene Claudia Ratmoko Ellen Arnetta Ellen Yumanda Erwien Christianto Evangs Evangs Mailoa Falensky, Lee Valdho Faradisia, Adeline Febriyanti, Monica Dias Federick Jonathan Felik Darmawan Wijaya Felix David Fernando, Fery Ferryan Nur Setyawan Feybiola Agustine Andrea Ompo Geraldie Tanu Saputra Getsemani Salisa Margaretha Gwen Theresia Grandis Aritonang Harjono, Rhaka Pradena Heinricho Dimas Prasetya Helena Dorthea Fiay Hendrawan Suprayogi Herdaning Sandra Kumalasari Jaya, Deni Supimum Jesajas, Marthen Billy Jessica Christiani Irawan Jonathan Nandika Gustin Jordan Johan Josafat Simanjuntak Juan Andrew Suthendra Juan Keinan thimothi Paparang Julio, Erry Kaferin, Eggia Kevin Alexander Harjanto Kevin Setiawan Klaudius Nikotino P Kristoko Dwi Hartomo Kumbara, Perdana Bagas Tirta Lenda, Julita Veronika Letuna, Noliyanti Ria Lusman, Chrizanny Winifred Mei Irawati Michael, Sean Mochammad Iqbal Tawakal Muhammad Haidar Wijaya Nadya Glorya Najoan Najoan, Nadya Glorya Nanda Choirul Ngantung, Ronaldo Kristoforus Ni Made Grace Advendi Nina Setiyawati Pali'pangan, Prihart Julian Pattipeilohy, Rioldy Leonard Perdana Bagas Tirta Kumbara Prasetya, Ezra Inti Pratama, Leonnyndra Putra Puspitasari, Pipit Putra, Arios Wardana Putra, Oktavian Alle Mahenswa Radithya Airlangga Ramos Somya Rheyna Atalya Setiadi Ririn Ayu Ardila Riza Jeheskiel N. Tarigan Rizki, Muhammad Bagus Robby Adrian Fajar Sulistya Sulistya Saghoa, Evifania Chayu Salama, Aditya Santoso, Chrys Nathanael Saputra, Denny Agusto Seli, Francelia Regina Simamora, Lasriama Agnes E Sindhi Diah Ayu Palupi Sofia Sofia Sonny Endrawan Stevanus Januar Latuluma Talahaturuson, Januar C. Tarigan, Aldy Alvharo Tobing, Prihantoro Manahan Tolanda, Dominus Alfin Tuah, Oliver Vincent Vincent Exelcio Susanto Virgelius Hendrawan Taralandu Wicaksana, Prasetya Wicaksono, Embang Aulia William Chrisnando Ekasaputra Willson Mangoki Yoridi, Maria Leonila Yawa Yos Richard Beeh Yosepinus Trinaldo Yoshua Kenny Nugroho Yuliadi, Yusup