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Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Published by Universitas Muhammadiyah Riau
ISSN : 20893353     EISSN : 28089162     DOI : https://doi.org/10.37859/jf.v11i3.2781
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) is expected to be a media of scientific study of research result, a thought and a study criticial analysis to a System engineering research, Informatics Engineering, Information Technology, Computer Engineering, Informatics Management, and Information System. We accept research papers which focused to these following topics: System Engineering Expert System Decision Support System Data Mining Artificial Intelligent Computer engineering Digital Image Processing Computer Graphic Computer Vision Genetic Algorithm Machine Learning Deep Learning Information System Design Business Intelligence and Knowledge Management Database System Big Data IOT Enterprise Computing ICT and Islam Technology Management and other relevant topics to field of Information Technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 397 Documents
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Pengenalan Pola Huruf Bahasa Isyarat Menggunakan Framework You Only Look Once (YOLO) JABAR, Tri Ahmad Jabar; Heriansyah, Rudi; Purnamasari, Evi
JURNAL FASILKOM Vol. 15 No. 2 (2025): 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.v15i2.10018

Abstract

Bahasa isyarat merupakan bentuk komunikasi visual yang penting bagi penyandang disabilitas rungu wicara. Namun, masih banyak masyarakat yang belum memahami Sistem Isyarat Bahasa Indonesia (SIBI), sehingga menimbulkan hambatan komunikasi. Penelitian ini bertujuan untuk mengembangkan sistem pengenalan huruf bahasa isyarat SIBI menggunakan framework You Only Look Once (YOLO). Data citra huruf dikumpulkan dari tangan penulis, dianotasi menggunakan Roboflow, dan dilatih dengan algoritma YOLOv11. Hasil deteksi huruf tidak hanya dikenali secara individu, tetapi juga disusun menjadi kalimat secara real-time melalui input kamera menggunakan pemrosesan sekuens huruf. Model terbaik menunjukkan nilai precision sebesar 0,835, recall 0,928, serta mean Average Precision (mAP) dengan mAP@50 (IoU 50%) sebesar 0,968 dan mAP@50–95 (rata-rata pada berbagai ambang IoU) sebesar 0,774. Sistem juga mencapai akurasi rata-rata 0,831 dan F1-score sebesar 0,865 dalam pengenalan huruf. Pada pengujian real-time, sistem berhasil menyusun kalimat sederhana “VINA SEDANG MAKAN” dengan akurasi 86,6%. Hasil ini membuktikan bahwa sistem tidak hanya mampu mendeteksi huruf, tetapi juga dapat merangkai huruf menjadi kalimat bermakna. Penelitian ini diharapkan dapat memberikan kontribusi nyata dalam pengembangan teknologi inklusif yang dapat menjembatani komunikasi antara penyandang disabilitas rungu wicara dengan masyarakat umum, serta berpotensi diimplementasikan dalam bidang pendidikan, pelayanan publik, maupun aplikasi sehari-hari
Evaluasi UI/UX pada Aplikasi Smart Campuss Unisbank Menggunakan Metode Design Thinking Mahendra, Nabiel Pramudya; Supriyanto, Edy
JURNAL FASILKOM Vol. 15 No. 2 (2025): 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.v15i2.10036

Abstract

Aplikasi Smart Campuss telah digunakan secara luas oleh mahasiswa Unisbank, namun masih ditemukan berbagai keluhan terkait kemudahan penggunaan aplikasi sehingga diperlukan evaluasi serta analisis pada aspek usability. Penelitian ini bertujuan untuk mengukur tingkat usability aplikasi Smart Campuss dan merancang perbaikan desain guna meningkatkan aspek pengalaman pengguna. Evaluasi dilakukan menggunakan metode system usability scale (SUS) yang dipadukan dengan pendekatan design thinking. Sampel penelitian terdiri dari 30 responden kuantitatif sesuai teori Laura Faulkner dan 5 responden kualitatif untuk wawancara mendalam mengacu pada rekomendasi Jakob Nielsen dengan partisipan mencakup mahasiswa aktif kelas reguler serta karyawan Unisbank. Hasil penelitian menunjukkan bahwa aplikasi Smart Campuss versi asli memperoleh skor SUS sebesar 60,75 dengan grade D yang mengindikasikan bahwa aplikasi belum memenuhi standar usability dan perlu dilakukan perancangan ulang. Setelah dilakukan perancangan ulang menggunakan pendekatan design thinking, skor meningkat sebesar 18,25 poin menjadi 79,00 dengan grade B. Temuan ini membuktikan bahwa desain ulang mampu secara signifikan meningkatkan tingkat usability sekaligus memperbaiki penerimaan pengguna terhadap aplikasi.
Perbandingan Algoritma Random Forest Dan Xgboost Untuk Klasifikasi Penyakit Jantung Berdasarkan Data Medis Pramudya, Muhammad Rayenra Azthi; Celvin Arafat; Muhammad Cavin Ramadhan; Fikri Abdul Jafar; Edi Ismanto
JURNAL FASILKOM Vol. 15 No. 2 (2025): 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.v15i2.9927

Abstract

Penyakit jantung merupakan salah satu penyebab kematian terbanyak di dunia, sehingga deteksi dini menjadi penting untuk mengurangi risiko fatal. Penelitian ini bertujuan untuk membandingkan kinerja dua algoritma pembelajaran mesin, yaitu Random Forest dan XGBoost, dalam mengklasifikasikan penyakit jantung berdasarkan data medis. Dataset yang digunakan tersedia untuk umum dan mencakup fitur-fitur darah seperti usia, tekanan, kadar kolesterol, denyut jantung maksimum, hasil EKG, dan tanda-tanda talasemia. Proses penelitian melibatkan eksplorasi data (EDA), pembersihan, transformasi, dan pelatihan model menggunakan kedua algoritma tersebut. Evaluasi dilakukan dengan menggunakan metrik seperti akurasi, presisi, recall, skor F1, dan ROC AUC. Hasilnya menunjukkan bahwa Random Forest berkinerja lebih baik dalam hal sensitivitas dan akurasi dibandingkan dengan XGBoost, terutama dalam mengidentifikasi pasien yang benar-benar menderita penyakit jantung. Temuan ini menunjukkan bahwa metode ensemble berdasarkan keputusan pohon, Random Forest, dapat menjadi pendekatan yang efektif untuk sistem prediksi penyakit jantung dini berdasarkan data medis.
Analisis Sentimen Ulasan Google Play Store: Studi Komparatif Algoritma SVM, Naïve Bayes, dan Logistic Regression Jatmiko, Singgih; Dometian, Charles
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

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

Abstract

This research aims to compare Support Vector Machine (SVM), Naïve Bayes, and Logistic Regression methods in sentiment analysis of app reviews on Google Play Store to identify the best method based on accuracy, precision, recall, and F1-Score using 2000 GoPay and LinkAja reviews from Google Play Store respectively. The methodology consists of six stages, namely, data collection, labeling method evaluation, preprocessing evaluation, SMOTE testing to overcome imbalanced data, hyperparameter tuning optimization, and consistency validation with a combination of TF-IDF and three classification methods. The data were split using an 80:20 ratio, with 80% of the data used for training and 20% for testing. Experimental results show SVM gives the best performance with 93% accuracy, 92% precision, 93% recall, and 92% F1-Score on the GoPay dataset due to its ability to find the optimal hyperplane, followed by Logistic Regression with 92% accuracy and the third Naïve Bayes despite identical accuracy but showing greater bias towards the majority class. Validation using the LinkAja dataset proves SVM still maintains the best performance with 95% accuracy, so the research concludes SVM is the best method for sentiment analysis of app reviews on the Google Play Store which is proven to provide optimal and consistent performance
Analisis Sentimen Calon Gubernur Jawa Tengah 2024 Menggunakan Metode Naïve Bayes Nuh Hanan, Martin; Hamid Muhammad Jumasa; Ike Yunia Pasa
JURNAL FASILKOM Vol. 15 No. 3 (2025): 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.v15i3.10184

Abstract

Social media platform X (formerly Twitter) has become a public space where people can freely express their opinions, including in the context of regional elections. These opinions can be processed into useful information for decision-makers, especially in political contexts. This study aims to analyze public sentiment toward the candidates for Governor of Central Java for the 2024–2029 period using the Naïve Bayes method. The data was collected through a crawling process on X using Tweet-Harvest and relevant keywords. The raw data then underwent preprocessing, including cleaning, case folding, normalization, stopword removal, tokenization, and stemming. Sentiment labeling was performed automatically using the TextBlob library, which classified tweets into positive, negative, or neutral categories. Naïve Bayes was chosen for its effectiveness and efficiency in text classification tasks. The results showed model accuracy of 90.28% for Andika Perkasa and 84.51% for Ahmad Luthfi, using a 90:10 training-to-testing data ratio. Out of 452 total tweets, Andika Perkasa received 350 positive sentiments, slightly more than Ahmad Luthfi, who received 336. These findings indicate that public perception toward both candidates is generally positive, with a slight edge for Andika Perkasa.
Integrasi Metode Forward Chaining dan Teorema Bayes Untuk Identifikasi Diagnosa Penyakit Kulit Pada Kucing Anugerah, Ade; Sucipto; Syarifah Putri Agustini Alkadri
JURNAL FASILKOM Vol. 15 No. 3 (2025): 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.v15i3.10288

Abstract

Skin diseases in cats are among the most common health issues, yet many cat owners still lack awareness of their symptoms. Limited access to veterinary services, especially in regions such as West Kalimantan, poses a significant challenge in early identification and treatment. This study aims to develop a web-based expert system capable of automatically diagnosing skin diseases in cats based on symptoms inputted by users. The system utilizes the Forward Chaining method for rule-based inference and the Bayes Theorem for probabilistic calculation to determine the likelihood of diseases. The system was built using the Laravel framework and MySQL database, based on a total of 83 case data obtained through direct interviews with veterinary experts. Testing using black-box and user acceptance methods showed that the system functions effectively and delivers accurate and informative diagnostic results. The system achieved an accuracy rate of 100% when tested on validated expert data. Therefore, this system can serve as an effective tool for cat owners to quickly and independently gain initial insights into their cat’s skin health before consulting a veterinarian.
Model Rekomendasi Destinasi Wisata Kreatif di Indonesia Berdasarkan Data Cuaca dan Ulasan Wisatawan Kharisma; Irmma Dwijayanti; Ulfi Saidata Aesyi; Alfirna Rizqi Lahitani
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

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

Abstract

Indonesia holds vast potential for creative tourism through its rich cultural heritage, natural beauty, and local creativity. However, travelers still face challenges in planning optimal trips due to the lack of context-aware and real-time recommendation systems. In practice, tourists often rely on Google Maps reviews, which are unorganized thematically, and there is limited integration with weather conditions—an important factor that significantly impacts travel experiences, particularly for nature-based destinations. This study aims to develop a recommendation model for creative tourism destinations in Indonesia by integrating two key aspects: sentiment analysis of Google Maps reviews and real-time weather data. The research utilizes tourist reviews from Google Maps alongside up-to-date weather information from destinations across Indonesia. The reviews are analyzed using the Support Vector Machine (SVM) algorithm to classify sentiments as positive or negative. These sentiment results are then combined with real-time weather data to build a Content-Based Filtering (CBF) recommendation system capable of providing more relevant and adaptive suggestions. The study successfully produced a recommendation system model with a testing accuracy of 90%.
Inovasi Agen AI Dalam Sistem Pencatatan Struk Digital Otomatis Berbasis n8n Pratama, Wahyu; Ahda, Fadhli Almu'iini
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

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

Abstract

The utilization of workflow automation and multimodal artificial intelligence introduces a new approach to developing an intelligent digital receipt recording system. This study aims to design an automatic transaction processing system by integrating n8n as a workflow engine, Google Gemini AI as a multimodal inference model, and Telegram Bot as a conversational interface. The system is implemented in a self-hosted Docker-based environment to ensure local execution without cloud dependence, enhancing data security and reducing operational costs. An experimental software engineering method was applied using 33 test scenarios consisting of 20 image inputs and 13 text inputs. The system successfully extracted key transaction information such as store name, total amount, and transaction date under various real-world conditions, including blurred images, faded ink, missing text segments, tilted receipts, and imperfect handwriting. Evaluation using a Confusion Matrix produced perfect classification results with 100% accuracy, precision, recall, and F1-score, confirming that all system outputs aligned with actual conditions. The system also demonstrated stable performance with average processing times of 15.8 seconds for text and 17–18.5 seconds for low-resolution images. These results indicate that combining workflow automation and multimodal AI provides an effective and adaptive solution for automatic transaction recording.
Implementasi Otomatisasi Data Lifecycle Management (DLM) untuk Peningkatan Skalabilitas dan Keandalan Sistem Informasi Pemesanan Kelas Sybil Auzi; Atta Zulfahrizan; Dedi Kiswanto; Josua Tampubolon
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

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

Abstract

The rapid growth of historical data in the class booking information system can significantly degrade performance, impacting system scalability and reliability. This research addresses the issue by designing and implementing an automated Data Lifecycle Management (DLM) framework. The primary objectives are: (1) to develop a functional automated DLM prototype using the Laravel framework and its task scheduler, and (2) to analyze how this implementation enhances system scalability and reliability. This study adopts a system implementation method by applying the seven stages of DLM, supported by a Dual Connection database architecture that separates operational data (Hot Storage) from historical archives (Cold Storage). The results demonstrate the successful implementation of all DLM stages, from data creation to automated deletion. The system automatically archives weekly transactional data and permanently deletes them after a retention period of one semester plus a 30-day grace period. Furthermore, a secure public API was developed to facilitate data sharing for academic purposes. The implementation of automated DLM proves effective in managing data lifecycle, reducing the burden on the primary database, and maintaining system performance, thereby ensuring better scalability and reliability.
Analisis Sentimen Media Sosial X Terhadap Kebijakan Presiden Republik Indonesia Prabowo Subianto Najiyah, Ina; Rizal, Miftahul
JURNAL FASILKOM Vol. 15 No. 3 (2025): 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.v15i3.10385

Abstract

This study aims to identify and measure the tendency of public sentiment towards the implementation of the policies of the President of the Republic of Indonesia, Prabowo Subianto. The methodology used is text mining-based sentiment analysis, utilizing a data corpus taken from the social media platform X. This study adopts the SEMMA (Sample, Explore, Modify, Model, Assess) workflow as a procedural framework. Data retrieval is carried out automatically using crawling techniques. Next, the data goes through a comprehensive text pre-processing stage, including cleaning, case folding, normalization, convert negation, tokenizing, stopword removal, stemming. Sentiment polarity is determined automatically through a lexicon-based approach, implemented with the VADER (Valence Aware Dictionary for Sentiment Reasoning) algorithm. The modeling phase uses two machine learning classification algorithms, namely Naïve Bayes and Support Vector Machine (SVM). Performance testing is carried out on three different training and testing data distribution schemes (90:10, 80:20, and 70:30). The evaluation findings show that the Naïve Bayes algorithm achieved the highest accuracy rate of 81.25% at a ratio of 80:20. Meanwhile, SVM consistently recorded superior accuracy, reaching a maximum value of 92.60% at a ratio of 90:10. Based on a comprehensive assessment of performance metrics (accuracy, precision, recall, and f1-score), the Support Vector Machine (SVM) algorithm was proven to provide significantly superior performance compared to Naïve Bayes in this sentiment classification task

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