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All Journal Infotech Journal IJFAC (Indonesian Journal of Fundamental and Applied Chemistry) Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Resona : Jurnal Ilmiah Pengabdian Masyarakat Jurnal Serambi Engineering Jurnal Informatika Kaputama (JIK) Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology Jurnal Tika Journal of Renewable Energy, Electrical, and Computer Engineering Sisfo: Jurnal Ilmiah Sistem Informasi Journal of Advanced Computer Knowledge and Algorithms Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Ar Razi
Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Development and Characterization of HDPE Waste/Ramie Fiber Composites for Geomembrane Applications in Litopenaeus vannamei Shrimp Cultivation Ahmad Fikri; Ar Razi; Mainisa Mainisa; Arif Fadhilah; Apryza Mila
IJFAC (Indonesian Journal of Fundamental and Applied Chemistry) Vol 11, No 1 (2026): February 2026
Publisher : IJFAC (Indonesian Journal of Fundamental and Applied Chemistry)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24845/ijfac.v11.i1.10

Abstract

Geomembranes are thin geosynthetic materials composed of polymeric layers that function as barriers in direct contact with soil. The properties of HDPE geomembranes suitable for application as vannamei shrimp (Litopenaeus vannamei) aquaculture. HDPE geomembranes applied in vannamei shrimp aquaculture are subjected to thermal loads (UV radiation), oxidative agents, and mechanical force such as interfacial friction and tensile loading in surface wear.  This study aims to improve the mechanical properties of the material by incorporating natural fibers as reinforcement into HDPE–ramie fiber biocomposite. The process of making HDPE–ramie fiber biocomposite uses an single screw extruder with a temperature of 170 oC, a rotation of 20 rpm with a ratio of HDPE waste and ramie fiber of 97.5:2.5, 95:5, and 92.5:7.5. The results of tensile and impact tests show the highest strength and toughness are shown in the HDPE sample and ramie fiber 92.5:7.5 with a value of 5.23 MPa and 96.17 kJ/m2. The highest material ductility was demonstrated in the HDPE and ramie fiber composite 97.5:2.5 with a value of 1.32%. In addition, the highest stiffness was demonstrated in the HDPE and ramie fiber composite 95:5 with a value of 845.40 MPa. The results of the morphological investigation showed that the bond between the matrix and filler was partially formed and the smallest fiber diameter was 4 µm.Keywords: HDPE, Ramie, Composite, Geomembrane, Vanamei
IMPLEMENTASI SISTEM MONITORING DAN MENENTUKAN UKM TERBAIK MENGGUNAKAN METODE TOPSIS: IMPLEMENTATION OF A MONITORING SYSTEM AND DETERMINING THE BEST UKM USING THE TOPSIS METHOD Rizka Salsabila Nasution; Dahlan Abdullah; Ar Razi
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.v10i2.6512

Abstract

Student Activity Units (SAUs) play a crucial role in developing students' potential, enhancing soft skills, and fostering a healthy competitive environment on campus. However, the performance monitoring system for SAUs at some universities is still conducted manually, which is time-consuming, labor-intensive, and prone to data inaccuracies. This study aims to design and implement a web-based UKM performance monitoring information system using the TOPSIS method as a decision-support tool for identifying the best UKMs. The research was conducted through the following stages: needs analysis, system design using the waterfall method, development, and system testing. The system is equipped with features for inputting activity data, evaluating criteria such as member activity, number of achievements, and annual activities, as well as TOPSIS calculations to obtain an objective ranking of student organizations. The implementation results show that the system functions well, facilitating administrators and campus authorities in monitoring and evaluating the performance of each student organization in real-time. Black-box testing indicates that all features function as expected. The final ranking based on TOPSIS calculations placed the Mahadasa Batalyon VII Satria Pasee Student Regiment (MENWA) as the best student organization with the highest preference score of 0.6841, followed by the Pramuka Racana Meurah Giri Ratu Nur Ilah with a score of 0.6254, and the Bidikmisi/KIP Kuliah Student Forum (Formadiksi) with a score of 0.6248. This system is expected to enhance transparency, motivation, and the quality of student activity management at one of the universities.
ANALISIS DATA MINING PERBANDINGAN ALGORITMA SUPPORT VEKTOR MACHINE DAN RANDOM FOREST PADA KLASIFIKASI SUBTIPE ANEMIA : Data Mining Analysis A Comparative Study of Support Vector Machine and Random Forest Algorithms in Anemia Subtype Classification Tiara Oktavia; Munirul Ula; Ar Razi
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.v10i2.6565

Abstract

Anemia is a medical condition characterized by hemoglobin levels or red blood cell counts below normal, which disrupts the distribution of oxygen throughout the body. Early detection and classification of anemia subtypes are crucial for determining appropriate medical treatment. This study was conducted at Cut Meutia Regional General Hospital in North Aceh Regency with the aim of developing a classification model for anemia subtypes using Support Vector Machine (SVM) and Random Forest (RF) algorithms. The research follows the CRISP-DM methodology, which includes business understanding, data exploration, data preparation, modeling, evaluation, and implementation. The dataset consists of medical parameters such as age, gender, diagnosis, and results from Complete Blood Count (CBC) tests. During the data preparation phase, normalization, missing data handling, and data balancing using the SMOTE technique were performed. The tuning process was carried out using the RBF kernel for SVM. Model validity was tested using 5-fold cross-validation. The results showed that the Random Forest algorithm achieved the highest accuracy of 96.94% with a processing time of 18.31 seconds, while SVM reached an accuracy of 92.15% with a processing time of 1.63 seconds. Based on these results, the Random Forest algorithm is considered more effective in classifying anemia subtypes and is recommended for development as a decision support system in the healthcare sector.  
SISTEM PENGUJIAN HAFALAN AL-QUR'AN SURAT AL-GHASYIYAH MENGGUNAKAN METODE TRANSFORMASI WALSH Fikri Akbar; Fadlisyah; Ar Razi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7721

Abstract

Memorizing the Qur’an is a highly recommended act of worship for Muslims, yet traditional learning methods often face constraints such as limited availability of competent teachers. With technological advancements, innovations to support the memorization process independently, objectively, and efficiently are greatly needed. This research aims to develop and measure the performance of an automated system for testing the memorization of Surat Al-Ghasyiyah (verses 1–26) through voice recognition using the Walsh Transform method. The Walsh Transform is applied to convert voice signals from the time domain to the frequency domain using basis functions valued at +1 and −1, extracting unique features from each verse to be compared with reference voice samples. The system was tested on all 26 verses using 6 training voice samples and 4 test voice samples per verse (total 260 samples: 156 training and 104 test). System performance was evaluated using four variations of probability constants: 0.3, 0.4, 0.5, and 0.6. Results indicate that the probability constant significantly affects system accuracy. Detection rates achieved were 70.2% (constant 0.3), 84.6% (constant 0.4), 89.4% (constant 0.5), and peaked at 93.3% (constant 0.6). With an overall average detection rate of 84.4%, it is concluded that the Walsh Transform method is highly effective and the developed system has potential as a reliable aid for Qur’an memorizers.
Improving Sentiment Classification of Indonesian Skincare Reviews through Fine-Tuned IndoBERT and Data Augmentation Nadia Thahira; Ar Razi
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1835

Abstract

This study analyzes sentiment in customer reviews of local skincare serum products on the Tokopedia e-commerce platform using a fine-tuned IndoBERT model enhanced with data augmentation techniques. A total of 5,000 reviews were collected from ten local skincare brands through web scraping and labeled according to star ratings into three sentiment classes: Positive, Neutral, and Negative. The dataset exhibited extreme class imbalance, with the Positive class representing 94.66% of all observations, creating substantial challenges for minority-class recognition. The data were divided through stratified sampling into 70% training, 15% validation, and 15% test sets to preserve class distributions. To mitigate imbalance, back-translation from Indonesian to English and back to Indonesian, together with synonym replacement, was applied exclusively to minority classes within the training set. The IndoBERT-base-p1 model was subsequently fine-tuned using focal loss combined with class weighting and compared against a baseline model trained without augmentation. Experimental results show that the proposed model achieved 94.40% accuracy, a Macro F1-score of 61.43%, and a Weighted F1-score of 95.14%. Although the baseline model obtained higher overall accuracy of 97.47%, it completely failed to identify the Neutral class, producing an F1-score of 0.00%. In contrast, the proposed approach increased the Neutral F1-score to 23.53% and improved the Macro F1-score by 2.30 percentage points, demonstrating more balanced performance across sentiment classes. The resulting model was deployed as SerumSense, a web-based application developed using Streamlit and SQLite, supporting both single-review and batch sentiment analysis. Black-box testing across 20 functional scenarios confirmed that all application features operated successfully as intended. These findings demonstrate that combining IndoBERT fine-tuning, targeted data augmentation, focal loss, and class weighting offers a practical approach for improving minority-class recognition in highly imbalanced Indonesian e-commerce review datasets.
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.
Rancang Bangun Game Edukasi Belajar Hijaiyah Berbasis Android Studi Kasus di Paud Pembangunan Ahklak Matang Guru Zulfikar Zulfikar; Muthmainnah Muthmainnah; Arrazi Arrazi
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 4 No. 1 (2020): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2020
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v4i1.6285

Abstract

Al-Quran merupakan pedoman hidup bagi seluruh umat islam yang ada di dunia. Al-Quran memiliki 114 surat yang di dalamnya terdapat 6236 ayat dan 77.845 kata. Jadi untuk memahami dan membaca Al-Quran sangatlah perlu ilmu-ilmu dasar tata cara membaca Al-Quran dengan benar. Huruf hijaiyah merupakan ilmu dasar membaca Al-Quran yaitu sebagai sarana untuk bisa membaca dan memahami Al-Quran dengan benar, sesuai dengan apa yang ada di dalam Al-Quran itu sendiri. Maka dari itu penulis merancang dan membuat sebuah aplikasi dasar belajar mengenal huruf hijaiyah, sebagai sarana untuk memudahkan anak-anak dalam memahami huruf-huruf dasar yang di perlukan untuk bisa membaca Al-Qur'an yaitu ada 28 huruf hijaiyah dan semua huruf hihjaiyah ini diaplikasikan dalam bentuk game edukasi mengenal membaca huruf hijaiyah  berbasis mobile android. Penulis menggunakan sistem Construct 2 sebagai sarana untuk membangun pembuatan game pembelajaran mengenal huruf hijaiyah. Hasil yang diperoleh dari penelitian ini adalah sebuah aplikasi game edukasi dasar belajar mengenal membaca huruf-huruf hijaiyah berbasis android offline yang mampu melakukan pencocokan huruf, perpindahan huruf dan menampilkan keseluruhan huruf-huruf hijaiyah yang di barengi dengan suaranya, guna mempermudahkan anak-anak dalam memahami ilmu dasar membaca Al-Quran yaitu mengenal huruf hijaiyah dengan cara cepat dalam mengingat. Yang disajikan dalam dunia permainan anak-anak yaitu dalam bentuk aplikasi game edukasi.
PENERAPAN FRAMEWORK COBIT 5 DOMAIN APO(ALIGN, PLAN AND ORGANISE) PADA AUDIT TATA KELOLA TEKNOLOGI INFORMASI Eni Yustanti; Angga Pratama; Arrazi Arrazi
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 4 No. 2 (2020): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2020
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v4i2.6297

Abstract

Penelitian ini membahas tentang pentingnya Tata Kelola Teknologi Informasi, karena peningkatan peran Teknologi Informasi nantinya harus berbanding   lurus   dengan   investasi   yang   dikeluarkan.   Investasi   Teknologi informasi biasanya mengeluarkan uang dalam jumlah besar. Untuk itulah diperlukan adanya tata kelola Teknologi Informasi yang baik pada suatu perusahaan, agar investasi yang dikeluarkan tidaklah sia-sia dan memberikan manfaat yang diinginkan oleh perusahaan. frameworkCOBIT 5 menyediakan ukuran, indikator, proses dan kumpulan praktik terbaik untuk membantu perusahaan optimal dari pengelolaan Teknologi Informasi yang pantas bagi suatu organisasi. Dengan demikian maka dilakukan penelitian di Bank Rakyat Indonesia menggunakan  FrameworkCOBIT  5  fokus  domain  Align,  Plan  and Organize(APO).
Comparison of Coffee Bean Sales Predictions at the Ketiara Coffee Traders Cooperative (KOPEPI) Using Linear Regression and Random Forest Methods Nada Syadzwina; Defry Hamdhana; Ar Razi
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.25974

Abstract

Most of Indonesia's land is used for agriculture and plantations because it is an agrarian country. Harvests or agricultural products can be exported to help the country's economic recovery. Coffee, the most traded tropical crop in the world, is one of the most valuable commodities. Approximately 25 million farming households contribute up to 80% of global coffee production (FAO Organizational 2023). Indonesia's coffee industry continues to experience significant annual growth. To optimize their production and distribution, Indonesian coffee producers must understand coffee bean sales trends. This study compares two methods for predicting coffee bean sales at the KOPEPI Ketiara Aceh Tengah Cooperative using Linear Regression and Random Forest methods. The research methods used in this study are data collection and system design. The results show a comparison of the Linear Regression and Random Forest methods in predicting coffee bean sales. Linear regression provides fairly good accuracy for the price variable with low MAPE values (3.35%–4.55%) and MAE that is still within reasonable limits, but produces large prediction errors for the export variable with high MAPE (67.84%–80.65%) and large MAE (5982–7960). In contrast, Random Forest shows superior performance with very low MAPE (2.69%–3.46%) and smaller MAE (4275–6038) on price variables, as well as more stable and consistent export predictions even though the MAPE values are still quite high (54.25%–84.97%). Overall, Random Forest is a more appropriate model to use because it provides accurate price predictions and more consistent export performance compared to Linear Regression.
Co-Authors Afta Adiyatma Wicaksana Afta Adiyatma Wicaksana afwandi afwandi Ahmad Fikri Aina Rahmadani Alvin Alvesaldy Angga Pratama Apryza Mila Arif Fadhilah Asrianda Asrianda Asrillah Asrillah Dahlan Abdullah Dedi Fariadi Eni Yustanti Fadlisyah Fikri Akbar Hafizh Al Kautsar Aidilof Hamdhana, Defry Mainisa Mainisa Muhammad Fauzan Muhammad Muhammad Munirul Ula Mutammimul Ula Muthmainnah Muthmainnah Nada Syadzwina Nadia Thahira Rini Meiyanti Rizka Salsabila Nasution Syifa Anjanira Tiara Oktavia Wahyu Fuadi Yulisda, Desvina Zulfadli Zulfadli