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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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PERANCANGAN UI/UX SISTEM INFORMASI CERDAS UNTUK MONITORING PERTUMBUHAN DAN PREDIKSI PANEN MANGGA BERBASIS WEB: UI/UX Design of an Intelligent Information System for Web-Based Mango Growth Monitoring and Harvest Prediction Dian Pramadhana; Darsih; Riyan Farismana
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.v11i2.8229

Abstract

Mango is one of Indonesia's leading horticultural commodities with high economic value and significant potential to support the productivity of the agricultural sector. However, monitoring plant growth, recording plant development, and scheduling harvest activities in most mango plantations are still carried out manually, resulting in fragmented information and increasing the risk of inaccurate harvest timing. This study aims to design the User Interface (UI) and User Experience (UX) of a web-based Intelligent Information System for Mango Growth Monitoring and Harvest Prediction using the Design Thinking method. The proposed method consists of five stages, namely Empathize, Define, Ideate, Prototype, and Testing, which emphasize a user-centered approach to developing an intuitive and user-friendly interface. The results of this study produced a web-based prototype consisting of five main interfaces: Dashboard, Plant Monitoring, Harvest Prediction, Harvest Calendar, and Reports. Each interface was designed to support plant growth monitoring, harvest prediction, harvest schedule management, and integrated reporting of harvest activities. The proposed UI/UX design is expected to serve as a reference for developing a more effective mango monitoring and harvest prediction information system, improve system usability, and support decision-making in mango plantation management.
STUDENT PERCEPTIONS OF CHATGPT FOR STATISTIC LEARNING: A TECHNOLOGY ACCEPTANCE MODEL (TAM) APPROACH: ANALISIS PERSEPSI MAHASISWA TERHADAP CHATGPT DALAM PEMBELAJARAN STATISTIKA: PENDEKATAN TECHNOLOGY ACCEPTANCE MODEL (TAM) Siva Nur Samrotissaadah -; Kemas Muslim Lhaksmana
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.v11i2.8240

Abstract

Many students consider statistics one of the difficult subjects to learn throughout their academic period because of many complex theories and statistical analysis methodologies, which can increase anxiety in the learning process. Generative Artificial Intelligence gives birth to ChatGPT, which provides interesting descriptions and allows independent learning. Nevertheless, not enough literature is found regarding the use of ChatGPT in the context of statistics learning, particularly in Indonesia. In this study, we aim to analyze the perceptions of using ChatGPT as a tool in statistics education through Technology Acceptance Model (TAM). A survey with a quantitative approach was conducted with 203 statistics learners (103 females, 100 males) who have already used ChatGPT. To obtain the data, Likert scale questionnaire was used, and PLS-SEM was applied in analyzing data through five constructs of Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude Toward Using (AT), Behavioral Intention (BI), and Actual Use (AU). It was found that all five hypotheses were confirmed, and all relationships between constructs of TAM were positive and significant. Particularly, PEOU significantly affected PU (β = 0.347) and AT (β = 0.255), while AT had the highest impact on BI (β = 0.330). These findings indicate that ChatGPT is perceived as an assisting technological tool for learning statistics, where usability and attitudes of users become very crucial factors.
REKAYASA ULANG PERANGKAT LUNAK MENGGUNAKAN ITERATIF PADA WEBSITE SMART VILLAGE KABUPATEN SAMPANG: SOFTWARE REENGINEERING USING ITERATIVES ON THE SMART VILLAGE WEBSITE OF SAMPANG DISTRICT Almas'Udi; Wildan Suharso
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.v11i2.8247

Abstract

Digital transformation through Smart Villages aims to improve the effectiveness of village governance. However, the implementation of the Smart Village Website in Sampang Regency previously faced technical constraints, such as an unresponsive user interface, suboptimal database structure, slow processing, and sub-optimal features. This study aims to improve service quality and system efficiency through software reengineering using an iterative approach. The stages include legacy system analysis, data classification, database and function redesign, component adaptation, data migration, equivalence testing (Black-Box Testing), and residual data clearance. The results show that reengineering successfully restructured the database by removing irrelevant attributes and residual data. Redesigning functions in main components resulted in a more interactive interface. Performance testing indicates a significant increase in page loading speed, averaging 71% to 73% across all main modules. In conclusion, the iterative reengineering method is effective in producing a more stable, efficient, and significantly better-performing system than the legacy system.
DATA MINING UNTUK PENGAMBILAN KEPUTUSAN DAN KEBIJAKAN KESEHATAN MASYARAKAT: NARRATIVE REVIEW Dela Riadi; Dewi Nirmala Sari
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.v11i2.8251

Abstract

The increasing availability of large-scale health data has created opportunities for using data mining to support evidence-based decision-making and public health policy development. This narrative review aims to describe the role of data mining in identifying public health priorities, supporting healthcare decision-making, facilitating program planning and evaluation, and strengthening evidence-based health policies. Relevant literature published between 2024 and 2026 was identified through searches of the Scopus and PubMed databases using keywords related to data mining, machine learning, predictive analytics, health policy, decision-making, and public health. Eligible articles were selected based on their relevance and synthesized narratively. The findings indicate that data mining techniques, including machine learning, predictive analytics, classification, clustering, and knowledge discovery, are widely used to identify disease patterns, high-risk populations, healthcare needs, and to support resource allocation and program evaluation. These approaches improve the targeting of public health interventions and strengthen evidence-based policy development. However, challenges related to data quality, interoperability, privacy protection, and model interpretability remain. Strengthening data governance and analytical capacity is essential to optimize the use of data mining in public health.
LIGHTGBM VS CATBOOST FOR SMART BUILDING ENERGY FORECASTING: A TIME-SERIES CROSS-VALIDATION APPROACH Ryan Wahyu Pratama; Devi Ajeng Efrilianda
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.v11i2.8257

Abstract

Electrical energy consumption in the smart building sector continues to rise with the growth of urbanization and IoT adoption. Accurate electrical load forecasting is crucial for efficient and sustainable energy management. This study aims to analyze and compar the performance of two modern gradient boosting algorithms, LightGBM and CatBoost, in predicting hourly electricity consumption at smart building network scale. The dataset used is the PJME Hourly Energy Consumption dataset, comprising 145,198 hourly observations recorded over a 16-year period from 2002 to 2018. The methodology involves temporal feature engineering through the extraction of calendar components (hour, day_of_week, month, quarter, day_of_year, is_weekend) and lag features (lag_1h, lag_24h, lag_1week) to equip the models with historical pattern memory. Model validation was conducted using a chronological 80:20 data split and Time-Series Cross-Validation (k=5) to ensure robust generalization. Experimental results demonstrate that LightGBM outperforms across all evaluation metrics with RMSE = 417.11, MAE = 312.72, MAPE = 0.99%, and R² = 0.9959, while also being computationally more efficient with a training time of 2.00 seconds compared to CatBoost at 4.97 seconds. Both models surpassed the R² accuracy threshold of 99%, validating the effectiveness of the proposed time-series-based feature engineering framework. This research contributes a reliable and efficient energy prediction framework for the implementation of energy management systems in smart buildings.  
PENERAPAN WEIGHTED K-MEANS DAN HEATMAP OVERLAY UNTUK REKOMENDASI LOKASI STRATEGIS UMKM KOPI KELILING: IMPLEMENTATION OF WEIGHTED K-MEANS AND HEATMAP OVERLAY FOR STRATEGIC LOCATION RECOMMENDATION OF MOBILE COFFEE MSMES Ridho Alif Naufaldy; Asep Wahyudin; Herbert Siregar
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.v11i2.8258

Abstract

Determining the operational routes for MSME mobile coffee vendors currently relies on subjective intuition, leading to time inefficiencies and income fluctuations. This study designs a mobile-based Decision Support System (DSS) using a Prototyping approach to provide objective strategic location recommendations. The system processes 10,961 historical transactions using the Weighted K-Means algorithm. Cluster center determination is modified using the center of mass principle based on profit and sales quantity weights, then visualized through a heatmap overlay. Functional testing using the Black Box method confirmed that all application parameters are valid. Field trials empirically demonstrated the system's effectiveness through a 16.6% surge in the fleet's average daily net profit (from Rp1,654,254 to Rp1,929,709). In conclusion, this DSS has proved to be successful in transforming conventional route determination into a data-driven precision strategy to boost MSME profitability.
ANALISIS SENTIMEN ULASAN HOTEL TRIPADVISOR MENGGUNAKAN TF-IDF DAN MACHINE LEARNING: PERBANDINGAN KINERJA SUPPORT VECTOR MACHINE DAN LOGISTIC REGRESSION Muhammad Fadli; Yunita Fitri Yanti; Tina Nurzachra Latifah Rizki Liana; Rifka Simbolon; Ratu Sinar Sari Tanjung; Hadori Rosadi; Budi Rahman
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.v11i2.8261

Abstract

Online reviews on platforms like TripAdvisor play a crucial role in consumer decision making and hotel reputation management. However, the massive and unstructured volume of data requires a fast and accurate automated analysis method. This study aims to analyze the sentiment of TripAdvisor hotel reviews and compare the performance of the Support Vector Machine (SVM) and Logistic Regression (LR) algorithms. The dataset consists of 20,491 reviews classified into three sentiment classes: positive, negative, and neutral. Feature extraction was performed using the Term Frequency-Inverse Document Frequency (TF-IDF) method. To handle the class imbalance issue and ensure robust evaluation, the models were evaluated using Stratified 10-Fold Cross-Validation. The results indicate that Logistic Regression outperforms Support Vector Machine, achieving an average accuracy of 81.46% and an F1-Score of 83.02%, compared to Support Vector Machine which obtained an accuracy of 80.41% and an F1-Score of 82.20%. Both models showed excellent performance on the positive class but faced challenges on the neutral class due to semantic ambiguity and majority class dominance. In conclusion, Logistic Regression proves to be a more optimal, stable, and efficient model for sentiment classification on imbalanced data, making it highly applicable for assisting hotel management in monitoring customer feedback in real time.
SISTEM MONITORING IOT BOBOT MUATAN DAN LIVE TRACKING PADA KENDARAAN PENGANGKUT TBS BERBASIS MQTT-NEXTJS Muhammad Hatta Ridho; Andi Prayogi; Ratu Mutiara Siregar
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.v11i2.8264

Abstract

The development of Internet of Things (IoT) technology provides opportunities for to improve the efficiency of Fresh Fruit Bunches (FFB) transportation in oil palm plantations. Common challenges include difficulties in monitoring vehicle locations in real time and recording load weights manually. This study aims to design and develop a load weight monitoring and live tracking system for an FFB transportation vehicle prototype using an ESP32 microcontroller, an HX711 Load Cell sensor, a Neo-6M GPS module, the Message Queuing Telemetry Transport (MQTT) protocol, and a NextJS-based monitoring dashboard. The research methodology included hardware design, software development, system integration, and functional testing. The Load Cell sensor measured the load weight, while the GPS module obtained real-time vehicle coordinates. The ESP32 processed the collected data and transmitted them via MQTT to a broker and database for visualization on the dashboard. The test results showed that the system successfully monitored load weight and tracked the prototype vehicle in real time, achieving a 100% data transmission success rate under normal network conditions. The developed system provides effective, accurate, and integrated transportation monitoring, improving the efficiency of FFB transportation management in oil palm plantations.
PEMETAAN TIPOLOGI PEMBANGUNAN WILAYAH DI KABUPATEN BOYOLALI MENGGUNAKAN ANALISIS KLASTER UNTUK MENDUKUNG PERENCANAAN BERBASIS DATA Pramudya Amara Setyaningtyas; Devi Afriyantari Puspa Putri
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.v11i2.8266

Abstract

Equitable regional development requires policies that match the characteristics of each region; however, to date there has been no comprehensive mapping that quantitatively classifies regional typologies in Boyolali Regency. This study aims to apply cluster analysis to map regional development typologies across 267 villages in 22 sub-districts of Boyolali Regency. Annual demographic data sourced from the Central Statistics Agency (BPS) were processed through data cleaning, feature engineering, and standardization, then modeled using the K-Means clustering algorithm. The optimal number of clusters was determined using the elbow method, and the quality of the results was evaluated using the Silhouette Score. The results show four regional typologies with a silhouette score of 0.2116: Perkotaan Mapan (78 villages), Perdesaan Luas Agraris (52 villages), Desa Berkembang (76 villages), and Desa Taat Pajak (61 villages). The mapping results were implemented as a website-based information system in the form of an interactive map displaying the cluster distribution of each village. User Acceptance Testing (UAT) with 13 prospective users obtained a score of 71.21%, categorized as Feasible. This system can serve as a strategic tool for local governments in formulating more targeted development policies tailored to the specific needs of each region.  
Mrs les endahti ANALISIS PENGARUH SMOTE TERHADAP BIAS KEPUTUSAN MODEL MACHINE LEARNING PADA PREDIKSI DROPOUT MAHASISWA: ANALISIS PENGARUH SMOTE TERHADAP BIAS KEPUTUSAN MODEL MACHINE LEARNING PADA PREDIKSI DROPOUT MAHASISWA LESENDAHTI JHONDIEN
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.v11i2.8275

Abstract

Student dropout prediction is one of the most widely used Machine Learning applications to support academic decision-making. However, predictive model performance is often affected by imbalanced data distributions, where the number of non-dropout students significantly exceeds the number of dropout students. This condition may cause Machine Learning models to favor the majority class and reduce their ability to identify students at risk of dropping out. This study aims to analyze the effect of applying the Synthetic Minority Oversampling Technique (SMOTE) on decision bias in Machine Learning models for student dropout prediction. The dataset used in this research is the Predict Students Dropout and Academic Success dataset obtained from Kaggle, consisting of 3,630 records. Two classification algorithms were employed: Random Forest, and XGBoost. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, ROC-AUC, and False Negative Rate (FNR). The results demonstrate that SMOTE improves the detection of at-risk students. For Random Forest, recall increased from 0.8908 to 0.9120, while FNR decreased from 0.1092 to 0.0880. For XGBoost, FNR decreased from 0.0986 to 0.0915. These findings indicate that SMOTE effectively reduces decision bias caused by data imbalance and improves the reliability of Machine Learning models for academic early warning systems.

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