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All Journal Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal INSYPRO (Information System and Processing) Ainet : Jurnal Informatika Madaniya Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Informatika Progres ARUS JURNAL SAINS DAN TEKNOLOGI Journal of Muhammadiyah’s Application Technology
Fachrim Irhamna Rachman
Universitas Muhammadiyah Makassar
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 Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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ANALISIS DETEKSI DINI PENYAKIT JANTUNG DENGAN METODE ENSEMBLE LEARNING PADA DATA PASIEN Adrianingsih, Rizka; Irhamna Rachman, Fahrim; Yusliana Bakti, Rizki; Wahyuni, Titin
Jurnal INSYPRO (Information System and Processing) Vol 10 No 1 (2025)
Publisher : Prodi Sistem Informasi UIN Alauddin

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

Abstract

Heart disease is one of the leading causes of death, requiring early detection for prompt and accurate treatment. This study aims to develop a heart disease prediction model using ensemble learning methods, specifically the Adaptive Boosting (AdaBoost) technique. This method combines several weak models to improve the accuracy of heart disease classification based on patient data. The results show that applying the ensemble learning technique with the AdaBoost method produces a highly accurate model, especially after adding demographic features such as gender and age. The model's accuracy increased from 93.75% to 100%, with precision, recall, and F1-score reaching a perfect score of 1.00 for both classes. With these excellent results, the AdaBoost method has proven to be effective in detecting heart disease at an early stage, providing opportunities for more timely and effective medical interventions. This research is expected to make a significant contribution to the development of early heart disease detection technology and improve patient quality of life through more accurate diagnoses.
KLASIFIKASI SARAN DAN KRITIK PADA SIMAK UNISMUH DENGAN MENGGUNAKAN ALGORTIMA RECCURENCT NEURAL NETWORK (RNN faisal, Ahmad; Wahyuni, Titin; Rachman, Fahrim Irhamna
Ainet : Jurnal Informatika Vol. 6 No. 2 (2024): September (2024)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/ainet.v6i2.15736

Abstract

SIMAK Unismuh Makassar merupakan platform penting yang digunakan oleh mahasiswa untuk menyampaikan saran dan kritik terkait berbagai aspek akademik. Dalam penelitian ini, peneliti mengimplementasikan algoritma Recurrent Neural Network (RNN) untuk mengklasifikasikan saran dan kritik yang diterima melalui SIMAK Unismuh. Tujuan dari penelitian ini adalah untuk mengetahui implementasi Algoritma RNN dalam mengklasifikasi saran dan kritik di laman SIMAK Unismuh dan bagaimana keberhasilan Algoritma RNN dalam mengklasifikasi saran dan kritik di laman SIMAK Unismuh. RNN dipilih karena kemampuannya dalam mengolah data teks yang berurutan, seperti masukan dalam bentuk kalimat, yang memungkinkan model untuk menangkap konteks dari masukan tersebut secara lebih efektif. Dataset yang digunakan dalam penelitian ini terdiri dari sejumlah data saran dan kritik yang telah dikategorikan secara manual. Model RNN yang dibangun kemudian dilatih dan diuji menggunakan data tersebut untuk menilai akurasi dan performanya. Hasil penelitian menunjukkan menunjukkan bahwa model mencapai akurasi tertinggi sebesar 91% dan akurasi terendah sebesar 90%. Meskipun terdapat variasi dalam performa model, hasil ini menunjukkan bahwa RNN memiliki potensi yang baik dalam mengklasifikasikan teks saran dan kritik. Model RNN dapat membantu institusi dalam memahami dan merespon masukan dari pengguna dengan lebih efektif, meskipun masih memerlukan optimasi lebih lanjut untuk meningkatkan konsistensi dan akurasi hasil. Kesimpulan dari penelitian ini menunjukkan bahwa model RNN mampu mengklasifikasikan saran dan kritik dengan tingkat akurasi yang memadai. Penerapan model ini diharapkan dapat membantu pihak administrasi Unismuh dalam mengelola masukan dari mahasiswa secara lebih efisien, serta memberikan respons yang lebih tepat dan cepat terhadap kebutuhan akademik.
IMPLEMENTASI METODE HYBRID FUZZY JARO WINKLER DAN COSINE SIMILARITY PADA SISTEM PENCARIAN AYAT AL-QURAN BERBASIS TRANSLITERASI LATIN Tahir, Gempar Perkasa; Habi Talib, Emil Agusalim; Rachman, Fahrim Irhamna
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i2.482

Abstract

This research addresses the challenge of retrieving Qur’anic verses in Latin transliteration, which is hindered by the absence of a standardized orthography, leading to diverse spelling variations. The study aims to design and implement a hybrid information retrieval system that integrates Fuzzy Jaro-Winkler for lexical similarity and Cosine Similarity on fine-tuned DistilBERT embeddings for semantic relevance. The system workflow begins with preprocessing and normalization of the dataset, followed by initial candidate selection using Jaro-Winkler, and final reranking through semantic similarity scoring. Evaluation was conducted using black-box testing across scenarios including ideal queries, spelling variations, incomplete queries, and varying query lengths. Results show high accuracy for ideal (96%) and varied spelling queries (92%), with performance improving as query length increases, reaching 96% for four-word queries. The hybrid approach effectively bridges lexical and semantic gaps, outperforming single-method baselines, and demonstrates robustness in handling non-standard transliteration in Qur’anic text retrieval.
Prediksi Tingkat Kelulusan Menggunakan K-Means Pada Program Studi Informatika Unismuh Makassar Irhamna Rachman, Fahrim; Mujadilah, Siti; Wahyuni, Titin; Anas, Lukman
JURNAL FASILKOM Vol. 13 No. 3 (2023): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v13i3.6061

Abstract

Predicting timely graduation brings numerous benefits not only to students but also to the university itself. Creating a graduation prediction model assists students and academic advisors in fostering a positive environment that encourages on-time graduation by developing a predictive model for graduation rates using the K-means data mining method in the Informatics study program at Universitas Muhammadiyah Makassar. This method is used to cluster students based on attributes such as total credits taken, semester Grade Point Average (GPA), and overall Cumulative Grade Point Average (CGPA). The clustering aims to identify patterns and characteristics of student graduation. Data from several semesters is collected and preprocessed, including data normalization and transformation. The research steps involve data preprocessing, cluster labeling, distance calculation to cluster centers, and result analysis. The analysis shows that the K-means method can generate student clusters with varying graduation rate patterns. The formed clusters can be interpreted as groups of students with potential for timely graduation or groups needing more attention to achieve on-time graduation. Empirical validation is performed by comparing K-means prediction results with actual graduation data. Accuracy measurement involves calculating the percentage of similarity between predictions and actual data. Empirical validation results demonstrate the accuracy level, which can serve as a benchmark for assessing the performance of this prediction model. This study aims to provide deeper insights into factors influencing student graduation and potentially support decision-making at the academic level. Keywords: Graduation Prediction, Data Mining, K-Means, Analysis, Clustering, Empirical Validation.
Analisis Sentimen Text Dengan Metode CNN Study Kasus Tempat Wisata Makassar Kamal, Safutri; Rachman, Fahrim Irhamna; Wahyuni, Titin
Ainet : Jurnal Informatika Vol. 7 No. 1 (2025): Maret (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/n1gcbb74

Abstract

Penelitian ini bertujuan untuk mengevaluasi dan menentukan sejauh mana metode CNN (Convolutional Neural Network) dapat menghasilkan prediksi sentimen yang akurat terhadap ulasan mengenai tempat wisata Makassar. Metode analisis sentimen ini menggunakan data ulasan yang dikumpulkan dari platform Google Maps. Dalam penelitian ini, dilakukan tahap preprocessing untuk membersihkan data, seperti cleaning, transform cases, tokenizing, stopword dan stemming. Selanjutnya, dilakukan pembagian dataset menjadi data latih dan data uji dengan scenario 90 : 10, 80 : 20 dan 70 : 30 untuk melatih dan menguji model dengan tiga kategori ulasan yaitu positif, negatif dan netral. Hasil dari analisis sentimen menunjukkan bahwa metode CNN memiliki kemampuan yang baik dalam memprediksi sentimen positif, negatif, dan netral pada ulasan mengenai Tempat Wisata Makassar. Tingkat akurasi yang tinggi pada tahap pelatihan menunjukkan bahwa model mampu belajar dengan baik dari dataset yang disediakan. Meskipun tingkat akurasi pada tahap validasi sedikit lebih rendah, tetapi masih mencapai angka yang memadai, menunjukkan bahwa model memiliki kemampuan generalisasi yang cukup baik dalam mengklasifikasikan sentimen pada ulasan-ulasan tersebut. Diperoleh hasil akurasi tertinggi dengan Training Accuracy yang meningkat memperoleh nilai akurasi training 95%, serta Validation Accuracy memperoleh nilai 73%.
KLASIFIKASI SARAN DAN KRITIK PADA SIMAK UNISMUH DENGAN MENGGUNAKAN ALGORTIMA RECCURENCT NEURAL NETWORK (RNN) faisal, Ahmad; Wahyuni, Titin; Rachman, Fahrim Irhamna
Ainet : Jurnal Informatika Vol. 7 No. 1 (2025): Maret (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/8ttaxq04

Abstract

SIMAK Unismuh Makassar is an important platform used by students to submit suggestions and criticisms related to various academic aspects. In this study, researchers implemented the Recurrent Neural Network (RNN) algorithm to classify suggestions and criticisms received through SIMAK Unismuh. The purpose of this study was to determine the implementation of the RNN Algorithm in classifying suggestions and criticisms on the SIMAK Unismuh page and how successful the RNN Algorithm was in classifying suggestions and criticisms on the SIMAK Unismuh page. RNN was chosen because of its ability to process sequential text data, such as input in the form of sentences, which allows the model to capture the context of the input more effectively. The dataset used in this study consists of a number of suggestion and criticism data that have been categorized manually. The RNN model that was built was then trained and tested using the data to assess its accuracy and performance. The results showed that the model achieved the highest accuracy of 91% and the lowest accuracy of 90%. Although there were variations in model performance, these results indicate that RNN has good potential in classifying suggestion and criticism texts. The RNN model can help institutions understand and respond to user input more effectively, although it still requires further optimization to improve the consistency and accuracy of the results. The conclusion of this study shows that the RNN model is able to classify suggestions and criticisms with an adequate level of accuracy. The application of this model is expected to help the Unismuh administration in managing student input more efficiently, as well as providing more appropriate and faster responses to academic needs.Keywords: Text Classification, Recurrent Neural Network (RNN), SIMAK Unismuh, Suggestions and Criticisms, Academic Information System.
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.
Pendeteksi Penyakit Daun Padi Menggunakan Algoritma YOLOv8 di Desa Jangan-Jangan Kecamatan Pujananting Kabupaten Barru Aritmawijaya, Suandi; Rachman, Fahrim Irhamna; Bakti, Rizki Yusliana; suandi_17, suandi_aritmawijaya
Journal of Muhammadiyah’s Application Technology Vol. 4 No. 3 (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/kn1zxt55

Abstract

ABSTRAKProduksi padi di Indonesia memiliki peran penting dalam menjaga ketahanan pangan nasional, namun produktivitasnya sering mengalami penurunan akibat serangan penyakit pada daun padi. Penyakit seperti blast, bercak coklat, dan hawar daun bakteri merupakan penyakit utama yang dapat menimbulkan kerugian signifikan jika tidak terdeteksi sejak dini. Identifikasi penyakit daun padi secara konvensional umumnya masih dilakukan secara manual dan bergantung pada pengalaman petani, sehingga berpotensi menimbulkan kesalahan diagnosis. Oleh karena itu, penelitian ini bertujuan mengembangkan sistem pendeteksi otomatis penyakit daun padi berbasis deep learning menggunakan algoritma YOLOv8. Dataset diperoleh dari pengambilan citra langsung di lahan pertanian Desa Jangan-Jangan, Kabupaten Barru, yang merepresentasikan kondisi lapangan nyata dan mencakup tiga jenis penyakit utama. Tahapan penelitian meliputi anotasi data menggunakan Roboflow, pelatihan model dengan Google Collab, serta evaluasi performa menggunakan confusion matrix, precision, recall, F1-score, dan mean Average Precision. Hasil pengujian menunjukkan bahwa model YOLOv8 mampu mendeteksi penyakit daun padi dengan akurasi tinggi dan waktu inferensi cepat, sehingga berpotensi diterapkan sebagai solusi deteksi dini penyakit padi secara real-time. Kata Kunci: YOLOv8, Deteksi Penyakit Padi, Deep learning, Citra Digital, Pertanian Presisi, Roboflow,CNN.   ABSTRACTRice production in Indonesia plays a crucial role in maintaining national food security, but productivity often declines due to leaf disease attacks. Diseases such as blast, brown spot, and bacterial leaf blight are major diseases that can cause significant losses if not detected early. Conventional rice leaf disease identification is generally still done manually and relies on farmer experience, potentially leading to misdiagnosis. Therefore, this study aims to develop an automatic rice leaf disease detection system based on deep learning using the YOLOv8 algorithm. The dataset was obtained from direct imagery captured in agricultural fields in Jangan-Jangan Village, Barru Regency, which represents real-world conditions and includes three main types of diseases. The research stages include data annotation using Roboflow, model training with Google Colab, and performance evaluation using a confusion matrix, precision, recall, F1-score, and mean Average precision. The test results show that the YOLOv8 model is capable of detecting rice leaf diseases with high accuracy and fast inference time, thus potentially being implemented as a real-time early detection solution for rice diseases. Keyworsds: YOLOv8, Rice Disease Detection, Deep learning, Digital Imagery, Precision Farming, Roboflow,CNN.
Peningkatan Akurasi Prediksi Kebutuhan Obat BPJS PRB melalui Integrasi Analisis Diferensial dan Deep Learning Hermanto, Chalidah Azzahrah; Rachman, Fachrim Irhamna; A.M Hayat, Muhyiddin; H, chalidah_azzahra00
Journal of Muhammadiyah’s Application Technology Vol. 4 No. 3 (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/k6t40472

Abstract

ABSTRAKProgram Rujuk Balik (PRB) BPJS Kesehatan bertujuan menjamin keberlanjutan pengobatan pasien penyakit kronis. Namun, fluktuasi kebutuhan obat sering menimbulkan permasalahan overstock dan stockout di apotek mitra BPJS. Penelitian ini bertujuan mengintegrasikan analisis diferensial dan algoritma deep learning Long Short-Term Memory (LSTM) untuk meningkatkan akurasi prediksi kebutuhan obat PRB. Data yang digunakan berupa transaksi penjualan obat pasien BPJS PRB di Apotek Kimia Farma Cendrawasih periode Januari 2022 hingga Juli 2024. Analisis diferensial digunakan untuk menghitung perubahan tingkat pertama (delta 1) dan tingkat kedua (delta 2) penjualan, yang selanjutnya dijadikan fitur tambahan pada model LSTM. Evaluasi model dilakukan menggunakan metrik Mean Squared Error (MSE), Mean Absolute Error (MAE), dan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa integrasi analisis diferensial dengan LSTM mampu meningkatkan akurasi prediksi, dengan model terbaik menghasilkan nilai MAE rata-rata di bawah 20 untuk sebagian besar produk. Temuan ini berimplikasi pada peningkatan efektivitas perencanaan dan pengadaan obat PRB berbasis data historis dan tren perubahan.Kata Kunci: Prediksi Obat, BPJS PRB, LSTM, Deep Learning, Analisis Diferensial ABSTRACTThe BPJS Kesehatan Rujuk Balik Program (PRB) aims to ensure the continuity of treatment for patients with chronic diseases. However, fluctuations in medicine demand frequently cause overstock and stockout problems at BPJS partner pharmacies. This study aims to integrate differential analysis and the Long Short-Term Memory (LSTM) deep learning algorithm to improve the accuracy of PRB medicine demand forecasting. The data used consist of transaction records of PRB patient medicine sales at Kimia Farma Cendrawasih Pharmacy from January 2022 to July 2024. Differential analysis was applied to calculate the first-order change (delta 1) and second-order change (delta 2) in sales, which were subsequently incorporated as additional features in the LSTM model. Model performance was evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results indicate that integrating differential analysis with LSTM improves prediction accuracy, with the best-performing model achieving average MAE values below 20 for most products. These findings have important implications for enhancing data-driven planning and procurement of PRB medicines based on historical trends and demand dynamics.Keyworsds: Medicine Forecasting, BPJS PRB, LSTM, Deep Learning, Differential Analysis
Konversi Tulisan Tangan Huruf Kapital Menjadi Teks Menggunakan Metode Deep Learning Berbasis YOLOv8 dan CTC Bakti, Rizki Yusliana; Rachman, Fahrim Irhamna; nur, makmur jaya
Journal of Muhammadiyah’s Application Technology Vol. 4 No. 3 (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/9wdk0e43

Abstract

ABSTRAKPenelitian ini mengkaji pengembangan sistem konversi tulisan tangan ke teks digital menggunakan metode deep learning dengan mengombinasikan arsitektur Convolutional Neural Network (CNN), YOLOv8, dan Connectionist Temporal Classification (CTC). Dataset yang digunakan terdiri dari 700 citra tulisan tangan huruf kapital (A–Z) yang diperoleh dari dokumen resmi Dinas Kependudukan dan Pencatatan Sipil Kabupaten Barru. Tahapan penelitian meliputi prapemrosesan citra berupa grayscale, normalisasi, perataan teks, serta augmentasi data, dilanjutkan dengan anotasi bounding box menggunakan Roboflow. Dataset kemudian dibagi menjadi data pelatihan, validasi, dan pengujian. Model YOLOv8 dilatih untuk mendeteksi karakter dan hasilnya diproses menggunakan CTC untuk menghasilkan teks akhir. Evaluasi menunjukkan performa yang baik dengan precision 98,38%, recall 87,25%, F1-score 92,44%, serta mAP@0.5 sebesar 87,19%. Hasil ini menunjukkan metode yang diusulkan efektif untuk mendukung digitalisasi dokumen administrasi publik.Kata Kunci: YOLOv8, Konversi Tulisan Tangan, Deep Learning, Citra Digital, Administrasi Publik, Roboflow, CNN, CTC ABSTRACTThis study investigates the development of a handwritten text-to-digital text conversion system using deep learning by combining Convolutional Neural Network (CNN), YOLOv8, and Connectionist Temporal Classification (CTC) architectures. The dataset consists of 700 images of uppercase handwritten letters (A–Z) obtained from official documents of the Department of Population and Civil Registration of Barru Regency. The research stages include image preprocessing such as grayscale conversion, normalization, text alignment, and data augmentation, followed by bounding box annotation using Roboflow. The dataset is then divided into training, validation, and testing sets. The YOLOv8 model is trained to detect characters, and the outputs are processed using CTC to generate the final text. Evaluation results demonstrate strong performance, achieving a precision of 98.38%, recall of 87.25%, an F1-score of 92.44%, and an mAP@0.5 of 87.19%. These findings indicate that the proposed method is effective in supporting the digitalization of public administrative documents.Keyworsds: YOLOv8, Handwriting Conversion, Deep Learning, Digital Image, Public Administration, Roboflow, CNN, CTC  
Co-Authors A.M Hayat, Muhyiddin Adrianingsih, Rizka Ahmad Faisal Anas, Lukman Aritmawijaya, Suandi Bakti, Rizki Yusliana H, chalidah_azzahra00 Habi Talib, Emil Agusalim Hermanto, Chalidah Azzahrah Indriani, Lis Iskandar, Aryansyah Kamal, Safutri La Ode Taufik Ismail La Ode Taufik Ismail Lukman Lukman Mujadilah, Siti Nur Alam Nur, Makmur Jaya Reski Awalia Salam, Abd Setiawan, Tommy Reynaldy Sri Hastati suandi_17, suandi_aritmawijaya Tahir, Gempar Perkasa Titin Wahyuni Wahyuni , Titin Wibawa. Ar, Arya Yanti, Wilda