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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 ALGORITMA MACHINE LEARNING UNTUK PENGELOMPOKAN SISWA BERDASARKAN ASPEK AKADEMIK DAN NON-AKADEMIK Hesti Sabrila Aulia; Muhammad Arifin; Diana Laily Fithri
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.v11i1.7249

Abstract

This study aims to develop a student potential clustering system as a strategy to address the limitations of academic identification processes that have traditionally been conducted manually, subjectively, and are prone to observational bias. The K-Means Clustering and K-Medoids algorithms were applied to a dataset consisting of 1,023 student records from SMP Negeri 2 Jekulo Kudus and SMP Negeri 3 Jekulo Kudus, using variables such as semester report card grades, core subjects including Mathematics, Science, and Indonesian Language, overall average scores, attitude assessments, and participation in extracurricular activities. The study employed a cluster number of (k = 3), representing High, Medium, and Low student potential categories for educational mapping purposes. The data preprocessing stage included missing value imputation using mean values and normalization of numerical features using RobustScaler to minimize the influence of outliers without removing student data. The evaluation results indicate that the K-Means algorithm achieved better clustering performance than K-Medoids based on evaluation metrics, with a Silhouette score of 0.529 and a Davies–Bouldin Index of 0.879, making it more suitable for the characteristics of the student dataset used. The system was subsequently implemented as an interactive web-based application developed in Python using the Flask framework and a MySQL database, enabling centralized data management, real-time access, and visualization of clustering results through a user-friendly interface. With this system, schools are expected to be able to map student potential more objectively, efficiently, and in a data-driven manner, thereby supporting learning strategy planning, intervention programs, and more targeted and inclusive educational decision-making.
DETEKSI EMOSI PADA TWITTER BERBASIS FASTTEXT: EVALUASI PERFORMA ARSITEKTUR CNN DAN GRU Eka Saraswati; Muljono
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.v11i1.7252

Abstract

Text on social media platforms such as Twitter generates large volumes of data that can be utilized to automatically understand user emotions. However, the characteristics of Twitter text, which are short, unstructured, and dominated by informal language and the use of emojis, pose significant challenges for emotion detection, particularly in Indonesian-language texts. This study aims to develop and evaluate a specific preprocessing pipeline to improve the performance of deep learning–based emotion classification models. The proposed preprocessing pipeline includes social media text cleaning, informal language and slang normalization, removal of irrelevant characters, and emoji-to-text conversion, while word representation is performed using FastText word embedding. An experimental method is employed to compare the performance of three deep learning architectures, namely Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and a hybrid CNN–GRU model, in predicting five emotion categories: anger, joy, sadness, fear, and love. The dataset consists of 5,079 tweets, which are divided using a stratified split with an 80:20 ratio between training and testing data. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the CNN with FastText model outperforms the other models, achieving an accuracy of 81.2%, precision of 81.1%, recall of 81.9%, and an F1-score of 81.2%, confirming that a specifically designed preprocessing pipeline plays a crucial role in improving emotion detection accuracy for Indonesian Twitter text.
PENERAPAN LOGIKA FUZZY MAMDANI UNTUK MENENTUKAN DURASI PENYIRAMAN TANAMAN TOMAT Nadya Almas; Erna Astuti
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.v11i1.7254

Abstract

The determination of tomato plant water requirements is influenced by changes in temperature, air humidity, and soil moisture content, which are constantly changing. This study uses the Mamdani Fuzzy Logic method to determine the duration of adaptive irrigation based on these three environmental parameters. The data used came from the Mendeley Data Repository with a total of 3,000 observations. The fuzzy system was designed with three input variables, one output variable, and 27 reasoning rules. The calculation process was performed using the scikit-fuzzy library in Google Colab. The results showed that the irrigation duration ranged from 2.05 to 19.99 minutes with an average of 9.60 minutes. The pattern of results shows a consistent response, namely that the watering duration increases when the temperature is hot and the soil is dry, and decreases when the humidity is high and the soil is wet. These findings prove that Mamdani Fuzzy Logic is effective as a computational approach to aid decision-making in smart irrigation systems for tomato plants.  
PENGEMBANGAN SISTEM INFORMASI P3M TERINTEGRASI MELALUI REFACTORING DAN PENAMBAHAN FITUR DENGAN METODE R&D Zulkarnaini; Muhammad Noval; Andre Mariza Putra; Ayu Octarina
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.v11i1.7268

Abstract

This study aims to develop an integrated Information System for the Center for Research and Community Service (P3M) at Politeknik Negeri Sriwijaya through the addition of new features and refactoring of the existing system. The previous system suffered from several limitations, including a monolithic architecture, data duplication across modules, and limited flexibility for further development, which negatively affected the efficiency of research and community service management. This study employed the Research and Development (R&D) method, consisting of requirement analysis, system design, prototype development, testing, evaluation, and refinement stages. The results show that the developed system successfully integrates the management of research and community service proposals, reviewer assessment processes, real-time activity monitoring, and automated report generation. Code refactoring improves readability, modularity, and system sustainability. User testing involving administrators, lecturers, and reviewers indicates improvements in administrative efficiency, data accuracy, and user satisfaction. This study contributes to the development of research management information systems in vocational higher education by offering a fully integrated system and a systematic refactoring approach to legacy applications.
PETUALANGAN INSPIRATIF BERBASIS ANDROID MEMPERKENALKAN FIGUR PAHLAWAN PAPUA KEPADA GENERASI DIGITAL DI KEPULAUAN RAJA AMPAT: INSPIRATIONAL ANDROID-BASED ADVENTURE INTRODUCES PAPUAN HEROES TO THE DIGITAL GENERATION IN THE RAJA AMPAT ISLANDS Fitriyani Tella; Virasanty Muslimah; Agniel Lorensyus Malino; Ridho Bintang Ramadhan
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.v11i1.7272

Abstract

Papua has a number of national heroes who have made significant contributions to the integration of the Papua region into the Unitary State of the Republic of Indonesia (NKRI). Although their role is very important in the history of the nation, there are still many young people, especially in the Papua region, who do not know or understand the values of these figures' struggles. Therefore, innovative learning media is needed to convey history in an interesting and interactive way. The development of an Android-based educational game with the theme of Papuan heroes' adventures. The purpose of this research is to design an Android-based adventure game to introduce Papuan heroes to the digital generation and to implement an Android-based adventure game about Papuan heroes for the younger generation in the Raja Ampat Islands. The research method used the Game Development Life Cycle (GDLC) approach with the stages of Initiation, Pre-production, Production, Alpha Testing, Beta Testing, and Release. The application development process was carried out using Unity software, while system testing was conducted through blackbox testing to ensure that all functions and interfaces in the game worked properly. In addition, usability testing was also conducted to assess the level of feasibility and comfort of users in playing the game. The test results showed that all functions worked very well without any bugs. Meanwhile, the usability score of 94% indicated that the game was highly feasible and had the potential to be an effective educational medium.  
ANALISIS PERBANDINGAN KINERJA ARSITEKTUR RESNET50 DAN EFFICIENTNETB1 MENGGUNAKAN METODE FINE-TUNING UNTUK KLASIFIKASI PNEUMONIA Daiyan Akbar Setiyadi; Sindhu Rakasiwi
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.v11i1.7273

Abstract

Early detection of pneumonia through chest X-ray images is a crucial step in medical treatment but is often hampered by class imbalance issues in datasets, leading to biased deep learning models. This research aims to conduct a holistic performance evaluation of two modern Convolutional Neural Network (CNN) architectures, ResNet50 and EfficientNetB1, to determine the optimal model under imbalanced data conditions. The methodology employed is transfer learning with an optimized two-phase fine-tuning protocol, supported by data augmentation techniques and class_weight strategies to address data imbalance. Evaluation was performed on the public "Chest X-Ray Images (Pneumonia)" dataset using accuracy, precision, recall, F1-score, and confusion matrix analysis. The results indicate that although ResNet50 achieved the highest total accuracy (89%) with low False Negatives (21 cases), the EfficientNetB1 model (87% accuracy) proved to be fundamentally more balanced. This superiority is demonstrated by a significant increase in the recall of the minority class (NORMAL) to 0.84, along with a 24% reduction in False Positive errors. This study concludes that a clinical trade-off exists where architecture selection must align with specific needs: ResNet50 for high-sensitivity screening, or EfficientNetB1 for prediction reliability and balance.
EXPERT SYSTEM FOR DETERMINING POULTRY FEED NUTRITION USING THE BACKWARD CHAINING METHOD Setiyowati; Hasman Budiadi; Sri Siswanti; Ahmad Muhariya
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.v11i1.7276

Abstract

Nutritional fulfillment in poultry feed is a critical factor in supporting optimal livestock growth, yet many farmers still face significant challenges due to limited knowledge regarding appropriate feed compositions for various growth phases. To address this gap, this study develops a web-based expert system designed to provide precise nutritional recommendations for various poultry types, including laying hens, ducks, quail, and broiler chickens. The system utilizes the Backward Chaining method as its inference engine, operating through a knowledge base structured with 13 nutritional goals and 8 distinct symptom/phase indicators. The system was subjected to rigorous evaluation through Black Box testing to ensure full functional integrity. Furthermore, diagnostic accuracy was assessed using 15 comparative test cases between the system's output and expert assessments. The results demonstrate that the expert system achieves an 80% accuracy rate in determining the optimal nutritional composition of poultry feed. This research concludes that the application of a backward chaining-based expert system can effectively assist both novice and experienced farmers in optimizing livestock productivity and reducing the risk of nutritional errors.
SKIN DISEASE CLASSIFICATION USING EFFICIENT TRANSFER LEARNING AND ATTENTION MECHANISM KURNIA ADI CAHYANTO; KUSWORO ADI; CATUR EDI WIDODO
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.v11i1.7278

Abstract

Skin diseases are a common health issue that is often underestimated, as most are mild and can be treated with over-the-counter medications. However, some types, such as melanoma, can be cancerous and deadly if not treated properly. Melanoma is caused by excessive exposure to ultraviolet rays and has a recovery rate of 99% if diagnosed on time, but it decreases to 20% in advanced stages. This study developed a multi-category skin disease classification model using transfer learning through a previously trained model such as EfficientNetV2S with Attention Mechanism to overcome overfitting and improve accuracy. The dataset used is ISIC2019 with 8 classes of skin diseases and 25,331 samples, after data augmentation was performed to increase the sample size. The EffCANet model showed a test accuracy of 94.81%, higher than previous studies, indicating a decrease in the overfitting gap and an improvement in test accuracy results.
IMPLEMENTASI METODE CERTAINTY FACTOR DALAM MENDIAGNOSIS HEAT STRESS PADA PEKERJA Nurul Hakim; Siska Anraeni; St. Hajrah Mansyur
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.v11i1.7280

Abstract

Heat stress is a significant health problem in tropical work environments such as Indonesia, which can have serious physical and cognitive effects on workers. This study aims to implement the Certainty Factor method in a web-based expert system to address the uncertainty of clinical symptoms in accurately diagnosing heat stress conditions. System knowledge was acquired from general practitioners covering 7 types of diseases and 35 clinical symptoms. The research methods included knowledge acquisition, knowledge modeling, method implementation, and accuracy testing. The evaluation was conducted by comparing the system's diagnosis results with 20 test cases validated by experts. The results showed that the system successfully identified 18 cases correctly and 2 cases incorrectly. Based on these results, this expert system has an accuracy rate of 90%. This accuracy achievement is competitive and in line with previous studies that implemented the Certainty Factor in other health domains. It is concluded that the implementation of this method is feasible and effective in providing an initial diagnosis of heat-related illnesses, thereby assisting healthcare workers and workers in high-risk environments in making appropriate treatment decisions.
PENERAPAN METODE CNN RESNET152 PADA PENGEMBANGAN APLIKASI VANILLATECH BERBASIS MOBILE UNTUK IDENTIFIKASI PENYAKIT TANAMAN VANILI Mush'ab Al Mubarak; Lilis Nur Hayati; St Hajrah Mansyur
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.v11i1.7285

Abstract

Vanilla is a high-value plantation commodity whose productivity is significantly affected by plant diseases that are difficult to identify accurately using conventional methods. This study aims to develop a mobile-based vanilla plant disease identification system using a Convolutional Neural Network (CNN) with the ResNet152 architecture. The dataset consists of primary field-acquired images, which were augmented to produce a total of 1,616 images across five disease classes. The model was trained using a transfer learning scheme with parameter adjustments designed to handle variations in field lighting conditions, image angles, and real-world visual characteristics. Experimental results demonstrate that the proposed ResNet152 model achieves high and stable classification accuracy. The integration of the trained model into a mobile application enables fast and practical disease diagnosis in real plantation environments. The novelty of this study lies in the field-oriented optimization of the ResNet152 model and its direct deployment in a mobile diagnostic system tailored for vanilla plant disease identification.

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