Infotek: Jurnal Informatika dan Teknologi
INFOTEK Jurnal Informatika dan Teknologi Fakultas Teknik Universitas Hamzanwadi selanjutnya disebut Jurnal Infotek (e-ISSN: 2614-8773) merupakan Jurnal yang dikelola oleh Fakultas Teknik Universitas Hamzanwadi yang mempublikasikan artikel ilmiah hasil penelitian atau kajian teoritis (invited authors) dalam bidang (1) keilmuan informatika, (2) Rekayasa Perangkat Lunak, (3) Multimedia, (4) Jaringan Komputer, (5) Data Mining, (6) Image Processing, (7) Komputer Vision, (8) Mikrokontroller, (9) Robotik, (10) IOT yang belum pernah dipublikasikan. Sejak Tanggal 09 Februari 2023 Jurnal Infotek telah terakreditasi Peringkat 4 (SINTA 4) berdasarkan Keputusan Mentri Riset dan Teknologi Nomor : 225/E/KPT/2022 Terhitung dari Artikel yang diterbitkan mulai dari Volume 5 No 2 Tahun 2022. Semua artikel pada Jurnal Infotek : Jurnal Informatika dan Teknologi telah memiliki Digital Object Identifier (DOI) dengan DOI Prefix : 10.29408 by Crossref. Jurnal Infotek diterbitkan oleh Fakultas Teknik Universitas Hamzanwadi dua kali setahun yaitu pada bulan Januari dan Juli. Jurnal Infotek Telah Terindeks oleh sejumlah lembaga pengindeks, diantaranya GARUDA, Dimensions, Google Scholar, dan lainnya. Nama Jurnal : Infotek : Jurnal Informatika dan Teknologi Inisial : Infotek Jadwal Terbit : 2 terbitan per tahun | Januari dan Juli DOI : 10.29408 /jit e-ISSN : 2614-8773 Ketua Penyunting : Aris Sudianto Penerbit : Universitas Hamzanwadi Analisa Kutipan : Google Scholar | Garuda | SINTA
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Pengembangan Sistem Penerjemah Kalimat Bahasa Isyarat Bisindo To Text Dengan Kinect Real Time
People with hearing and speech impairments often have difficulty communicating with the general public due to a lack of understanding of sign language. This results in social isolation and barriers to accessing information and education. The development of sign language translator technology is expected to improve communication and independence of people with disabilities. The methodology used in this research includes data collection through literature studies, questionnaires, and documentation. The BISINDO data processing algorithm in this research uses the Long short-term memory (LSTM) method to detect skeletons on the hands, face and body. The system implementation uses kinect to capture real-time hand movements. System development uses the Agile method to ensure functionality and fulfillment of user needs. From the evaluation results by testing system performance using confusion matrix by calculating accuracy, recall, precision and F1-Score values. As well as datasets taken in realtime with a total of 90 data, each consisting of 30 actions of my sign language, 30 actions of good sign language, 30 actions of iloveyou sign language. The results show that the system has an accuracy value of 1.0, recall 1.0, precision 1.0 and F-1 Score 1.0 with LSTM algortima, epoch 140 and response data that shows positive towards the system
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Penerapan Algoritma Decision Tree Untuk Menentukan Terapi Pada Anak Autis
People with hearing and speech impairments often have difficulty communicating with the general public due to a This research focuses on the development of a classification system for autism levels in children using the Decision Tree algorithm. This system considers various aspects of the psychomotor abilities of autistic children, such as balance, hand coordination, flexibility, and muscle strength. Observation data was taken from autistic children aged 5-8 years who attended SLB Anugerah Colomadu, and was used to build a Decision Tree model. The results showed that this model had 0,9 accuracy in classifying autism rates based on the observed psychomotor features. In addition to classification, the system also provides recommendations for psychomotor therapy that are specific and according to the level of autism that the model has classified. This approach is expected to improve the quality of interventions for autistic children by facilitating the development of their psychomotor skills. With this system, it is hoped that educators and therapists can provide more targeted treatment, so that autistic children can develop more optimally according to their individual needs. This structured and data-based approach is also expected to be a reference in the development of other intervention methods in the future
Pengembangan Website Speech To Video Bahasa Isyarat Indonesia (Bisindo) Berbasis Algoritma Long Shot Term Memory
Indonesian Sign Language (BISINDO) is an essential communication tool for more than one million deaf people in Indonesia. This research aims to develop a Speech to BISINDO website based on the Long Short-Term Memory (LSTM) algorithm to overcome barriers in communication and learning processes for the deaf community. The research stages include data collection through questionnaires and literature studies to understand user needs, as well as system development using the Rational Unified Process (RUP) method. The system is designed to convert voice input into sign language videos and support the sign language learning process through interactive features. Testing was conducted to ensure that the system meets functional and non-functional needs. The results show that the Speech to BISINDO website is effective in translating speech into sign language videos with high accuracy, as well as supporting better communication and learning between deaf people and the general public. The system offers an innovative solution to improve access to information and learning process for people with hearing impairment.
Perspective Owner Pada Enterprise Architecture Menggunakan Zachman Di Perusahaan Distributor Mesin
Machinery distributor companies have an important role in the industrial supply chain, where organizations are faced with various challenges, such as the possibility of stock management which is vulnerable to data errors and operational losses. This can result in operational inefficiencies and hinder the achievement of business goals. Implementing Enterprise Architecture (EA) using the Zachman Framework can play a role in helping overcome these challenges and maximizing the benefits of implementing information systems. This research aims to design an EA blueprint for machine distributor companies using the Zachman Framework mapping method. Data collection was carried out through interviews and observations to analyze the organization's value chain and develop scope, enterprise model and system model with a focus on data, function and network aspects. The results of this research produced a comprehensive and structured EA blueprint design, with a focus on three main aspects, namely data, function and network from the perspective of the owner and designer. This design is expected to help machine distributor companies achieve goals in the stock management process to increase data accuracy and operational efficiency
Model Hibrida K-Nearest Neighbors Berbasis Genethic Algorithm untuk Prediksi Penyakit Ginjal Kronis
Chronic Kidney Disease, which is often abbreviated as PGK, is a serious disease that is of major concern to society and the medical world. This disease can cause various serious complications if not treated properly and early. Therefore, accurate prediction of CKD is very important to support early intervention that can slow disease progression, prevent further complications, and increase the patient's chances of recovery. This research aims to increase the accuracy of PGK predictions by developing a hybrid model that combines the K-Nearest Neighbors (KNN) algorithm with optimization using the Genetic Algorithm (GA). In this approach, the KNN algorithm is used to build a prediction model, while GA acts as an optimization tool that improves model performance. The effectiveness of the optimized model is evaluated using key metrics such as accuracy, precision, recall, and area under the curve (AUC). The results show a significant increase in performance, with accuracy increasing by 17.75%, precision increasing by 23.84%, and recall increasing by 5.34%. This research makes an important contribution to the development of data mining technology for clinical applications and opens up opportunities for further improvements in the future in increasing the prediction accuracy of chronic diseases such as CKD
Pengelompokan Keaktifan Anggota Perpustakaan Menggunakan Algoritma K-Means
This study aims to analyze membership activity at the Rimba Baca Library in South Jakarta using the K-Means algorithm. The background of this research is the library's need to understand membership patterns and improve services based on visit and book borrowing data. The dataset for this study consists of 81 membership records collected from 2023 to 2024. The methodology involved collecting visit and book borrowing data, then applying the K-Means algorithm to cluster members based on their activity levels. The results of the study indicate the presence of three clusters with different characteristics. Cluster 1 comprises very active members, while Clusters 0 and 2 exhibit lower levels of activity. These findings provide insights for the library to develop more effective service strategies, such as special promotions and programs to increase activity among less active member groups. Additionally, the study shows that membership types allowing for more book borrowings do not necessarily correlate with high activity levels. With this information, the library can enhance member engagement and optimize the use of existing resources, thereby creating a more dynamic and interactive environment for all visitors
Implementasi Discovery Learning untuk Meningkatkan Kecerdasan Digital Mahasiswa
The digital era demands students to have comprehensive digital intelligence, including students' ability to access, manage, analyze, and present digital information, alongside a grasp of digital ethics and safety. This research seeks to examine how the Discovery Learning approach influences the enhancement of students' digital intelligence. The study employed a pre-experimental design utilizing a one group pretest-posttest, which included 31 students in their fifth semester from the Faculty of Teacher Training and Education. The results showed a significant increase in students' digital intelligence. Statistical analysis produced a significance probability of p = 0.0000 (p <0.05), an average increase of 25.01%. Through the stages in this model, students are given the opportunity to not be passive in the learning process, so they can have critical thinking skills, problem solving, and digital skills needed in today's digital era. Further research should explore other elements, including lecturer readiness, availability of digital learning resources, and supportive institutional policies
Implementasi Metode RAD pada Sistem Informasi Manajemen Penelitian, Pengabdian Masyarakat dan Luaran
Higher education institutions frequently struggle to manage research, community service, and output data. Manual data and information management is prone to mistakes. It is challenging for leaders and stakeholders to monitor and evaluate performance. This study intends to address these issues by offering a platform that meets the needs of the Community Service Research Institute (LPPM), leaders, and stakeholders. The primary features produced are statistics on research, service, and output data, as well as report generating automation. This study employs RAD to create the system. The system developed has been declared functional based on the results of blackbox testing with equivalent partitions. According to the 91% usability testing estimate, users are pleased with the information system built, and the system is simple to use. This system's specific benefits and potential implications include improved data management efficiency, consolidated data access, accreditation and reporting support, and higher institutional reputation
Implementasi Internet of Things (IoT) Untuk Sistem Pemantauan Kebakaran Dini Dengan Notifikasi Telegram dan Alarm
Fire is a serious threat that can cause major losses, both material and non-material. This research aims to develop an Internet of Things (IoT) based fire monitoring system at the Tangen District Office to detect fires early. This system uses smoke, temperature and fire sensors integrated with a NodeMCU microcontroller to process data in real-time. Data from sensors is analyzed to detect signs of fire, and notifications are sent instantly via the Telegram application and accompanied by alarm activation. Tests show that the system has a detection accuracy rate of up to 95% in detecting various types of fire media, such as wood, paper and plastic, with an effective sensor distance of up to 20 cm. This system is able to reduce response time by up to 70% compared to conventional methods, increasing the readiness of officers in handling fires. It is hoped that the results of this research can minimize the risk and impact of fire, as well as significantly increase building safety
Rancang Bangun Sistem TOEFL Untuk Analisis Kelemahan Peserta Dengan Penerapan Algoritma K-Means Clustering
English proficiency is a critical skill across various sectors, particularly in educational and professional contexts. The TOEFL test assesses English language skills, covering aspects such as Reading, Listening, Speaking, and Writing. However, TOEFL results often only provide a total score without detailed insights into participants' weaknesses. To address this, this study utilizes the K-Means Clustering algorithm for its simplicity, efficiency in processing multidimensional data, and ability to produce statistically meaningful groupings. The research aims to identify the weaknesses of TOEFL participants at the Language Laboratory of ITN Malang. The dataset comprises scores from 520 students across Reading, Structure and Written Expression, and Listening sections. This method classifies participants into three primary clusters: Reading Boosters (C1), Grammar Builders (C2), and Listening Improvers (C3). The clustering process involves randomly selecting initial centroids, calculating distances using the Euclidean Distance formula, and iterating until cluster stability is achieved. The analysis results show that 36.54% of participants fall into C1, 41.35% into C2, and 22.21% into C3. The implementation of this algorithm offers valuable insights for developing more effective learning programs.
Penggunaan Metode SAW dalam Sistem Pendukung Keputusan Pemilihan Pegawai Terbaik
This research aims to develop a website-based Decision Support System (DSS) to facilitate the selection of the best employees at the Ngrampal District Office. Employee performance assessments carried out manually have so far been considered less effective due to high subjectivity and a time-consuming process. The system built uses the Simple Additive Weighting (SAW) method which is able to give weight to each assessment criterion such as absenteeism, roll call attendance and employee performance. SAW was chosen because of its ability to provide objective ranking results based on measurable data. Implementation of this system shows faster and more accurate results in determining the best employees compared to manual assessments. Apart from improving the quality of performance appraisals, the implementation of this web-based system is also expected to motivate employees to improve their performance. This decision support system was created only within the scope of the Ngrampal District Office with three assessment criteria. It is hoped that this research can be developed further by adding new assessment criteria and applied in other agencies to expand the validity and effectiveness of the system
Penerapan Sistem Informasi Berbasis Web Untuk Pengajuan Judul Skripsi Mahasiswa Sebagai Upaya Peningkatan Efesiensi Waktu
This research aims to design an online-based thesis title submission information system using Rapid Application Development (RAD). This system is designed to facilitate students who previously submitted thesis titles manually. The research method used is a quantitative approach by conducting observations, interviews, and questionnaires. The development stages include Requirement Planning, User Design, Development, and Cutover. Results In the research and development of this thesis title submission information system, the main objectives have been achieved well, namely being able to minimize time in the submission process and there is user satisfaction with this system because it has helped to do the submission process online. This system is designed to simplify and speed up the process of submitting, verifying, and determining a supervisor, which was previously done manually and took longer. By using PHP technology as a programming language and MySQL as a database system, this system successfully automates various administrative processes involved in managing thesis titles
Pengembangan Aplikasi Point of Sales untuk Prediksi Penjualan Harian Usaha Minuman Menggunakan Algoritma Random Forest Regression
The jumbo iced tea business is growing rapidly, but one partner in Solo faced challenges in manual transaction recording and sub-optimal stock management due to sales fluctuations. This problem has an impact on operational efficiency and customer satisfaction. This research aims to design and build a point of sales application with sales prediction features using random forest regression algorithms that are able to cope with non-linear data and have high accuracy. The method used is waterfall, which includes the stages of identification and planning, analysis, system design, implementation, and maintenance. The dataset for prediction is taken from historical daily sales data, with variables used including day of the week, weekend status, previous day's sales, and average sales of the previous 7 days. The test results show that the random forest regression model with a total dataset of 30 data has a good level of accuracy, with an average absolute error percentage of 2.85%. The application system developed is able to improve the operational efficiency of the jumbo iced tea business, support better decision making, increase profitability, and improve customer satisfaction through more structured stock and transaction management
Analisis Pengaruh Bayesian Optimization Terhadap Kinerja SVM Dalam Prediksi Penyakit Diabetes
Diabetes is a prevalent and serious chronic illness that impacts millions of individuals globally. Early detection of diabetes is essential to mitigate severe health complications. This study investigates the application of Support Vector Machine (SVM) enhanced by Bayesian Optimization for the early prediction of diabetes. While SVM is a robust machine learning algorithm, its performance heavily depends on the proper selection of parameters. Bayesian Optimization is an efficient approach to fine-tune SVM parameters, such as the regularization parameter (C) and the kernel parameter (gamma). The research utilizes a Kaggle dataset that includes various diabetes risk factors. The study compares the performance of SVM optimized using Bayesian Optimization against SVM without optimization. The findings reveal that SVM with Bayesian Optimization achieves an accuracy of 95%, surpassing the 94% accuracy of the unoptimized SVM. These results highlight that Bayesian Optimization enhances SVM's effectiveness in predicting diabetes early
Implementasi Aplikasi Mobile untuk Meningkatkan Keselamatan Turis di Tujuan Wisata Berisiko Tinggi
The competitiveness of smart tourism destinations depends on the natural and cultural attractions, infrastructure, and quality of services offered, including security and safety as the main factors in choosing a tourist destination by tourists. The purpose of this study is to determine mobile applications to improve tourist safety in high-risk tourist destinations. This research is a systematic review (Systematic Literature Review). Data is processed and presented in the form of a description. The study results show that a mobile application can be an alternative to improve tourist safety, especially in high-risk tourist destinations. A mobile application can be developed to improve tourist safety, especially in high-risk tourist destinations so that they feel satisfactory service. Satisfactory service can certainly provide a positive experience for tourists. The application can also be developed with the application of artificial intelligence and various features to improve user experience. These services can be in the form of safe transportation route features for tourist destinations such as medical tourism. Mobile applications with real-time features can also make it easier for tourism managers to monitor tourists so that they continue to carry out activities safely. Therefore, the application can also be made specifically to overcome disasters and can be a tool to provide information to tourists, increase their awareness of risks, and improve their preparedness for disasters
Implementasi Algoritma K-Means Untuk Rekomendasi Pengadaan Buku
One of the main challenges faced by libraries is determining the procurement of book collections that align with the needs and interests of borrowers. At the Sragen Regency Archives and Library Service, book procurement is often based on intuition or unstructured requests, resulting in many books that are less popular among visitors. This leads to a low number of book borrowings, meaning the library cannot provide optimal services. Based on this issue, the author attempts to cluster the book borrowing data for the year 2023 from the Sragen Regency Archives and Library Service by age group, book categories, number of borrowings, number of titles, and number of copies using data mining techniques with the k-means clustering algorithm. For the initial data processing, the author uses the Min-Max normalization method. After normalization, k-means is calculated with 3 clusters, followed by finding the optimal cluster using the elbow method, silhouette, and gap statistics. The results of the optimal cluster are compared with the results from the Dunn Index method. The research identifies three clusters: Cluster 1 contains book groups with low interest, consisting of 7 categories: General Works, Social Sciences, Language, Pure Sciences, Applied Sciences, Arts and Sports, History and Geography; Cluster 2 contains book groups with moderate interest, consisting of 2 categories: Philosophy and Psychology, Religion; and Cluster 3 contains the book group with the highest interest, consisting of 1 category: Literature
Penerapan Metode AHP untuk Sistem Pendukung Keputusan Pemilihan Siswa Teladan
Students are an important element in education who are expected to become quality individuals. The selection of exemplary students is important to do, this aims to motivate students to excel, but a system that only relies on academic grades can hinder non-academic development. Therefore, non-academic aspects such as morals and extracurricular activities are also included in the assessment. Decision Support Systems (DSS) help analyze situations with criteria that are not always clear, using the Analytical Hierarchy Process (AHP) method to organize complex problems. This study developed a DSS for selecting exemplary students at SMP Muhammadiyah Kawali with four criteria: attendance, discipline, achievement, and extracurricular. The results showed that exemplary students with the highest weighted scores scored 0.1920. The resulting DSS was able to select exemplary students more objectively and transparently based on existing criteria, namely discipline, attendance, achievement and extracurricular activities. With the implementation of this system, it is able to increase objectivity and fairness in selecting exemplary students and motivate students to achieve better
Implementasi Algoritma K-Means Clustering Dalam Mengelompokkan Kepatuhan Wajib Pajak Bumi dan Bangunan Dengan Optimasi Elbow
Land and Building Tax (Pajak Bumi dan Bangunan, or PBB) is one of the primary sources of regional revenue that plays a significant role in supporting development across various regions. Therefore, efforts to improve tax compliance must be enhanced through various strategies, such as continuous socialization and education, to raise awareness of the importance of paying taxes. Additionally, improving the quality of services is essential. This study aims to classify the compliance levels of PBB taxpayers in Sakra District using the K-Means Clustering algorithm. The data used in this research is the 2023 PBB dataset for Sakra District, comprising 376 entries and involving five key attributes: land area, building area, PBB assessment, payment status, and penalties. The results obtained from processing using the K-Means algorithm indicate an optimal number of clusters, as follows: Cluster 1 represents a high compliance level, consisting of 355 items; Cluster 2 represents a moderate compliance level, consisting of 18 items; and Cluster 3 represents a low compliance level, consisting of 3 items. These clustering outcomes can serve as a reference for authorities in formulating more targeted strategies to enhance tax compliance through improved education and services in the future.
Prediksi Diabetes Menggunakan Algoritma K-Nearest (KNN) Teknik SMOTE-ENN
Nowadays, diabetes is a common disease affecting millions of people worldwide, and it is generally more prevalent among women. Recent health research has adopted various innovative and advanced technologies to diagnose individuals and predict diseases based on clinical data. One such technology is Machine Learning (ML), which enables more accurate diagnosis and prediction. The data used in this study is the Pima Indian women diabetes dataset from Kaggle and the UCI data repository. This study focuses on predicting diabetes using the KNN algorithm model by applying optimization to the dataset using the SMOTE-ENN technique to enhance prediction accuracy for Pima Indian women. The dataset was trained and tested with five different splits using Jupyter Notebook to determine the best accuracy for the KNN algorithm model. Parameters such as classification accuracy, classification error, and the ROC curve were evaluated, along with identifying the variables influencing the risk of diabetes. The results showed that applying SMOTE-ENN optimization to the research dataset significantly improved the prediction accuracy using the KNN algorithm model. With a 70% training and 30% testing data split, the model achieved a classification accuracy of 0.96, a classification error of 0.04, and an AUC of 0.95. These predictions indicated that Pima Indian women are more likely to develop diabetes due to factors such as age above 33 years, the number of pregnancies, excessive sugar consumption, blood pressure, skin thickness, insulin levels, BMI (Body Mass Index), and genetic predisposition to diabetes
Pengembangan Sistem untuk Deteksi dan Identifikasi Lokasi Kecelakaan pada Lansia sebagai Bentuk Peningkatan Keselamatan berbasis Internet of Things (IoT)
fainting, which can be fatal to their health and quality of life. The elderly population in Indonesia continues to increase, creating an urgent need for technological solutions to effectively monitor their health conditions. This study aims to design and develop an Internet of Things (IoT)-based accident detection and identification system for the elderly. The system utilizes several sensors, including an accelerometer, pulse sensor, and GPS, to monitor the physical conditions of the elderly and track their location in real-time. The research method employed is the Research and Development (R&D) method. Testing was conducted on a non-elderly subject by simulating falls with several scenarios, repeated multiple times. When the sensors detect an accident, the system automatically sends notifications to the user’s or the elderly family’s smartphone, enabling quick actions that can prevent further impacts. The results showed that the system achieved a detection accuracy rate of 91% in identifying falls and weak pulse conditions out of 30 test trials
Optimalisasi Akurasi Algoritma Naïve Bayes Dengan Metode Syntetic Minority Oversampling Technique (Smote) Pada Data Numerik
This research will classify numerical data, namely loan data taken from Kaggle. The data used amounted to 9578 datasets which included data classes with borrowers able to complete credit as many as 8045 records and loans that could not complete credit as many as 1533 records. From the amount of data there is an imbalance of classes so it is necessary to do balancing in order to get more accurate classification results. The purpose of this research is to improve the accuracy of the Naïve Bayes algorithm in classifying numerical data. Fraud in financial transactions is an example of a case of imbalanced data, where the number of legitimate transactions is much greater than those that are fraudulent. Optimizing accuracy in minority (fraud) classes is very important to avoid losses. The method used to improve the accuracy of the algorithm is the Synthetic Minority Oversampling Technique (SMOTE) by over sampling the minority of the dataset. In addition, it also uses the K-Fold Cross Validation method to evaluate the performance of the algorithm process used. Data preprocessing is done to clean the data from missing and invalid values and normalize the data so that all features are on the same scale and suitable for classification analysis. Based on the results of the analysis conducted, before the application of SMOTE the model's ability to recognize minority classes was 16.1%, while after the application of SMOTE the model's ability to recognize minority classes became 48.8%. besides that, before the application of SMOTE the model was able to predict the minority class correctly in 10 cases while after the application of SMOTE, the model was able to predict the minority class correctly in 102 cases. So it can be concluded that the SMOTE technique is able to improve the ability of the model
Penerapan Algoritma K-Means Untuk Klasterisasi Produktivitas Tanaman Jahe
Sumenep Regency is one of the regions that produces ginger plants in East Java province, and has become one of the mainstays of the community's economy. However, there are obstacles faced, namely the size of the planting area, erratic weather changes and the lack of modern technology in ginger cultivation which are the main causes in affecting crop yields and product quality. This study aims to implement data mining using a web-based K-Means clustering algorithm to group ginger-producing areas in Sumenep Regency. The data used was 108 data covering the area of harvest areas and the amount of production per sub-district from 2020 to 2023 taken from the official website of the Central Statistics Agency (BPS) of Sumenep Regency. The K-Means algorithm was chosen because of its advantages in efficiently managing large amounts of numerical data, helping to recognize distribution patterns such as harvest area and production amount to produce the right cluster, its relatively simple implementation and easy-to-interpret clustering results. The results of the study show that the Sumenep Regency area can be grouped into 2 clusters based on ginger plant productivity, namely cluster 1 with 104 sub-districts and cluster 2 with 4 sub-districts. This research is expected to help farmers and the government in identifying areas with high and low productivity, optimizing land and resource use, and increasing productivity and income
Implementasi Data Mining Menggunakan Algoritma Naïve Bayes Untuk Klasifikasi Penyaluran Dana Zakat
Zakat is one of the pillars in Islam that aims to reduce economic disparity and assist those in need. Effective distribution of zakat requires a system capable of accurately identifying and targeting mustahik (zakat recipients). This study aims to implement data mining techniques using the Naïve Bayes Algorithm for the classification of zakat fund distribution at the National Amil Zakat Agency (BAZNAS) in East Lombok Regency. The Naïve Bayes Algorithm was chosen for its ability to predict categories based on probability and historical data. The data used is private data obtained through the financial reports of BAZNAS East Lombok Regency for the years 2022-2023, with 461 mustahik including family members, income, and economic status. There are 8 attributes used in this study. Data processing is conducted using RapidMiner software with the Naïve Bayes algorithm. The results show that the Naïve Bayes Algorithm achieved the best accuracy rate from the 3-fold validation test, amounting to 99.57% with an AUC value of 0.997%. The tests conducted provided excellent classification results. With this comprehensive and data-driven approach, it is hoped that this study can provide effective solutions to the current zakat fund distribution issues
Klasterisasi Daerah Rawan Bencana Alam Menggunakan Algoritma K-Means
East Java is a province with high vulnerability to disasters, such as floods, landslides, earthquakes, and strong winds, which have an impact on material losses, casualties, and deterioration of socio-economic conditions, especially in rural areas. The lack of mitigation strategies and resource allocation worsens disaster management. This study aims to classify disaster data using the K-Means algorithm to overcome the limitations of descriptive analysis conducted by BPBD East Java. The data used includes 1125 disaster events with variable disaster frequency, total damage, and the number of casualties per sub-district in East Java districts and cities during 2021-2022, obtained from the official website of the East Java BPBD. The K-Means algorithm was chosen because of its efficiency in managing big data and its flexibility in cluster formation. The results of the study show that in 2021, the region in East Java is divided into three clusters based on the level of disaster risk: Cluster 1 (low risk) with 192 sub-districts, Cluster 2 (medium risk) with 35 sub-districts, and Cluster 3 (high risk) with 10 sub-districts. In 2022, significant changes were seen in Cluster 1, which includes 462 sub-districts, Cluster 2 with 20 sub-districts, and Cluster 3 with 11 sub-districts. The results of this study are expected to support the government's decision-making priorities, especially in disaster management and resource allocation based on risk levels
Perancangan Aplikasi KWH Meter Dan Sistem Monitoring Konsumsi Listrik Berbasis Internet Of Things Untuk Kamar Kos-Kosan
The use of electrical energy in boarding houses is currently quite important for the residents. However, in its use there are often problems related to fees between boarding house residents which are within 1 KWH Meter, needs between boarding house residents with unequal electricity usage and bill information can be monitored by boarding house owners. Therefore, we need a system that can monitor and calculate the cost of electricity usage in boarding houses in real-time. This research aims to develop a digital KWH meter application design and IoT (Internet of Things) based electrical energy consumption information using ESP8266, PZEM sensors, web server. The results of this research are a KWH prototype that is able to monitor electrical energy consumption in boarding houses in real-time and calculate electricity costs based on the applicable electricity tariffs where the PZEM sensor is used as a tool to measure electrical energy consumption in boarding houses connected to the ESP8266 module to transmit data to the server via WiFi. Then, the data received is processed and presented in the form of values and a dashboard display on the web application in the form of voltage, current and energy data. With this Internet of Things-based electrical energy consumption information application, it is hoped that boarding house residents can monitor electrical energy usage more easily and help in determining payments for the electricity consumption used.
Penerapan Sistem Payment Gateway Pada E-Commerce Sebagai Upaya Peningkatan Penjualan
The development of information technology and the internet has changed consumers’ behavior in buying and selling transactions. This causes the online buying and selling business to grow rapidly. We can find this online buying and selling activity in various media such as social media platforms and online shop websites. However, most of the payment systems are though bank transfer system and then confirmation of proof of payment, which is arguably not safe and efficient. This is impractical and vulnerable from a security point of view, especially if payments are made online. Therefore, this study aims to design a web-based online store application by implementing a payment gateway system for safer and more efficient payment transactions. The method used in this study is the waterfall method with the stages of needs analysis, design, implementation, testing and maintenance. The result of this study is the design of an online store application that has been equipped with online payment services using the Midtrans payment gateway. This system provides transaction security for customers and can make it easier for buyers to make online payments when placing orders for products because with the payment gateway system, there are more choices of payment methods, including credit cards, bank transfers, electronic wallets (GoPay), or Indomaret and Alfamart, and many more. In addition, this research is also expected to contribute to online buying and selling businesses, especially the development of better online shop applications and provide inspiration for the public to start innovate in promoting their business products.
Analisis Kompetensi Siswa Dalam Ilmu Robotika Menggunakan Mixed Methods
His study aims to evaluate the impact of robotics training on the technical and analytical skills of students at SMK NWDI Renco. The background of this research is based on the importance of developing technical and analytical skills in the era of the Industrial Revolution 4.0, where technology and automation have become integral parts of various industrial sectors. Vocational education at the high school level plays a strategic role in preparing a competent and adaptive workforce to meet technological advancements.The research method used is a mixed-method approach, combining both quantitative and qualitative methodologies. An experimental design was implemented with pre-test and post-test evaluations to measure changes in students' technical and analytical skills before and after the robotics training. Quantitative data were collected through specially developed technical and analytical skill tests, while qualitative data were gathered through in-depth interviews and Focus Group Discussions (FGDs) with students and teachers involved in the training. The robotics training program was conducted over eight weeks, covering topics such as the fundamentals of robotics, programming, and practical applications through small-scale projects. Participants were purposively sampled and consisted of 30 eleventh-grade students from SMK NWDI Renco. A pre-test was conducted prior to the training to assess the students' baseline skills. During the training, direct observations were made to document students’ progress, and activities were documented. A post-test was conducted after the training to evaluate changes in technical and analytical skills. The results of the study showed a significant improvement in students’ understanding and skills. The average pre-test score was 63.2, while the average post-test score increased to 84.5. Specifically, the improvements included a 41.7% increase in the understanding of robotics concepts, a 35.4% improvement in microcontroller utilization, and a 24.6% improvement in sensor and actuator applications.
Pengembangan Media Pembelajaran Ke NWDI-An Berbasis Mobile
The rapid development of information and communication technology (ICT) has brought significant changes in various aspects of life, including in the field of education. The need for effective and efficient learning media is increasingly urgent along with demands to improve the quality of education that is relevant to current developments. However, there are still several obstacles such as the lack of active role of students in the learning process and the limited learning media used. Learning resources in presenting material still use PowerPoint slides which are not yet interactive, resulting in a lack of student interest in learning. Therefore, this research aims to develop learning media, to support a more effective and interactive teaching and learning process. The method used in this research is Research and Development with the ADDIE model (Analyze, Design, Development, Implementation, and Evaluation) which is a teaching design system that is centered on individual learning, has immediate and long-term phases, is systematic, and uses a systems approach to knowledge. and human learning. The result of this research is a mobile-based learning media application with the hope that it can help increase students' understanding and interest in learning in NWDI courses, especially at the Faculty of Engineering, Hamzanwadi University.
Pengembangan Model Convolutional Neural Network (CNN) untuk Klasifikasi Penyakit Kulit Berbasis Citra Digital
This study aims to develop a skin disease classification model based on Convolutional Neural Networks (CNN) specifically designed to classify three types of skin diseases: acne, ringworm, and tinea versicolor. Unlike some previous studies that utilized datasets from various domains such as textile images, plants, and blood cell images, this research specifically employs a dataset relevant to skin diseases. The dataset used in this study consists of 810 skin disease images, divided into 600 images for training (200 images each for acne, tinea versicolor, and ringworm) and 210 images for testing. To enhance data variation and support model generalization, the dataset was processed using augmentation techniques. The model's performance evaluation showed promising results, with an average accuracy of 87.14%. Additionally, the model achieved precision, recall, and F1-score values of 87% each, demonstrating its ability to detect and classify skin diseases consistently. This study is expected to serve as a foundation for developing more accurate and efficient technology-based diagnostic tools, particularly for skin diseases, in the future
Penerapan Automatic Drip Irrigation System (ADIS) berbasis Internet Of Things (IoT) untuk Monitoring dan Meningkatkan Produktivitas Cabai
Padamara Village is one of the villages in Sukamulia District with the largest agricultural expanse reaching 155 Ha, of which 90% is a productive area in the first and second planting seasons after rice. The cayenne pepper plant is a plant that is suitable for cultivation in highland and lowland areas. The optimal growth and yield of cayenne pepper plants is very dependent on adequate soil quality, proper watering management and fertilizer application. Checking soil conditions is very important for the growth of cayenne pepper plants which have optimal humidity of 50%-70% so that they are not too dry or wet with a soil pH of 6-7 and a temperature of 24-28 degrees Celsius. The "SEHATI" farmer group in Padamara Village, Sukamulia District, East Lombok Regency has around 45 farmers with an agricultural land area of ± 15.89 Ha as partners in the Implementation of an Automatic Drip Irrigation System (ADIS) based on the Internet of Things (IoT) for Monitoring and Increasing Productivity Chili in solving problems. Components such as Soil Moisture YL-69 for monitoring soil moisture and pH which will then be sent to the web server for subsequent reading of the results. ESP 32 as a microcontroller to turn on/off the pump water drops.
Survei Teknik Pemilihan Fitur Untuk Sistem Deteksi Intrusi Berbasis Machine Learning
With the increasing threat to cybersecurity, Machine Learning (ML)-based Intrusion Detection Systems (IDS) are becoming increasingly important for detecting and preventing network attacks. The selection of appropriate features is a key factor in improving the performance of IDS, as it can enhance detection accuracy, reduce model complexity, and save computation time. This article examines various feature selection techniques used in ML-based IDS, including filter, wrapper, embedded, and hybrid techniques. Each technique has its advantages and disadvantages, depending on the characteristics of the dataset and the type of attack encountered. This research also evaluates the effectiveness of these techniques using popular datasets such as KDD Cup 99, NSL-KDD, and CICIDS 2017. The results show that filter techniques are more efficient in terms of time, while wrapper and hybrid techniques offer higher detection accuracy, although they require more resources. The embedded technique combines efficiency and accuracy with time savings in model training. This article also discusses the importance of good feature selection for classification in IDS, as well as the challenges faced by IDS in overcoming its limitations. This research provides a comprehensive overview of feature selection in ML-based IDS and recommendations for further development and implementation to address increasingly complex threats.