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Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika
Published by Universitas Pakuan
ISSN : 16937554     EISSN : 26543990     DOI : https://doi.org/10.33751/komputasi.v22i2.29
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Mathematics
Komputasi is a journal that publishes scientific papers in the fields of computer science and mathematics. This journal, published by the Department of Computer Science, Faculty of Mathematics and Natural Sciences, Pakuan University, Bogor. This journal provides an opportunity for researchers or academics to submit papers in the field of computer science, as well as management policies related to all aspects of computers and their subdisciplines. The journal is published twice a year, is well-documented in book form, which includes a wide range of computer science and mathematics papers by authors from various backgrounds. In addition, we also have partners from local editors who graduated as professors from several universities who will review each article before it is published. Every article or paper published in this Journal will definitely be useful for all visitors and readers. Articles submitted to this journal will be reviewed by reviewers before being published by a blind review.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 48 Documents
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Real-Time Detection of Huanglongbing (HLB) Disease in Citrus Leaves Using Enhanced YOLO V8 Algorithm Sumanto Sumanto; Rachmat Adi Purnama; Hendra Supendar; Ade Christian; Teuku Vaickal Rizki irdian; Kaisar Ages Querio
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i1.82

Abstract

This study addresses the complex challenge of detecting Huanglongbing (HLB) disease in citrus leaves, which is known as one of the most lethal plant diseases with no known cure. The primary issue in HLB detection is the difficulty in identifying symptoms early and accurately, particularly in dynamic and uncontrolled field environments. Therefore, the main focus of this research is the development of a real-time detection approach using the YOLO V8 algorithm to more accurately detect and classify HLB symptoms in citrus leaf images. The objective of this study is to design a technique that can enhance the detection of HLB disease and compare its performance with the conventional YOLO V8 method. This research also aims to address the limitations of previous studies that used the Support Vector Machine (SVM) method, which only achieved an accuracy of 80%. To achieve this objective, the study utilizes a dataset consisting of 1200 citrus leaf images, representing various levels of severity, including mild, moderate, severe, and healthy leaves. The method employed in this research involves the use of the YOLO V8 algorithm to detect and classify HLB symptoms in citrus leaf images. This approach was tested through a series of experiments to measure accuracy, precision, recall, and computational efficiency. The experimental results consistently demonstrate that the developed approach outperforms the basic YOLO V8 and previous methods using SVM, with an improvement in HLB disease detection accuracy reaching 98%. This study provides critical insights into early detection of HLB disease, potentially serving as a powerful tool to support efforts in preventing the spread of this disease across citrus orchards. Additionally, this research opens opportunities for further development in real-time plant disease detection by integrating more advanced AI technologies and applying similar methods to other plant diseases. Future research can focus on developing more efficient and scalable algorithms for use in various field conditions, as well as exploring the integration of sensors and IoT technology for more comprehensive plant health monitoring.
Design and Implementation of a Teaching Assistant Information System Using Laravel Filament and Extreme Programming Nasrul; Edi Wibowo; David Wahyu Wismanindra
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i1.83

Abstract

The management of teaching assistants at Sekolah Tinggi Teknologi Terpadu Nurul Fikri (STT-NF) has traditionally been conducted using manual processes, resulting in time inefficiencies, data inaccuracies, and limited integration of information. To address these challenges, a web-based Teaching Assistant Information System was developed using the Laravel Framework with Filament as the administrative interface and PostgreSQL as the database management system. The system is designed to streamline teaching assistant recruitment, class and assistant scheduling, and honorarium calculation in a structured and efficient manner. The development process applies the Extreme Programming (XP) methodology, which emphasizes iterative development, intensive user involvement, and adaptability to changing requirements. The results indicate that the proposed system improves efficiency and data accuracy while supporting administrative and academic staff in monitoring activities and making informed decisions.
A Metaheuristic Hybrid Approach for University Timetabling- Genetic Algorithm and Simulated Annealing Septian Cahyadi; Thesya Mercella
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i1.84

Abstract

This study addresses the recurrent course scheduling problem in universities. The problem involves constructing an optimal timetable by allocating courses, lecturers, and student groups to rooms and time slots while satisfying mandatory hard constraints and improving quality through soft constraints. Given the scale—nine study programs, 148 courses, 123 classrooms, 82 class groups, and 147 active lecturers—the problem exhibits combinatorial complexity. We propose a hybrid metaheuristic that integrates Genetic Algorithm (GA) and Simulated Annealing (SA) to balance global exploration and local exploitation. GA is selected for its robust exploration of large solution spaces and its proven applicability to university timetabling, while SA offers principled local refinement guided by an annealing schedule to reduce constraint violations. Prior work indicates that GA–SA hybrids can improve convergence and reduce computation time relative to standalone GA. We formalize the constraints, define a fitness function that prioritizes feasibility, and design neighbourhood operators tailored to timetabling moves. The proposed approach aims to deliver a robust timetable that satisfies institutional requirements and enhances operational efficiency.
Design and Development of Emergency Mobile Application Using Design Thinking and Agile Scrum: A Case Study of Batam City Li Cen; Martius Lim; Ricky Roy Nardson; Herman
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i1.86

Abstract

Emergency response in Batam City, an area with high urban mobility and dynamics, is often hamperedby slow reporting mechanisms. Residents must contact different numbers for each service in differentareas, such as the fire department, ambulance, or police, causing confusion and wasting valuable time,especially in panic situations. The absence of an integrated system specifically creates significantinefficiencies in emergency service response. This study aims to design and develop an integrated mobileapplication that serves as a tool to accelerate the reporting and handling of emergency conditions andpublic utility services in Batam City. The methodology used combines the Design Thinking approach,which ensures that solutions are designed based on the real needs and experiences of users, with the AgileScrum method, which allows for a flexible and iterative development process. This research successfullyproduced a functional application that was then comprehensively tested. Functionality testing using theUsability Testing Method showed that all core features worked well, were valid, and performed asexpected. To measure usability, a System Usability Scale (SUS) test was conducted, resulting in anexceptional score of 88.82. This score places the application in the “Excellent” category, with an“Acceptable” user acceptance rating, indicating a highly intuitive and easy-to-use interface. The mainconclusion is that the developed application has proven to be highly viable, functional, and well-receivedby users. This application shows significant potential as an effective tool for improving the speed andefficiency of emergency service responses, and could serve as a model technological solution for publicservice integration in the City of Batam.
Design and Performance Analysis of a Floating Solar Power System in Situ Tunggilis Evyta Wismiana; Fikri Adzikri; Waryani; Ahmad Zulfa Zulhilmi Rizqi; Jaelani; Josua Panca Arjuna Manurung
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i1.87

Abstract

Situ Tunggilis is a regional government asset managed by the Public Works Department of West Java Province and is part of an optimization plan by the Central Government through the Ministry of Public Works. One way to utilize the Situ Tunggilis area is by developing it as a tourist destination. To support this, adequate infrastructure such as public street lighting and reliable internet facilities is essential. The implementation of a Floating Solar Power Plant offers a sustainable solution to meet these energy needs, especially considering the limited available land and the demand for operational cost efficiency. This study focuses on designing a floating solar power plant system to supply independent energy for lighting and internet services in the tourist area. The approach involves technical calculations based on field measurements and secondary data, combined with system performance simulations. The simulation results demonstrate that the floating solar power plants system can meet the electricity demand for lighting and internet services in the pedestrian area, achieving a performance ratio of 71% and utilizing approximately 75% of the available solar energy. Moreover, the planned Wi-Fi network meets the required internet coverage radius for the tourist area.
Sentiment Analysis of Electric Vehicles on Social Media Using Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM): BERT, LSTM, Sentiment Analysis, Electric Vehicles , Social Media Muhammad Fadhillah Harahap; Yusma Yanti; Prihastuti Harsani
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i1.88

Abstract

Electric vehicles (EVs) are widely recognized as an environmentally sustainable alternative capable of reducing greenhouse gas emissions; however, their adoption in Indonesia remains limited. Data from the Indonesian Ministry of Transportation, as recorded in the Type Approval Registration System (SRUT), indicate that approximately 195,084 Battery Electric Vehicles (BEVs) were registered nationwide by early 2024. This study investigates public sentiment toward electric vehicles using social media data from X, Instagram, and TikTok, while also comparing the effectiveness of two text classification approaches: Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM). A total of 5,172 Indonesian-language comments were collected through crawling and scraping techniques using electricvehicle-related keywords over the period January 2021 to January 2025. The comments were categorized into five sentiment classes: very positive, positive, neutral, negative, and very negative. The analytical process followed the Knowledge Discovery in Databases (KDD) framework, including data preprocessing, transformation, classification, and evaluation using a confusion matrix. The results indicate that IndoBERT substantially outperformed LSTM, achieving an accuracy of 91% compared to 36% for LSTM. Sentiment analysis reveals a dominance of negative and very negative opinions, primarily reflecting public concerns regarding cost, performance, and maintenance of electric vehicles. These findings offer important insights for policymakers and the automotive industry in designing targeted promotion strategies, improving public awareness, and strengthening supporting infrastructure. Future research is encouraged to explore data augmentation techniques to improve model performance, particularly for deep learning models such as LSTM, in order to better support evidence-based electric vehicle adoption policies.
The Neural Network Based Multiple Linear Regression Model for Financial Profit Prediction: A Single Layer Perceptron Approach Integrated into a Financial Information System Sandy Budi Wirawan
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Accurate financial forecasting plays a vital role in supporting business decision-making, particularly for organizations that depend on operational efficiency and strategic planning. CV. Surya Cipta Estetica Mandiri still manages its financial records manually, resulting in inaccurate estimations and limited predictive capabilities. This study proposes a profit prediction model using a Neural Network–based Multiple Linear Regression approach implemented through a Single Layer Perceptron (SLP) and integrated into a financial information system. The dataset consists of 3,127 daily transaction records from January 2021 to April 2025, including date, category, total income, and total expense. The preprocessing phase includes handling missing values, label encoding, RobustScaler-based feature scaling, and an 80:20 train–test split. The model was developed using TensorFlow with a Sequential architecture, trained using 2,501 data points and evaluated using 626 data points. The model demonstrates excellent predictive performance, achieving R² = 0.9988, MAPE = 1.03%, MAE = 258,080, RMSE = 893,381, and an overall accuracy of 98.97%. These results indicate that the SLP-based Multiple Linear Regression model provides stable and highly accurate profit predictions. Furthermore, the model can be expanded into a broader ERP system by integrating additional business modules such as finance, HR, and inventory management. Future research may incorporate external market variables, adaptive learning mechanisms, and non-linear algorithms such as Random Forest or Gradient Boosting to improve model robustness and predictive generalization.
Prediction and Analysis of Factors Affecting Marketplace Sales Using a Bidirectional LSTM Model for Inventory Estimation Indah Cahyani; Tjut Awaliyah Zuraiyah; Aries Maesya; Fitri Mintarsih
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i2.96

Abstract

Forecasting weekly sales at the product level can help marketplace sellers avoid both excess inventory and stock shortages. The purpose of this study is to investigate the sales forecasting of the Shopee store antianshop by applying the Bidirectional Long Short-Term Memory (BiLSTM) method in conjunction with correlation-based feature selection to predict the inventory. The raw dataset contained 5,426 weekly product records from Shopee Seller Centre covering 22 May 2023 to 31 May 2025; 5,312 records remained after preprocessing. For each product, the data were ordered by week and converted into ten-week input sequences. Pearson correlation showed that Add to Cart (r = 0.5835$) and Enter Cart (r = 0.5579$) were the strongest retained predictors of weekly Units Sold. BiLSTM was then compared with a unidirectional LSTM under the same experimental settings for ten products. LSTM recorded slightly lower average errors, with MAE of 1.7462 units, RMSE of 2.3977 units, and non-zero MAPE of 69.57%, while BiLSTM produced 1.8505 units, 2.4611 units, and 71.18%, respectively. The results indicate that BiLSTM was competitive but did not outperform the simpler LSTM model consistently. We combined the two models in a Streamlit dashboard that shows product forecasts and weekly inventory guidance. Generalizability of the findings should be done cautiously since the analysis used one store, relatively short product histories, and no systematic hyperparameter search.
Multi-Horizon LSTM Forecasting of Digital Product Transactions in Retail Counter Dervio Rahmatdianto
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study proposes a multi-horizon forecasting approach using a stacked Long Short-Term Memory (LSTM) neural network to predict digital retail transaction volumes in a micro-scale counter environment. Historical transaction data were obtained from MyFastbiller Telegram logs and transformed into a 739-day daily time series containing 27,540 valid transactions. Temporal feature engineering, normalization, and 60-day sequence generation were applied prior to model training. The LSTM architecture was evaluated across three operational forecasting horizons: H-1, H-7, and H-30. Results show consistent prediction accuracy with MAPE values of 15.24%, 15.14%, and 16.35% for H-1, H-7, and H-30 respectively, all below the 20% operational threshold. The H-7 horizon achieved the best performance due to alignment with weekly seasonality patterns in transaction behavior. Visualization results indicate that predicted transaction series closely follow actual temporal dynamics across all horizons. The trained model was integrated into a web-based decision-support dashboard to provide multi-horizon transaction forecasts for operational planning. The findings demonstrate that multi-horizon LSTM forecasting effectively captures seasonal and intermittent characteristics of digital retail transactions and provides practical value for data-driven balance management in micro-scale retail counters.
Solar Panel Energy Analysis on Remote Control Mobile Robots Agus Ismangil; Syarif Hidayatulloh Hidayatulloh; Teguh Puja Negara; Syaeful Anwar
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The ever-increasing energy demand due to technological and industrial developments is driving the search for environmentally friendly energy solutions. Renewable energy, particularly solar energy, offers great potential to replace dependence on dwindling fossil fuels. As a manifestation of innovation in the field of robotics, this Android-based remote-controlled mobile robot is designed to use a 2WP monocrystalline solar panel as the main energy source, with a battery as additional power storage. This robot system integrates an Arduino Uno microcontroller for data processing and an HC-05 Bluetooth module that can control the robot remotely through an Android application. By utilizing solar energy through the solar panel, the robot can reduce dependence on conventional batteries, while increasing energy efficiency. Trials were conducted to evaluate system performance, including measuring the voltage, current, and efficiency of the solar panel. This research aims to contribute to the development of environmentally friendly robotics technology utilizing solar energy. The results of this study indicate that the designed system can work according to plan and has the potential for further development.

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