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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 Sciences 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 Pangulu Abdi: Jurnal Pengabdian Kepada Masyarakat VISA: Journal of Vision and Ideas Jurnal Intelek Dan Cendikiawan Nusantara 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
Religion Agriculture, Biological Sciences & Forestry Humanities Chemistry 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Penggunaan CNN Dalam Analisis Sentimen Pada Review Tempat Wisata Makassar Kamal, Safutri; Rachman, Fahrim Irhamna; Wahyuni, Titin
Ainet : Jurnal Informatika Vol. 7 No. 2 (2025): September (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/73mrdb71

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen pada ulasan tempat wisata di Makassar menggunakan metode Convolutional Neural Network (CNN). Makassar, sebagai salah satu destinasi wisata utama di Indonesia, menerima banyak ulasan dari pengunjung yang beragam. Setiap ulasan diproses secara tekstual melalui tahapan pembersihan data, tokenisasi, penghapusan kata-kata umum (stop words), dan stemming. Model CNN yang dibangun terdiri dari beberapa lapisan konvolusi dan pooling yang berfungsi untuk mengekstraksi fitur penting dari teks ulasan. Hasil penelitian ini memberikan wawasan yang berharga mengenai persepsi pengunjung terhadap tempat wisata di Makassar. Analisis sentimen ini dapat digunakan oleh pengelola tempat wisata dan pihak terkait untuk meningkatkan kualitas layanan dan pengalaman wisatawan.
Optimasi Ukuran Dataset untuk Analisis Sentimen Menggunakan Teknik Pembelajaran Mesin dan Pembelajaran Mendalam Halisah Duli, St Nur; Rahman, Fahrim Irhamna; Wahyuni, Titin
Ainet : Jurnal Informatika Vol. 7 No. 2 (2025): September (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/xsq0pg68

Abstract

Penelitian ini bertujuan untuk mengoptimalkan ukuran dataset yang digunakan dalam analisis sentimen melalui penerapan teknik pembelajaran mesin dan pembelajaran mendalam. Metode pembelajaran mesin yang digunakan mencakup Naive Bayes, Regresi Logistik, dan Support Vector Machine, sedangkan Convolutional Neural Network digunakan untuk metode pembelajaran mendalam. Data yang digunakan dalam penelitian ini berasal dari ulasan Google Maps mengenai beberapa tempat wisata, seperti Bugis Waterpark, Akkarena, Tanjung Bayang, Pantai Bosowa, dan Wisata Kebun. Tahap pra-pemrosesan data meliputi pembersihan data, casefolding, penghapusan stopwords, tokenisasi, dan stemming. Pengujian dilakukan dengan sembilan ukuran dataset yang berbeda (4500, 4000, 3500, 3000, 2500, 2000, 1500, 1000, dan 500) serta pembagian data latih dan data uji dengan rasio 90:10, 80:20, dan 70:30. Hasil pengujian menunjukkan bahwa Regresi Logistik dengan ukuran dataset 1000 dan Pembagian 90:10 mencapai tingkat akurasi tertinggi sebesar 85%. Studi ini menyimpulkan bahwa ukuran dataset yang optimal bervariasi tergantung pada metode yang digunakan dan menggarisbawahi pentingnya pemilihan ukuran dataset yang tepat untuk meningkatkan kinerja analisis sentimen. .
Menentukan Tingkat Kemiripan Judul Mahasiswa Fakultas Keguruan dan Ilmu Pendidikan Unismuh Makassar Menggunakan Metode Cosine Similarity Lukman; Wahyuni, Titin; Baba, Haedir
Ainet : Jurnal Informatika Vol. 7 No. 2 (2025): September (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/zzptwc89

Abstract

Plagiarism and duplicate thesis titles pose serious challenges to maintaining research originality among students at the Faculty of Teacher Training and Education (FKIP), Universitas Muhammadiyah Makassar. This study aims to implement the cosine similarity method to detect thesis title similarity and evaluate its performance using standard metrics. The research data comprised 1,000 thesis titles processed through preprocessing stages, TF-IDF feature extraction, cosine similarity calculation, and model evaluation. Results show the system can detect similarity with 87.33% accuracy, 100% precision, 58.70% recall, and 73.97% F1-score. Perfect precision indicates the system is highly reliable in identifying similar titles without false positives. However, the relatively low recall indicates that some similar titles remain undetected. This research provides practical contributions as a tool for verifying the authenticity of thesis titles and encourages the development of more sensitive similarity-detection systems in the future.
OPTIMALISASI DISTRIBUSI PEMILIH TERHADAP TPS MENGGUNAKAN METODE CLUSTERING FUZZY C-MEANS Djalil, Sony Achmad; Muhammad Faisal; Muhyiddin AM Hayat; Titin Wahyuni
Ainet : Jurnal Informatika Vol. 7 No. 2 (2025): September (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/6jc0a759

Abstract

General elections are a fundamental pillar of modern democratic systems, requiring an implementation that is efficient, fair, and inclusive. One of the key factors influencing the success of an election is the determination of polling station locations, as their placement directly affects voter accessibility, travel distance, and public participation. Inappropriate polling station allocation can lead to service inequality, voter congestion, and a decline in the overall quality of the voting process. At the local administrative level, polling station determination is still largely conducted manually by grouping voters based on neighborhood or administrative boundaries. This conventional approach is often time consuming, prone to administrative errors, and frequently results in an uneven distribution of voters across polling stations. In addition, electoral regulations impose limits on the maximum number of voters per polling station to ensure smooth and orderly voting procedures, which are not always optimally satisfied through manual methods. As voter data complexity and geographic dispersion increase, computational approaches are needed to support more effective decision making. Clustering techniques in unsupervised learning enable objective grouping of voters based on spatial characteristics. The Fuzzy C-Means method represents a suitable approach because it can accommodate data uncertainty and overlapping service areas. The application of this method is expected to produce a more efficient, equitable, and data driven distribution of polling stations, thereby contributing to the improvement of election management quality and democratic integrity
Kombinasi Vader Lexicon dan Svm dalam Mengklasifikasi Sentiment Transportasi Online (Grab) pada Ulasan Play Store Agustiawal Agustiawal; Fachrim Irhamma Rahman; Titin Wahyuni
Jurnal Intelek Insan Cendikia Vol. 2 No. 12 (2025): Desember 2025
Publisher : PT. Intelek Cendikiawan Nusantara

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

Abstract

Perkembangan teknologi digital telah mendorong meningkatnya penggunaan layanan transportasi online di Indonesia, salah satunya adalah aplikasi Grab. Banyaknya ulasan pengguna di Google Play Store menjadi sumber data yang berharga untuk mengetahui tingkat kepuasan dan persepsi masyarakat terhadap layanan tersebut. Penelitian ini bertujuan untuk mengklasifikasikan sentimen ulasan pengguna Grab menggunakan kombinasi metode VADER Lexicon dan algoritma Support Vector Machine (SVM). VADER digunakan untuk memberikan skor awal sentimen secara leksikal, kemudian hasil tersebut digunakan sebagai data anotasi untuk melatih model SVM. Hasil penelitian menunjukkan bahwa kombinasi kedua metode ini efektif dalam mengelompokkan sentimen ulasan menjadi positif, negatif, dan netral secara lebih akurat. Pendekatan ini dapat membantu penyedia layanan memahami kebutuhan dan keluhan pengguna, serta meningkatkan kualitas layanan secara berkelanjutan.
PERBANDINGAN CNN DAN YOLO PADA SISTEM PENGENALAN WAJAH BERBASIS PRESENSI Nurfadillah; Ida; Darniati; Yusliana Bakti, Rizki; Wahyuni, Titin; Faisal, Muhammad
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.532

Abstract

Face recognition based on image data has been widely applied in automated attendance systems; however, it still faces challenges related to accuracy and efficiency under varying lighting conditions and facial pose variations. This study aims to compare the performance of Convolutional Neural Network (CNN) and You Only Look Once (YOLO) methods for face detection and recognition in a deep learning–based attendance system. The dataset consists of facial images collected from students in a limited campus environment with several variations in viewpoint and illumination. The research stages include image preprocessing, training of CNN and YOLO models, and performance evaluation using accuracy, precision, recall, and computation time metrics. The experimental results indicate that YOLO outperforms CNN in terms of detection speed and performance stability, while CNN demonstrates competitive classification performance on limited datasets. This study provides empirical insights into the characteristics of both methods in attendance system scenarios and can serve as a reference for selecting appropriate models for real-world implementation. The main limitations of this study are the dataset size and the restricted data acquisition scope.
PENERAPAN ALGORITMA MOBILENETV2 UNTUK KLASIFIKASI HURUF HIJAIYAH BERBASIS GESTUR TANGAN Riswan, Muh.; Wahyuni, Titin; Danuputri, Chyquitha; Habi Talib, Emil Agusalim; Faisal, Muhammad; Anas, Lukman; Agung, Andi
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.535

Abstract

The digitalization of religious education offers significant opportunities to enhance Hijaiyah letter learning, particularly for the hearing-impaired community through visual gesture recognition. This study aims to develop and evaluate a real-time web-based classification system for 28 Hijaiyah hand gestures using the MobileNetV2 architecture. The research methodology involves a quantitative approach utilizing transfer learning with a balanced dataset of augmented images. The model was trained using fine-tuning techniques and deployed on a web platform using TensorFlow.js and MediaPipe for efficient on-device inference. Experimental results demonstrate that the model achieved an overall accuracy of 84% on the independent test set, with specific classes reaching near-perfect detection in real-time scenarios, although misclassification persisted among visually similar gestures. The system effectively balances computational efficiency with classification performance, minimizing latency during user interaction. In conclusion, the implementation of MobileNetV2 facilitates a responsive and accessible educational tool, proving the viability of computer vision in creating inclusive religious learning environments without requiring complex server-side infrastructure.
PENERAPAN MODEL ESRGAN UNTUK UPSCALING CITRA DAN VIDEO DIGITAL Suhardi, Syahrul; Habi Talib, Emil Agusalim; Rachman, Fahrim Irhamna; Wahyuni, Titin; Faisal, Muhammad; S.Kuba, Muhammad Syafaat
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.539

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 Hasbir, Syahrul; Habi Talib, Emil Agusalim; Rachman, Fahrim Irhamna; Wahyuni, Titin; Faisal, Muhammad; S.Kuba, Muhammad Syafaat
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.540

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.
DETEKSI DAN REKOMENDASI PENYAKIT DAUN JAGUNG OTOMATIS MENGGUNAKAN CNN DAN BASIS DATA REKOMENDASI fikar, zul; Fahrim Irhmna Rachman; Titin Wahyuni
Ainet : Jurnal Informatika Vol. 8 No. 1 (2026): Maret (2026)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/xz2dp745

Abstract

Jagung merupakan salah satu komoditas pangan utama di Indonesia yang memiliki peran penting bagi ketahanan pangan dan perekonomian masyarakat. Namun, produktivitas jagung sering terkendala oleh serangan penyakit daun seperti hawar daun dan karat daun yang dapat menurunkan hasil panen secara signifikan. Selama ini proses identifikasi penyakit masih dilakukan secara manual melalui pengamatan visual, yang sering kali tidak akurat dan membutuhkan waktu lama. Penelitian ini bertujuan untuk mengembangkan sistem deteksi dan penanganan penyakit daun jagung secara otomatis menggunakan metode Convolutional Neural Network (CNN) yang terintegrasi dengan basis data rekomendasi. Dataset citra daun jagung dikumpulkan dari lapangan dan melalui proses preprocessing seperti resize, normalisasi, serta augmentasi sebelum digunakan untuk pelatihan model. Model CNN yang dibangun mampu mengklasifikasikan daun jagung ke dalam tiga kategori, yaitu sehat, hawar daun, dan karat daun, dengan akurasi pengujian mencapai 96,94%. Sistem ini diimplementasikan dalam bentuk aplikasi berbasis web yang memungkinkan petani mengunggah gambar daun jagung untuk dideteksi secara otomatis, sekaligus memperoleh rekomendasi penanganan sesuai basis data yang tersedia.
Co-Authors . Darniati 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 Amelia, Azarine Nahdah Amir Ali Anang Sulistyo Andi Agung ANDI AGUNG DWI ARYA BULU Andi Makbul Syamsuri 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 Darniati 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 Irhmna Rachman 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 Kamal, Safutri Kazman Riyadi Khafi, Moh. Zainul Krisnita Dwi Jayanti Krisnita Dwi Jayanti, Krisnita Dwi La Ode Taufik Ismail Listiawan, Nadhila 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 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 Ridwang Ridwang Ridwang Ridwang Ridwang, Ridwang Rinaldy, Muh Risal Haris Riswan, Muh. Rizky Maulia Rizky Yusliana Bakti 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 Suhardi Syarifuddin, Nur Annisa TANTRI INDRABULAN Titik Khawa Abd Rahman Umi Khoirun Nisak Wibawa. Ar, Arya xss, aa xx Yulianita, Novi Eka Zul fikar