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All Journal Jurnal Keperawatan PROtek : Jurnal Ilmiah Teknik Elektro Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Promotif: Jurnal Kesehatan Masyarakat Jurnal INSYPRO (Information System and Processing) Jurnal Manajemen Kesehatan Yayasan RS.Dr. Soetomo JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Informasi Kesehatan Indonesia (JIKI) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Health Science Jurnal Manajemen Informasi Kesehatan Indonesia (JMIKI) Ainet : Jurnal Informatika Jurnal Ilmiah Perekam dan Informasi Kesehatan Imelda (JIPIKI) Babali Nursing Research Madaniya J-PEN Borneo : Jurnal Ilmu Pertanian Tangible Journal Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Abdimas Indonesia : Jurnal Abdimas Indonesia Buletin Sistem Informasi dan Teknologi Islam Joutica : Journal of Informatic Unisla Jurnal Ilmu Komputer PELS (Procedia of Engineering and Life Science) Jurnal Informatika Progres Media Bina Ilmiah International Journal of Health and Information System (IJHIS) Jurnal Abdimas Jatibara Journal of Embedded Systems, Security and Intelligent Systems Pangulu Abdi: Jurnal Pengabdian Kepada Masyarakat VISA: Journal of Vision and Ideas Jurnal Intelek Dan Cendikiawan Nusantara Journal of Computer Science and Information Technology ARUS JURNAL SAINS DAN TEKNOLOGI Jurnal Intelek Insan Cendikia International Journal of Research in Counseling Journal of Green Complex Engineering Journal of Muhammadiyah’s Application Technology Kajian Ilmiah Mahasiswa Administrasi Publik (KIMAP) Jurnal Medis Umum
Titin Wahyuni
Universitas Muhammadiyah Makassar
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PENERAPAN MODEL ESRGAN UNTUK UPSCALING CITRA DAN VIDEO DIGITAL Syahrul Suhardi; Emil Agusalim Habi Talib; Fahrim Irhamna Rachman; Titin Wahyuni; Muhammad Faisal; Muhammad Syafaat S.Kuba
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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Abstract

Low-resolution images and videos remain a common problem in various digital applications due to limited visual quality. Conventional interpolation-based upscaling methods often produce blurry results and lead to the loss of important texture details. This study aims to apply the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to improve the resolution of digital images and videos. The dataset used consists of low-resolution images and videos that are processed through preprocessing, model training, and testing stages using the Google Colab environment. The ESRGAN model is trained to generate high-resolution images while preserving visual details and structural information. Model performance is evaluated using the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and visual comparison between images before and after the upscaling process. The results show that ESRGAN significantly improves the quality of images and videos compared to conventional interpolation methods, both quantitatively and qualitatively. Therefore, the application of ESRGAN is considered effective for enhancing the resolution of digital images and videos and can be utilized in applications that require high visual quality.
MONITORING DAN NOTIFIKASI REAL-TIME PERUBAHAN FILE PADA WEB SERVER MENGGUNAKAN WATCHDOG DAN TELEGRAM BOT SEBAGAI SISTEM PERINGATAN DINI Syahrul Hasbir; Emil Agusalim Habi Talib; Fahrim Irhamna Rachman; Titin Wahyuni; Muhammad Faisal; Muhammad Syafaat S.Kuba
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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Abstract

Web servers are critical infrastructures for delivering digital services and are highly vulnerable to unauthorized file changes that may threaten system security and service availability. However, many conventional monitoring systems still rely on periodic checking mechanisms, which often fail to provide timely detection of security incidents. This study aims to design and implement a real-time file change monitoring system on a web server using the Watchdog library and a Telegram Bot as an early warning mechanism. The research adopts an applied research method with an experimental approach. The system is developed using the Python programming language and evaluated in a local XAMPP-based web server environment, with the uploads directory selected as the monitoring target. Experimental results demonstrate that the proposed system is capable of detecting various file change events, including file creation, deletion, content modification, and file renaming, in real time without event loss. Notifications delivered via the Telegram Bot provide clear, timely, and actionable information to administrators. These findings indicate that the proposed event-driven monitoring system is effective and efficient in enhancing web server security and improving incident response capabilities.
A Hybrid Salp Swarm Optimization and Behavioral Nudge Framework for Optimizing Software Developer Task Allocation Ashabul Kahfi; Muhammad Faisal; Titin Wahyuni; Desi Anggreani; Darniati Darniati; Muhammad Syafaat S Kuba; Andi Makbul Syamsuri; Ida Mulyadi
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.106920

Abstract

Effective task allocation is critical in Agile software development, yet most optimization-based approaches treat it as a purely technical scheduling problem and disregard behavioral factors such as motivation, fairness, and engagement. This study proposes a Hybrid Salp Swarm Optimization–Behavioral Nudge Framework (HSSO–BNF) for developer–task allocation that integrates technical constraints with human-centered cues. The model formulates allocation as a multi-objective function combining workload balance, skill mismatch, deadline penalties, and a motivation score derived from three nudge components: Motivational Cue (MC), Social Comparison (SC), and Effort–Reward Feedback (ERF). These behavioral signals are embedded directly into the SSO position update and fitness evaluation, enabling the swarm to adapt simultaneously to performance and motivational states. Experiments on real developer–task records collected from GitHub compare HSSO–BNF against GA, PSO, and standard SSO using convergence behavior, allocation cost, fairness, satisfaction, and motivation dynamics. The results show that HSSO–BNF achieves faster and more stable convergence, reduces allocation cost by approximately 32% compared with GA and SSO and about 25% compared with PSO, and improves workload fairness and developer satisfaction while preserving psychologically sustainable specialization patterns. Heatmap visualizations and motivation trends further confirm that the behavioral layer produces more coherent and interpretable task assignments, indicating that behavior-aware metaheuristics are a promising direction for intelligent, human-centered task allocation in Agile teams.
Turning Turnitin Highlights into Structured Data with Report-Aware HSV Segmentation and GPU-Enabled OCR Mutiara yusuf; Rizki Yusliana Bakti; Titin Wahyuni
Journal of Green Complex Engineering Vol. 4 No. 1 (2026): August
Publisher : Gio Architect

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59810/greenplexresearch.v4i1.284

Abstract

Turnitin similarity reports contain rich spatial, color, and textual information, yet highlighted passages are usually inspected visually rather than converted into structured data. This study develops a report-aware pipeline that transforms color-coded Turnitin highlights into analyzable records. The method combines PDF rendering, page-type triage, hue–saturation–value (HSV) segmentation, morphological processing, connected-component analysis, geometric filtering, and GPU-enabled optical character recognition (OCR), with PDF text-layer inspection as supporting information. The evaluation corpus comprised 106 reports and 3,057 pages. Page triage identified 1,315 highlight-bearing pages and routed 56.98% of pages away from detailed processing. HSV segmentation produced eight color classes and 11,471 pre-filter components. After filtering and text extraction, 7,530 structured records were retained; 76.75% satisfied the predefined good-readability criterion, 20.04% were very short fragments, and 3.21% were noisy, garbled, or empty. Document workload was strongly right-skewed. The results establish systems-level feasibility for auditable extraction without treating textual similarity as automatic evidence of plagiarism or the readability indicator as OCR accuracy.
KLASIFIKASI RISIKO INFEKSI SALURAN PERNAPASAN AKUT MENGGUNAKAN ENSEMBLE SOFT VOTING BERBASIS REKAM MEDIS Alvian Syah Burhani; Muhammad Faisal; Fahrim Irhamna Rachman; Darniati; Titin Wahyuni; Muhammad Syafaat S. Kuba; Farida Gaffar
PROGRESS Vol 18 No 2 (2026): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i2.467

Abstract

This study proposes a risk classification model for Acute Respiratory Infections using a Soft Voting-based Ensemble Learning approach, which integrates prediction probabilities from the Random Forest and Extreme Gradient Boosting algorithms. The research utilized 456 patient medical record data from RSUD Latemmamala spanning January 2020 to December 2025. Comparative evaluation results show that the Random Forest model achieved an accuracy of 94.57%, Extreme Gradient Boosting reached 95.65%, and the Soft Voting Ensemble model delivered the best performance with an accuracy of 96.74%, precision of 96.97%, recall of 96.88%, and an F1-Score of 96.77%. Furthermore, the Soft Voting Ensemble model successfully achieved a perfect recall score for the Severe Acute Respiratory Infection category, ensuring that no high-risk patients went undetected. In conclusion, the Soft Voting Ensemble model serves as a reliable decision-support tool to assist medical professionals in triaging Acute Respiratory Infection patients quickly, objectively, and accurately.
OPTIMASI PENJADWALAN KULIAH FAKULTAS TEKNIK MENGGUNAKAN PENDEKATAN HIBRIDA GREEDY DAN CONSTRAINT SATISFACTION PROBLEM Rezki Asriani; Rizki Yusliana Bakti; Titin Wahyuni
PROGRESS Vol 18 No 2 (2026): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i2.473

Abstract

Course scheduling is a complex academic process that must consider the availability of lecturers, classrooms, time slots, student groups, and specific regulations of each study program. This study aims to implement the Greedy Algorithm combined with a Constraint Satisfaction Problem (CSP) approach to optimize course scheduling in the Faculty of Engineering. The system was developed using Python and tested using scheduling data from the Even Semester of the 2025/2026 Academic Year at the Faculty of Engineering, Universitas Muhammadiyah Makassar. The dataset consisted of 432 courses from six study programs, 136 lecturers, 16 classrooms, seven operational days, and five class sessions per day. The Greedy Algorithm was employed to generate an initial schedule using a first-fit strategy based on priority and slot availability. Subsequently, CSP was applied to model and resolve constraints, including lecturer conflicts, room conflicts, student conflicts, Zoom-class regulations, MKDU scheduling rules, non-regular class schedules, and fixed PWK schedules. The experimental results indicate that the proposed system successfully generated a conflict-free schedule with a Constraint Satisfaction Rate of 100%. All seven hard constraints were satisfied without any lecturer, room, or student conflicts. The core scheduling process required only 0.679 seconds and successfully resolved nine room conflicts within two iterations. These findings demonstrate that the integration of the Greedy Algorithm and CSP is effective in producing valid, efficient, and adaptive course schedules that meet the academic requirements of the Faculty of Engineering.
Ensemble Learning for Android Privacy-Risk Flow Pre-Screening Using Permission and Metadata Features Tri Wahyuni; Muhammad Faisal; Titin Wahyuni; Nurnawaty; Rio Prasetyo Lukodono; Titik Khawa Abd Rahman
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.13180

Abstract

Purpose - This study develops a privacy-oriented pre-screening framework for identifying Android applications with potential Sensitive Data Exposure by combining lightweight permission and metadata features with ensemble learning. Design/methods/approach - Android applications obtained from the AndroZoo repository were analyzed using FlowDroid to construct reference labels based on sensitive source–sink flows. Privacy-oriented features were derived from permissions, application metadata, source and sink indicators, and interaction patterns. An Ensemble Stacking model integrating Random Forest, Support Vector Machine, and Extreme Gradient Boosting with Logistic Regression as the meta-classifier was evaluated under class imbalance. Additional circularity, ablation, repeated validation, and clean-feature experiments were conducted to assess robustness and deployment feasibility. Findings - The proposed framework demonstrated strong capability in distinguishing applications containing FlowDroid-defined potential privacy-risk flows. FlowDroid-derived source and sink indicators were highly discriminative, while permission-only features were less effective. Importantly, the clean-feature configuration retained strong discriminatory capability without requiring FlowDroid at inference time, supporting its use as a lightweight first-stage screening mechanism before more computationally intensive taint analysis. Research implications/limitations - The framework can support developers, security auditors, and platform administrators in prioritizing applications for deeper privacy inspection. However, the study relies on static analysis, a single application repository, and FlowDroid-derived reference labels. Originality/value - This study contributes a two-stage Android privacy-risk screening framework that combines lightweight deployable features with targeted static taint analysis while explicitly addressing label-feature circularity and inference-time feasibility.
IMPLEMENTASI NLP UNTUK SUMMARIZATION DOKUMEN AKADEMIK BERBAHASA INDONESIA MENGGUNAKAN TEXTRANK DAN TRANSFORMER MT5 Yaumul Furqan; Muhyiddin A.M Hayat; Titin Wahyuni
Journal of Computer Science and Information Technology Vol. 3 No. 4 (2026): September
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jcsit.v3i4.4363

Abstract

Perkembangan dokumen akademik digital meningkatkan kebutuhan akan sistem automatic text summarization yang mampu menyajikan informasi secara ringkas tanpa menghilangkan makna utama. Penelitian ini mengimplementasikan Natural Language Processing (NLP) pada sistem summarization dokumen akademik berbahasa Indonesia berbasis web menggunakan metode TextRank dan Transformer mT5. Dataset terdiri atas dua dokumen akademik yang dievaluasi menggunakan gold summary. Tahapan penelitian meliputi ekstraksi teks, preprocessing, pembangkitan ringkasan, dan evaluasi menggunakan ROUGE-1, ROUGE-2, dan ROUGE-L. Model Transformer mT5 diimplementasikan dengan konfigurasi maksimum input 512 token, output 256 token, beam search 4, length penalty 1,0, dan repetition penalty 1,2. Hasil pengujian menunjukkan bahwa performa kedua metode dipengaruhi oleh karakteristik dokumen. TextRank memperoleh rata-rata ROUGE-1 F1-score sebesar 0,4363, sedangkan Transformer mT5 memperoleh 0,4329. TextRank menghasilkan kesesuaian leksikal yang lebih tinggi terhadap gold summary, sedangkan Transformer mT5 mampu menghasilkan ringkasan yang lebih ringkas berdasarkan hasil observasi. Penelitian ini menunjukkan bahwa kedua pendekatan memiliki karakteristik yang saling melengkapi dalam automatic text summarization dokumen akademik berbahasa Indonesia.
DIGITALISASI ADMINISTRASI ADIYUSWA PPSLU MAPPAKASUNGGUH: IMPLEMENTASI USER CENTERED DESIGN DAN VALIDASI SYSTEM USABILITY SCALE A.Fachri; Titin Wahyuni; Lukman
Journal of Computer Science and Information Technology Vol. 3 No. 4 (2026): September
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jcsit.v3i4.4374

Abstract

Pengelolaan administrasi Adiyuswa di PPSLU Mappakasungguh masih mengandalkan Microsoft Excel, Microsoft Word, dan dokumen fisik, sehingga pencarian, pembaruan, pengelompokan, serta penyusunan laporan memerlukan waktu dan berisiko menimbulkan ketidakkonsistenan data. Penelitian ini bertujuan merancang sistem digitalisasi administrasi berbasis web menggunakan pendekatan User Centered Design (UCD) dan mengukur tingkat usabilitasnya menggunakan System Usability Scale (SUS). Penelitian dilaksanakan pada Januari–Juli 2026 dengan pendekatan mixed methods. Data kebutuhan diperoleh melalui observasi, pemeriksaan dokumen, dan wawancara dengan tiga petugas administrasi. Tahapan UCD meliputi identifikasi konteks penggunaan, penentuan kebutuhan pengguna, perancangan solusi, serta evaluasi dan penyempurnaan. Sistem dikembangkan menggunakan PHP, MySQL, Bootstrap, dan lingkungan server XAMPP. Evaluasi fungsional dilakukan melalui 23 skenario black-box, sedangkan evaluasi usabilitas melibatkan 11 petugas dan pengelola yang dipilih secara purposive. Sistem yang dihasilkan menyediakan pengelolaan data Adiyuswa terpusat, pencarian dan penyaringan, analisis data, unggah foto, laporan PDF, manajemen pengguna, serta mekanisme permintaan dan persetujuan akses edit. Seluruh skenario black-box berhasil dengan tingkat keberhasilan 100%. Evaluasi SUS menghasilkan skor rata-rata 82,05 dengan simpangan baku 8,28, yang berada pada kategori excellent. Sistem ini diharapkan mengurangi ketergantungan pada berkas fisik dan mempercepat penyusunan laporan administrasi. Hasil ini menunjukkan bahwa keterlibatan pengguna melalui UCD menghasilkan sistem yang sesuai dengan kebutuhan operasional dan memiliki tingkat penerimaan usabilitas yang tinggi.
IMPLEMENTASI MEKANISME QUEUE DAN WORKER UNTUK OPTIMASI UTILISASI GPU DALAM PEMROSESAN LAYANAN AI PADA BACKEND Syahrul Ramadhan; Muhyiddin AM Hayat; Rizki Yusliana Bakti; Titin Wahyuni; Fahrim Irhamna Rachman; Darniati
Journal of Computer Science and Information Technology Vol. 3 No. 4 (2026): September
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jcsit.v3i4.4439

Abstract

Pengembangan layanan Kecerdasan Buatan (AI) berbasis Large Language Models (LLM) telah meningkatkan kebutuhan akan sistem backend yang mampu menangani proses inferensi secara efisien. Proses inferensi yang berjalan pada Graphics Processing Units (GPU) membutuhkan komputasi yang intensif, yang berpotensi menyebabkan hambatan (bottleneck) dan penggunaan GPU yang tidak optimal. Penelitian ini bertujuan untuk mengimplementasikan mekanisme queue dan worker pada backend layanan AI berbasis GPU, menganalisis dampak variasi jumlah worker terhadap kinerja sistem dan penggunaan GPU, serta menentukan konfigurasi worker yang paling optimal. Sistem ini dikembangkan menggunakan bahasa pemrograman Go dengan arsitektur berlapis, Redis sebagai message queue, Asynq sebagai pengelola queue dan worker, serta vLLM sebagai layanan inferensi AI berbasis GPU. Pengujian dilakukan menggunakan K6 dengan beban 50 virtual users selama 60 detik pada konfigurasi dengan 1, 2, 4, 8, dan 16 worker. Parameter yang dianalisis meliputi waktu respons (response time), throughput, tingkat kesalahan (error rate), penggunaan GPU (GPU utilization), dan waktu GPU menganggur (GPU idle time). Hasil penelitian menunjukkan bahwa peningkatan jumlah worker meningkatkan throughput dari 26,60 menjadi 142,91 requests/s, mengurangi waktu respons dari 1.904 menjadi 343,54 ms, dan meningkatkan penggunaan GPU dari 80,21% menjadi 100%. Semua konfigurasi menghasilkan error rate sebesar 0%, sementara waktu GPU menganggur menurun dari 18,3% menjadi 0%. Berdasarkan seluruh parameter pengujian, konfigurasi 8 worker memberikan keseimbangan terbaik antara kecepatan respons, kapasitas pemrosesan, dan efisiensi penggunaan GPU, sehingga menjadikannya konfigurasi paling optimal dalam lingkungan pengujian penelitian ini.
Co-Authors . Darniati A.Fachri Abdul Rakhim Nanda Achmad Yanu Aliffianto Adi Malik Muhammad Mutsuhito Aditya, Dwi Martha Nur Adrianingsih, Rizka Agung, Andi Agustiawal Agustiawal Agustin Dwi Syalfina Ahmad Faisal Ahmad Risal Aiman , Ailul Alfina Aisatus Saadah Alfina Aisatus Saadah Alvian Syah Burhani Amelia, Azarine Nahdah Amir Ali Anang Sulistyo Andi Agung ANDI AGUNG DWI ARYA BULU Andi Makbul Syamsuri Andi Nailah Mashfufah Sulfa Andi Yusri andi Yusri Andi Yusri Anita Dahliana Arfandi, Viki Fahril Arianti, Kencana Indah arikal khairat Arshy Prodyanatasari Arvianda Asep Indra Syahyadi Ashabul Kahfi Aswad, Muh. Akhwan Adam Baba, Haedir Bakti, Riski Yusliana Bakti, Rizki Yusliana Bambang Nudji Bisono, Eva Firdayanti Cantika Aprilia Santi Chatarina Umbul Wahyuni Cholifah . Cholifah, Cholifah Christine Christine Chyquitha Danuputri Danuputri, Chyquitha Darniati Desi Anggreani Dewi, Syamrilla Djalil, Sony Achmad Dzakki Adam, Ahmad Wildan Emil Agus Salim Habi Talib Erick Yusuf Kotte Erika Yanti Fachrim Irhamma Rahman Fachrim Irhamna Rachman Fahmi Ramadhan S Fahrim Irhamna Rachman Fahrim Irhmna Rachman Farida Gaffar Ferdiansyah Firdaus , Abidatu Zahrotul Firman Firman Firman Firman Fitrianti, Dwi Framz Hardiansyah Haidul, Haidul Halisah Duli, St Nur Haruna, Hanjas Hasbir, Syahrul Hidayanti, Sukria Hidayat, Andra Dwitama Ida Ida Ida Mulyadi, Ida Indriani, Lis Jaelan Usman, Jaelan Jufri . Kamal, Safutri Kazman Riyadi Khafi, Moh. Zainul Krisnita Dwi Jayanti Krisnita Dwi Jayanti, Krisnita Dwi La Ode Taufik Ismail Listiawan, Nadhila Lukman Lukman LUKMAN ANAS Lukman Lukman Maharani, Eva Ratih Masyfufah, Lilis Masyfufah, Lilis  Maylina Surya Wirawati Pribadi Mone, Ansyari Muh. Akhwan Adam Aswad Muh. Riswan Muhadi, Muhadi Muhammad Faisal Muhammad Hasraddin Hasnan Muhammad Syafaat S. Kuba Muhyiddin A.M Hayat Mujadilah, Siti Muslimah, Nurul Aulia Mustakim Mustakim Mutiara yusuf Nadhila Listiawan Naila, Faiqotun Nandy Rizaldy Najib Natsir, Fitra M. Nisha, Khairun Nova Mellania Novianti, Siti Nur Alam Nur Annisa Syarifuddin Nurfadilla, Destiani Irma Nurfadillah Nurfadillah Nurnawaty Octavia, Winda Dwi Pandin, Maria Yovita R. Pribadi, Maylina Surya Wirawati Puspadewi, Intan Putra, Yunior Bimasekti RAHMANIA Rahmania Rahmawati, Ayu Isnaini Ramadhan S, Fahmi Reski Awalia Retnowati Prihandini Rezki Asriani Ridwang Ridwang Ridwang Ridwang Ridwang, Ridwang Rinaldy, Muh Rio Prasetyo Lukodono Risal Haris Riswan, Muh. Rizky Maulia Rosyiari, Ahniyatul Ilmiyah Salsabila, Damai Arsila Sari, Selvi Permata Sa’adah, Alfina Asiatus Setiawan, Mohammad Yusuf Setiawan, Tommy Reynaldy Shafira Trisnanda Fatimatus Zahra Siti Fatimatuz Zahroh Siti Mujanah Slamet Riyadi Sri Hastati Suhardi, Syahrul Sukmantoro, Agung Anjar SULASTRI Suryadinata, Rivan Virlando Sutha, Diah Wijayanti SYAFAR, A. MUHAMMAD Syahrul Hasbir Syahrul Ramadhan Syahrul Suhardi TANTRI INDRABULAN Titik Khawa Abd Rahman Titik Khawa Abd Rahman Tri Wahyuni Umi Khoirun Nisak Wibawa. Ar, Arya xss, aa xx Yaumul Furqan Yulianita, Novi Eka Zul fikar