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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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PENERAPAN METODE ALGORITMA SVM (SUPPORT VECTOR MACHINE) UNTUK KLASIFIKASI PENDERITA PENYAKIT GASTROESOPHAGEAL REFLUX DISEASE: APPLICATION OF SVM (SUPPORT VECTOR MACHINE) ALGORITHM METHOD FOR CLASSIFICATION OF GASTROESOPHAGEAL REFLUX DISEASE PATIENTS Teuku Ferynanda Ramadhan; Asrianda; Risawandi
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.v10i2.6466

Abstract

Gastroesophageal Reflux Disease (GERD) is a digestive disorder caused by the backflow of stomach acid into the esophagus, with symptoms that often resemble those of other conditions, making diagnosis challenging. This study aims to implement the Support Vector Machine (SVM) algorithm to develop a classification system for GERD patients based on clinical symptom data, including chest pain, swallowing disorders, regurgitation, and others. The research was conducted at Sakinah General Hospital in Lhokseumawe City using patient data from the 2020–2023 period. The classification system was designed through a series of stages including data preprocessing, normalization, and the application of a polynomial kernel in the SVM algorithm. The results demonstrate that the SVM algorithm achieved an accuracy of 82.5% and an F1-score of 58.3%, indicating a strong classification performance in distinguishing between GERD and non-GERD patients, and suggesting its potential as an effective diagnostic support tool for medical professionals.
PEMANTAUAN PENGOBATAN PASIEN TUBERKULOSIS DENGAN METODE VIDEO OBSERVED TREATMENT (VOT) BERBASIS MOBILE DI UPTD PUSKESMAS SAMBAS KOTA SIBOLGA: TUBERCULOSIS PATIENT TREATMENT MONITORING INFORMATION SYSTEM WITH MOBILE-BASED VIDEO OBSERVED TREATMENT (VOT) METHOD Deli Kartika Abrianisyah; Raissa Amanda Putri
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.v10i2.6475

Abstract

in Indonesia, due to its high morbidity and mortality rates. Tuberculosis is caused by bacteria from the Mycobacterium group, specifically Mycobacterium Tuberculosis. Although TB can be cured with appropriate therapy, the relatively long treatment duration of 6 to 9 months often poses a challenge for patients to remain compliant with the treatment regimen. The success of TB therapy heavily depends on patients' adherence to treatment. Given the importance of monitoring medication intake during the recovery period, strategies are needed to improve patient adherence, one of which is through the use of information technology in healthcare services. This application is designed to improve TB patient adherence at the UPTD Puskesmas Sambas in Sibolga City. The system was developed using the Rapid Application Development (RAD) method, which includes the stages of requirements planning, design workshop, and implementation. For monitoring patient medication, the Video Observed Treatment (VOT) method is used, where patients record their medication intake process, which is then verified by healthcare workers to ensure adherence to the treatment schedule. VOT enables remote monitoring without direct interaction, thereby saving time and costs for both patients and healthcare workers. Testing results indicate that the application can monitor tuberculosis patients' medication adherence through video reports during medication consumption. The system was designed using Android Studio with the Java programming language, Firebase Database, and Firestore cloud storage. The integration of this technology successfully replaces the Directly Observed Treatment (DOT) method, providing a modern solution to support digital transformation in healthcare services. This research resulted in a mobile-based Tuberculosis Patient Treatment Monitoring system that is expected to support healthcare workers in monitoring treatment in a more effective and efficient manner
PENERAPAN PERCEIVED STRESS SCALE DALAM PSYCHOLOGICAL SELF-ASSESSMENT UNTUK MENGUKUR TINGKAT STRES MENGGUNAKAN METODE K-NEAREST NEIGHBORS Nadia Rizatul Riski; Safwandi Safwandi; 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.v10i2.6479

Abstract

Stress is a common psychological issue experienced by university students, particularly in high-pressure academic environments such as engineering faculties. This study aims to develop a digital self-assessment system to measure student stress levels using the Perceived Stress Scale (PSS-10) and the K-Nearest Neighbors (KNN) classification method. Data were collected from 100 respondents from the Faculty of Engineering at Universitas Malikussaleh. The system classifies stress levels into three categories: mild, moderate, and severe. Testing was conducted using 5-fold cross-validation. The evaluation results show an average accuracy of 83%, weighted precision of 79.18%, weighted recall of 84.70%, and weighted F1-score of 80.10%. These findings indicate that the system is capable of providing fairly accurate stress classification and can serve as a useful tool for independent stress detection.
COMPARING SIMPLE EXPONENTIAL SMOOTHING AND ADVANCED TIME SERIES FORECASTING FOR CEMENT STOCK PREDICTION AT PT. SOLUSI BANGUN ANDALAS Uzia Ulfa; Sujacka Retno; Safwandi
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.v10i2.6483

Abstract

Accurate cement stock prediction is crucial for optimizing supply chain management, ensuring operational efficiency, and achieving long-term sustainability and profitability within the global cement industry. Inaccurate predictions can lead to significant costs due to overstocking or stockouts, impacting customer satisfaction and overall economic development. The complex market environment and dynamic demand fluctuations within the construction sector further exacerbate these challenges. This study compares time series forecasting algorithms to predict cement stock levels. The methodologies investigated include traditional statistical models: Simple Moving Average (SMA), Double Moving Average (DMA), Simple Exponential Smoothing (SES), and Double Exponential Smoothing (Holt's Method). Additionally, advanced machine learning and deep learning models, namely ARIMA (Autoregressive Integrated Moving Average), LSTM (Long Short-Term Memory), and Prophet, are also evaluated. This research aims to identify the most suitable algorithm for cement stock forecasting by assessing their performance using standard metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Initial findings from existing literature suggest that while traditional methods offer simplicity, modern models like LSTM often achieve superior accuracy for complex and non-linear patterns, whereas Prophet excels at automatically handling seasonality and missing data.ARIMA provides computational efficiency for simpler, stationary patterns but may struggle with non-linearity. This study contributes to the field by providing a structured comparison of diverse forecasting techniques specifically tailored for cement inventory, offering practical guidance for industry practitioners and informing strategic decision-making in supply chain optimization.  
APLIKASI TES MINAT BAKAT SERTA REKOMENDASI JURUSAN UNTUK SISWA SMA MENGGUNAKAN METODE FORWARD CHAINING Nurul Husna; Munirul Ula; Yesy Aflillia
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.v10i2.6490

Abstract

In the Industrial Revolution 4.0 era, human resources play a crucial role in workplace competitiveness, requiring individuals to develop their skills and knowledge through education. High school graduates who wish to pursue higher education face difficulties in choosing the right major. This situation is influenced by external pressures, causing many students to make choices that are not in line with their abilities. This study aims to develop an Interest and Talent Test Application to facilitate high school students in providing explanations about their interests and talents as well as recommendations for appropriate majors. This application is built on a website using the Multiple Intelligences theory and the Forward Chaining method. In this process, data samples will be used to test the methods used in this study. The samples used came from 60 students of SMA Negeri 1 Jeumpa who had completed the trial. The results of this test showed that the average type of intelligence of students in the high school was Verbal (Linguistic) intelligence for 16 students with majors that correspond to their type of intelligence, namely Communication Science, Language and Literature, International Relations, Law, and Political Science. This system has achieved an average user satisfaction rating of 81.91%, indicating that users are satisfied with consulting using this aptitude and interest testing application.
IMPLEMENTASI METODE PREFERENCE SELECTION INDEX (PSI) DALAM PEREKRUTAN MITRA KURIR J&T: IMPLEMENTATION OF THE PREFERENCE SELECTION INDEX (PSI) METHOD IN RECRUITING J&T COURIER PARTNERS Alfisyahrin; Zara Yunizar; 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.v10i2.6492

Abstract

The recruitment process for courier partners is an important aspect in maintaining the quality of delivery services in logistics companies such as J&T Express. Selecting the right candidate requires an objective and systematic method so that the decisions taken can be accounted for. This study aims to apply the Preference Selection Index (PSI) method to assist the decision-making process in the selection of courier partners. The PSI method is used to evaluate and rank prospective partners based on a number of criteria, including work experience, communication skills, problem-solving skills, discipline, and teamwork skills. This research was conducted at J&T Bireun. The analysis process was carried out through the stages of normalization, calculation of preference deviations, and determination of the final preference index value. The data used amounted to 10 employee candidates with the highest ranking results, namely Ridwan with a score of 8.6, Zikra with a score of 8.55 and Syakir with a score of 8.55. The results of the study show that the PSI method is able to provide objective and efficient selection results, and makes it easier for companies to determine the best courier partner candidates. Thus, this method is worthy of consideration as a tool in a multi-criteria-based recruitment system in the logistics industry.The courier partner recruitment process is one of the important aspects of maintaining the quality of delivery services in logistics companies such as J&T Express. The selection of appropriate candidates requires objective and systematic methods for the decision to be taken accountable. The Preference Selection Index (PSI) method to assist the decision-making process on the selection of courier partners. PSI method is used to evaluate and rank prospective partners based on a number of criteria, including work experience, communication skills, problem solving capabilities, discipline, and teamwork capabilities. This study was conducted on J&T Bireuen. The analysis process is performed through the normalization stage, preference deviation calculation, and the determination of the final preference index value. The data sample used amounted to 10 candidates with the highest ranking results, namely Ridwan with value (8.6), Zikra with value (8.55), and Syakir with value (8.55). Research results in the application of PSI methods for labor selection in the logistics sector, especially in the operational position of courier partners indicate that it is able to provide objective and efficient selection results, and facilitate the company in determining the best courier partner candidates. Thus, this method is worth considering as an alterative tool in a proof-criterion-based recruitment decision support system in the logistics industry.
PENDEKATAN METODE MAUT PADA FITUR SELEKSI DALAM SISTEM INFORMASI ADMINISTRASI BERBASIS WEB KWARTIR RANTING BATANG KUIS Suci Syah Putri; Suendri
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.v10i2.6497

Abstract

The administration and coaching systems in the Batang Kuis Scout Branch are still carried out manually, including data recording, correspondence management, and participant selection processes. This leads to workflow delays, increased risk of data recording errors, and low accuracy in decision-making. These issues significantly affect organizational efficiency and fairness in participant selection. This study aims to develop a web-based information system integrated with the Multi-Attribute Utility Theory (MAUT) method as a decision support feature to ensure objective, transparent, and efficient participant selection. A quantitative research approach was applied, direct user testing to evaluate system effectiveness. The developed system includes features for managing scoutmasters, Scout Units (Gudep), correspondence (incoming and outgoing letters), activity documentation, and a MAUT-based selection module that processes five main criteria: Administration, Scout Knowledge, National Insight, Personal Characteristics, and Marching Ability (LKBB). Selection data was collected through observation and participant score documents, then processed using the MAUT method to generate accurate participant rankings. The results show that the system achieved 92.5% selection accuracy and improved selection efficiency by up to 78% compared to conventional methods. In conclusion, the system successfully improves the effectiveness and accuracy of the selection process while strengthening organizational administrative governance. The implication of this research is that the system can serve as an integrated digital solution, which can be adopted by other scout branches as a foundational step toward digital transformation in scouting organizational management, ensuring sustainability and transparency.
STACKING ENSEMBLE MACHINE LEARNING MODEL FOR EARLY DETECTION OF CHRONIC KIDNEY DISEASE IN INDONESIA Agusviyanda; Hamdani; M. Khairul Anam; Agustin; M. Ikhsan Wibowo
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.v10i2.6501

Abstract

Chronic Kidney Disease (CKD) is one of the major health problems that continues to rise and requires accurate early detection to prevent progression to end-stage renal failure. This study proposes a hybrid machine learning approach to automatically detect CKD by combining data balancing techniques, ensemble learning, and cross-validation. The dataset used was obtained from the Kaggle platform, consisting of 1,089 patient records, and was balanced using the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Three boosting algorithms—Adaboost, XGBoost, and LightGBM—were used as base models and combined through a stacking approach with Logistic Regression as the meta-classifier. Evaluation was conducted using a 5-fold cross-validation scheme with accuracy, precision, recall, and F1-score as performance metrics. The results show that the stacking model achieved an average accuracy of 99.40%, outperforming individual models (LightGBM: 98.87%; Adaboost: 98.76%; XGBoost: 98.61%) and exceeding the performance of several previous studies. These findings indicate that the stacking approach, when combined with SMOTE and cross-validation, significantly enhances classification performance for CKD detection.
KLASIFIKASI TEMA AYAT AL-QUR'AN PADA JUZ 30 MENGGUNAKAN TEXT MINING DAN ALGORITMA C4.5 BERBASIS WEB: CLASSIFICATION OF QUR'ANIC VERSE THEMES IN JUZ 30 USING TEXT MINING AND THE C4.5 ALGORITHM BASED ON WEB Jihan Adila; Fadlisyah Fadlisyah; Said Fadlan Ansari
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.v10i2.6502

Abstract

This study aims to develop a classification system for themes of the verses of the Qur'an in Juz 30 using the web-based C4.5 algorithm. The C4.5 algorithm has proven effective in the classification process, providing high accuracy in data processing. This system is designed to help users access and understand the themes contained in the verses automatically. By using text mining, this study allows processing and analysis of the text of the Qur'an, while the C4.5 algorithm is used to build a decision tree to classify themes such as morality, monotheism, and life after death. The data used consists of 38 themes that have been categorized based on the meaning and messages contained in the verses of Juz 30. The evaluation results of the model show an accuracy of 89.77%, with other evaluation metrics such as precision, recall, and F1-score showing satisfactory results. This system is implemented in the form of a web-based application, which allows users to select a theme and get related verses quickly and efficiently. With a simple and easy-to-use interface, this application makes it easy to search and understand themes in the Qur'an, and contributes to the development of understanding of Islamic teachings through modern technology.
PENERAPAN ALGORITMA RANDOM FOREST UNTUK MENENTUKAN KELAYAKAN PENERIMA BLT: IMPLEMENTATION OF THE RANDOM FOREST ALGORITHM FOR DETERMINING BLT RECIPIENTS' ELIGIBILITY Muhammad Iqbal; Dahlan Abdullah; 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.v10i2.6503

Abstract

Indonesia faces significant challenges regarding poverty, particularly among low-income communities. The government has introduced various programs, including the Direct Cash Aid Program (BLT), to alleviate poverty. However, identifying eligible recipients in Bandar Dua Subdistrict, Pidie Jaya, remains complex and time-consuming. This research aims to implement the Random Forest algorithm to determine the eligibility of cash aid recipients in Bandar Dua, Pidie Jaya, providing accurate and efficient classification based on predefined criteria. The study focuses on BLT recipients in Bandar Dua, comprising 45 villages, using data from 2020 to 2022. The Random Forest algorithm is applied, considering criteria such as income, housing conditions, and the number of dependents. The expected outcomes include valuable insights for the local government in determining the eligibility of cash aid recipients, a classified dataset using the Random Forest algorithm, and a reference for future research. According to the test results using the Random Forest algorithm, an accuracy of 83.33%, precision of 89.04%, and recall of 91.55% were achieved.  

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