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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Resona : Jurnal Ilmiah Pengabdian Masyarakat ILKOM Jurnal Ilmiah Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Ilmiah Mahasiswa Fakultas Hukum Universitas Malikussaleh TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Natural Jurnal Rekayasa elektrika Kanun: Jurnal Ilmu Hukum Jurnal Teknik Sipil
Asrillah Asrillah
Fakultas Hukum Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Neuroscience Physics Transportation Other

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PENGKLASTERAN DATA KUALITAS AIR TAMBAK MENGGUNAKAN METODE GAUSSIAN MIXTURE MODEL: Statistical Approach to Identifying Pond Water Quality Patterns Assri Yani Sibuea; Asrianda Asrianda; Hafizh Al-Kautsar Aidilof
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6426

Abstract

This study aims to cluster pond water quality data to support decision making in fish farming management. The Gaussian Mixture Model (GMM) method is used as a probabilistic approach in clustering water quality parameters, namely pH, temperature, turbidity, and total dissolved solids (TDS). Data were collected from ponds in Kuala Kerto Village, North Aceh Regency, which is a traditional fish farming area. Before clustering, the data were cleaned from outliers and normalized using the Z-score method to improve the modeling quality. The model evaluation results showed that the GMM with 3 clusters provided the best results with a Silhouette Score of 0.55, Davies-Bouldin Index of 1.01, and the lowest BIC Score. Based on the standards for aquaculture water quality (pH 6.5–8.5, TDS <3000 mg/L, turbidity <300 NTU), each cluster was interpreted into good, moderate, and poor quality categories. Visualization of the results using PCA shows quite clear separation between clusters. This research provides a practical contribution in helping fish farmers monitor and evaluate pond water conditions in a more structured and data-driven manner.   Keywords: Gaussian Mixture Model, Pond Water Quality, Clustering, Z-Score, Silhouette Score  
PENERAPAN DECISION TREE C5.0 DALAM APLIKASI ANALISIS SENTIMEN TERHADAP BOIKOT PRODUK PRO-ISRAEL DI MEDIA SOSIAL X: SENTIMENT CLASSIFICATION ON BOYCOTT-RELATED TWEETS USING C5.0 DECISION TREE ALGORITHM Juliar Husriansyah; Asrianda Asrianda; Said Fadlan Anshari
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6451

Abstract

The movement to boycott products believed to support Israel reflects global solidarity with the Palestinian fight. In Indonesia, support for this movement continues to grow, especially through social media platform X (formerly Twitter). After the release of MUI Fatwa Number 83 of 2023, which advises Muslims to refrain from using products linked to Israel. The objective of this study is to analyze the sentiment of users on social media X regarding the boycott of products that support Israel, using the Decision Tree C5.0 algorithm. The data were collected through a scraping technique targeting tweets containing relevant boycott-related keywords, then processed using text preprocessing and analyzed using Term Frequency-Inverse Document Frequency (TF-IDF) for the extraction of features. The dataset was divided into 80% for training and 20% for testing in order to train and assess the classification model. The classification results revealed that out of 1,840 tweets, 1,257 were positive, 318 negative, and 265 neutral, indicating that 68.32% of users expressed support for the boycott movement. The evaluation of the model resulted in an accuracy of 83.26%, a precision of 86.51%, a recall of 83.26%, and an f1-score of 84.29%, demonstrating that the C5.0 algorithm effectively and accurately classifies sentiment. This research is anticipated to act as a guide for creating systems that analyze public opinion and provide insights for policymakers and industry players in responding to social issues emerging on digital platforms.
Clustering Level of Cigarettes Addiction Among Malikussaleh University Students Using K-Means Method Alvin Alvesaldy; Asrianda Asrianda; Ar Razi
Journal of Renewable Energy, Electrical, and Computer Engineering Vol. 5 No. 1 (2025): March 2025
Publisher : Institute for Research and Community Service (LPPM), Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v5i1.18165

Abstract

Cigarettes are a form of tobacco product produced by rolling dried tobacco leaves into small cylindrical sticks. Cigarettes are usually used for smoking, namely smoking and inhaling the smoke produced when tobacco leaves are burned. Cigarettes generally contain ingredients such as tobacco leaves, which can contain nicotine, an addictive substance that causes dependence. Apart from that, cigarettes also contain various other dangerous chemicals such as tar, carbon monoxide and formaldehyde. The smoke produced when a cigarette is burned creates more than 4,000 chemicals, of which about 70 are known to cause cancer. This research aims to help students at the Faculty of Engineering, Malikussaleh University to help students find out the level of their addiction to cigarettes. This research also gave birth to a grouping system that uses the Python programming language and MySQL as the database. The K-Means Clustering algorithm used in this grouping system states that out of 200 students at the Faculty of Engineering, Malikussaleh University, 28 people are smokers who have a low level of addiction (C1), 77 people have a moderate level of addiction (C2), 55 people have a heavy level of addiction. (C3), 40 people had a very severe level of addiction (C4). This system can be used to determine the level of cigarette addiction among students at the Faculty of Engineering, Malikussaleh University in the future.
Konsep Finite State Machine dan implementasinya pada Game Asrianda Asrianda; Zulfadli Zulfadli
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 6 No. 1 (2022): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2022
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v6i1.8352

Abstract

Game merupakan  sesuatu yang digunakan untuk bermain yang dimainkan dengan aturan-aturan tertentu. Penerapan FSM banyak digunakan dalam gameuntuk mendapatkan variasi respon NPC (Non Playable Charackter) antar pemain pada sebuah game. Sistem melakukan aksi yang sama pada state sampai sistem menerima event, baik berasal dari perangkat luar atau komponen sistem itusendiri. Setiap state terhubung oleh transisi yang mengarah ke satu state lainnya.  Dalam Game ini proses FSM berjalan dengan mendapatkan aturan jika misiterselesaikan atau quest terpenuhi, maka akan berpindah state ke level berikutnya, dan apabila misi tidak terselesaikan atau quest tidak terpenuhi makan akan tetap pada state awal. NPC diprogram untuk melakukan tugas atau peran tertentu kepada pemain dengan memberikan misi, atau membantu dalam pertempuran atau sekedar berjalan-jalan untuk memberikan ramainya suasana.
Phishing Email Detection Using SVM with RBF Kernel Based on Manhattan Distance Asrianda Asrianda; Sujacka Retno; Beno Jange; Mansur Mansur
ILKOM Jurnal Ilmiah Vol 18, No 2 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i2.2806.268-280

Abstract

This study examines the performance of a Support Vector Machine (SVM) model with a Radial Basis Function (RBF) kernel for phishing email detection using Euclidean and Manhattan distance measures. The dataset consists of 3,600 email samples, including 2,400 legitimate emails and 1,200 phishing instances. The features are designed to capture both linguistic and structural characteristics of emails, including word count, vocabulary diversity, stop word usage, number of links and domains, presence of email addresses, spelling errors, and urgency-related terms. The experiments were conducted using two train-test split ratios, 80:20 and 70:30, combined with hyperparameter tuning of C and gamma across 15 iterations. The findings indicate that the Manhattan distance consistently outperforms the Euclidean distance, particularly in terms of recall and F1-score, which are critical for detecting the minority class. The model achieved a best accuracy of 78.33%, accompanied by noticeable improvements in recall and F1-score. These results suggest that the choice of distance function within the RBF kernel plays a crucial role in enhancing model sensitivity and generalization when dealing with imbalanced data. Furthermore, the iterative hyperparameter tuning process contributes significantly to improving both performance and model stability. Overall, the SVM-RBF approach with Manhattan distance provides an effective and reliable framework for phishing email detection in machine learning applications.
Co-Authors Alvin Alvesaldy Ar Razi Arif Rahman Assri Yani Sibuea Beno Jange Budi Bahreisy DESI D Eka Intan Kumala Putri Fajriana, Fajriana Gartika Setiya Nugraha Hadi Iskandar Hafizh Al Kautsar Aidilof Hafizh Al-Kautsar Aidilof Herinawati Herinawati Ibnu Rusydy Jamaluddin J Juliar Husriansyah Lolia Lusiana Rahayu Malahayati Malahayati Mansur Mansur Marwan Abu Bakar Mochammad Afifuddin Muhammad Fachri Hibatullah Muhammad Muhammad Muhammad Nasir Muhammad Ridho Fadli Mulyana Mulyana Nurarafah Nuribadah Nuribadah Ramziati Ramziati riana dewi Roiyani R Said Fadlan Anshari Saputra, Ferdy Sofyan Jafar Suci Mahara Sujacka Retno Teuku Budi Aulia Tiara T Yoga Pangestu Zaki Al Abrar Zulfadli Zulfadli