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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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The SISTEM PENJADWALAN PADA DAYAH DARUL MUARRIF AL-AZIZIYAH MENGGUNAKAN METODE ALGORITMA GENETIKA: SCHEDULING SYSTEM AT DAYAH DARUL MUARRIF AL-AZIZIYAH USING GENETIC ALGORITHM METHOD Zikratul Maulana; Fadlisyah Fadlisyah; Fajriana Fajriana
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.v10i2.6405

Abstract

The scheduling of subjects at Dayah Darul Muarrif Al-Aziziyyah is a complex problem due to multiple constraints, such as teacher availability, classroom capacity, and student learning time. This problem falls into the NP-hard category, making conventional methods less effective. This study utilizes a genetic algorithm to efficiently solve the scheduling problem. The schedule is represented as a chromosome containing subject, time, room, and teacher data. Evaluation is performed using a fitness function based on hard constraints (such as schedule conflicts) and soft constraints (such as time preferences). The results demonstrate that the genetic algorithm can produce feasible and more efficient schedules compared to manual scheduling. This study supports the development of adaptive automatic scheduling systems for educational institutions. In addition, the system is designed with a user-friendly interface and is capable of evolving the population iteratively to find an optimal or near-optimal solution. Testing was conducted using real data from Dayah Darul Muarrif Al-Aziziyyah, demonstrating the system’s ability to handle various combinations of constraints with a high degree of accuracy. Thus, this approach not only accelerates the scheduling process but also improves the overall quality of schedule management.
IMPLEMENTASI SMART CLASSROOM BERBASIS IOE: STUDI EVALUATIF DENGAN PENDEKATAN PDCA (Plan-Do-Check-Act) DAN MODEL TAM (Technology Acceptance Model): IMPLEMENTATION OF SMART CLASSROOM BASED ON IOE: EVALUATIVE STUDY WITH PDSA APPROACH AND TAM MODEL Bismar Fadli; Seno Adi Putra; Hanif Fakhrurroja
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.v10i2.6410

Abstract

Modern universities are required to transform by embracing digital technologies to support the implementation of the Tri Dharma of Higher Education. This study explores how Indonesian higher education institutions adopt the Smart Classroom concept as part of digital transformation to improve the quality of learning. One of the key initiatives is the creation of interactive and connected learning environments through technology integration. This research proposes a standardized Smart Classroom model based on the Internet of Everything (IoE), utilizing devices such as Interactive Flat Panels, PTZ cameras, smart door locks, Wireless Presentation Displays, and audio systems. The methodology includes literature review, conceptual design, prototype development, and evaluation using the Plan-Do-Check-Act (PDCA) approach. Additionally, the Technology Acceptance Model (TAM) is employed to analyze students’ perceptions and intentions to adopt Smart Classroom technologies. The results show that the application of IoE in Smart Classrooms enhances interaction between lecturers and students, operational efficiency, and provides a more adaptive and comfortable learning experience. The main contribution of this study is the development of a comprehensive implementation guide for IoE-based Smart Classrooms, incorporating technical, managerial, and pedagogical aspects to support the advancement of higher education in the digital era.
PERBANDINGAN KINERJA KNN DAN DECISION TREE DALAM KLASIFIKASI POTENSI AKADEMIK SISWA SMP WILAYAH MEJOBO: COMPARISON OF KNN AND DECISION TREE PERFORMANCE IN CLASSIFICATION OF ACADEMIC POTENTIAL OF JUNIOR HIGH SCHOOL STUDENTS IN MEJOBO AREA Muhammad Fahrino Haykal Febrian; Wiwit Agus Triyanto; Diana Laily Fithri
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.v10i2.6412

Abstract

Identification of student academic potential, which still relies on traditional methods, is often inaccurate and time-consuming, potentially hindering early intervention for students who need support. This study offers a solution, comparing the effectiveness of the K-Nearest Neighbor (KNN) and Decision Tree algorithms in classifying the academic potential of 7th-grade students at SMP Negeri 1 Mejobo Kudus and SMP Negeri 2 Mejobo Kudus. This study utilized a dataset of 1,100 student data, with key features including Indonesian and Mathematics scores, reading, writing, and arithmetic test results, and behavioral records. Our goal is to help schools precisely identify students who require special attention early on. This system was developed through comprehensive data collection and the application of refined classification models. The results showed that the KNN model achieved 99% accuracy, while the Decision Tree model fell slightly short at 98%. Despite the high accuracy achieved, cross-validation and in-depth analysis were conducted to ensure model generalization and mitigate potential overfitting. Both algorithms proved highly effective in providing accurate mapping of academic potential, with KNN demonstrating slightly superior performance. With the presence of this web-based system, it is hoped that schools can more easily and quickly identify student potential, reduce misidentification, and make more appropriate and inclusive educational decisions, for the sake of better student learning quality.  
IMPLEMENTASI RULE-BASED CHATBOT DAN FUZZY STRING MATCHING DALAM SISTEM INFORMASI LAYANAN HAJI DAN UMRAH DI PT ALFATA WAFIQAH WISATA Nafi Beckhamsyah Siahaan; Muhamad Alda
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.v10i2.6413

Abstract

The development of digital technology has driven various sectors to improve efficiency and competitiveness, including the tourism industry. PT Alfata Wafiqah Wisata, a company engaged in the tour and travel sector, still applies a semi-manual system for registration and information services, which involves filling out physical forms and manually transferring the data to Microsoft Excel. This approach poses risks such as data entry errors, difficulties in data management, and delays in responding to common inquiries – especially outside of working hours. This research aims to address these issues by developing a web-based information system for serving prospective Hajj and Umrah pilgrims. The system is equipped with a rule-based chatbot to provide information and manage data related to registration and payments. The system development follows the waterfall methodology and incorporates Fuzzy String Matching to handle typing errors when users input keywords. The result of this research is a web-based information system that assists PT Alfata Wafiqah Wisata in managing pilgrim data and providing fast, automated responses to frequently asked questions – even outside operational hours.
DETEKSI DINI KANKER KULIT MENGGUNAKAN CNN, DNN, DAN EFFICIENTNET: PENDEKATAN DEEP LEARNING BERBASIS WEB Shindy Maheswari; Dedi Gunawan
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.v10i2.6417

Abstract

Skin cancer is one of the most commonly diagnosed types of cancer globally. Early detection is crucial for improving the chances of recovery and preventing further complications. This study implements and compares three deep learning models—Convolutional Neural Network (CNN), Deep Neural Network (DNN), and EfficientNet—to detect skin cancer using the HAM10000 dataset. The research process includes preprocessing, model training, performance evaluation, and integration into an interactive web application based on Flask. Evaluation was conducted using accuracy, precision, recall, F1-score, and AUC metrics. The test results show that EfficientNet provides the best performance with a test accuracy of 78.44%, followed by CNN at 69.76%, while DNN only reaches 40.52% due to loss of spatial information. To improve interpretability, the system is also equipped with Grad-CAM visualization that highlights important areas in the lesion image that influence the model's decision. This study demonstrates that the EfficientNet architecture can provide more accurate and stable classification of skin lesions compared to the other two models. The practical implications of these results are the potential use of EfficientNet in clinical decision support systems to assist in the early detection of skin cancer in an automated, efficient, and accurate manner, particularly in healthcare facilities with limited resources.  
PERBANDINGAN CNN, RESNET50, DAN VISION TRANSFORMER UNTUK KLASIFIKASI KANKER PAYUDARA BERBASIS WEB Stella Juventia Grace; Dedi Gunawan
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.v10i2.6420

Abstract

This research aims to compare three deep learning algorithm-based image processing models, namely CNN, ResNet50, and Vision Transformer (ViT), in classifying breast cancer based on mammography images. The CBIS-DDSM dataset from Kaggle was used and processed through pre-processing steps such as data cleaning, image resizing, normalization, augmentation, and data splitting into training and testing sets. The models were evaluated using a 5-Fold Cross Validation scheme to ensure performance stability. The results show that ResNet50 achieved the highest accuracy of 97%, followed by CNN at 92%, and Vision Transformer at 71%. All three models were implemented into a web application using Flask to support the automatic diagnosis process. These findings are expected to help develop a faster and more accurate breast cancer detection system for medical professionals.
PENERAPAN SUPPORT VECTOR MACHINE UNTUK ANALISIS SENTIMEN PADA TANGGAPAN MASYARAKAT DI MEDIA SOSIAL TERHADAP PROGRAM MAKAN SIANG GRATIS : APPLICATION OF SUPPORT VECTOR MACHINE FOR SENTIMENT ANALYSIS ON PUBLIC RESPONSE TOWARDS FREE LUNCH PROGRAM Mutiara Sintia Dewi; Abdul Halim Hasugian
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.v10i2.6425

Abstract

The Free Nutritious Lunch Program initiated by the government has become a public spotlight, as it is considered a strategic effort to address the issues of malnutrition and stunting in Indonesia. This study aims to analyze public sentiment toward the program using a technology-based approach. The method used is the Support Vector Machine (SVM) algorithm with the Term Frequency-Inverse Document Frequency (TF-IDF) approach to classify public opinions on social media. This study utilized 1,000 tweets collected from the Twitter platform and processed using Google Colaboratory. SVM was chosen because it can handle high-dimensional data and has proven effective for text classification in various previous studies. The analysis results show that the majority of public sentiment is positive. The SVM model achieved an accuracy of 67%, with a precision of 98%, recall of 99%, and F1-score of 98%, demonstrating its effectiveness in classifying textual data. These findings indicate that sentiment analysis using machine learning approaches can serve as an important tool in evaluating public perception of government policies.
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  
IMPLEMENTASI METODE MULTI OBJECT OPTIMIZATION ON THE BASIS OF RATIO ANALYSIS (MOORA) DALAM PENERIMAAN PESERTA DIDIK BARU SMKN 2 LHOKSEUMAWE: IMPLEMENTATION OF MOORA AS A TOOL TO HELP DETERMINE THE BEST PROSPECTIVE STUDENTS Gilang Sidiq; Nurdin; Fajriana
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.v10i2.6432

Abstract

SMK N 2 Lhokseumawe is a public high school located on Jln. Ocean, Kampung Jawa Lama, Banda Sakti, Lhokseumawe City, Aceh. The process of admitting new students to make it more effective, the school certainly has a scoring system with predetermined criteria. SMK N 2 Lhokseumawe itself has a data system and value selection process which makes it a problem, because the data is processed only using Microsoft Excel and calculated manually. from the test results. Therefore it is necessary to change the old system with changes to the new system. By using the SPK (Decision Support System) in the MOORA (Multi Object Optimization on The Basis of Ratio Analysisis) Method in order to optimize two or more conflicting attributes simultaneously and to make it easier for SMK N 2 Lhokseumawe in determining PPDB according to the criteria set out set. This system is supported by the PHP programming language as a system development application, and the MySQL database as data storage. The results of calculations using the MOORA method show that SMK N 2 Lhokseumawe students on behalf of Afrilia Fransciska are ranked first with a percentage of 0.305 and Beriana Amelia Febrianti is ranked last with a percentage of 0.166. very useful for SMK N 2 Lhokseumawe.
SISTEM PAKAR DIAGNOSA PENYAKIT TANAMAN PEPAYA BERBASIS FUZZY LOGIC MAMDANI: PENDEKATAN EFISIEN UNTUK IDENTIFIKASI DINI: IMPLEMENTATION OF THE MAMDANI FUZZY ALGORITHM TO SUPPORT DISEASE DIAGNOSIS DECISIONS IN PAPAYA BASED ON AN EXPERT SYSTEM Arifinal; Wahyu Fuadi; 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.v10i2.6433

Abstract

Papaya is one of the most widely cultivated horticultural commodities in Indonesia. However, this plant is susceptible to various diseases that can reduce productivity and fruit quality. Limited knowledge among farmers in identifying early disease symptoms is a major factor in failed disease management. This research aims to develop an expert system using the Mamdani Fuzzy Logic method to assist in determining types of diseases in papaya plants. The system utilizes fuzzy inference techniques capable of handling uncertain and linguistic data, and is built using the Python programming language and MySQL database. Diagnosis is performed based on symptoms entered by users, processed through fuzzification, inference, and defuzzification stages to produce the most probable disease type and its certainty level. The test results show that the system provides accurate diagnoses that align with manual expert analysis using predefined fuzzy rules. Therefore, the expert system serves as an effective solution for supporting fast and accurate identification of diseases in papaya plants.

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