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RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Published by Universitas Abdurrab
ISSN : 24772062     EISSN : 2502891X     DOI : https://doi.org/10.36341/rabit
Core Subject : Science, Engineering,
Computer Science & IT Engineering
This journal is called RABIT, where the name comes from two words namely, RAB which means Abdurrab University and IT which means information technology, it can be interpreted as a journal of this journal Journal of Informatics Engineering Study Program Pekanbaru Abdurrab University. This RABIT journal contains various sciences related to the world of computers especially information technology and information systems, namely, this journal is published twice a year where the initial publication is on January 10 while for the second issue which is on July 10.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 696 Documents
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KEAMANAN ENDPOINT API MENGGUNAKAN OAUTH2 PADA UNIT LAYANAN TERPADU UNIVERSITAS MALIKUSSALEH: APPLICATION PROGRAMMING INTERFACE (API) SECURITY AT THE INTEGRATED SERVICE UNIT OF MALIKUSSALEH UNIVERSITY Gilang Ramadhan Purba; Rizal; Yesy Afrillia
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.v10i2.6543

Abstract

Malikussaleh University, as a higher education institution, strives to improve service quality through its Integrated Service Unit (ULT). However, monolithic systems often become an obstacle due to their rigidity and difficulty in development. This research aims to design and build an efficient and secure ULT system model by adopting a microservice architecture. The methodology includes designing the microservice architecture, developing APIs, and implementing security using Laravel Passport, which supports the OAuth2 standard to protect Machine-to-Machine (M2M) communication. Each service, such as administration, academic, and personnel services, is broken down into independent services packaged in Docker containers. The results show the successful implementation of a system prototype where each service can communicate securely via API. Testing proved that the token-based and scope-based authentication and authorization mechanisms successfully protected endpoints from unauthorized access. Thus, the implemented microservice architecture model and API security are proven to be a solution for a modular, secure, and efficient service digitalization in the university environment.
PENERAPAN METODE DECISION TREE CART UNTUK KLASIFIKASI PENYAKIT PADA TANAMAN KELAPA SAWIT: APPLICATION OF THE CART DECISION TREE METHOD FOR CLASSIFYING DISEASES IN OIL PALM PLANTS Alfin Syatriawan; Fadlisyah; Kurniawati
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.v10i2.6544

Abstract

This study developed a web-based oil palm disease detection system to help farmers and related parties quickly and accurately identify diseases based on symptoms observed in the field. The system was built using the CRISP-DM framework, which includes the stages of business understanding, data understanding, data preparation, modelling, evaluation, and implementation. The classification method used is Classification and Regression Tree (CART) due to its ability to handle categorical data and provide easy-to-understand interpretations. The symptom dataset was compiled based on literature references, direct observations, and official data from the North Aceh District Food Crop Agriculture Office and a number of scientific journals. The data consists of 11 main symptoms with a predetermined severity scale. The evaluation results showed excellent model performance, with an accuracy of 95.45%, precision of 97%, recall of 95%, and an F1-score of 95%. The trained model was then integrated into a Flask-based web application, enabling users to input symptoms to obtain disease predictions and management solutions. The novelty of this research lies in the use of relevant local data, the adoption of a symptom-based approach instead of image-based methods, and the integration of the classification model into an applicable web-based system. This system is expected to enhance efficiency, accessibility, and accuracy in decision-making related to disease management.
SISTEM PAKAR DIAGNOSIS PENYAKIT PARU MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DAN RULE BASED SYSTEM: EXPERT SYSTEM FOR LUNG DISEASE DIAGNOSIS USING CONVOLUTIONAL NEURAL NETWORK AND RULE-BASED SYSTEM Putri Syifa; Safwandi; Zahratul Fitri
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.v10i2.6548

Abstract

Lung disease remains a major health problem in Indonesia, accounting for 25.8% of respiratory-related deaths according to the Ministry of Health. Data from Muhammad Ali Kasim Gayo Lues Regional Hospital, Aceh, shows approximately 3,600 cases recorded since 2022. This study designs an artificial intelligence-based diagnostic system combining 224x224 pixel chest X-ray image analysis with clinical parameter evaluation using a rule-based system. The Rule-Based System implements standardized weighting where each clinical manifestation contributes proportionally to the total diagnostic score through normalization to a 100-point scale.The dataset consists of 983 images (786 training, 197 validation) collected during the 2022-2025 period, covering tuberculosis (300 cases), pneumonia (300 cases), and pneumothorax (383 cases) with 25 disease symptoms. Evaluation results show the system achieves 94.97% accuracy with 93.2% sensitivity and 95.4% specificity. The F1-scores for each disease were 0.9375 (Tuberculosis), 0.9125 (Pneumonia), and 0.987 (Pneumothorax). Therefore, this system can assist the diagnostic process and support clinical decision-making through both radiographic image analysis and patient symptom evaluation.
PERBANDINGAN METODE LOGISTIC REGRESSION DAN RANDOM FOREST DALAM KLASIFIKASI PENYAKIT KULIT MULTIKELAS: COMPARISON OF LOGISTIC REGRESSION AND RANDOM FOREST METHODS IN MULTICLASS SKIN DISEASE CLASSIFICATION Syatriani Jauhari; Rozzi Kesuma Dinata; 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.v10i2.6551

Abstract

This study compares two classification algorithms, namely Logistic Regression and Random Forest, in classifying eight types of skin diseases based on ten clinical symptoms. The data used consists of 271 patient medical records from Cut Meutia General Hospital, which are divided into 80% training data and 20% test data. The pre-processing stage included imputing missing data, encoding categorical variables, and normalizing numerical features for the Logistic Regression model. Both algorithms were implemented using the Scikit-learn library in the Python programming language. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results show that Logistic Regression achieved an accuracy of 94.55%, slightly higher than Random Forest, which reached 92.73%. Validation using 5-fold cross-validation and a paired t-test yielded a p-value of 0.0371, indicating that the performance difference between the two models is statistically significant. However, limitations such as the relatively small amount of data and class imbalance impacted the low performance of the model in minority categories such as Psoriasis. This study is expected to serve as a foundation for the development of data-based medical diagnosis assistance systems to improve efficiency and accuracy in healthcare services.
IMPLEMENTASI ALGORITMA BOYER-MOORE DAN BRUTE FORCE UNTUK PENCARIAN TAFSIR DI ENSIKLOPEDIA AL-QUR’AN BERBASIS WEBSITE: MPLEMENTATION OF BOYER-MOORE AND BRUTE FORCE ALGORITHMS FOR TAFSIR SEARCH IN A WEB-BASED QUR’ANIC ENCYCLOPEDIA Siti Nadilla; Rizal Tjut Adek; Rizki Suwanda
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.v10i2.6552

Abstract

The growing demand for structured and efficient access to Qur’anic content based on specific themes underscores the need for a thematic search system for verses and tafsir. This study aims to develop a web-based Qur’anic encyclopedia featuring a keyword-based search function that employs string matching algorithms. We implemented and compared two algorithms Boyer-Moore and Brute Force to assess their performance in processing large textual tafsir datasets. The system was built using PHP and MySQL, utilizing a dataset comprised of categorized tafsir and verses according to themes. Our methodology included a literature review, interface design, system development, algorithm implementation, and performance testing. The results indicate that the Boyer-Moore algorithm is significantly faster and more efficient than the Brute Force algorithm for large-scale text searches. Testing with ten different keywords revealed that the average search time for the Boyer-Moore algorithm was 0.030 seconds, while the Brute Force algorithm took 0.069 seconds, making Boyer-Moore approximately 56% faster. The search feature reliably retrieved relevant verses and tafsir based on user input. Additionally, the system demonstrated user-friendliness through a responsive web interface. These findings highlight the effectiveness of string matching algorithms in digital Islamic tools and emphasize the advantage of the Boyer-Moore algorithm in optimizing the complexity of thematic Qur’anic searches.  
ANALISIS PERBANDINGAN KINERJA ALGORITMA AGGLOMERATIVE HIERARCHICAL CLUSTERING DAN K-MEDOIDS UNTUK KLASTERISASI JENIS PENYAKIT PASIEN RAWAT INAP: COMPARATIVE ANALYSIS OF THE PERFORMANCE OF AGGLOMERATIVE HIERARCHICAL CLUSTERING AND K-MEDOIDS ALGORITHM FOR CLUSTERING DISEASE TYPES OF INPATIENTS Lailatul Husna; Defry Hamdhana; Munirul Ula
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.v10i2.6554

Abstract

Rumah Sakit Arun Lhokseumawe memiliki data jenis penyakit pasien rawat inap yang beragam dan terus bertambah, namun belum dimanfaatkan secara optimal untuk analisis lebih lanjut. Selama ini, data hanya menjadi laporan administratif tanpa dilakukan pengolahan guna memperoleh informasi bermakna, seperti pola dominasi penyakit. Riset ini bertujuan untuk mengelompokkan jenis penyakit pasien rawat inap menggunakan dua metode klasterisasi, yaitu Agglomerative Hierarchical Clustering dan K-Medoids, serta menganalisis perbandingan kinerjanya. Data yang dipergunakan mencakup 84 jenis penyakit yang direkam pada periode Desember 2024 hingga Januari 2025, dengan atribut jumlah pasien laki-laki, pasien perempuan, dan umur pasien. Klasterisasi Agglomerative Hierarchical Clustering dilakukan dengan pendekatan average linkage dan jarak Manhattan Distance, sedangkan K-Medoids menggunakan jarak Euclidean Distance. Hasil memperlihatkan bahwa metode Agglomerative Hierarchical Clustering membentuk 3 cluster, yaitu C1 dengan 4 jenis penyakit, C2 menghasilkan 79 jenis penyakit, dan C3 menghasilkan 1 jenis penyakit. Sedangkan metode K-Medoids juga menghasilkan 3 cluster dengan C1 dengan 11 jenis penyakit, C2 menghasilkan 13 jenis penyakit, dan C3 menghasilkan 60 jenis penyakit. Evaluasi hasil dilakukan menggunakan Silhouette Coefficient. Berdasarkan pengujian validitas cluster Agglomerative Hierarchical Clustering menggunakan Silhouette Coefficient, algoritma Agglomerative Hierarchical Clustering memperlihatkan kinerja lebih baik dengan rerata nilai 0,5837. Sedangkan pengujian validitas cluster K-Medoids menggunakan Silhouette Coefficient pada seluruh data, diperoleh nilai rerata sejumlah -0.3558. Nilai ini memperlihatkan bahwa hasil kualitas cluster yang kurang optimal. Perbedaan hasil klasterisasi antara AHC dan K-Medoids terjadi karena kedua algoritma memiliki cara kerja yang berbeda. AHC membentuk cluster secara bertahap dengan menggabungkan data yang paling dekat satu per satu hingga membentuk struktur hierarki menggunakan jarak Manhattan, sedangkan K-Medoids langsung membagi data ke dalam jumlah cluster di awal dengan menggunakan jarak Euclidean. Perbedaan ini memengaruhi jumlah dan susunan anggota pada tiap cluster. Kata Kunci: Klasterisasi, Agglomerative Hierarchical Clustering, K-Medoids, Silhouette Coefficient
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.  
PERAMALAN DAN ANALISIS PERPUTARAN PERSEDIAAN PRODUK DAPUR KERIPIK RIDA DENGAN METODE SINGLE MOVING AVERAGE Tri Wulandari; Nuriadi Manurung; Sudarmin
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.v11i1.6588

Abstract

Rida Chip Kitchen is a business engaged in the production and sale of various types of chips. The problem faced by Rida Chip Kitchen is the difficulty in determining the optimal inventory level for each period, resulting in excessive inventory storage that increases operational costs and inventory shortages that lead to lost sales opportunities over the past seven months. Production is carried out without clear planning, leading to an imbalance between demand and inventory, as well as the risk of excess or shortage of inventory due to the absence of accurate forecasts. The Single Moving Average (SMA) method is the simplest type of Moving Average that does not use weighting in its calculations of closing price movements. The research method used in this study is a quantitative research method. From the analysis results at Rida's Chip Kitchen, it is possible to predict product demand more accurately, improve product availability, and reduce the risk of customers switching to competitors due to stock shortages
PENERAPAN TOPSIS DALAM EVALUASI KINERJA GUNA PENENTUAN BONUS PEGAWAI NON - ASN PADA KUA SIMPANG EMPAT Anggi Anggraini; Muhammad Ardiansyah Sembiring; Santoso
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.v11i1.6589

Abstract

Optimal public services at the Office of Religious Affairs (KUA) are highly dependent on fair performance evaluations, including those of non-civil servant employees. Currently, the performance appraisal system at the KUA Simpang Empat is still subjective and unstandardized, which has the potential to cause unfairness in the distribution of bonuses. This study aims to develop a decision support system using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to evaluate the performance of non-ASN employees objectively, measurably, and transparently. The research was conducted using a qualitative approach through observation and interviews, followed by the design and implementation of a web-based system. Performance evaluation is based on five main criteria: overtime hours, attendance, timeliness of task completion, teamwork, and initiative and creativity, with a sample size of 10 non-ASN employees evaluated. The results of the study indicate that the developed system is capable of providing a fairer evaluation, aiding data-driven decision-making, and minimizing subjectivity in bonus distribution.
IMPLEMENTASI AUGMENTED REALITY UNTUK PENGENALAN TANAMAN TOGA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK Melita Saldila; Rozzi Kesuma Dinata; Said Fadlan Anshari
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.v11i1.6602

Abstract

This research aims to develop an Augmented Reality (AR) based application integrated with Convolutional Neural Network (CNN) method to help communities recognize Family Medicinal Plants (TOGA) interactively and increase awareness of their potential benefits. The developed application uses AR technology to provide direct information about TOGA plants detected through mobile phone cameras, with a dataset covering 10 types of TOGA plants, each containing 200 images per label. The research results show that the system successfully performs plant recognition in real-time with an accuracy rate of 58.53%, precision of 58.76%, and recall of 99.40%. The CNN model is capable of recognizing various visual variations of plants under different lighting conditions and viewing angles. Model training was conducted up to 125,000 steps with the best performance achieved at the 72,000th checkpoint. Although the application can provide an engaging and effective learning experience, the main challenge faced is the diversity of physical forms of plants within each category that affects system accuracy. This research proves that the combination of AR and CNN technologies can be used as an innovative solution for medicinal plant education, although further development is still needed to improve recognition accuracy.

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