Jurnal Teknoif Teknik Informatika Institut Teknologi Padang
ISSN : 2598-9197 (online), 2338-2724(print) Subject: Informatics Engineering Frequency: Semiannual Language: Indonesia and English Indexed at: Crossref, PKP Index, Dimension Citation: Google Scholar DOI :10.21063/jtif
This journal is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). Authors retain copyright and grant the journal the right of first publication. The work may be shared and adapted, even for commercial purposes, as long as appropriate credit is given and any new creations are licensed under the identical terms.
Rethinking Graph-Based Approaches: An Empirical Study of Feature Engineering in Network Intrusion Detection
Although graph-based feature engineering has become widely used in network intrusion detection systems (NIDS), there is a severe lack of empirical research on determining whether the addition of network topology features results in a real positive improvement over the operation of the system or it simply adds complexity to the system. Our paper gets into this gap by critically assessing the performance of graph-based methods as compared to conventional statistical features via systematic comparative analysis across several machine learning paradigms. Using the UNSW-NB15 dataset, we employed a graph-theoretic characteristics that included measures of centrality, the community structure identification and the topological analysis, which were compared to traditional traffic-based characteristics. Results revealed a counterintuitive finding where incorporating graph features consistently degraded detection performance across all algorithms, with statistically significant accuracy reductions observed in multiple classifiers. Random Forest experienced modest performance decline, while Support Vector Machines and RBF Networks showed more substantial degradation. Computational analysis also demonstrated that graph feature extraction imposed substantial overhead compared to traditional feature computation, representing approximately nineteen-fold increase in processing time.
Copyright (c) 2026 Richard Nwachukwu https://creativecommons.org/licenses/by-sa/4.0
Analysis of Vegetation Changes in Siak Regency's Oil Palm Plantations
Land cover change driven by the expansion of oil palm plantations has become a critical environmental issue in Siak Regency, Riau Province, necessitating periodic vegetation monitoring for sustainable land management. This study aimed to integrate Google Earth Engine and Quantum Geographic Information System to analyze vegetation changes in oil palm plantations using Sentinel-2 imagery from the 2022 to 2024 period. The methods involved processing Sentinel-2 Level-2A imagery through cloud masking, generating annual median-based composites, and calculating vegetation indices, including the Normalized Difference Vegetation Index, Enhanced Vegetation Index, and Soil Adjusted Vegetation Index. These indices were classified into several density classes to map land cover conditions, while changes in oil palm and non-oil palm areas were identified annually. All image processing stages were performed on a cloud computing platform, and the results were exported for spatial visualization and further analysis. The findings indicated that Siak Regency remained predominantly characterized by moderate to dense vegetation, particularly in the central and eastern regions. However, annual vegetation dynamics were detected in the western region, which was dominated by residential and industrial activities. In Koto Gasib District, there was a measurable increase in non-oil palm classes and expanding land cover changes over time. Overall, the integration of cloud-based processing and desktop geographic information systems proved effective in producing accurate spatial information for multitemporal analysis to support land use planning and oil palm plantation management in Siak Regency.
Copyright (c) 2026 Elvira Nurrahma, Faiza Aulia Supriatna, Inggit Lolita Sari, Emilia roza, Harry Ramza https://creativecommons.org/licenses/by-sa/4.0
Alzheimer’s Disease Classification using Lightweight Network MobileNet-V3
Alzheimer’s disease is a major public health concern characterized by progressive cognitive decline due to irreversible neuronal damage. Alzheimer’s disease represents a major global health concern in the twenty-first century. Although magnetic resonance imaging (MRI) is widely used for early diagnosis, manual interpretation is time-consuming and subject to variability. This study proposes an automated classification system based on the lightweight MobileNetV3 architecture to improve diagnostic efficiency. The model leverages depthwise separable convolutions to reduce computational complexity while maintaining high performance. MobileNetV3 models is then evaluated using appropriate metrics to assess the effectiveness of the proposed classification approach. Data augmentation techniques, including random rotation and flipping, are applied to enhance model generalization. Experimental results demonstrate that the MobileNetV3 Small model achieves superior performance, with an accuracy and F1-score of approximately 0.94, compared to 0.90 for the MobileNetV3 Large model. These findings indicate that the compact architecture provides better efficiency and reliability for Alzheimer’s disease classification. The proposed approach is suitable for deployment in resource-constrained medical environments.
Copyright (c) 2026 Muhammad Sadewa Wicaksana Wibowo, M Cahyo Kriswantoro, Neni Purwati https://creativecommons.org/licenses/by-sa/4.0
Classification of Daily Rainfall Using XGBoost with SMOTE in The Special Region of Yogyakarta
Rainfall is a meteorological parameter that influences various sectors, such as agriculture, water resource management, and disaster mitigation; however, the process of classifying it still faces challenges, particularly due to imbalanced data across categories. This study aims to evaluate the performance of the XGBoost algorithm in classifying daily rainfall in the Special Region of Yogyakarta using NASA POWER data from 2000 to 2025, with input variables including air temperature, relative humidity, wind speed, and surface pressure. The evaluation was conducted using accuracy, precision, recall, and F1-score metrics to provide a more comprehensive overview of the model’s performance. The results indicate that the model achieved an accuracy of 0.82 and performed well in identifying light rain, and began to identify moderate rain, although not yet optimally; however, its performance remains limited for higher-intensity rain classes. This suggests that imbalanced data distribution remains a primary factor affecting model performance, making data quality and balance critical considerations in the development of rainfall classification models.
Copyright (c) 2026 Ayyesa Azzahra Mulia Ramadani, Danur Wijayanto https://creativecommons.org/licenses/by-sa/4.0
Real-Time IoT-Based Solar Power Monitoring System Using Threshold Decision Algorithm and Mobile Push Notifications
The establishment of solar power installations as a renewable energy option necessitates dependable monitoring systems to ensure peak performance. Nonetheless, current monitoring solutions typically emphasize only data visualization, lacking intelligent early warning features, which results in slower fault detection and faster battery deterioration. This research tackles this gap by creating a mobile monitoring system based on the Internet of Things (IoT), where both the threshold-based decision algorithm and notification logic are fully integrated into the mobile application, rather than the IoT hardware. The system employs an ESP8266 microcontroller exclusively for gathering data from a PZEM-017 DC sensor, transmitting raw readings to Firebase Realtime Database. A mobile application built on Flutter fetches data, checks it against user-defined thresholds (voltage, current, power), and produces immediate push notifications with anti-spam delays, audio alerts, and optional Telegram sending. Experimental outcomes from 30 measurement locations and 50 anomaly simulations indicate that the system reaches a voltage measurement error of 1.22% and a current error of 9.90% in comparison to a calibrated multimeter. The typical notification delay is 3.4 seconds, significantly less than the 10-second goal. In contrast to edge-based thresholding (on microcontroller), the mobile-centric method enables users to modify thresholds flexibly through the app without the need for hardware reprogramming, offers more extensive notification options (local pop-up, sound, Telegram), and streamlines IoT device upkeep. The specialized mobile interface provides enhanced customization and data logging features in comparison to standard platforms like Blynk or Telegram-based options.
Copyright (c) 2026 Afif Fathur Rachman, Yasdinul Huda, Ika Parma Dewi, Hadi Kurnia Saputra, Asnal Effendi https://creativecommons.org/licenses/by-sa/4.0
WEB-BASED SANTRI MANAGEMENT AND MONITORING SYSTEM INTEGRATED WITH WHATSAPP
The increasing number of students in Islamic boarding schools (pesantren) has made administrative tasks such as recording student violations, achievements, permissions, and health records more complex. Many schools still manage these data manually, resulting in duplication, delayed communication, and inefficient supervision. This study addresses this issue by developing a Web-Based Santri Management and Monitoring System integrated with WhatsApp, aiming to streamline data management and enhance real-time communication between administrators, guardians, and health staff. The system was developed using the Waterfall methodology, which includes requirement analysis, system design, implementation, testing, and maintenance stages. Three main user roles were defined: administrators, guardians, and health staff, each with specific responsibilities. A User Acceptance Test (UAT) was conducted with 32 respondents, including one administrator, 30 guardians, and one health staff. The results showed satisfaction levels of 100% for administrators, 87% for guardians, and 100% for health staff, indicating high user acceptance. These findings suggest that the system effectively addresses the challenges of manual data management, improves administrative efficiency, and strengthens communication through real-time WhatsApp notifications, providing a comprehensive solution for student monitoring and supervision in Islamic boarding schools.
Copyright (c) 2025 Lucita Aprillia, Yang Agita Rindri, Tri Agusti Farma https://creativecommons.org/licenses/by-sa/4.0
DESIGN OF SUTI WATER SYSTEM INVENTORY APPLICATION USING THE USER-CENTERED DESIGN (UCD) METHOD
Inventory management (stock) is an important factor for business operational continuity. The Suti Water bottled water business unit, managed by the Indonesian Islamic Boarding School Cooperation Agency (BKsPPI), was facing serious constraints where the manual recording system often resulted in delays and data inaccuracies, which had a significant impact on operational efficiency. This study aimed to overcome these challenges by designing and developing an inventory application prototype with a focus on optimizing the User Interface (UI) and User Experience (UX). The User-Centered Design (UCD) methodology was adopted to ensure that the application design was centered on the real needs of the Suti Water Main Distributor warehouse staff. Following development, the application's usability was evaluated using the System Usability Scale (SUS). The testing results showed a SUS score of 93.0, which placed the application in the 'Acceptable' category with an "Excellent" rating. These findings demonstrate that the developed prototype is an effective and highly accepted solution by the users. Furthermore, the application proved to reduce transaction recording time and significantly minimize data errors..
Copyright (c) 2025 Muhammad Farhan, Hersanto Fajri, Berlina Wulandari, Muh. Arbiyansyah Nur https://creativecommons.org/licenses/by-sa/4.0
IMPLEMENTATION OF FEEDFORWARD NEURAL NETWORK FOR CARDIOVASCULAR DISEASE PREDICTION WITH PERFORMANCE EVALUATION
Disease is crucial to prevent more serious complications. This study implemented a Feedforward Neural Network (FNN) algorithm to build a cardiovascular disease risk prediction model using patient clinical data. The dataset used was sourced from open sources and underwent preprocessing stages such as one-hot encoding and normalization. The model architecture consists of two hidden layers with ReLU and dropout activation functions, and an output layer with a sigmoid function for binary classification. Training was conducted for 100 epochs with a data split ratio of 80:20. Evaluation was carried out using accuracy, precision, recall, F1-score, and confusion matrix metrics. The evaluation results showed that the model achieved a training accuracy of 92% and a testing accuracy of 88%, with an average F1-score of 87.2%. The Confidence Factor value also indicated a high level of confidence in each prediction. These results indicate that the FNN model is effective for cardiovascular disease risk prediction and has the potential to be used as a tool for rapid and accurate medical decision-making.
Copyright (c) 2025 Muhammad Rafli, Misbahuddin, Bulkis Kanata https://creativecommons.org/licenses/by-sa/4.0
SISTEM INFORMASI GEOGRAFIS PEMETAAN JENIS KEKERASAN TERHADAP PEREMPUAN DI JAWA TENGAH MENGGUNAKAN METODE K-MEANS CLUSTERING
Violence against women is a social issue with widespread impacts and remains highly prevalent in Indonesia, including in Central Java Province. The forms of violence include physical, psychological, and sexual abuse, exploitation, neglect, and others. Presenting data in a general form without spatial mapping often makes it difficult to identify regions with high levels of vulnerability. This study aims to cluster regencies/municipalities in Central Java based on types of violence against women by integrating the K-Means Clustering method with Geographic Information Systems (GIS). The data used are records of violence against women in 2024 from 35 regencies/municipalities. The K-Means method was applied iteratively until reaching a convergent condition, resulting in three main clusters. The clustering results were visualized using QGIS software in the form of thematic maps, facilitating the interpretation of spatial patterns. The evaluation shows that spatial classification was successfully applied with a spatial match rate of 100%, and a Silhouette Score of 0.577, indicating a moderately good cluster quality. The majority of regions are included in the low cluster, while only one region is in the high cluster. This study concludes that the combination of K-Means and GIS is effective in detecting and visualizing regional vulnerability to violence against women and has the potential to serve as a basis for developing more targeted and evidence-based protection policies. It is recommended that future research expand the dataset, include additional risk variables, and explore alternative clustering methods or advanced spatial analyses to improve the accuracy and understanding of violence patterns.
Copyright (c) 2025 Novan Maulana, Aris Tri Jaka Harjanta, Mega Novita https://creativecommons.org/licenses/by-sa/4.0
COMPARISON OF MACHINE LEARNING CLUSTERING ALGORITHMS FOR ANALYSING ELECTRICITY USAGE PATTERNS IN CAMPUS AREAS
Electricity consumption in campus environments varies based on building functions, occupancy patterns, and time-of-day usage. Understanding these variations is essential for efficient energy management. Uncontrolled electricity use often results in high operational costs, highlighting the need for accurate methods to uncover consumption patterns. This study analyzes electricity consumption data from multiple campus buildings at a polytechnic in Jakarta during 2023 and 2024. Each dataset consists of six columns and 365 rows in a year. Since the data is unlabeled, three clustering algorithms: K-Means, Hierarchical Clustering, and DBSCAN are applied to identify usage patterns across campus areas. Pre-processing included imputation and normalization, followed by clustering. Cluster quality was evaluated using the Silhouette Score. A key novelty of this study is the year-to-year comparative analysis, showing that clustering performance can vary significantly depending on data structure and noise. The 2023 dataset (dataset 1) achieved the highest Silhouette Score of 0.48 using DBSCAN, while the 2024 dataset (dataset 2) produced the best result with Hierarchical Clustering at 0.53. These results emphasize the importance of selecting clustering methods based on data characteristics and temporal context. The findings contribute to developing adaptive, data-driven strategies for managing energy use in non-residential settings, particularly in educational institutions like campuses.
Copyright (c) 2025 Diya Namira Purba, Muhammad Ridha, Rida Indah Fariani, Harkiapri Yanto https://creativecommons.org/licenses/by-sa/4.0
DEVELOPMENT OF REAL-TIME MULTI-LOCATION INVENTORY SYSTEM AT PT. AFA KARYALOKA NATA UTAMA USING V-MODEL
Inventory management in companies with multiple locations often faces challenges when conventional tools such as spreadsheets are still used, causing inefficiency and difficulties in maintaining accurate records. Previous studies have largely focused on single-location systems without real-time integration, leaving a gap in supporting centralized and synchronized inventory control. This study aims to develop a web-based inventory information system that supports real-time and multi-location operations to improve efficiency and accuracy in stock management. The system was developed using the V-Model methodology, which includes requirements analysis, system design, architectural design, module design, implementation, and testing. Research data were obtained through interviews with company employees and literature review. System testing was conducted using the Black Box Testing method, which focuses on validating functionality based on inputs and outputs without producing quantitative data. Therefore, the evaluation was based on the conformity of the system with the specified requirements. The results show that the developed system is capable of handling core functions such as warehouse and counter stock management, shipping records, return records, and user management, all integrated into a centralized database with real-time synchronization across locations. This ensures that inventory data remains consistent and up to date. In conclusion, the research successfully addresses the identified gap by presenting a web-based, multi-location inventory information system with real-time integration, thereby supporting greater efficiency, accuracy, and control in inventory management.
Copyright (c) 2025 Arif Sa’banna Hasibuan; Tedi Setiadi https://creativecommons.org/licenses/by-sa/4.0
CLASSIFICATION OF TRAFFIC TICKET CASES AT THE PAGAR ALAM DISTRICT ATTORNEY'S OFFICE USING THE C4.5 ALGORITHM
The increasing number of traffic violations in Pagar Alam City has led to a yearly rise in ticketing case data at the Pagar Alam District Attorney’s Office. This accumulation of data creates difficulties in effective data management and hinders the extraction of meaningful insights. The current classification process for ticketing cases remains limited in its accuracy and efficiency, making it difficult to identify patterns or trends. This study aims to address this issue by developing a classification model for traffic ticket cases using data mining techniques, specifically the C4.5 algorithm. The model classifies cases based on attributes such as the relevant article of law, type of vehicle, evidence submitted, and the fine imposed. The CRISP-DM framework is used to guide the process through six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. RapidMiner is used as the primary tool for data processing, and the model is evaluated using the X-Cross Validation technique. The results show that the C4.5 algorithm achieves a high classification accuracy of 99.75%. The “Article” attribute emerged as the most influential factor with the highest gain ratio value. These findings can support law enforcement and policymakers in identifying the most frequent violations and developing more targeted strategies to improve traffic law enforcement and public safety.
Copyright (c) 2025 Sabella Azhari, Muntahanah https://creativecommons.org/licenses/by-sa/4.0
DATA MINING ANALYSIS OF SHELL OIL SALES USING THE C4.5 ALGORITHM AT CV. HARAPAN KARYA MANDIRI
This study focuses on the analysis of Shell oil sales at CV. Harapan Karya Mandiri (HKM) Bengkulu, which faces challenges in predicting consumer demand and managing stock efficiently. CV. HKM Bengkulu is an official distributor of PT. Shell Indonesia, competing in the vehicle lubricant industry. To address the challenges of competition and demand uncertainty, this study applies data mining methods, particularly the C4.5 algorithm, to analyze historical sales data and uncover significant patterns and trends. Data mining is a technique that helps identify hidden patterns and insights in large datasets to support decision-making. The C4.5 algorithm is employed to build a predictive model through a decision tree, which classifies data based on certain variables such as oil type, sales region, or time period. This model is expected to assist CV. HKM in predicting customer demand, optimizing sales strategies, and improving stock planning efficiency. Additionally, the results from the C4.5 algorithm provide practical benefits by enabling CV. HKM to optimize inventory management, target marketing efforts more effectively, and enhance operational efficiency. The insights derived from the model support data-driven decisions, improve business performance, and maximize profits by aligning stock levels with demand trends, thereby reducing wastage and improving profitability.
Copyright (c) 2025 Seci Monika, Muhammad Husni Rifqo https://creativecommons.org/licenses/by-sa/4.0
IMPLEMENTASI SISTEM KEAMANAN DAN HIGH AVAILABILITY PADA CLOUD SERVER MENGGUNAKAN AMAZON WEB SERVICES (AWS)
Service availability and security are critical aspects in web server management, particularly for educational institutions utilizing cloud-based technologies. This study aims to implement a high availability and security system on the SMKN 5 Padang web server using Amazon Web Services (AWS). The methods employed include load balancing using Elastic Load Balancing to evenly distribute workloads, Auto Scaling to dynamically adjust the number of instances based on traffic load, and Wazuh as a security monitoring system to detect and respond to cyber threats such as Denial of Service (DoS) attacks. System performance was evaluated through stress testing and security log analysis. The implementation results indicate that load balancing effectively optimizes network traffic distribution, Auto Scaling maintains service availability during traffic spikes, and Wazuh successfully detects and mitigates security threats in real time. The main contribution of this research is the enhancement of cloud-based web server architecture through a high availability and adaptive security approach, which can be adopted by other educational institutions. Future research is recommended to optimize the system using artificial intelligence to improve proactive cyber threat detection and response efficiency.
Copyright (c) 2025 Andre Febrian Kasmar; Wahyuna Wahyuna, Fanni Sukma, Sitti Amalia https://creativecommons.org/licenses/by-sa/4.0
RANCANG BANGUN SISTEM INFORMASI PENERIMAAN SISWA BARU BERBASIS WHATSAPP E-REMINDER
In the ever-evolving digital era, managing data and information in educational environments has become increasingly crucial. The new student admission process at SDIT Rabbani Kota Bengkulu still faces several challenges, such as document accumulation, difficulties in data management, and delays in administrative procedures. These issues hinder the smooth and efficient execution of the admission process. To address these problems, this study aims to design and develop a web-based student admission information system equipped with an e-Reminder feature through a WhatsApp Gateway. The research adopts the Waterfall development methodology, which includes analysis, design, implementation, testing, and maintenance stages. The system is built using the Laravel and Vue.js frameworks and is integrated with WhatsApp services to send automatic notifications regarding important admission steps and schedules. The implementation results indicate that the system has successfully streamlined the admission process, reduced administrative delays, and improved communication between the school and prospective student guardians. Therefore, this system enhances administrative performance in student admissions and provides a more modern and informative service experience.
Copyright (c) 2025 Haris Suryamen, Ullya Mega Wahyuni, Muhammad Fairuzi Iszam Aziz https://creativecommons.org/licenses/by-sa/4.0
IDENTIFIKASI KEPADATAN PENDUDUK DI PROVINSI JAWA BARAT MENGGUNAKAN HIERARCHICAL CLUSTERING
This research applies a hierarchical clustering algorithm to identify population density patterns in West Java (18 regencies, 9 cities) as a basis for natural disaster management. Population density data for 2020-2022 from the West Java Population Office was analyzed to group areas into three categories: densest, medium, and lowest. The hierarchical clustering method was used to group areas based on population density and flood potential, with the additional attribute of river presence. The clustering results were evaluated using the Davies-Bouldin index. The results showed that the algorithm was successfully applied, grouping 20 districts/cities with the lowest population density (Cluster 0), 3 districts/cities with medium density (Cluster 1), and 4 districts/cities with the densest density (Cluster 2).This research is expected to provide insight to the government and related institutions in planning disaster mitigation based on population density patterns, so as to reduce the risk of natural disasters in the future. This research takes data from the official source https://jabar.bps.go.id/indicator/12/245/1/kepadatan-penduduk-menurut-kabupaten-kota.html. The main objective of this research is to understand population density patterns that can provide an indication of the level of risk to certain natural disasters in the West Java region. This information is expected to be used as a basis for more effective and efficient disaster management strategies in the future. The implication of this research shows that by understanding the pattern of population density and river distribution through the hierarchical clustering method, the government and related institutions can formulate more targeted disaster management strategies.
Copyright (c) 2025 Wahyu Iskandar Simanjuntak, Yusran Timur Samuel https://creativecommons.org/licenses/by-sa/4.0
MODEL REGRESI LOGISTIK DAN JARINGAN SYARAF TIRUAN UNTUK KLASIFIKASI MAHASISWA BERPOTENSI DROP OUT
Every university faces students who leave without notice, including those who fail to complete their studies and are declared as dropouts (DO). An initial step in addressing student dropout issues can be undertaken using classification techniques. This study aims to classify dropout students using logistic regression, which is compared with the Artificial Neural Network (ANN) method to categorize data into five classifications: Active, Graduated, Potential to Graduate, Potential DO, and DO. The dataset consists of academic records of undergraduate students from the Computer Science program, obtained from PUSTIPADA at UIN Sumatera Utara. The data includes entry year, study duration, semester GPA, cumulative GPA, credits per semester, total credits, and tuition fees. A total of 1,337 student records were divided into 80% training and 20% testing sets. The logistic regression model achieved an accuracy of 93% on the test data, while the ANN model performed better with an accuracy of 96%. This indicates that ANN is more effective in capturing complex and variable patterns in student data. The findings of this study contribute to academic institutions and educational policymakers, particularly in the Computer Science program, by providing insights for decision-making and developing intervention programs to prevent potential dropouts among students with similar characteristics to those in the dataset.
Copyright (c) 2025 Armansyah Armansyah, Suhardi Suhardi https://creativecommons.org/licenses/by-sa/4.0
IMPLEMENTASI INCREMENTAL METHOD PADA RANCANG BANGUN WEBSITE PENERBIT PNP PRESS
PNP Press publisher is one of the publishers in Indonesia which is under the auspices of the Padang State Polytechnic State University. This publisher publishes two publishing models, namely magazines and books. The types of books published at PNP Press are scientific books, texts, and references. Meanwhile, the types of articles published in magazines are popular articles and articles from several columns. With the increasing demand for book and magazine publications at this publisher, a system is needed that can manage the publication process effectively and efficiently. This research aims to design and implement a website that can facilitate the publication and distribution process of magazines and books at PNP Press using the Incremental method. This method was chosen because it can accommodate various needs during the system development process. Using the Incremental Method in building publisher websites has proven capable of producing PNP Press publisher websites that suit user needs and ensure the system can be implemented effectively, namely in the form of books published using the open monograph press (OMP) and magazines using the open journal system (OJS).
Copyright (c) 2025 Ikhsan https://creativecommons.org/licenses/by-sa/4.0
ANALISIS LAHAN DAN REKOMENDASI TANAMAN PADA SISTEM PERTANIAN CERDAS BERBASIS IoT (Kasus : Lahan Petani Durian Tarung, Kec. Kuranji, Kota Padang)
Technology-based agriculture is a modern approach that utilises the Internet of Things (IoT) to efficiently support the production process. In Kampung Durian Tarung, Pasar Ambacang, Kuranji, Padang, West Sumatra, a group of horticultural farmers face challenges in determining the type of plants that are suitable for the soil quality of their land. This research develops an IoT-based system for land analysis that can recommend optimal crop types according to soil conditions. IoT technology is implemented to collect soil data, such as pH, moisture, and soil temperature, through wireless sensors. The data is analysed using a fuzzy method, which enables decision-making based on a number of soil quality parameters to provide more precise and real-time recommendations. The trial results show that the system is able to accurately measure soil conditions and provide appropriate crop recommendations based on the soil quality data obtained. The use of fuzzy methods proved effective in filtering data from sensors and adjusting crop recommendations based on threshold values on pH, humidity, and soil temperature. This research contributes to the field of smart agriculture by presenting an IoT-based solution specific to the needs of local horticulture farmers. Combining wireless sensor technology and fuzzy methods enables analyses that are more adaptive and relevant to the soil conditions on the farm. The success of this research provides direction for the development of similar systems that can be applied in other regions, and underlines the role of IoT technology in supporting agricultural sustainability through optimal utilisation of data.
Copyright (c) 2024 Muhammad Vajri Djauhari, Busran Busran, Eva Yulianti, Eko Kurniawanto Putra, Anna Syahrani https://creativecommons.org/licenses/by-sa/4.0
PENGEMBANGAN WEB SERVICE MENGGUNAKAN FRAMEWORK FASTAPI UNTUK MENINGKATKAN KEMUDAHAN INTEGRASI SISTEM INFORMASI AKADEMIK MULTIPLATFORM
Academic activities at Dian Nuswantoro University are managed using a web-based and mobile-based (Android and IOS) academic information system for students, lecturers, educators, and parents (guardians). The data retrieval process used by the academic information system is currently in each system itself so that it is prone to errors. Therefore, a back-end service is needed in the form of a web service that acts as a portal for the data retrieval process that can be used by the multiplatform academic information system. In addition to helping to avoid data retrieval errors, the web service that is built also provides complete and easy-to-understand documentation of web service usage. The average time required for the web service to provide a response when accessed by 1000 users is 6198ms (minimum 17ms and maximum 10017ms), meaning that the web service has good performance under high loads. The Extreme Programming method was chosen for the development of the web service in this study. This method consists of four stages, namely planning (analysis of what the system needs), design (visualization with Use Case diagrams), coding (using FastAPI Framework), and the last is testing (using BlackBox and JMeter for testing functions and security). The simplicity of this method can support the achievement of the desired results, namely a back-end service in the form of a web service, which can be used by a multi-platform academic information system to exchange data easily and accurately so that errors can be avoided, especially inconsistencies in presenting academic data.
Copyright (c) 2024 Aprilyani Nur Safitri, Imanuel Harkespan https://creativecommons.org/licenses/by-sa/4.0
SISTEM PENYIRAMAN OTOMATIS PADA PEMBIBITAN PRE-NURSERY KELAPA SAWIT BERBASIS INTERNET OF THINGS
The application of automation technology in the agricultural sector is a highly effective solution for improving the efficiency and productivity of seedling cultivation, particularly in oil palm nurseries. CV Pangean Raya TBS is an oil palm nursery business located in Solok Selatan Regency. CV Pangean Raya TBS is currently facing challenges in the watering process of oil palm seedlings, as it is still done manually. This manual method leads to wasted time, labor, and water. Manual watering often results in uneven water distribution, which can affect the quality of the oil palm seedlings. This research aims to design and implement an efficient Internet of Things (IoT)-based automatic watering system using a soil moisture sensor, ESP32 module, and RTC. The system is designed to monitor soil moisture conditions in real time and regulate watering automatically. The automatic watering is based on the moisture values detected by the sensor. Watering can also be manually controlled via a smartphone when needed, such as during rainfall, to prevent water wastage and overwatering of the oil palm seedlings. This system can help plantation owners optimize water usage, increase seedling productivity, and reduce dependence on manual labor. The research results indicate that the watering system can operate automatically based on the moisture data received, making it effective in conserving resources, improving productivity, and providing better control over plant conditions.
Copyright (c) 2024 Dian Eka Putra, Edwar Rosman, Katrina Flomina G, Miftahul Hasanah, Riyan Ikhbal Salam https://creativecommons.org/licenses/by-sa/4.0
PERANCANGAN SISTEM INFORMASI ANALIS BREAK EVEN POINT DAN STOK PADA BUDIDAYA AYAM POTONG BERBASIS WEB
Although broiler chicken farming offers many benefits, there are also risks of losses, especially when production costs are not accurately recorded, leading to inaccurate per-unit pricing that can harm farmers. To assist farmers in accurately determining the Break Even Point (BEP) price for each chicken, a web-based cost and stock management information system for broiler farming has been developed. To provide a clear and sequential software lifecycle approach, this system was built using the waterfall method, which includes the stages of requirements analysis, design specifications, coding, testing, and maintenance. Each stage produces measurable outcomes before proceeding to the next. The development results indicate that the system successfully records the total production costs, including detailed costs for feed, medicine, operational expenses, and chicken stock. By using the formula BEP = total production cost/number of live chickens, the system achieves accurate pricing for broiler chickens. Based on these findings, future research is suggested to improve the user interface for greater appeal, optimize system accessibility on mobile devices, and integrate this system with other systems.
Copyright (c) 2024 khoiril afif, Yudi Kristyawan, Slamet Kacung https://creativecommons.org/licenses/by-sa/4.0
INOVASI E-COMMERCE UNTUK OPTIMALISASI PROSES KONSUMEN, PRODUKSI, DAN PEMASARAN BAGI PELAKU UMKM
E-commerce technology enables efficient online transactions, providing customers with a convenient and efficient purchasing experience. In terms of production, e-commerce information systems can be integrated with production management, enabling monitoring of the production process, optimizing production efficiency and reducing the risk of stock shortages. E-commerce technology also opens up opportunities for more sophisticated marketing strategies, as MSMEs can utilize consumer data analysis, increasing customer appeal and ordering and sales. E-commerce technology facilitates direct interaction between consumers, fostering closer relationships and improving customer experience. The development of e-commerce technology has significantly increased the effectiveness and efficiency of MSMEs specifically in the fried chicken sector, enabling them to compete in the digital business era. The purpose of this study is to develop an E-Commerce information system in the "Maju Makmur" Kalasan fried chicken industry center that manages MSMEs fried chicken. The research method is a prototype model. This model starts from data collection, system design and testing. The stages of the prototyping development model consist of: communication, rapid planning, rapid modeling, construction, and delivery. The results of this study are a web-based system to optimize consumer, production, and marketing processes in order to increase operational efficiency and market access through an online platform.
Copyright (c) 2024 Zaidir Zaidir, V Wiratna Sujarweni, Erizal Erizal https://creativecommons.org/licenses/by-sa/4.0
PERANCANGAN APLIKASI PERHITUNGAN BEBAN KERJA DOSEN TERINTEGRASI DENGAN PENDEKATAN WATERFALL
Digital transformation in higher education emphasizes the importance of information technology in enhancing management efficiency, including the management of lecturers' workloads. This study aims to design a Full-Time Equivalent Teaching Load (EWMP) calculation system integrated with the Integrated Resource Information System (SISTER), implemented by the Ministry of Education, Culture, Research, and Technology (KEMDIKBUDRISTEK). The application was developed using the Waterfall methodology and leverages the SISTER Application Programming Interface (API) to automate the collection of lecturer activity data at Universitas Dian Nuswantoro Semarang (UDINUS). By integrating the workload calculation application into the internal management system, this solution streamlines data recording, reduces manual errors, and enhances accuracy in the evaluation of lecturer performance. The test results indicate that the application successfully synchronizes data with SISTER in an accurate and real-time manner, supporting more effective workload management for lecturers. Additionally, the system provides reports and analyses of lecturer workloads, facilitating resource planning and allocation. This application is expected to contribute to a more transparent, accurate, and quality-driven human resource management process in higher education.
Copyright (c) 2024 Danny Oka Ratmana, Muhammad Syaifur Rohman, Fahri Firdausillah, Galuh Wilujeng Saraswati https://creativecommons.org/licenses/by-sa/4.0
PERANCANGAN PROTOTIPE APLIKASI PERUMAHAN TAMAN KENARI SENSOR MONITOR MENGGUNAKAN METODE AGILE
Taman Kenari Housing Complex is a residential area located in Bogor Regency, West Java. The complex is relatively quiet during the day, evening, and night, which can lead to security issues for the homes. One of the problems faced by the residents of Taman Kenari Housing Complex is the lack of effective monitoring and supervision of their home security. To address this issue, the author designed a prototype application called Taman Kenari Sensor Monitor using the Agile method. The purpose of this study is to design the Taman Kenari Sensor Monitor prototype as an effective solution to enhance the security and efficiency of home monitoring in the Taman Kenari Housing Complex. Through this prototyping approach, the Taman Kenari Sensor Monitor application can be visually represented in the form of a mockup that meets user needs while being efficient in terms of functionality and aesthetics. The prototype is designed using UML (Unified Modeling Language) modeling and implemented in the form of a mockup created in Figma. Application testing was carried out using the Blackbox method to test the overall functionality of the application. This study provides a structured solution to address the home security issues in the Taman Kenari Housing Complex.
Copyright (c) 2024 Ichramsyah Abdurrachman Universitas Paramadina, Diki https://creativecommons.org/licenses/by-sa/4.0
IMPLEMENTASI FUZZY TSUKAMOTO DALAM SISTEM PREDIKSI PANEN PADI DI KABUPATEN INDRAMAYU
Strategic efforts to maintain rice production, which is the staple food of the Indonesian people, must continue to be carried out, one of which is maintaining production in rice-producing areas such as Indramayu Regency is a step that must be prioritized. Knowing the prediction of the upcoming rice harvest is the right effort to make. Fuzzy Tsukamoto logic is a method that can be used as a calculation to make predictions, the selection of fuzzy Tsukamoto is due to its ability to predict with factors that have uncertainty such as rainfall, in addition, land area and harvest yields can also be used as variables to obtain predictions through fuzzy Tsukamoto. This study was conducted to implement fuzzy Tsukamoto for predicting rice harvest yields, where the results of the fuzzy Tsukamoto logic process that have been carried out produce six rules at the inference stage in the category of increasing or decreasing harvests, which are used as the basis for the defuzzification process to find the output value of the harvest. The calculation results are then applied in a website-based system using the Laravel framework and MySql DBMS so that they can be used to predict rice harvest yields. Based on the prediction process carried out through a system based on the fuzzy Tsukamoto method, out of 31 sub-districts, 16 sub-districts have the potential to experience a decline, and 15 sub-districts have experienced an increase in rice harvest production in Indramayu Regency.
Copyright (c) 2024 riyan farismana, Debi Nabilah Sholihah, Dian Pramadhana, Sonty Lena https://creativecommons.org/licenses/by-sa/4.0
IMPLEMENTASI CHATBOT YANG TERINTEGRASI LAYANAN KEPOLISIAN DALAM PEMBUATAN SIM
This research explores the deployment of a chatbot integrated with police services, focusing on the driver's license (SIM) application process. The urgency of this research arises from the need to enhance the accessibility and efficiency of public services. By leveraging Natural Language Processing (NLP), the chatbot is designed to provide rapid and accurate information regarding the SIM application procedures, renewal guidelines, and necessary documents. The study employs a mixed-methods approach, combining quantitative methods such as surveys and statistical analysis with qualitative methods including observations and interviews. This comprehensive approach allows for the development and assessment of the chatbot system within the context of SIM applications. The findings reveal that the chatbot significantly reduces processing time, improves user satisfaction, and streamlines the overall application experience. This integration demonstrates substantial potential for advancing public service delivery by making it more accessible and efficient. The research underscores that chatbot technology can play a crucial role in modernizing and optimizing public services, offering scalable solutions for future improvements in service delivery.
Copyright (c) 2024 Ekananda Naufal Allaam Eka, Eri Zuliarso https://creativecommons.org/licenses/by-sa/4.0
IMPLEMENTASI QUESTION ANSWERING SYSTEM TAFSIR AL- AZHAR MENGGUNAKAN LANGCHAIN DAN LARGE LANGUAGE MODEL BERBASIS CHATBOT TELEGRAM
Tafsir is a main gateway for a Muslim to study and understand the content of the verses in the Quran. One example is Tafsir Al-Azhar. Tafsir Al-Azhar is a commentary authored by Professor Dr. Hamka, which demonstrates how Dr. Hamka connects modern Islamic history with Quranic studies. Tafsir Al-Azhar has a large number of pages, requiring extra effort when searching for information within it. This research aims to create a system capable of receiving questions about Tafsir Al-Azhar in natural language and answering them in user-friendly terms. The technology used in this research includes Langchain and Large Language Models, implemented using a Telegram chatbot. Telegram was chosen for its popularity and user-friendly interface. The Question Answering system was tested using User Acceptance Testing (UAT) and the DeepEval framework. The UAT resulted in an accuracy score of 83.71%, while testing using the DeepEval framework yielded a hallucination score of 41%, contextual precision of 90%, contextual recall of 81%, and contextual relevancy of 79%.
Copyright (c) 2024 Aji Bayu Permadi, Nazruddin Safaat H, Lestari Handayani, Yusra https://creativecommons.org/licenses/by-sa/4.0
SIMULASI MONTE CARLO UNTUK ESTIMASI PENGADAAN ATK (STUDI KASUS DI INSTITUT TEKNOLOGI PADANG)
Office Stationery (ATK) is one of the operational needs at the Padang Institute of Technology (ITP). Stationery procurement is carried out twice a year with an annual budget submission. ATK management is carried out manually starting from requests to the transaction system. Current conditions have resulted in procurement planning not being measured, resulting in running out of certain items in the middle of the period or excess of certain items at the end of the year. How to help the procurement department to determine procurement estimates, so that they can provide goods optimally and use the budget more effectively and efficiently for the coming period. Processing is carried out on ATK transaction data using the Monte Carlo simulation method. Simulation stages include determining probability, calculating cumulative distribution, determining intervals of random numbers, forming random numbers and creating simulations. The results of the simulations carried out can determine the estimated quantity of goods procurement and can reduce procurement costs by 25.6% when compared to procurement in the previous even semester period. The level of accuracy of results in predicting future conditions depends on the amount of historical data involved in this process. The simulation method used can be adjusted to the object being studied and the variables that object has.
Copyright (c) 2024 Hamdi Habdillah, Jufriadif Na’am https://creativecommons.org/licenses/by-sa/4.0
TOPIC ANALYSIS VIDEO DEBAT JELANG PEMILU PRESIDEN DAN WAKIL PRESIDEN TAHUN 2024 DENGAN LATENT DIRICHLET ALLOCATION
Several debates were held among presidential and vice presidential candidates to convey their ideas for the 2024 presidential general election (PEMILU). This research analyzes the topics discussed in the debates using Latent Dirichlet Allocation (LDA), K-means Clustering, and word tagging methods for each candidate pair. The K-Means Clustering method yielded more diverse and evenly distributed topics for each candidate pair, while LDA produced fewer topics but was more effective in identifying topics for candidate A. The K- Means Clustering method yielded more diverse and evenly distributed topics for each candidate pair, while LDA produced fewer topics but was more effective in identifying topics for candidate. These are somewhat consistent with previos works. A. In dataset 1 using the LDA model, candidate pairs A have a probability of 60%, B have a probability of 25%, and C have a probability of 0%. In dataset 2 using the K-Means model, candidate pairs A have a probability of 37.04%, B have a probability of 25%, and C have a probability of 17.24%. In dataset 2 using the LDA model, candidate pairs A have a probability of 100%, B have a probability of 40%, and C have a probability of 0%. In dataset 2 using the K-Means model, candidate pairs A have a probability of 35.71%, B have a probability of 14.29%, and C have a probability of 28.57%.
Copyright (c) 2024 Ivana Valentina, Aziz Mu’min, Devion Tanrico, Oscar Karnalim https://creativecommons.org/licenses/by-sa/4.0