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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Malikussaleh Mengabdi
Fadlisyah
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Detection of Qur’anic Ikhfa Patterns in Digital Images Using Binary Similarity Distance Measures (BSDM) with 3W-Jaccard Formula Julianansa, Ririn; Fadlisyah; Yesy Afrillia
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9814

Abstract

Recitation rules in the Qur'anic script form various visual patterns. One of the selected rules for this study is the Ikhfa pattern. Ikhfa is a recitation rule pronounced subtly when the nun sukun (نْ) or tanwin (ـَــًـ, ـِــٍـ, ـُــٌـ) is followed by one of 15 specific letters, namely: ta’ (ت), tsa’ (ث), jim (ج), dal (د), dzal (ذ), za’ (ز), sin (س), syin (ش), shad (ص), dhad (ض), tha’ (ط), zha’ (ظ), fa’ (ف), qaf (ق), and kaf (ك). In this study, the primary challenge is the difficulty of automatically detecting the Ikhfa pattern in both digital and printed Qur'anic texts. This challenge arises from the subtlety of the recitation rule, which makes it difficult to distinguish from other recitation patterns. To address this, the Ikhfa pattern is detected using image processing techniques, and pattern classification is performed using the Binary Similarity and Distance Measures (BSDM) method. The results indicate that the pattern detection system, employing BSDM with the 3W-Jaccard formula, achieved a detection rate of 83.84%. This suggests that the 3W-Jaccard formula is an effective approach for detecting similar recitation patterns. One advantage of the 3W-Jaccard formula is its ability to recognize patterns with a relatively small amount of reference data, making it highly suitable for implementation in the detection system.
PKM Strategi Pemanfaatan Teknologi Informasi dalam Pencegahan Cyberbullying untuk Siswa Ula, Mutammimul; Fasdarsyah; Bustami; Rizal Tjut Adek; Fadlisyah; salahuddin
Jurnal Malikussaleh Mengabdi Vol. 4 No. 1 (2025): Jurnal Malikussaleh Mengabdi, April 2025
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v4i1.24208

Abstract

PKM Strategi Pemanfaatan Teknologi Informasi dalam Pencegahan Cyberbullying untuk Siswa di SMK Negeri 3 kota lhokseumawe untuk mengantisipasi salah satu dampak negatif dari perkembangan teknologi digital yang memberikan ancaman serius bagi kesehatan mental dan sosial siswa. Fenomena ini menuntut adanya strategi pencegahan yang efektif melalui pemanfaatan teknologi informasi. Penelitian ini bertujuan untuk mengkaji strategi penggunaan teknologi informasi dalam upaya pencegahan cyberbullying di kalangan siswa. Metode yang digunakan adalah studi literatur terhadap berbagai penelitian terdahulu dan analisis praktik implementasi teknologi di bidang pendidikan. Hasil pengabdian ini menunjukkan bahwa pemanfaatan teknologi informasi dapat dilakukan melalui tiga pendekatan utama: (1) penggunaan aplikasi pengawasan dan pelaporan untuk mendeteksi serta menangani kasus cyberbullying secara cepat, (2) penguatan literasi digital siswa melalui platform e-learning dan konten edukatif interaktif, serta (3) kolaborasi sekolah, orang tua, dan penyedia layanan digital dalam menciptakan sosial secara online dan aman. Kesimpulan dari pengabdian ini adalah Pemanfaatan teknologi informasi dalam pencegahan cyberbullying bagi siswa terbukti efektif bila diarahkan pada tiga aspek utama: deteksi dini melalui aplikasi pengawasan, peningkatan literasi digital siswa, dan kolaborasi antara sekolah, orang tua, serta penyedia layanan digital. Strategi ini tidak hanya menekan potensi terjadinya cyberbullying, tetapi juga membangun budaya digital yang sehat dan aman bagi siswa. Dengan demikian, integrasi teknologi informasi dalam program pendidikan dan kebijakan sekolah menjadi langkah krusial dalam pencegahan cyberbullying.
Enhancing Oil Palm Leaf Disease Classification using a Pruned SqueezeNet Architecture Nugraha Rahmadan Diyanto; Muhathir; Fadlisyah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16524

Abstract

The SqueezeNet architecture is known to be effective but possesses a considerable number of parameters, which can be optimized using pruning—a compression technique that significantly reduces model parameters without sacrificing accuracy. This research aims to apply the L2-Norm based pruning method to the SqueezeNet architecture and compare its performance (accuracy and efficiency) against the default SqueezeNet model for classifying four classes of oil palm leaf diseases. The study used a primary dataset of 4,000 images, divided into training (70%), validation (20%), and testing (10%) sets. The SqueezeNet architecture was pruned using L2-Norm structured pruning with a uniform distribution at rates from 10% to 50%, followed by fine-tuning. The results show that the default SqueezeNet achieved 97.50% accuracy with 724,548 parameters. Significantly, a 10% pruning rate actually increased the accuracy to a high of 99.25% while simultaneously reducing the parameters to 579,036. Overly aggressive pruning, such as 40%, drastically decreased accuracy to 93.25%. It is concluded that the 10% pruning rate is the most optimal, proving that this method not only makes SqueezeNet lighter but also more effective. This 10% pruned model is highly suitable for application implementation due to its enhanced efficiency. Future research is recommended to validate these findings using a more diverse dataset and to test the model on actual edge devices.
KLASIFIKASI TINGKAT KECANDUAN PENGGUNA APLIKASI TIKTOK PADA MAHASISWA UNIVERSITAS MALIKUSSALEH MENGGUNAKAN METODE NAÏVE BAYES Wardina Ningsih; Mukti Qamal; Fadlisyah
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6603

Abstract

Penggunaan media sosial, khususnya TikTok , meningkat pesat di kalangan pelajar. Hal ini dapat menyebabkan kecanduan yang mempengaruhi pendidikan dan kehidupan sehari-hari mereka. Penelitian ini bertujuan untuk mengetahui tingkat Kecanduan TikTok di kalangan mahasiswa Universitas Malikussaleh dengan mengumpulkan data dari 466 mahasiswa dari tujuh fakultas dan menggunakan metode Naïve Bayes . Kelas yang akan dihasilkan tiga kategori yaitu Kecanduan ingan, Kecanduan Sedang, dan Kecanduan Berat. Dataset dibagi menjadi data pelatihan dan pengujian dengan rasio 80:20. Hasil analisis menunjukkan bahwa sebagian besar siswa (258 orang atau 69,4%) berada pada tingkat kecanduan sedang, diikuti oleh tingkat kecanduan ringan (22,6%) dan berat (8,1%). Menurut evaluasi kinerja model, metode Naive Bayes menunjukkan akurasi sebesar 85%, presisi rata-rata sebesar 84%, dan recall sebesar 83% pada data pengujian, yang menjadikannya pilihan yang tepat. Fakultas Pertanian memiliki proporsi kecanduan ringan tertinggi sebesar 14,6%, sedangkan Fakultas Ekonomi memiliki proporsi kecanduan ringan tertinggi sebesar 27,1%. Penelitian ini menunjukkan bahwa teknik Naive Bayes efektif dalam mengklasifikasikan tingkat kecanduan pengguna TikTok dan dapat digunakan sebagai acuan untuk upaya pencegahan dampak negatif Kecanduan media sosial di dunia akademik.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN FRAMEWORK WEB MENGGUNAKAN METODE WEIGHTED PRODUCT: DECISION SUPPORT SYSTEM FOR WEB FRAMEWORK SELECTION USING WEIGHTED PRODUCT METHOD Muhammad Alfath Frandhana Sihotang; Fadlisyah; Fajriana
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6610

Abstract

There are many types of web frameworks available today, each with its own characteristics, advantages, and limitations. This diversity often makes it difficult for developers to determine the most appropriate framework to meet all project needs. This study aims to help developers determine the best framework that can be used optimally on small and large-scale projects. The research process includes determining framework selection criteria based on literature studies, assessing the weight of criteria from developers and students, and collecting framework data through online platforms such as Official Documentation (official websites of the frameworks studied), Github, and Stack Overflow. Based on the results of the analysis and evalution of the collected data, it was found that the Next.js framework ranked highest as the best front-end framework with a final score of 0.04566 on a scale of 0-1, and the FastAPI framework ranked highest as the best back-end framework with a final score of 0.04385 on a scale of 0-1. Thus, Next.js (front-end framework) and FastAPI (back-end framework) are considered to have superior performance, high scalability, and extensive community support, making it easier for developers to find solutions, documentation, and development references. If an error or bug occurs, developers can ask questions on the Github website, Stack Overflow and the Official Documentation of the framework.  
Co-Authors Bustami Fajriana, Fajriana Fasdarsyah Julianansa, Ririn Muhammad Alfath Frandhana Sihotang Muhathir Mukti Qamal Mutammimul Ula Nugraha Rahmadan Diyanto Rizal Tjut Adek Salahuddin Salahuddin Wardina Ningsih Yesy Afrillia