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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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ANALISIS PENGARUH INSTITUTIONAL SUPPORT TERHADAP JOB SATISFACTION MELALUI AI-ENHANCED INNOVATION MENGGUNAKAN STRUCTURAL EQUATION MODELING muhammad galih wonoseto; Imam Riadi; Rusydi Umar
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.v11i2.8120

Abstract

Lecturers are at the forefront of implementing Artificial Intelligence in higher education. Although research on AI adoption continues to grow, most studies are still dominated by the Technology Acceptance Model, which primarily focuses on technology acceptance. Research examining the role of AI-Enhanced Innovation as a mechanism linking institutional support and work-related outcomes remains limited. Moreover, previous studies have largely focused on primary and secondary school teachers, leaving the application of AI among university lecturers underexplored. Lecturers’ readiness to utilize AI is a critical factor in the digital transformation of higher education. This study aims to examine the effect of Institutional Support on Job Satisfaction through AI-Enhanced Innovation among university lecturers. Partial Least Squares Structural Equation Modeling was employed to analyze data collected from 32 lecturers representing 17 higher education institutions in Indonesia. The results indicate that Institutional Support has a positive and significant effect on AI-Enhanced Innovation (β = 0.411; p = 0.019), while AI-Enhanced Innovation has a positive and significant effect on Job Satisfaction (β = 0.569; p < 0.001). However, the direct effect of Institutional Support on Job Satisfaction is not significant (β = 0.137; p = 0.337). These findings suggest that institutional support does not directly enhance lecturers’ job satisfaction but does so indirectly by fostering AI-based innovation in teaching and learning. AI-Enhanced Innovation fully mediates the relationship between Institutional Support and Job Satisfaction. This study contributes to the literature by integrating Institutional Support, AI-Enhanced Innovation, and Job Satisfaction into a single research model.
EVALUASI SKEMA FUNGSI TERKOMPRESI PADA SMALL LANGUAGE MODEL MENGGUNAKAN QLORA PADA DOMAIN APARATUR SIPIL NEGARA Alfan Lily Armansyah; Imam Tahyudin; Azhari Shouni Barkah
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.v11i2.8124

Abstract

As government institutions undergo digitalization, demand has grown for intelligent assistants that can resolve personnel inquiries autonomously by means of function calling. Commercial large language models remain difficult to deploy in this setting owing to recurring subscription fees and potential exposure of confidential records, which positions locally hosted Small Language Models (SLMs) as a viable substitute. However, conventional verbose JSON schemas exhaust the context window and degrade the accuracy of small models. This study proposes the Compressed Function Schema (CFS), a concise notation replacing verbose JSON schemas, combined with QLoRA fine-tuning on the Qwen3.5-0.8B model for the personnel domain of the Indonesian Civil Service (ASN). Evaluation was conducted on 173 test samples under three controlled conditions, namely baseline (verbose schema, zero-shot), ablation (compressed schema, zero-shot), and proposed (compressed schema, fine-tuned), using Function Name Accuracy (FNA), Parameter Accuracy (PA), and Exact Match Accuracy (EMA) metrics. Results show that CFS reduces schema tokens from 1,739 to 302 tokens, or 5.76 times more compact, a significant reduction, while the proposed model achieves FNA of 95.95%, PA of 97.20%, and EMA of 91.03%, outperforming the baseline across all metrics. The ablation study reveals that the compressed schema does not function without fine-tuning, indicating that the two are mutually complementary. Accordingly, an 0.8-billion-parameter SLM can operate on a 16 GB commodity GPU with high accuracy for personnel information services.
DIGITAL TWIN-BASED SYSTEM FOR SMART MONITORING AND OPERATIONAL SIMULATION OF MSMES: SISTEM BERBASIS DIGITAL TWIN UNTUK MONITORING CERDAS DAN SIMULASI OPERASIONAL USAHA MIKRO, KECIL, DAN MENENGAH (UMKM) Muhammad Jufri; Agung Saputra; Kadek Jemmy Waciko; Sriwanti Belani; Abdurrahman Ridho; Deosa Putra Caniago; Mohd Rizki
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.v11i2.8127

Abstract

Usaha Mikro, Kecil, dan Menengah (UMKM) memiliki peran penting dalam mendukung pertumbuhan ekonomi dan ketahanan ekonomi daerah. Namun, banyak UMKM masih menghadapi permasalahan dalam monitoring operasional, pengelolaan inventori, serta analisis performa bisnis secara real-time akibat keterbatasan implementasi teknologi digital yang terintegrasi. Penelitian ini mengusulkan Digital Twin-Based System for Smart Monitoring and Operational Simulation of MSMEs untuk meningkatkan efisiensi operasional dan mendukung proses pengambilan keputusan bisnis yang lebih adaptif. Sistem yang diusulkan mengintegrasikan data operasional bisnis seperti kondisi inventori, transaksi penjualan, dan aktivitas usaha ke dalam lingkungan digital twin yang mampu merepresentasikan kondisi operasional secara dinamis dan real-time. Sistem dikembangkan menggunakan arsitektur berbasis web yang terintegrasi dengan dashboard smart monitoring dan modul simulasi operasional. Beberapa fitur utama yang diimplementasikan meliputi monitoring inventori, pengelolaan transaksi penjualan, visualisasi operasional, serta simulasi operasional bisnis. Pengujian sistem dilakukan menggunakan metode Black Box Testing dan User Acceptance Testing (UAT). Hasil pengujian menunjukkan bahwa seluruh fungsi utama sistem berjalan dengan baik sesuai kebutuhan sistem. Selain itu, hasil evaluasi UAT yang melibatkan pelaku dan staf operasional UMKM memperoleh rata-rata skor kepuasan sebesar 4,46 dari skala 5 dengan tingkat penerimaan sistem sebesar 89,2% yang termasuk dalam kategori Very Good. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu meningkatkan visibilitas operasional, mempermudah proses monitoring, dan mendukung analisis operasional yang lebih adaptif pada lingkungan UMKM.
IMPLEMENTASI ALGORITMA SUPPORT VECTOR MACHINE (SVM) UNTUK MENDETEKSI PENYAKIT PADA DAUN KELAPA SAWIT : IMPLEMENTATION OF SUPPORT VECTOR MACHINE (SVM) ALGORITHM FOR DETECTING DISEASES IN OIL PALM LEAVES Bintang Ananta
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.v11i2.8128

Abstract

This study aims to implement the Support Vector Machine (SVM) algorithm for classifying oil palm leaf conditions using digital image processing. Accurate and timely identification of leaf conditions is essential for maintaining seedling quality and supporting oil palm productivity. However, manual observation methods are often limited by subjectivity and differences in observer experience. The dataset used in this study consisted of oil palm leaf images representing three conditions: healthy leaves, yellow leaves, and spotted leaves. The research process included image preprocessing, color and texture feature extraction, and classification using the SVM algorithm. The dataset was divided into training and testing sets to evaluate the model's performance. The experimental results showed that the SVM model achieved an accuracy of 91.53% in classifying oil palm leaf conditions. These findings indicate that the SVM algorithm has strong potential as an effective approach for supporting the automatic identification of oil palm leaf conditions based on digital image processing.
IMPLEMENTASI KONTROL PEMOTRETAN PHOTOBOOTH BERBASIS IoT MENGGUNAKAN DETEKSI SUARA DAN VALIDASI FRAME DENGAN METODE FUZZY: IMPLEMENTATION OF IOT-BASED PHOTOBOOTH SHOOTING CONTROL USING VOICE DETECTION AND FRAME VALIDATION WITH FUZZY METHOD Adithya Putra; Suroso; Irma Salamah
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.v11i2.8138

Abstract

The development of the Internet of Things (IoT) has encouraged the implementation of automation systems in various fields, including photobooth services. This study aims to design an automatic photo capture control system for Deebooth Photobooth by integrating a sound sensor, OpenCV, and the Fuzzy Mamdani method. An ESP32 microcontroller is used to acquire sound intensity data, while OpenCV with the Haar Cascade method is employed to detect human objects within the photo capture area. Sound intensity data and frame validation results are processed using Fuzzy Mamdani through fuzzification, inference, and defuzzification stages to generate photo capture decisions. The results show that the sound sensor can distinguish sound intensity levels under different environmental conditions, while OpenCV successfully detects human objects in real time. The implementation of Fuzzy Mamdani produces three output categories: No Capture, Standby, and Capture. The system performs automatic photo capture only when a human object is detected and the sound intensity satisfies the predefined fuzzy rules. The proposed system improves the flexibility and reliability of automatic photo capture in IoT-based photobooth applications.
ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI GOJEK MENGGUNAKAN METODE RANDOM FOREST, SVM, DAN LOGISTIC REGRESSION Anugrah Ramadhani; Fikri Budiman
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.v11i2.8141

Abstract

The development of digital applications such as Gojek has generated a wide range of user reviews that can be used to assess service quality and user satisfaction. However, the large volume and unstructured nature of these reviews make manual sentiment identification inefficient, which encourages the use of sentiment analysis techniques to automatically process user opinions. This study compares the performance of Random Forest, Support Vector Machine (SVM), and Logistic Regression in analyzing opinions from Gojek user reviews. The dataset consists of 8,091 Indonesian-language reviews obtained from Kaggle. The research process includes data preprocessing such as cleaning, removing irrelevant words, and stemming using the Sastrawi library. The reviews are then converted into numerical features using the TF-IDF method. The dataset is split into 80% training data and 20% testing data before classification using the three algorithms. Model performance is evaluated using accuracy, precision, recall, and F1-score. The results show that Logistic Regression achieves the highest accuracy of 91.84%, followed by SVM with 89.99% and Random Forest with 86.09%. In addition, Logistic Regression shows more balanced performance across positive, negative, and neutral sentiment classes based on precision, recall, and F1-score. Based on the results, Logistic Regression is identified as the most effective method for sentiment analysis of Gojek user reviews in this study.  
ANALISIS KOMPARATIF ARSITEKTUR MOBILENETV3SMALL DAN EFFICIENTNETV2S PADA CITRA DIGITAL PENYAKIT DAUN TANAMAN Abito Setyaji; Sri Winarno
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.v11i2.8142

Abstract

Plant leaf diseases are one of the major factors that reduce agricultural productivity, making fast and accurate identification methods essential. This study aims to compare the performance of two lightweight deep learning architectures, MobileNetV3 Small and EfficientNetV2S, for plant leaf disease identification using digital images. The dataset used was the Plant_leaf_diseases_dataset_without_augmentation obtained from Mendeley Data. The research workflow consisted of image preprocessing, data augmentation, model training with hyperparameter configuration, fine-tuning, and performance evaluation using TensorFlow based on accuracy, precision, recall, F1-score, training time, model size, and inference time. The results showed that EfficientNetV2S achieved the best classification performance, with an accuracy of 97.66%, a precision of 97.80%, a recall of 97.66%, and an F1-score of 97.67%. Meanwhile, MobileNetV3 Small achieved an accuracy of 96.40%, with a model size of 9.96 MB, an inference time of 1.46 ms, and a frame rate of 686.95 FPSse findings indicate that the two models have different strengths: EfficientNetV2S excels in classification accuracy, whereas MobileNetV3 Small offers superior computational efficiency.
TINJAUAN LITERATUR SISTEMATIS TERHADAP DETEKSI NYERI DENGAN MEMANFAATKAN TEKNOLOGI NATURAL LANGUAGE PROCESSING Hari Rejeki Ginting; Andrian Reinaldo Crispin
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.v11i2.8149

Abstract

Pain detection is an important aspect in healthcare services, especially for patients who have difficulty communicating verbally, such as infants, critically ill patients, and individuals with neurological disorders. Advances in Natural Language Processing (NLP) have enabled automated analysis of medical text to support pain assessment more objectively and efficiently. This study aims to analyze the development of NLP methods used in pain detection through a Systematic Literature Review (SLR) approach. The study adopted the PRISMA guideline to identify, screen, and evaluate relevant articles obtained from Google Scholar and Scopus databases. The reviewed studies were published between 2017 and 2025. Based on the PRISMA selection process, 173 final articles met the inclusion criteria and were included in the final analysis. The results indicate that transformer-based models such as BERT, BioBERT, and ClinicalBERT achieved better performance compared to traditional machine learning and conventional deep learning methods. In addition, Electronic Health Records (EHR), clinical notes, and patient reports were identified as the most frequently used datasets in pain detection research. However, several challenges remain, including limited dataset availability, lack of evaluation standardization, and high computational requirements. This study is expected to provide a comprehensive overview of NLP-based pain detection and support future research in intelligent healthcare systems.
SISTEM KINERJA DAN KEPEGAWAIAN MENGGUNAKAN MULTI FACTOR EVALUATION PROCESS (STUDI KASUS: SMK MA'ARIF 1 TEMON YOGYAKARTA) Agus Sidiq Purnomo; Anief Fauzan Rozi; Dina Yulina Heriyani
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.v11i2.8150

Abstract

A personnel information system is an important requirement for educational institutions in managing employee data digitally. SMK Ma'arif 1 Temon Yogyakarta currently does not have an integrated personnel information system (SIMPEG), so the process of recruitment, attendance, employee profiles, leave applications, career paths, and performance appraisal are still done manually. This study aims to produce a prototype of a personnel information system and to apply the Multi Factor Evaluation Process (MFEP) method for employee performance assessment. The system development method uses the Waterfall Model with assessment criteria including years of service, education level, position, rank, and additional duties. The results showed that the system was able to perform MFEP calculations properly, producing the highest ranking for Alternative A22 with a score of 4.30. System functionality testing shows all features run well. This prototype can be a solution for digitalization of personnel management at SMK Ma'arif 1 Temon.
SIMULASI DAN VALIDASI MPPT-FUZZY LOGIC PADA WECS SKALA MIKRO UNTUK APLIKASI MEDICAL COLD CHAIN Lailatul Inayah
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.v11i2.8153

Abstract

The availability of reliable energy is an important for maintaining the continuity of the medical cold chain, particularly for the storage of vaccines and other medical products that require continuous temperature control. Limited access to electricity and frequent power outages in certain regions highlight the need for sustainable alternative energy sources. This study aims to develop a micro-scale WECS based on a PMSG and a Fuzzy Logic-based MPPT controller to support the energy requirements of medical cold chain systems. The proposed system was modeled and simulated in MATLAB/Simulink and consisted of a wind turbine, a PMSG, a three-phase rectifier, a boost converter, and an MPPT-Fuzzy Logic controller. Wind speed variations were applied as system inputs to evaluate performance in terms of turbine aerodynamic characteristics, power coefficient (),), tip speed ratio (TSR), output power, and energy generation. Model validation was conducted by comparing the simulated output power with the theoretical power of the wind turbine. The simulation results indicate that the turbine achieved a maximum power coefficient of 0.44 at the optimum TSR ( ≈ 6). The MPPT-Fuzzy Logic controller successfully maintained turbine operation near the maximum power point, increasing the generated energy from 0.384 kWh to 0.552 kWh, representing an improvement of 43.75% compared with the system operating without MPPT. However, the system was only able to satisfy 47.9% of the energy demand required by the medical cold chain. These findings demonstrate that the proposed WECS with MPPT-Fuzzy Logic has significant potential as a renewable energy solution for healthcare facilities located in areas with limited access to electricity.  

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