Technology provides great opportunities for the plantation sector in increasing the efficiency and effectiveness of production management. PT. Adimulia Palmo Lestari (APL), as a company engaged in the oil palm plantation sector, faces challenges in the process of monitoring plantation results which are still carried out manually, so there is a risk of delays and inaccuracies in data. This study aims to design a website-based oil palm plantation monitoring service information system to facilitate the process of recording, reporting, and monitoring data in real time. The method used in designing this system is the waterfall method, which consists of the stages of needs analysis, system design, implementation, and testing. The results of this study are a web-based information system that is able to display harvest data in a structured manner, provide periodic reports, and facilitate access to information for management. The conclusion of this study shows that the implementation of a website-based information system can improve accuracy, efficiency, and transparency in the process of monitoring oil palm plantation results at PT. Adimulia Palmo Lestari (APL).

This study focuses on the analysis and design of a web-based online queue system prototype for banking services, aiming to improve service quality and operational efficiency. In the banking sector, managing customer waiting times is a significant challenge that impacts their comfort and satisfaction. The online queue system enables customers to obtain queue numbers online, thereby minimizing waiting times and making queue management more efficient. This research includes system requirements analysis, user interface design, and process flow development tailored to customer needs and banking operations. The resulting prototype is expected to serve as a foundation for developing a more comprehensive digital queue system, supporting digital transformation in the banking sector, and providing practical and theoretical contributions to web-based service development.

This study develops an image processing application to automatically detect the ripeness level of tomatoes using the YCbCr color space transformation. This method is effective because it is able to separate the luminance and chrominance components, so it can identify color changes that indicate the ripeness level of tomatoes, namely unripe, semi-ripe, and ripe tomatoes. The application is designed with matlab and uses a GUI interface that makes it easy for users to upload and process images. Based on trials on image samples, the system is able to classify tomato ripeness with 100% accuracy on a limited test dataset. The classification process is based on three main parameters: the red area ratio, the average value of the Cr channel, and the average value of the Cb channel. The results of the study indicate that this approach can be used as a digital solution in the automatic and efficient tomato sorting process

Penelitian ini bertujuan untuk mengevaluasi penerapan Sistem Informasi Manajemen Rumah Sakit (SIMRS) di Klinik Utama Amanah Medika menggunakan pendekatan Technology Acceptance Model (TAM). Penelitian ini mengidentifikasi faktor-faktor yang mempengaruhi penerimaan pengguna terhadap SIMRS, termasuk persepsi kemudahan penggunaan, persepsi kemanfaatan, sikap terhadap penggunaan, niat untuk menggunakan, dan penerimaan sistem yang sebenarnya. Data dikumpulkan melalui kuisioner yang disebarkan kepada 25 responden, yang terdiri dari tenaga medis, administrator, dan staf lainnya. Hasil penelitian menunjukkan bahwa mayoritas responden merasa SIMRS mudah digunakan dan memberikan manfaat signifikan dalam meningkatkan efisiensi dan akurasi pelayanan. Variabel persepsi kemudahan penggunaan dan kemanfaatan sistem menjadi faktor utama yang mempengaruhi sikap dan niat perilaku pengguna. Namun, beberapa masalah teknis seperti keterbatasan perangkat keras dan gangguan jaringan masih perlu diperbaiki. Secara keseluruhan, SIMRS diterima dengan baik oleh pengguna di Klinik Utama Amanah Medika, meskipun pengembangan lebih lanjut dalam infrastruktur dan pelatihan diperlukan untuk optimalisasi penggunaan sistem.

Digital interface design plays a vital role in user interaction with software, especially through visual elements such as color and typography. This study was motivated by the need to understand how these visual elements affect user comfort while interacting with an application or website. The purpose of this study was to analyze user perceptions of the use of color and typography, and their impact on visual comfort. The method used was a descriptive quantitative approach by distributing online questionnaires to 20 respondents from various age groups. The collected data were analyzed descriptively by comparing preferences and comfort based on the age of the respondents. The results showed that the majority of respondents felt that color and typography greatly influenced the comfort of using digital interfaces. Dark background colors and sans-serif fonts with proportional sizes proved to be the most preferred choices because they provided reading comfort and reduced eye fatigue. A balanced combination of color and typography was also considered important to improve readability and the overall user experience. In conclusion, the selection and use of appropriate visual elements contribute greatly to creating a friendly and comfortable interface design for users of various age ranges.

Digital image processing plays an important role in extracting visual information from natural objects, including the morphological structure of leaves. One of the crucial techniques in this process is edge detection, which is used to emphasize object boundaries and support pattern analysis. This study aims to compare three edge detection methods, namely Sobel, Prewitt, and Canny in recognizing patterns in leaf images using the Python programming language and the OpenCV library. The methods used include quantitative experiments by implementing the three edge detection techniques on grayscale leaf images, followed by visual result analysis based on the criteria of edge sharpness, morphological detail, noise, and computational efficiency. The results show that the Canny method produces the most accurate and clean edge detection, with the ability to capture small details and reduce noise significantly. Sobel shows quite good performance in highlighting the main structure of the leaf, while Prewitt produces simpler and less precise results. Based on the evaluation results, the Canny method is considered the most effective for the purposes of digital leaf pattern classification and analysis. This study provides an important contribution in selecting the optimal edge detection method for computer vision applications in the field of digital botany..

Improving the quality of education requires a comprehensive understanding of the conditions and characteristics of each educational institution. West Muna Regency has various secondary schools with diverse profiles and challenges. Until now, school grouping has often been done manually, which does not always accurately reflect the overall characteristics of the data. This study aims to cluster secondary schools in West Muna Regency using the K-Means algorithm as a clustering method to identify hidden patterns in school data, such as the number of students, teachers, staff, facilities, and location. The research method involves several stages, including data collection, method analysis, software implementation, and cluster testing. The clustering results produced three school clusters with different characteristics. Cluster 1 consists of schools with the most complete resources, Cluster 2 includes the largest number of schools with varying resources, and Cluster 3 represents schools with moderate conditions. These findings are expected to serve as a basis for formulating more targeted educational policies, ensuring equitable distribution of resources, and improving the quality of education in West Muna Regency.

Object recognition, particularly fruit classification, plays a crucial role in various fields, ranging from agricultural automation to digital marketplaces. This study proposes a fruit classification system based on RGB images, developed using a Convolutional Neural Network (CNN) architecture consisting of convolutional layers, pooling layers, fully connected layers, and dropout for model stability. The model was trained using the Adam optimization algorithm on an augmented dataset to enhance data variation and reduce overfitting. The resulting model achieved an average accuracy of 98%, demonstrating the reliability of CNNs in pattern recognition tasks. To enhance usability, the model was integrated into a graphical user interface (GUI) built with MATLAB R2023b App Designer, allowing users to add datasets, train the model, and predict new images without writing any code. The findings highlight that while the model performs well, its accuracy remains dependent on consistent image backgrounds; therefore, expanding the variety of fruit types and background conditions in the dataset is essential to improve the system's robustness in real-world applications.

Sosial media menghasilkan banyak informasi seperti informasi terkait politik, social, budaya, olahraga bahkan pengetahuan. Tidak hanya itu, banyak dari masyarakat yang mengomentari suatu acara dari stasiun televisi tertentu melalui social media. Komentar-komentar tersebut bisa berupa komentar positif atau negative. Disisi lain, statsiun telivisi juga juga membutuhkan hal tersebut guna menjadi dasar dalam merancang suatu acara yang disukai dan bernilai postif oleh masyarakat.  Salah satu metode machine learning yang dapat digunakan untuk menunjang hal tersebut adalah LSTM (Long Short Term Memory). Tujuan utama pada penelitian ini adalah bagaimana penggunaan metode LSTM dalam menentukan bentuk dari suatu komentar berupa komentar terhadap acara telivis tersebut, apakah acara tersebut bermuatan sentiment atau sebaliknya. Berdasarkan hasil pengujian menggunakan metode LSTM memperlihatkan tingkat akurasi sebesar 97% dan loss sebesar 12%.

Persediaan produk menjadi salah satu penentu keberhasilan dalam penjualan pada sebuah toko. Berdasarkan studi kasus yang terjadi di Sinarmart, persediaan produk dilakukan ketika produk telah habis. Dengan menggunakan data mining algoritma FP-Growth, diharapkan dapat membantu dalam mengambil keputusan kedepannya untuk menentukan persediaan produk. Penelitian ini menggunakan penerapan algoritma FP-Growth melalui aplikasi Rapidminer 9.10 dengan batasan minimum support ≥ 65% dan batasan minimum confidence ≥ 80%. Data yang digunakan adalah penjualan produk selama bulan agustus 2021 sebanyak 31 transaksi berasal dari 13 kategori produk. Hasil penelitian menunjukkan penjualan produk minuman, susu, snack, permen, ice cream, dan rokok memiliki nilai support kategori tinggi sebesar 96,77% dan penjualan produk perlengkapan dewasa dan anak-anak serta obat nyamuk memiliki nilai support kategori dibawah minimum support.