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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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RANCANG BANGUN SMART GREENHOUSE OTOMATIS BERBASIS INTERNET OF THINGS DENGAN KONTROL RULE-BASED UNTUK OPTIMALISASI PERTUMBUHAN TANAMAN TOMAT: DESIGN AND CONSTRUCTION OF AN AUTOMATIC SMART GREENHOUSE BASED ON THE INTERNET OF THINGS WITH RULE-BASED CONTROL FOR OPTIMIZING TOMATO PLANT GROWTH Muhammad Zikri; Muhammad Fikry; 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.v10i2.6435

Abstract

This study aims to develop and implement an automated greenhouse system based on the Internet of Things (IoT) to monitor and analyze environmental conditions, including temperature, humidity, and light intensity. The system utilizes an ESP32 microcontroller integrated with a DHT22 sensor, soil moisture sensor, and LDR light sensor. Sensor data is transmitted in real time to the Blynk application, enabling users to monitor and control the system remotely via smartphone. The system uses a rule-based approach, such as activating a fan when the temperature exceeds 32°C, activating a water pump when soil moisture is below 30%, and turning on lights when light intensity is low. The experiment was conducted using a laboratory-scale prototype with kangkung plants. The system demonstrated its ability to provide timely and accurate environmental updates, enhancing greenhouse management efficiency. This system shows potential to support optimal plant growth while conserving energy.
CLUSTERING TINGKAT KECANDUAN GAME MOBILE LEGENDS TERHADAP KEHARMONISAN KELUARGA MENGGUNAKAN METODE K-MEANS Muhammad Fadhil; Wahyu Fuadi; Maryana
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.v10i2.6439

Abstract

Penelitian ini bertujuan untuk menganalisis tingkat kecanduan game Mobile Legends dan dampaknya terhadap keharmonisan keluarga menggunakan pendekatan machine learning dengan algoritma K-Means Clustering. Metode penelitian menggunakan pendekatan kuantitatif dengan mengumpulkan data dari 297 responden melalui kuesioner yang mencakup 10 variabel, terdiri dari 5 variabel addiction dan 5 variabel keharmonisan. Data yang terkumpul kemudian diproses menggunakan preprocessing dengan teknik encoding dan scaling, selanjutnya dianalisis menggunakan algoritma K-Means Clustering dengan optimasi jumlah cluster melalui kombinasi Elbow Method, Silhouette Analysis, dan Davies-Bouldin Index. Hasil penelitian menunjukkan bahwa algoritma K-Means berhasil mengidentifikasi tiga cluster optimal (K=3) dengan kualitas clustering yang memadai, ditunjukkan oleh Davies-Bouldin Index sebesar 1.580, Silhouette Score 0.233, dan Inertia 2089. Distribusi cluster menunjukkan bahwa 60.3% responden berada dalam kategori kecanduan ringan dengan keharmonisan tinggi (Cluster 1), 32.7% dalam kategori sedang-sedang (Cluster 0), dan 7.1% dalam kategori kecanduan berat dengan keharmonisan rendah (Cluster 2). Temuan utama penelitian mengkonfirmasi hipotesis adanya hubungan invers yang signifikan antara tingkat kecanduan game Mobile Legends dengan keharmonisan keluarga, dimana semakin tinggi tingkat kecanduan semakin rendah keharmonisan keluarga. Principal Component Analysis menunjukkan bahwa dua komponen utama mampu menjelaskan 60% varians data, memberikan validasi visual terhadap hasil clustering. Penelitian ini memberikan kontribusi penting dalam memahami dampak psikologis gaming addiction terhadap dinamika keluarga dan dapat menjadi dasar pengembangan strategi intervensi yang tepat sasaran untuk meningkatkan keharmonisan keluarga.
PERBANDINGAN ALGORITMA MACHINE LEARNING: SVM, RANDOM FOREST, DAN XGBOOST UNTUK PREDIKSI STROKE: COMPARISON OF MACHINE LEARNING ALGORITHMS: SVM, RANDOM FOREST, AND XGBOOST FOR STROKE PREDICTION Hanifah Afkar Nabila; Endang Wahyu Pamungkas
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.v10i2.6444

Abstract

Stroke is a leading cause of death and disability worldwide, making early detection essential. This study compares three machine learning algorithms Support Vector Machine (SVM), Random Forest, and XGBoost for stroke prediction. The dataset includes Kaggle data for training and clinical data from Indonesian primary healthcare (Puskesmas) for external validation. Pre-processing involved handling missing values, encoding categorical features, normalization, and balancing using SMOTE. Performance was evaluated using accuracy, precision, recall, F1-score, AUC-ROC and AUC-PRC. Unlike most prior studies, this research incorporates clinical data to assess generalizability in real-world settings. Results show that Random Forest and XGBoost outperform SVM, especially with clinical data. This study contributes a practical perspective by validating models using local datasets and emphasizes the importance of robust algorithms and external validation in medical prediction systems.
ANALISIS SENTIMEN REVIEW APLIKASI STOCKBIT DI GOOGLE PLAY STORE DAN X(TWITTER) MENGGUNAKAN SUPPORT VECTOR MACHINE: SENTIMENT ANALYSIS OF STOCKBIT APPLICATION REVIEWS ON GOOGLE PLAY STORE AND X (TWITTER) USING SUPPORT VECTOR MACHINE Yusril; Wahyu Fuadi; 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.v10i2.6446

Abstract

This study examines sentiment analysis of user reviews of the Stockbit application obtained from the Google Play Store and platform X (formerly Twitter). The aim of this research is to classify user opinions into two sentiment categories: positive and negative, using the Support Vector Machine (SVM) method. A total of 3,000 review data points were used in this study, consisting of 2,100 training data points and 900 test data points (stratified split with a 70:30 ratio) to ensure balanced sentiment distribution. The research process includes text preprocessing, feature weighting using Term Frequency-Inverse Document Frequency (TF-IDF), sentiment classification with the SVM algorithm, and model performance evaluation. Based on the evaluation results, the SVM model demonstrated high performance with an accuracy of 95.5%, precision of 93.5%, recall of 97.4%, and an F1-score of 95.3%. Although its accuracy is lower than that of Maulana et al.'s (2024) study, which achieved 99.50% on the Pluang application, this research excels in using data from two different platforms and evaluating class imbalance, making the analysis results more representative of real-world conditions. These findings indicate that SVM remains an effective method for text-based sentiment analysis in digital financial service applications.
KLASIFIKASI KUALITAS UDARA DENGAN METODE NAIVE BAYES BERBASIS WEB: AIR QUALITY CLASSIFICATION USING WEB-BASED NAIVE BAYES METHOD Sugeng Dwi Budi Priantoro; M Ghofar Rohman; Moh Rosidi Zamroni
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.v10i2.6447

Abstract

Air quality is a critical indicator for public health and the environment. This study presents the first web‑based implementation for classifying the Air Pollutant Standard Index (ISPU) of Jakarta using the 2024 dataset from Satu Data Indonesia. The Gaussian Naive Bayes method was chosen for its efficiency and ability to handle continuous numerical data. Preprocessing steps included mean imputation, removal of “no data” and “very unhealthy” categories, and a random state 80:20 train‑test split. Evaluation results show 90.57% accuracy surpassing the KNN baseline of 86% with precision and recall F1‑scores for the “Unhealthy” category at 84.09% and 92.50%, respectively. A Flask‑based web application air quality prediction. These findings confirm the superiority of Gaussian Naive Bayes over KNN in handling data imbalance, while providing an accurate, accessible environmental monitoring tool. Contributions of this research include (1) the first deployment of ISPU Jakarta 2024 in a web system, and (2) a measured performance comparison between GNB and KNN.
PENERAPAN DECISION TREE C5.0 DALAM APLIKASI ANALISIS SENTIMEN TERHADAP BOIKOT PRODUK PRO-ISRAEL DI MEDIA SOSIAL X: SENTIMENT CLASSIFICATION ON BOYCOTT-RELATED TWEETS USING C5.0 DECISION TREE ALGORITHM Juliar Husriansyah; Asrianda Asrianda; 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.v10i2.6451

Abstract

The movement to boycott products believed to support Israel reflects global solidarity with the Palestinian fight. In Indonesia, support for this movement continues to grow, especially through social media platform X (formerly Twitter). After the release of MUI Fatwa Number 83 of 2023, which advises Muslims to refrain from using products linked to Israel. The objective of this study is to analyze the sentiment of users on social media X regarding the boycott of products that support Israel, using the Decision Tree C5.0 algorithm. The data were collected through a scraping technique targeting tweets containing relevant boycott-related keywords, then processed using text preprocessing and analyzed using Term Frequency-Inverse Document Frequency (TF-IDF) for the extraction of features. The dataset was divided into 80% for training and 20% for testing in order to train and assess the classification model. The classification results revealed that out of 1,840 tweets, 1,257 were positive, 318 negative, and 265 neutral, indicating that 68.32% of users expressed support for the boycott movement. The evaluation of the model resulted in an accuracy of 83.26%, a precision of 86.51%, a recall of 83.26%, and an f1-score of 84.29%, demonstrating that the C5.0 algorithm effectively and accurately classifies sentiment. This research is anticipated to act as a guide for creating systems that analyze public opinion and provide insights for policymakers and industry players in responding to social issues emerging on digital platforms.
PENENTUAN LOKASI KAFE UNTUK MAHASISWA TEKNIK UNIVERSITAS MALIKUSSALEH MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING: DETERMINATION OF CAFE LOCATION FOR ENGINEERING STUDENTS OF MALIKUSSALEH UNIVERSITY USING THE SIMPLE ADDITIVE WEIGHTING METHOD Ahmad Fajrul Amin; Munirul Ula; 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.v10i2.6453

Abstract

Engineering students at Universitas Malikussaleh require a comfortable, affordable, and academically supportive space for gathering, discussion, and study. However, financial limitationsparticularly among recipients of the Kartu Indonesia Pintar (KIP) program highlight the importance of location and pricing in café selection. This study aims to determine the optimal location for a student café using the Simple Additive Weighting (SAW) method as a Decision Support System. SAW is chosen for its effectiveness in processing multi-criteria decisions by applying specific weights to each criterion. The research was conducted within the Faculty of Engineering at Universitas Malikussaleh, using primary data collected through interviews and direct observation of potential café locations. Evaluation criteria include menu prices, comfort, service quality, distance to campus, and supporting facilities. The SAW method was applied to rank the alternatives, with the highest score representing the most suitable location for students' needs. A web-based application was developed using PHP and MySQL to implement the system, and functionality was validated using both black-box and white-box testing methods. This study offers a data-driven solution for optimizing campus facilities and provides a reference model for other institutions seeking to develop inclusive and strategic student service locations.
PREDIKSI PRODUKSI MINYAK MENTAH KELAPA SAWIT PT. BAKRIE PASAMAN PLANTATION MENGGUNAKAN METODE EXTREME LEARNING MACHINE (STUDI KASUS: DATA 2023-2024, SUMATERA BARAT) Reza Pratama; Dahlan Abdullah; Zara Yunizar
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.v10i2.6456

Abstract

The production of crude palm oil (CPO) in Indonesia experiences fluctuations influenced by various factors such as rainfall, number of rainy days, and the quantity of fresh fruit bunches (FFB). This study aims to develop a predictive model for estimating crude palm oil production using the Extreme Learning Machine (ELM) method, applied to production data from PT. Bakrie Pasaman Plantations in West Sumatra. ELM was chosen due to its fast learning capability and high accuracy in non-linear regression tasks. The study utilizes historical production data from the past two years. The research process involves data normalization, model training, testing, and performance evaluation using the Mean Absolute Percentage Error (MAPE). The results show that the developed model achieves a good level of accuracy with a MAPE value of 12.07%, which is considered reliable. The predictive model is also implemented as a web-based application that displays forecast results and comparative graphs between actual and predicted data. It is expected that this system can support more effective and efficient production planning.
PENGEMBANGAN APLIKASI AUGMENTED REALITY UNTUK PEMBELAJARAN BIOLOGI MENGGUNAKAN MARKER BASED TRACKING PADA PLATFORM ANDROID Teguh Brahmana; Taufiq; 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.v10i2.6460

Abstract

The development of Augmented Reality (AR) technology offers significant opportunities for creating interactive learning media, especially for abstract Biology topics. This study aims to develop an AR-based learning application using the marker-based tracking method on the Android platform. The developed application, named BioAR, presents a three-dimensional model of animal cell structures that can be displayed through the device’s camera with the help of markers. The development method used in this application is the Waterfall model, which consists of several stages: literature review, data collection, system design, application development, and testing. The 3D models of the organelles are created using Blender, then integrated into Unity with the use of Vuforia SDK. The application is tested using the black-box testing method, focusing on the functionality of navigation buttons, the distance between the camera and marker, light intensity, marker type, and testing on various Android devices with different specifications. The testing results show that the application performs well according to the designed functions. The application successfully displays 3D objects in real-time at an ideal distance between the camera and marker, ranging from 20–30 cm, with adequate lighting. Devices with at least 3 GB of RAM and high-quality cameras provide optimal results. This application is expected to serve as an alternative interactive learning media, easily accessible, and help visualize Biology material in a more engaging way.
PENGEMBANGAN APLIKASI SURVEI ANDROID TERENKRIPSI DENGAN VALIDASI LOKASI UNTUK ASESMEN PSIKOLOGIS: DEVELOPMENT OF AN ENCRYPTED ANDROID SURVEY APPLICATION WITH LOCATION VALIDATION FOR PSYCHOLOGICAL ASSESSMENT Nanda Nan Arif H; Muhammad Fikry; Zara Yunizar
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.v10i2.6461

Abstract

Digital data collection in psychological contexts requires systems that are not only efficient but also secure and context-aware. This study develops an Android-based survey application with layered security by integrating GPS-based location validation and AES-128 encryption. The system restricts access to predefined geographic areas, and all user data is encrypted exclusively on the server side (Laravel backend) to ensure confidentiality and integrity. Indoor testing of location validation showed high accuracy with a 50-meter tolerance and an average response time of 50 ms. Encryption was performed in 40–110 ms for data up to 19.7 KB. Communication between the mobile app and the server uses a REST API secured with HTTPS. The application was developed using the Waterfall model, which fits the structured and pre-defined system requirements. This study’s novelty lies in combining real-time location validation and server-side encryption in a single system—an approach often handled separately in prior research. Black-box testing confirmed that the system performs securely and reliably without burdening the device. This solution is suitable for mobile-based psychological assessments in controlled environments such as schools and is adaptable for other services requiring simultaneous data protection and location verification.

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