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Jurnal Teknoif Teknik Informatika Institut Teknologi Padang
Published by Institut Teknologi Padang
ISSN : 23382724     EISSN : 25989197     DOI : 10.21063/jtif
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
The editors of the Jurnal TeknoIf Institut Teknologi Padang (Teknoif) are pleased to present this call for papers on Information Technology. Teknoif specifically focuses on experimental study, design, planning and modeling, implementation method, and literature study. Topics include, but are not limited to: Artificial intelligence technologies Cloud computing Digital forensics Genetic algorithms and programming Grid computing Human Computer Interaction Image and speech recognition Internet of Things Mobile technology development Network architectures Network technologies Pattern recognition Sensor technologies Virtualization Wearable computing Wireless technologies ISSN : 2598-9197 (online), 2338-2724(print) Subject: Informatics Engineering Frequency: Semiannual Language: Indonesia Indexed at: Crossref, PKP Index, Citation: Google Scholar DOI :10.21063/jtif
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 254 Documents
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RANCANG BANGUN SISTEM INFORMASI PENERIMAAN SISWA BARU DENGAN FITUR E-REMINDER BERBASIS WHATSAPP GATEWAY : STUDI KASUS SDIT RABBANI KOTA BENGKULU Suryamen, Haris; Wahyuni, Ullya Mega; Aziz, Muhammad Fairuzi Iszam
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 13 No 1 (2025): TEKNOIF APRIL 2025
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2025.V13.1.17-27

Abstract

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.
IDENTIFIKASI KEPADATAN PENDUDUK DI PROVINSI JAWA BARAT MENGGUNAKAN HIERARCHICAL CLUSTERING Simanjuntak, Wahyu Iskandar; Samuel, Yusran Timur
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 13 No 1 (2025): TEKNOIF APRIL 2025
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2025.V13.1.28-39

Abstract

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.
IMPLEMENTASI SISTEM KEAMANAN DAN HIGH AVAILABILITY PADA CLOUD SERVER MENGGUNAKAN AMAZON WEB SERVICES (AWS) Wahyuna, Wahyuna; Sukma, Fanni; Amalia, Sitti; Febrian Kasmar, Andre
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 13 No 1 (2025): TEKNOIF APRIL 2025
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2025.V13.1.40-47

Abstract

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.  
DATA MINING ANALYSIS OF SHELL OIL SALES USING THE C4.5 ALGORITHM AT CV. HARAPAN KARYA MANDIRI Husni Rifqo, Muhammad; Monika, Seci
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 13 No 2 (2025): TEKNOIF OKTOBER 2025
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2025.V13.2.48-56

Abstract

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.
CLASSIFICATION OF TRAFFIC TICKET CASES AT THE PAGAR ALAM DISTRICT ATTORNEY'S OFFICE USING THE C4.5 ALGORITHM Muntahanah; Azhari, Sabella
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 13 No 2 (2025): TEKNOIF OKTOBER 2025
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2025.V13.2.57-65

Abstract

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.
DEVELOPMENT OF REAL-TIME MULTI-LOCATION INVENTORY SYSTEM AT PT. AFA KARYALOKA NATA UTAMA USING V-MODEL Sa’banna Hasibuan, Arif; Setiadi, Tedi; SA'BANNA HASIBUAN, ARIF
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 13 No 2 (2025): TEKNOIF OKTOBER 2025
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2025.V13.2.66-76

Abstract

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.
SISTEM INFORMASI GEOGRAFIS PEMETAAN JENIS KEKERASAN TERHADAP PEREMPUAN DI JAWA TENGAH MENGGUNAKAN METODE K-MEANS CLUSTERING Maulana, Novan; Harjanta, Aris Tri Jaka; Novita, Mega
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 13 No 2 (2025): TEKNOIF OKTOBER 2025
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2025.V13.2.77-86

Abstract

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.
COMPARISON OF MACHINE LEARNING CLUSTERING ALGORITHMS FOR ANALYSING ELECTRICITY USAGE PATTERNS IN CAMPUS AREAS Purba, Diya Namira; Muhammad Ridha; Rida Indah Fariani; Harkiapri Yanto
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 13 No 2 (2025): TEKNOIF OKTOBER 2025
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2025.V13.2.87-96

Abstract

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.
IMPLEMENTATION OF FEEDFORWARD NEURAL NETWORK FOR CARDIOVASCULAR DISEASE PREDICTION WITH PERFORMANCE EVALUATION Muhammad Rafli; Misbahuddin; Bulkis Kanata; Raflin, Muhammad Rafli
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 13 No 2 (2025): TEKNOIF OKTOBER 2025
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2025.V13.2.97-104

Abstract

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.
DESIGN OF SUTI WATER SYSTEM INVENTORY APPLICATION USING THE USER-CENTERED DESIGN (UCD) METHOD Farhan, Muhammad; Fajri, Hersanto; Wulandari, Berlina; Nur, Muh. Arbiyansyah
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 13 No 2 (2025): TEKNOIF OKTOBER 2025
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2025.V13.2.105-116

Abstract

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..

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