Jurnal Informatika: Jurnal Pengembangan IT
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Implementasi Aplikasi Internal Service Order (ISO) Berbasis Web pada Perusahaan Manufaktur Furniture
No full textTechnological advancements and competitive challenges in the industrial sector have driven an increasing need for information systems. The implementation of information systems is believed to significantly enhance a company’s operational effectiveness and efficiency. In the furniture manufacturing industry, production machines are critical assets that require regular maintenance. PT Ebako Nusantara operates more than 200 production machines. Overall, the demand for machine repairs at PT Ebako Nusantara is managed manually, leading to frequent delays and difficulties in tracking repair history. To address these issues, this study was conducted with the aim of utilizing information systems and data analytics, enabling the company to predict machine maintenance and minimize production downtime. The study results show that furniture manufacturing companies that adopt information systems experience a 22% increase in production efficiency and a 50% reduction in production errors. The development process of the ISO application adopts the Agile methodology with six stages: planning, implementation, software testing, documentation, application deployment, and maintenance. The features available in the application include submitting repair requests and tracking machine repairs, which are designed to streamline and expedite the machine repair workflow. This research has facilitated interdepartmental integration in terms of machine repair requests, significantly improving operational efficiency. The use of the ISO application also simplifies the maintenance department’s scheduling of technicians for machine repairs and routine maintenance
Developing Fishpond Control System for School Natural Laboratory Automation
No full textPandemi Covid-19 memaksa kegiatan belajar dilakukan secara daring. Sekolah berusaha melakukan kegiatan secara luring dengan membatasi jumlah siswa atau dengan melaksanakan kegiatan di laboratorium alam. Mengelola laboratorium alam membutuhkan banyak biaya terutama pada kondisi pasca covid-19. Internet of Things adalah teknologi yang memungkinkan kendali jarak jauh dan otomatisasi. Hal ini memungkinkan pengelolaan laboratorium alam dilakukan dari jarak jauh atau secara otomatis. Penelitian ini bertujuan untuk membuat desain dan sistem IoT yang meliputi penentuan modul dasar dan fungsinya, penentuan perangkat sensor dan aktuator yang dibutuhkan. Sistem dibangun menggunakan arsitektur MQTT. Aplikasi Android dibuat untuk mengontrol periferal IoT. Sistem yang telah berhasil dibangun diuji dengan metode blackbox testing. Berdasarkan hasil blackbox testing, aplikasi Android dan periferal IoT dapat berkomunikasi dan berfungsi dengan baik. Penelitian ini masih memiliki keterbatasan yaitu perlu dilakukannya kalibrasi perangkat IoT dan pengujian perangkat keras IoT dalam jangka waktu yang lama
Identifikasi Hukum Tajwid pada Citra Teks Al Quran menggunakan SSD MobileNet v2
No full textTajweed contains a set of rules for reciting the Qur'an correctly. These rules must be complied with to ensure each letter is pronounced accurately. Arabic script and language compose the Qur'an, yet not all readers are fluent in Arabic. Tajweed serves as a guide to prevent readers from making mistakes when reciting the Qur'an that could alter the meaning. However, Tajweed rules are quite numerous and diverse, causing readers to struggle in memorizing these rules. To address this issue, a preliminary development of a Quran reading assistance system will be established, focusing on detecting Tajweed rules in images of Quranic text. SSD MobileNet v2, a Deep Learning technique for object detection, will be utilized for detecting Tajweed rules. The development of the Tajweed rule identification model begins with the data collection stage by capturing screens of the Al-Quran text pages from the Kemenag Qur'an Application. A total of 520 collected data were divided into 80:10:10 for training, validation, and test data, respectively. All data were subsequently annotated and enclosed in bounding boxes using the tool labelImg. The pre-trained model, SSD MobileNet V2 FPNLite 320x320, was used as the initial weight configuration of the model. Then the identification model was constructed during the training stage using training and validation data. The reliability of the constructed model was tested using test data. The test results indicated that the model could successfully recognize two Tajwid rules, Mad Aridlisukun and Mad Layyin, achieving the minimum loss around 0.15 and the maximum precision around 0.96
Perancangan Model Deteksi Potensi Siswa Putus Sekolah Menggunakan Metode Logistic Regression Dan Decision Tree
No full textThe phenomenon of student dropouts is one of the main challenges in education, influenced by various factors such as absenteeism, economic pressures on families, low academic performance, and lack of motivation. This issue not only affects the personal development of students but also tarnishes the reputation of educational institutions. Therefore, an innovative technology-based approach, such as data mining, is needed to detect students at risk of dropping out early. This study aims to design a model for detecting the potential of school dropout students using Logistic Regression and Decision Tree methods based on student data from SMA N 4 Tegal. The variables used in the analysis include demographic, academic, and social information such as absenteeism, average semester grades, parental income, and transportation type. The dataset is processed using one-hot encoding and label encoding techniques to convert categorical data into numeric values. The results indicate that both methods have their respective advantages. The Decision Tree model achieves high precision, especially in predicting students who continue their education, with a precision of 0.99 for the "Continue School" class. However, recall for the "Dropout" class remains low (0.60), indicating the need for improvements in detecting students at risk of dropping out. On the other hand, the Logistic Regression model shows better balance in detecting both classes, with more balanced accuracy and recall. This study concludes that both models can be used to monitor the potential of school dropouts and provide data-driven recommendations for more accurate educational decision-making
Application of Optimization Algorithm to Machine Learning Model for Solar Panel Output Power Prediction: A Review
No full textSolar panels have become a popular source of renewable energy due to their sustainability and environmental friendliness. Accurate predictions of solar panel output are crucial for various applications, such as energy system optimization, power grid management, and economic planning. Many important factors pose challenges in predicting the output of solar panels, such as weather conditions that can change at any time, geographical factors, data quality, and the duration of data collection. Machine learning (ML) models show promising performance in this prediction; there are many types of machine learning models, some are single models and others are hybrid models. Optimization algorithms are used to optimize parameters and improve the prediction accuracy of machine learning models. This research reviews fifteen journals that have been filtered to obtain those discussing optimization algorithms in the predictive models of solar panel output power. This journal will examine the optimization algorithms used in machine learning models for predicting solar panel output power, discussing various types of optimization algorithms, their application in machine learning models, the prediction results from these models, the input data used, and the data collection locations that significantly influence the prediction outcomes. From the results of this research, it does not conclude which machine learning model is the best, due to the many factors that influence it. However, this research is expected to provide references on the application of machine learning models in predicting the output power of solar panels, thereby encouraging the use of renewable energy sources
Deteksi Malware menggunakan Metode Stacking berbasis Ensemble
No full textSerangan malware kian hari kian memprihatinkan. Evolusi malware yang cepat dan semakin destruktif menimbulkan kekhawatiran bagi banyak pihak. Oleh karena itu, deteksi malware yang efektif sangat dibutuhkan. Data mining memainkan peran yang krusial dalam bidang ini, mengingat algoritma-algoritma yang ada pada data mining bisa dilatih hingga menghasilkan akurasi yang paling tinggi. Untuk mengklasifikasi suatu file, apakah tergolong malware atau tidak, dalam penelitian ini metode stacking digunakan karena dapat meningkatkan akurasi jika dibandingkan dengan algoritma-algoritma klasifikasi konvensional. Empat Algoritma dilibatkan dalam eksperimen yang dilakukan, yaitu: Neural Network, Random Forest, kNN, dan Logistic Regression. Tiga algoritma pertama digunakan sebagai classifier pada level 0, sementara itu Logistic Regression digunakan classifier pada level 1 (meta classifier). Dengan kombinasi 4 algoritma tersebut, akurasi yang diperoleh adalah sebesar 98.7%, dan akurasi tersebut merupakan yang paling tinggi jika dibandingkan dengan masing-masing algoritma jika dieksekusi secara individual
Implementasi Aplikasi Sentimen Pada Data Twitter Jelang Pemilu 2024
No full textElections are one of the most important democratic processes, giving citizens the right to choose their leaders. In today's digital era, social media is an increasingly important information source influencing public perception. Twitter has been a social media from the past until now that still exists in finding information. Tweets are one of the most frequently used services to express opinions or opinions to the public. Sentiment analysis as an application of Natural Language Processing (NLP) is helpful in understanding public opinion towards prospective leaders and issues discussed during election campaigns. The motivation for this study is to conduct text classification using a deep learning model called LSTM and to compare the use of oversampling and non-oversampling methods. This research started by collecting datasets from Twitter, labelling, pre-processing, creating and evaluating the model, and implementing it into the web application. The experiment showed that the random oversampling technique gets more significant accuracy than non-oversampling. Random oversampling produces an accuracy of 0.82 at epoch 25, while non-oversampling reaches an accuracy of 0.61 at epoch 5
Pemanfaatan Algoritma K-Means untuk Membuktikan Implementasi Undang-Undang Pelanggaran Hukum Korupsi di Pengadilan Negeri Banjarmasin
No full textThis research aims to demonstrate the implementation of the Anti-Corruption Law in the Banjarmasin District Court by utilizing the K-Means algorithm. Corruption, which persists in Indonesia over a prolonged period, has reached a critical level, making it crucial to enforce the law fairly and firmly. In this study, the panel of judges in the Banjarmasin District Court was analyzed using the K-Means Clustering method and silhouette coefficient to decide corruption cases that result in state losses. The research findings indicate that the optimal number of clusters is 3, with a value of 0.686. However, there is also a lowest value among the 4 clusters, which is 0.454. These clusters are then divided into three categories of enforcement, namely cases that have been executed (108 cases), cases that will be executed (26 cases), and cases that have not been executed (2 cases). All clusters have a silhouette score of 0.742, indicating successful enforcement. This research provides concrete evidence that the panel of judges in the Banjarmasin District Court has implemented the Anti-Corruption Law while considering state losses. By utilizing the K-Means algorithm, this study also contributes to a better understanding of enforcement practices in the court. It is expected that the results of this research will support efforts to enhance the implementation of the Anti-Corruption Law in Indonesia, particularly in the Banjarmasin District Cour
Implementasi Algoritma Priority Scheduling Sistem Informasi Pelayanan Administrasi Kependudukan Desa
No full textPelayanan administrasi di desa seringkali masih menerapkan metode manual dalam pelaksanaanya yang mengharuskan masyarakat datang secara langsung ke kantor desa. Selain itu, pelayanan yang masih manual juga menyulitkan petugas kantor desa dalam menentukan urutan pemrosesan surat yang harus diverifikasi terlebih dahulu, akibatnya banyak surat yang seharusnya sudah diselesaikan namun prosesnya masih berjalan. Oleh sebab itu, tujuan dilakukan penelitian ini berfokus pada perancangan sistem informasi yang dapat membantu masyarakat dalam pengajuan pembuatan surat serta sistem yang dapat membantu petugas kantor desa dalam menentukan prioritas surat yang harus diverifikasi terlebih dahulu dengan mengimplementasikan algoritma priority scheduling. Dalam penelitian ini, metode yang digunakan melingkupi perancangan algoritma priority scheduling yang diimplementasikan ke dalam sistem serta perancangan perangkat lunak menggunakan metode SDLC waterfall. Perancangan algoritma priority scheduling berupa penentuan urutan prioritas serta pembuatan pseudocode dari algoritma. Hasil dari penelitian ini adalah sebuah sistem informasi pelayanan administrasi kependudukan yang mengimplementasikan algoritma priority scheduling dalam proses pengurutan surat. Berdasarkan hasil pengujian menggunakan metode blackbox aplikasi berjalan tanpa ada error, 0% kegagalan dan berjalan sesuai fungsinya. Sedangkan pengujian SUS menunjukkan bahwa aplikasi berada pada level good dengan skor 75,25
Performance Improvement of Random Forest Algorithm for Malware Detection on Imbalanced Dataset using Random Under-Sampling Method
No full textHandling imbalanced dataset has their own challenge. Inappropriate step during the pre-processing phase with imbalanced data could bring the negative effect on prediction result. The accuracy score seems high, but actually there are many problems on recall and specificity side, considering that the produced predictions will be dominated by the majority class. In the case of malware detection, false negative value is very crucial since it can be fatal. Therefore, prediction errors, especially related to false negative, must be minimized. The first step that can be done to handle imbalanced dataset in this crucial condition is by balancing the data class. One of the popular methods to balance the data, called Random Under-Sampling (RUS). Random Forest is implemented to classify the file, whether it is considered as goodware or malware. Next, 3 evaluation metrics are used to evaluate the model by measuring the classification accuracy, recall and specificity. Lastly, the performance of Random Forest is compared with 3 other methods, namely kNN, Naïve Bayes and Logistic Regression. The result shows that Random Forest achieved the best performance among evaluated methods with the score of 98.1% for accuracy, 98.0% for recall, and 98.2% for specificity