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All Journal JAAKFE UNTAN ( (Jurnal Audit Dan Akuntansi Fakultas Ekonomi Universitas Tanjungpura) JURNAL TEKNOLOGI DAN OPEN SOURCE JURIKOM (Jurnal Riset Komputer) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Teknisi Nusantara Journal of Computers and its Applications JITCoS : Journal of Information Technology and Computer System
Aulia, Muhammad Fathir
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences Other

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PENERAPAN ALGORITMA COLLABORATIVE FILTERING UNTUK REKOMENDASI SKEMA VSGA LANJUTAN DI BBPSDMP KOMINFO MEDAN Aulia, Muhammad Fathir; Sitorus, Nur Shafwa Aulia; Zufria, Ilka
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 1 (2025): February 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i1.2685

Abstract

Sistem rekomendasi membantu pengguna memilih alternatif terbaik berdasarkan data interaksi, terutama dalam konteks pelatihan dalam hal pendidikan. Salah satu metode yang digunakan adalah Collaborative Filtering, yang bekerja dengan menganalisis pola preferensi melalui rating pengguna. Penelitian ini bertujuan mengembangkan sistem rekomendasi berbasis Collaborative Filtering pada program Vocational School Graduate Academy (VSGA) untuk membantu lulusan memilih skema pelatihan yang sesuai dengan kebutuhan mereka. Dua metode utama diterapkan, yaitu Pearson Correlation dan Cosine Similarity. Pearson Correlation mengukur kesamaan pola preferensi melalui perhitungan rata-rata, sementara Cosine Similarity menghitung kemiripan antar pengguna dengan pendekatan vektor. Adapun dataset sebanyak 50 data yang digunakan yaitu dataset rating dari masing-masing skema pelatihan VSGA sebanyak 45 data dan menggunakan 5 data pembanding untuk di uji, untuk mencari rekomendasi pada 2 data. Sistem ini dibangun menggunakan Python dengan pustaka Pandas, NumPy, dan scikit-learn. Hasil pengujian menunjukkan bahwa kedua metode memberikan rekomendasi akurat sesuai skenario tertentu. Sistem ini diharapkan membantu lulusan menemukan pelatihan relevan, meningkatkan kualitas pembelajaran, dan mempersiapkan mereka menghadapi era industri 4.0. Kata kunci: Collaborative Filtering; Pearson Correlation; Cosine Similarity; Sistem Rekomendasi; Algoritma
APPLICATION OF WEIGHTED AVERAGE ALGORITHM IN RECREATIONAL PARK TOURIST DESTINATION RECOMMENDATION SYSTEM BASED ON GOOGLE MAPS USER RATINGS Faiza, Nayla; Siregar, Hervilla Amanda R.; Sitorus, Nur Shafwa Aulia; Nugroho, Agung; Aulia, Muhammad Fathir; Furqan, Mhd
JURNAL TEKNISI Vol 5, No 2 (2025): Agustus 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/teknisi.v5i2.3790

Abstract

Abstract: The development of digital technology has changed the behavior patterns of tourists in choosing travel destinations. Google Maps is now not only used to find restaurant locations but has also become the main source for searching nearby tourist destinations based on user ratings and reviews. This research aims to build a recommendation system for recreational park tourist destinations in Medan City by applying the Weighted Average algorithm using Google Maps user rating data. The data used comes from reviews by five users of five popular recreational parks in Medan City during the period from January 1, 2025, to April 30, 2025. The Weighted Average algorithm was chosen because it can provide a more objective and fair assessment by taking into account the weight of each rating given by users. As a result, this system can recommend the best recreational parks based on user experiences related to cleanliness, parking facilities, toilets, security, running paths, and accessibility. It is hoped that this system can help tourists choose destinations that meet their needs and preferences, as well as provide a more enjoyable and satisfying travel experience.Keywords : digital technology; google maps; recommendation system; weighted average algorithmAbstrak: Perkembangan teknologi digital telah mengubah pola perilaku wisatawan dalam memilih destinasi wisata. Google Maps kini tidak hanya digunakan untuk mencari lokasi restoran, tetapi juga menjadi sumber utama dalam mencari destinasi wisata terdekat berdasarkan rating dan ulasan pengguna. Penelitian ini bertujuan untuk membangun sistem rekomendasi destinasi wisata taman rekreasi di Kota Medan dengan menerapkan algoritma Weighted Average menggunakan data rating pengguna Google Maps. Data yang digunakan berasal dari lima ulasan pengguna terhadap lima taman rekreasi populer di Kota Medan selama periode 1 Januari 2025 hingga 30 April 2025. Algoritma Weighted Average dipilih karena mampu memberikan penilaian yang lebih objektif dan adil dengan memperhatikan bobot setiap rating yang diberikan pengguna. Hasilnya, sistem ini dapat merekomendasikan taman rekreasi terbaik berdasarkan pengalaman pengguna terkait aspek kebersihan, fasilitas parkir, toilet, keamanan, lintasan lari, dan aksesibilitas. Diharapkan sistem ini dapat membantu wisatawan dalam memilih destinasi yang sesuai dengan kebutuhan, preferensi, dan memberikan pengalaman wisata yang lebih menyenangkan dan memuaskan. Kata Kunci: google maps; sistem rekomendasi; teknologi digital; weighted average algorithm
Perbandingan Usability Mode Gelap dan Mode Terang WhatsApp Menggunakan System Usability Scale (SUS) pada Mahasiswa UINSU aulia, muhammad fathir; Nabila, Putri Salsa; Windriani, Nazla; M. Khalil Gibran, M. Khalil
NJCA (Nusantara Journal of Computers and Its Applications) Vol 10, No 1 (2025): Edisi Juni 2025
Publisher : Computer Society of Nahdlatul Ulama (CSNU) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36564/njca.v10i1.409

Abstract

Perkembangan teknologi digital telah mendorong peningkatan penggunaan aplikasi pesan instan seperti WhatsApp dalam berbagai aktivitas, termasuk dalam lingkungan akademik. WhatsApp menyediakan dua mode tampilan, yaitu mode terang dan mode gelap, yang dirancang untuk meningkatkan kenyamanan visual pengguna. Penelitian ini bertujuan untuk membandingkan tingkat kegunaan antara kedua mode tersebut berdasarkan persepsi mahasiswa Universitas Islam Negeri Sumatera Utara (UINSU). Metode yang digunakan adalah pendekatan kuantitatif dengan instrumen System Usability Scale (SUS). Sebanyak 80 responden diminta mengisi kuesioner berisi 20 pernyataan (10 untuk masing-masing mode), yang kemudian dianalisis dengan perhitungan skor SUS dan dilakukan uji statistik menggunakan uji t sampel berpasangan. Hasil penelitian menunjukkan bahwa rata-rata skor usability untuk mode terang sebesar 45.34, sedangkan mode gelap sebesar 48.69. Uji t menghasilkan nilai p sebesar 0.0004 yang berarti terdapat perbedaan signifikan antara kedua mode. Meskipun keduanya masih berada di bawah ambang batas kegunaan ideal, mode gelap cenderung memberikan pengalaman pengguna yang lebih nyaman. Temuan ini menunjukkan bahwa desain tampilan memiliki pengaruh terhadap kenyamanan persepsi dan perlunya menjadi perhatian dalam pengembangan antarmuka aplikasi.
Penerapan Algoritma Naive Bayes untuk Prediksi Financial Distress pada Perusahaan Publik sebagai Upaya Digitalisasi Analisis Akuntansi Sitorus, Nur Shafwa Aulia; Aulia, Muhammad Fathir; Faiza, Nayla; Siregar, Hervilla Amanda R.
JAAKFE UNTAN (Jurnal Audit dan Akuntansi Fakultas Ekonomi Universitas Tanjungpura) Vol 14, No 2 (2025): Jurnal Audit dan Akuntansi Fakultas Ekonomi Universitas Tanjungpura
Publisher : Jurusan Akuntansi, Fakultas Ekonomi dan Bisnis, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jaakfe.v14i2.101099

Abstract

Perkembangan teknologi digital mendorong penerapan kecerdasan buatan dalam bidang akuntansi untuk meningkatkan akurasi dan efisiensi analisis keuangan. Penelitian ini bertujuan menerapkan algoritma Naive Bayes guna memprediksi kondisi financial distress pada perusahaan publik sektor manufaktur di Indonesia sebagai bagian dari digitalisasi analisis akuntansi. Data yang digunakan berupa 150 entri rasio keuangan, meliputi Current Ratio (CR), Return on Assets (ROA), Net Profit Margin (NPM), dan Total Asset Turnover (TATO). Model dievaluasi menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan akurasi sebesar 86,67%, precision 75%, recall 25%, dan F1-score 37,5%. Temuan ini menunjukkan bahwa Naive Bayes cukup efektif dalam mengklasifikasi perusahaan Non-Distress, namun masih perlu pengembangan untuk meningkatkan deteksi kelas Distress. Model ini berpotensi menjadi dasar pengembangan sistem deteksi dini berbasis digital dalam analisis keuangan perusahaan.
Implementation of Finite State Automata on Pizza Vending Machine System Aulia, Muhammad Fathir; Suryandi, Diky; Nainggolan, Jesron
JITCoS : Journal of Information Technology and Computer System Vol. 1 No. 1 (2025): Volume 1 Number 1, June 2025
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v1i1.3

Abstract

This study aims to implement Finite State Automata (FSA) on a pizza machine. FSA is a theoretical computational model used to describe the behavior of a system that can change discretely from one state to another. A pizza machine is a machine used to make pizza automatically. In this study, we design and implement FSA on a pizza machine to regulate the pizza making process. FSA consists of a number of states and transitions between those states. Each state represents a certain stage in the pizza making process, such as adding ingredients, mixing dough, and baking. The programming language and algorithm used are appropriate for implementing FSA on a pizza machine. When the machine is turned on, it will start in the initial state. Then, based on the input given, the machine will switch between different states according to the specified transition rules. By implementing FSA, this study successfully automated the pizza making process on the machine. This reduces dependence on human intervention and increases production efficiency. By using FSA, the pizza machine can operate automatically and produce pizza with high accuracy and efficiency. This study contributes to the development of automation in the food industry and improves the understanding of how to apply FSA in the context of real-world applications. In this study, FSA is used to control a muffin machine, but the FSA concept can also be used in various other automation applications.
Transfer Learning Implementation with MobileNetV2 for Cassava Leaf Disease Detection Aulia, Muhammad Fathir; Gibran, M. Khalil; Sitorus, Nur Shafwa Aulia; Nugroho, Agung; Faiza, Nayla; Siregar, Hervilla Amanda R.
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4442

Abstract

Cassava (Manihot esculenta) is one of Indonesia’s key agricultural commodities but is vulnerable to various leaf diseases, such as Cassava Bacterial Blight (CBB) and Cassava Mosaic Disease (CMD). These diseases often exhibit similar visual symptoms, making it challenging for farmers to accurately identify them through manual observation. This study aims to develop an automatic cassava leaf disease detection system based on transfer learning, utilizing the MobileNetV2 architecture. The dataset used consists of 1,500 images, evenly distributed across three categories: CBB, CMD, and healthy leaves. The data underwent preprocessing, augmentation, and model training, including fine-tuning of the last 20 layers of the MobileNetV2 model. Evaluation results indicated that the model achieved an accuracy of 67% on the test set, with the highest performance in detecting Cassava Mosaic Disease, reflected by an F1-score of 0.75. These results demonstrate the potential of MobileNetV2 as a lightweight and efficient solution for detecting cassava leaf diseases, particularly when supported by a larger and more diverse dataset. This research serves as a foundation for developing mobile-based diagnostic tools to help farmers make faster and more accurate decisions in the field.
Perbandingan CNN Dan ResNet50 Dalam Klasifikasi Tuberkulosis Pada Citra X-Ray Paru Aulia, Muhammad Fathir; Ikhsan, Muhammad
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9554

Abstract

Tuberculosis (TB) remains a global health problem and requires rapid and consistent early screening. Chest X-rays are widely used because they are practical and economical, but manual interpretation is highly dependent on experts, which can lead to subjectivity, fatigue, and delayed diagnosis. This study aims to compare the performance of a basic Convolutional Neural Network (CNN) and a transfer learning-based ResNet50 in classifying lung X-ray images into two classes, namely TB and Normal, as well as to assess the trade-off between accuracy and computational efficiency. The dataset used is a balanced subset of 1,000 images (500 TB and 500 Normal) divided into 70% training data, 15% validation, and 15% testing with a fixed seed to ensure reproducible experiments. Preprocessing was performed by resizing the images to 224×224 pixels and normalizing the pixel values. ResNet50 used a preprocessing scheme in accordance with the pretrained model. Evaluation was performed using a confusion matrix and accuracy, precision, recall, and F1-score metrics. The test results show that CNN achieved an accuracy of 98.00% with three classification errors, while ResNet50 achieved an accuracy of 99.33% with one classification error and average precision, recall, and F1-score metrics above 0.99. In terms of efficiency, the CNN training time was approximately 40.46 seconds, while ResNet50 took a total of approximately 226.99 seconds. In the robustness test, the CNN inference time was approximately ±100 ms/image and ResNet50 was approximately ±1,900 ms/image. These findings indicate that ResNet50 excels in accuracy and generalization stability, while CNN is more efficient for fast response and limited resource requirements.
Co-Authors Faiza, Nayla Gibran, M. Khalil Ilka Zufria M. Khalil Gibran, M. Khalil Mhd Furqan Muhammad Ikhsan Nabila, Putri Salsa Nainggolan, Jesron Nugroho, Agung Siregar, Hervilla Amanda R. Sitorus, Nur Shafwa Aulia Suryandi, Diky Windriani, Nazla